"""Python wrappers around TensorFlow ops.

This file is MACHINE GENERATED! Do not edit.
Original C++ source file: bitwise_ops.cc
"""

import collections as _collections
import six as _six

from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.eager import execute as _execute
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import errors as _errors
from tensorflow.python.framework import tensor_shape as _tensor_shape

from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.deprecation import deprecated_endpoints
from tensorflow.python.util import dispatch as _dispatch
from tensorflow.python.util.tf_export import tf_export
from tensorflow.python.util.tf_export import kwarg_only as _kwarg_only
from tensorflow.tools.docs import doc_controls as _doc_controls


@_dispatch.add_dispatch_list
@tf_export('bitwise.bitwise_and')
def bitwise_and(x, y, name=None):
  r"""Elementwise computes the bitwise AND of `x` and `y`.

  The result will have those bits set, that are set in both `x` and `y`. The
  computation is performed on the underlying representations of `x` and `y`.

  For example:

  ```python
  import tensorflow as tf
  from tensorflow.python.ops import bitwise_ops
  dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64,
                tf.uint8, tf.uint16, tf.uint32, tf.uint64]

  for dtype in dtype_list:
    lhs = tf.constant([0, 5, 3, 14], dtype=dtype)
    rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
    exp = tf.constant([0, 0, 3, 10], dtype=tf.float32)

    res = bitwise_ops.bitwise_and(lhs, rhs)
    tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE
  ```

  Args:
    x: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`.
    y: A `Tensor`. Must have the same type as `x`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "BitwiseAnd", name, _ctx.post_execution_callbacks, x, y)
      return _result
    except _core._FallbackException:
      try:
        return bitwise_and_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              bitwise_and, x=x, y=y, name=name)
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  try:
    _, _, _op = _op_def_lib._apply_op_helper(
        "BitwiseAnd", x=x, y=y, name=name)
  except (TypeError, ValueError):
    result = _dispatch.dispatch(
          bitwise_and, x=x, y=y, name=name)
    if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
      return result
    raise
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op._get_attr_type("T"))
  _execute.record_gradient(
      "BitwiseAnd", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def BitwiseAnd(x, y, name=None):
  return bitwise_and(x=x, y=y, name=name)
BitwiseAnd.__doc__ = bitwise_and.__doc__
BitwiseAnd = _doc_controls.do_not_generate_docs(_kwarg_only(BitwiseAnd))
tf_export("raw_ops.BitwiseAnd")(BitwiseAnd)


def bitwise_and_eager_fallback(x, y, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function bitwise_and
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], _ctx)
  (x, y) = _inputs_T
  _inputs_flat = [x, y]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"BitwiseAnd", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "BitwiseAnd", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


@_dispatch.add_dispatch_list
@tf_export('bitwise.bitwise_or')
def bitwise_or(x, y, name=None):
  r"""Elementwise computes the bitwise OR of `x` and `y`.

  The result will have those bits set, that are set in `x`, `y` or both. The
  computation is performed on the underlying representations of `x` and `y`.

  For example:

  ```python
  import tensorflow as tf
  from tensorflow.python.ops import bitwise_ops
  dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64,
                tf.uint8, tf.uint16, tf.uint32, tf.uint64]

  for dtype in dtype_list:
    lhs = tf.constant([0, 5, 3, 14], dtype=dtype)
    rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
    exp = tf.constant([5, 5, 7, 15], dtype=tf.float32)

    res = bitwise_ops.bitwise_or(lhs, rhs)
    tf.assert_equal(tf.cast(res,  tf.float32), exp)  # TRUE
  ```

  Args:
    x: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`.
    y: A `Tensor`. Must have the same type as `x`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "BitwiseOr", name, _ctx.post_execution_callbacks, x, y)
      return _result
    except _core._FallbackException:
      try:
        return bitwise_or_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              bitwise_or, x=x, y=y, name=name)
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  try:
    _, _, _op = _op_def_lib._apply_op_helper(
        "BitwiseOr", x=x, y=y, name=name)
  except (TypeError, ValueError):
    result = _dispatch.dispatch(
          bitwise_or, x=x, y=y, name=name)
    if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
      return result
    raise
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op._get_attr_type("T"))
  _execute.record_gradient(
      "BitwiseOr", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def BitwiseOr(x, y, name=None):
  return bitwise_or(x=x, y=y, name=name)
BitwiseOr.__doc__ = bitwise_or.__doc__
BitwiseOr = _doc_controls.do_not_generate_docs(_kwarg_only(BitwiseOr))
tf_export("raw_ops.BitwiseOr")(BitwiseOr)


def bitwise_or_eager_fallback(x, y, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function bitwise_or
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], _ctx)
  (x, y) = _inputs_T
  _inputs_flat = [x, y]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"BitwiseOr", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "BitwiseOr", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


@_dispatch.add_dispatch_list
@tf_export('bitwise.bitwise_xor')
def bitwise_xor(x, y, name=None):
  r"""Elementwise computes the bitwise XOR of `x` and `y`.

  The result will have those bits set, that are different in `x` and `y`. The
  computation is performed on the underlying representations of `x` and `y`.

  For example:

  ```python
  import tensorflow as tf
  from tensorflow.python.ops import bitwise_ops
  dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64,
                tf.uint8, tf.uint16, tf.uint32, tf.uint64]

  for dtype in dtype_list:
    lhs = tf.constant([0, 5, 3, 14], dtype=dtype)
    rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
    exp = tf.constant([5, 5, 4, 5],  dtype=tf.float32)

    res = bitwise_ops.bitwise_xor(lhs, rhs)
    tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE
  ```

  Args:
    x: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`.
    y: A `Tensor`. Must have the same type as `x`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "BitwiseXor", name, _ctx.post_execution_callbacks, x, y)
      return _result
    except _core._FallbackException:
      try:
        return bitwise_xor_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              bitwise_xor, x=x, y=y, name=name)
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  try:
    _, _, _op = _op_def_lib._apply_op_helper(
        "BitwiseXor", x=x, y=y, name=name)
  except (TypeError, ValueError):
    result = _dispatch.dispatch(
          bitwise_xor, x=x, y=y, name=name)
    if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
      return result
    raise
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op._get_attr_type("T"))
  _execute.record_gradient(
      "BitwiseXor", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def BitwiseXor(x, y, name=None):
  return bitwise_xor(x=x, y=y, name=name)
BitwiseXor.__doc__ = bitwise_xor.__doc__
BitwiseXor = _doc_controls.do_not_generate_docs(_kwarg_only(BitwiseXor))
tf_export("raw_ops.BitwiseXor")(BitwiseXor)


def bitwise_xor_eager_fallback(x, y, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function bitwise_xor
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], _ctx)
  (x, y) = _inputs_T
  _inputs_flat = [x, y]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"BitwiseXor", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "BitwiseXor", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


@_dispatch.add_dispatch_list
@tf_export('bitwise.invert')
def invert(x, name=None):
  r"""Invert (flip) each bit of supported types; for example, type `uint8` value 01010101 becomes 10101010.

  Flip each bit of supported types.  For example, type `int8` (decimal 2) binary 00000010 becomes (decimal -3) binary 11111101.
  This operation is performed on each element of the tensor argument `x`.

  Example:
  ```python
  import tensorflow as tf
  from tensorflow.python.ops import bitwise_ops

  # flip 2 (00000010) to -3 (11111101)
  tf.assert_equal(-3, bitwise_ops.invert(2))

  dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
                dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64]

  inputs = [0, 5, 3, 14]
  for dtype in dtype_list:
    # Because of issues with negative numbers, let's test this indirectly.
    # 1. invert(a) and a = 0
    # 2. invert(a) or a = invert(0)
    input_tensor = tf.constant([0, 5, 3, 14], dtype=dtype)
    not_a_and_a, not_a_or_a, not_0 = [bitwise_ops.bitwise_and(
                                        input_tensor, bitwise_ops.invert(input_tensor)),
                                      bitwise_ops.bitwise_or(
                                        input_tensor, bitwise_ops.invert(input_tensor)),
                                      bitwise_ops.invert(
                                        tf.constant(0, dtype=dtype))]

    expected = tf.constant([0, 0, 0, 0], dtype=tf.float32)
    tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected)

    expected = tf.cast([not_0] * 4, tf.float32)
    tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected)

    # For unsigned dtypes let's also check the result directly.
    if dtype.is_unsigned:
      inverted = bitwise_ops.invert(input_tensor)
      expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32)
      tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32))
  ```

  Args:
    x: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name, "Invert",
        name, _ctx.post_execution_callbacks, x)
      return _result
    except _core._FallbackException:
      try:
        return invert_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              invert, x=x, name=name)
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  try:
    _, _, _op = _op_def_lib._apply_op_helper(
        "Invert", x=x, name=name)
  except (TypeError, ValueError):
    result = _dispatch.dispatch(
          invert, x=x, name=name)
    if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
      return result
    raise
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op._get_attr_type("T"))
  _execute.record_gradient(
      "Invert", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def Invert(x, name=None):
  return invert(x=x, name=name)
Invert.__doc__ = invert.__doc__
Invert = _doc_controls.do_not_generate_docs(_kwarg_only(Invert))
tf_export("raw_ops.Invert")(Invert)


def invert_eager_fallback(x, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function invert
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
  _inputs_flat = [x]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"Invert", 1, inputs=_inputs_flat, attrs=_attrs,
                             ctx=_ctx, name=name)
  _execute.record_gradient(
      "Invert", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


@_dispatch.add_dispatch_list
@tf_export('bitwise.left_shift')
def left_shift(x, y, name=None):
  r"""Elementwise computes the bitwise left-shift of `x` and `y`.

  If `y` is negative, or greater than or equal to the width of `x` in bits the
  result is implementation defined.

  Example:

  ```python
  import tensorflow as tf
  from tensorflow.python.ops import bitwise_ops
  import numpy as np
  dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64]

  for dtype in dtype_list:
    lhs = tf.constant([-1, -5, -3, -14], dtype=dtype)
    rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
    
    left_shift_result = bitwise_ops.left_shift(lhs, rhs)
    
    print(left_shift_result)

  # This will print:
  # tf.Tensor([ -32   -5 -128    0], shape=(4,), dtype=int8)
  # tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int16)
  # tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int32)
  # tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int64)

  lhs = np.array([-2, 64, 101, 32], dtype=np.int8)
  rhs = np.array([-1, -5, -3, -14], dtype=np.int8)
  bitwise_ops.left_shift(lhs, rhs)
  # <tf.Tensor: id=139, shape=(4,), dtype=int8, numpy=array([ -2,  64, 101,  32], dtype=int8)>
  ```

  Args:
    x: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`.
    y: A `Tensor`. Must have the same type as `x`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "LeftShift", name, _ctx.post_execution_callbacks, x, y)
      return _result
    except _core._FallbackException:
      try:
        return left_shift_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              left_shift, x=x, y=y, name=name)
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  try:
    _, _, _op = _op_def_lib._apply_op_helper(
        "LeftShift", x=x, y=y, name=name)
  except (TypeError, ValueError):
    result = _dispatch.dispatch(
          left_shift, x=x, y=y, name=name)
    if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
      return result
    raise
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op._get_attr_type("T"))
  _execute.record_gradient(
      "LeftShift", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def LeftShift(x, y, name=None):
  return left_shift(x=x, y=y, name=name)
LeftShift.__doc__ = left_shift.__doc__
LeftShift = _doc_controls.do_not_generate_docs(_kwarg_only(LeftShift))
tf_export("raw_ops.LeftShift")(LeftShift)


def left_shift_eager_fallback(x, y, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function left_shift
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], _ctx)
  (x, y) = _inputs_T
  _inputs_flat = [x, y]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"LeftShift", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "LeftShift", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


def population_count(x, name=None):
  r"""Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).

  For each entry in `x`, calculates the number of `1` (on) bits in the binary
  representation of that entry.

  **NOTE**: It is more efficient to first `tf.bitcast` your tensors into
  `int32` or `int64` and perform the bitcount on the result, than to feed in
  8- or 16-bit inputs and then aggregate the resulting counts.

  Args:
    x: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `uint8`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "PopulationCount", name, _ctx.post_execution_callbacks, x)
      return _result
    except _core._FallbackException:
      try:
        return population_count_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  _, _, _op = _op_def_lib._apply_op_helper(
        "PopulationCount", x=x, name=name)
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op._get_attr_type("T"))
  _execute.record_gradient(
      "PopulationCount", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def PopulationCount(x, name=None):
  return population_count(x=x, name=name)
PopulationCount.__doc__ = population_count.__doc__
PopulationCount = _doc_controls.do_not_generate_docs(_kwarg_only(PopulationCount))
tf_export("raw_ops.PopulationCount")(PopulationCount)


def population_count_eager_fallback(x, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function population_count
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
  _inputs_flat = [x]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"PopulationCount", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "PopulationCount", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


@_dispatch.add_dispatch_list
@tf_export('bitwise.right_shift')
def right_shift(x, y, name=None):
  r"""Elementwise computes the bitwise right-shift of `x` and `y`.

  Performs a logical shift for unsigned integer types, and an arithmetic shift
  for signed integer types.

  If `y` is negative, or greater than or equal to than the width of `x` in bits
  the result is implementation defined.

  Example:

  ```python
  import tensorflow as tf
  from tensorflow.python.ops import bitwise_ops
  import numpy as np
  dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64]

  for dtype in dtype_list:
    lhs = tf.constant([-1, -5, -3, -14], dtype=dtype)
    rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
    
    right_shift_result = bitwise_ops.right_shift(lhs, rhs)
    
    print(right_shift_result)
    
  # This will print:
  # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int8)
  # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int16)
  # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int32)
  # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int64)

  lhs = np.array([-2, 64, 101, 32], dtype=np.int8)
  rhs = np.array([-1, -5, -3, -14], dtype=np.int8)
  bitwise_ops.right_shift(lhs, rhs)
  # <tf.Tensor: id=151, shape=(4,), dtype=int8, numpy=array([ -2,  64, 101,  32], dtype=int8)>
  ```

  Args:
    x: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`.
    y: A `Tensor`. Must have the same type as `x`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `x`.
  """
  _ctx = _context._context or _context.context()
  if _ctx is not None and _ctx._thread_local_data.is_eager:
    try:
      _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
        _ctx._context_handle, _ctx._thread_local_data.device_name,
        "RightShift", name, _ctx.post_execution_callbacks, x, y)
      return _result
    except _core._FallbackException:
      try:
        return right_shift_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              right_shift, x=x, y=y, name=name)
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    except _core._NotOkStatusException as e:
      if name is not None:
        message = e.message + " name: " + name
      else:
        message = e.message
      _six.raise_from(_core._status_to_exception(e.code, message), None)
  # Add nodes to the TensorFlow graph.
  try:
    _, _, _op = _op_def_lib._apply_op_helper(
        "RightShift", x=x, y=y, name=name)
  except (TypeError, ValueError):
    result = _dispatch.dispatch(
          right_shift, x=x, y=y, name=name)
    if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
      return result
    raise
  _result = _op.outputs[:]
  _inputs_flat = _op.inputs
  _attrs = ("T", _op._get_attr_type("T"))
  _execute.record_gradient(
      "RightShift", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def RightShift(x, y, name=None):
  return right_shift(x=x, y=y, name=name)
RightShift.__doc__ = right_shift.__doc__
RightShift = _doc_controls.do_not_generate_docs(_kwarg_only(RightShift))
tf_export("raw_ops.RightShift")(RightShift)


def right_shift_eager_fallback(x, y, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function right_shift
  """
  _ctx = ctx if ctx else _context.context()
  _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], _ctx)
  (x, y) = _inputs_T
  _inputs_flat = [x, y]
  _attrs = ("T", _attr_T)
  _result = _execute.execute(b"RightShift", 1, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "RightShift", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def _InitOpDefLibrary(op_list_proto_bytes):
  op_list = _op_def_pb2.OpList()
  op_list.ParseFromString(op_list_proto_bytes)
  _op_def_registry.register_op_list(op_list)
  op_def_lib = _op_def_library.OpDefLibrary()
  op_def_lib.add_op_list(op_list)
  return op_def_lib
# op {
#   name: "BitwiseAnd"
#   input_arg {
#     name: "x"
#     type_attr: "T"
#   }
#   input_arg {
#     name: "y"
#     type_attr: "T"
#   }
#   output_arg {
#     name: "z"
#     type_attr: "T"
#   }
#   attr {
#     name: "T"
#     type: "type"
#     allowed_values {
#       list {
#         type: DT_INT8
#         type: DT_INT16
#         type: DT_INT32
#         type: DT_INT64
#         type: DT_UINT8
#         type: DT_UINT16
#         type: DT_UINT32
#         type: DT_UINT64
#       }
#     }
#   }
#   is_commutative: true
# }
# op {
#   name: "BitwiseOr"
#   input_arg {
#     name: "x"
#     type_attr: "T"
#   }
#   input_arg {
#     name: "y"
#     type_attr: "T"
#   }
#   output_arg {
#     name: "z"
#     type_attr: "T"
#   }
#   attr {
#     name: "T"
#     type: "type"
#     allowed_values {
#       list {
#         type: DT_INT8
#         type: DT_INT16
#         type: DT_INT32
#         type: DT_INT64
#         type: DT_UINT8
#         type: DT_UINT16
#         type: DT_UINT32
#         type: DT_UINT64
#       }
#     }
#   }
#   is_commutative: true
# }
# op {
#   name: "BitwiseXor"
#   input_arg {
#     name: "x"
#     type_attr: "T"
#   }
#   input_arg {
#     name: "y"
#     type_attr: "T"
#   }
#   output_arg {
#     name: "z"
#     type_attr: "T"
#   }
#   attr {
#     name: "T"
#     type: "type"
#     allowed_values {
#       list {
#         type: DT_INT8
#         type: DT_INT16
#         type: DT_INT32
#         type: DT_INT64
#         type: DT_UINT8
#         type: DT_UINT16
#         type: DT_UINT32
#         type: DT_UINT64
#       }
#     }
#   }
#   is_commutative: true
# }
# op {
#   name: "Invert"
#   input_arg {
#     name: "x"
#     type_attr: "T"
#   }
#   output_arg {
#     name: "y"
#     type_attr: "T"
#   }
#   attr {
#     name: "T"
#     type: "type"
#     allowed_values {
#       list {
#         type: DT_INT8
#         type: DT_INT16
#         type: DT_INT32
#         type: DT_INT64
#         type: DT_UINT8
#         type: DT_UINT16
#         type: DT_UINT32
#         type: DT_UINT64
#       }
#     }
#   }
# }
# op {
#   name: "LeftShift"
#   input_arg {
#     name: "x"
#     type_attr: "T"
#   }
#   input_arg {
#     name: "y"
#     type_attr: "T"
#   }
#   output_arg {
#     name: "z"
#     type_attr: "T"
#   }
#   attr {
#     name: "T"
#     type: "type"
#     allowed_values {
#       list {
#         type: DT_INT8
#         type: DT_INT16
#         type: DT_INT32
#         type: DT_INT64
#         type: DT_UINT8
#         type: DT_UINT16
#         type: DT_UINT32
#         type: DT_UINT64
#       }
#     }
#   }
# }
# op {
#   name: "PopulationCount"
#   input_arg {
#     name: "x"
#     type_attr: "T"
#   }
#   output_arg {
#     name: "y"
#     type: DT_UINT8
#   }
#   attr {
#     name: "T"
#     type: "type"
#     allowed_values {
#       list {
#         type: DT_INT8
#         type: DT_INT16
#         type: DT_INT32
#         type: DT_INT64
#         type: DT_UINT8
#         type: DT_UINT16
#         type: DT_UINT32
#         type: DT_UINT64
#       }
#     }
#   }
# }
# op {
#   name: "RightShift"
#   input_arg {
#     name: "x"
#     type_attr: "T"
#   }
#   input_arg {
#     name: "y"
#     type_attr: "T"
#   }
#   output_arg {
#     name: "z"
#     type_attr: "T"
#   }
#   attr {
#     name: "T"
#     type: "type"
#     allowed_values {
#       list {
#         type: DT_INT8
#         type: DT_INT16
#         type: DT_INT32
#         type: DT_INT64
#         type: DT_UINT8
#         type: DT_UINT16
#         type: DT_UINT32
#         type: DT_UINT64
#       }
#     }
#   }
# }
_op_def_lib = _InitOpDefLibrary(b"\n@\n\nBitwiseAnd\022\006\n\001x\"\001T\022\006\n\001y\"\001T\032\006\n\001z\"\001T\"\027\n\001T\022\004type:\014\n\n2\010\006\005\003\t\004\021\026\027\220\001\001\n?\n\tBitwiseOr\022\006\n\001x\"\001T\022\006\n\001y\"\001T\032\006\n\001z\"\001T\"\027\n\001T\022\004type:\014\n\n2\010\006\005\003\t\004\021\026\027\220\001\001\n@\n\nBitwiseXor\022\006\n\001x\"\001T\022\006\n\001y\"\001T\032\006\n\001z\"\001T\"\027\n\001T\022\004type:\014\n\n2\010\006\005\003\t\004\021\026\027\220\001\001\n1\n\006Invert\022\006\n\001x\"\001T\032\006\n\001y\"\001T\"\027\n\001T\022\004type:\014\n\n2\010\006\005\003\t\004\021\026\027\n<\n\tLeftShift\022\006\n\001x\"\001T\022\006\n\001y\"\001T\032\006\n\001z\"\001T\"\027\n\001T\022\004type:\014\n\n2\010\006\005\003\t\004\021\026\027\n9\n\017PopulationCount\022\006\n\001x\"\001T\032\005\n\001y\030\004\"\027\n\001T\022\004type:\014\n\n2\010\006\005\003\t\004\021\026\027\n=\n\nRightShift\022\006\n\001x\"\001T\022\006\n\001y\"\001T\032\006\n\001z\"\001T\"\027\n\001T\022\004type:\014\n\n2\010\006\005\003\t\004\021\026\027")
