from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__lowerCamelCase    :				int							     =	logging.get_logger(__name__)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[tf.Tensor, np.ndarray]			)							->      List[int]:







  """simple docstring"""





  if isinstance(__UpperCamelCase				,  np.ndarray			):
    return list(tensor.shape			)

  SCREAMING_SNAKE_CASE__    							=   tf.shape(__UpperCamelCase			)

  if tensor.shape == tf.TensorShape(__UpperCamelCase			):
    return dynamic

  SCREAMING_SNAKE_CASE__    							=   tensor.shape.as_list()

  return [dynamic[i] if s is None else s for i, s in enumerate(__UpperCamelCase			)]



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: tf.Tensor				,  __UpperCamelCase					: Optional[int] = None				,  __UpperCamelCase					: Optional[str] = None			)							->      tf.Tensor:







  """simple docstring"""





  return tf.nn.softmax(logits=logits + 1E-9				,  axis=__UpperCamelCase				,  name=__UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int				,  __UpperCamelCase					: Tuple=1E-5				,  __UpperCamelCase					: Union[str, Any]=-1			)							->      Union[str, Any]:







  """simple docstring"""





  if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__UpperCamelCase				,  __UpperCamelCase			):
    raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis."""			)

  # Get mean and variance on the axis to be normalized
  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   tf.nn.moments(__UpperCamelCase				,  axes=[axis]				,  keepdims=__UpperCamelCase			)

  if axis != -1:
    # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
    # on every dimension except axis
    SCREAMING_SNAKE_CASE__    							=   [1] * inputs.shape.rank
    SCREAMING_SNAKE_CASE__    							=   shape_list(__UpperCamelCase			)[axis]
    SCREAMING_SNAKE_CASE__    							=   tf.reshape(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   tf.reshape(__UpperCamelCase				,  __UpperCamelCase			)

  # Compute layer normalization using the batch_normalization
  # function.
  SCREAMING_SNAKE_CASE__    							=   tf.nn.batch_normalization(
      __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  offset=__UpperCamelCase				,  scale=__UpperCamelCase				,  variance_epsilon=__UpperCamelCase				,  )
  return outputs



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Tuple=0				,  __UpperCamelCase					: Any=-1			)							->      Optional[int]:







  """simple docstring"""





  if end_dim < 0:
    end_dim += input.shape.rank
  if start_dim < 0:
    start_dim += input.shape.rank

  if start_dim == end_dim:
    return input

  SCREAMING_SNAKE_CASE__    							=   tf.shape(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
  SCREAMING_SNAKE_CASE__    							=   tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]				,  axis=0			)
  return tf.reshape(__UpperCamelCase				,  __UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: tf.Tensor			)							->      tf.Tensor:







  """simple docstring"""





  if not isinstance(__UpperCamelCase				,  tf.Tensor			):
    SCREAMING_SNAKE_CASE__    							=   tf.convert_to_tensor(__UpperCamelCase			)  # Catches stray NumPy inputs
  if encoder_attention_mask.shape.rank == 3:
    SCREAMING_SNAKE_CASE__    							=   encoder_attention_mask[:, None, :, :]
  if encoder_attention_mask.shape.rank == 2:
    SCREAMING_SNAKE_CASE__    							=   encoder_attention_mask[:, None, None, :]
  # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
  # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
  # /transformer/transformer_layers.py#L270
  # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
  # encoder_extended_attention_mask.transpose(-1, -2))
  SCREAMING_SNAKE_CASE__    							=   (
      tf.cast(1				,  encoder_attention_mask.dtype			) - encoder_extended_attention_mask
  ) * encoder_extended_attention_mask.dtype.min

  return encoder_extended_attention_mask



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: tf.Tensor				,  __UpperCamelCase					: int				,  __UpperCamelCase					: str = "input_ids"			)							->      None:







  """simple docstring"""





  tf.debugging.assert_less(
      __UpperCamelCase				,  tf.cast(__UpperCamelCase				,  dtype=tensor.dtype			)				,  message=(
          f"""The maximum value of {tensor_name} ({tf.math.reduce_max(__UpperCamelCase			)}) must be smaller than the embedding """
          f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
      )				,  )



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: Optional[Any]			)							->      Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   6_45_12
  # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
  # because in that case even chunking the array would not make the saving
  # possible.
  SCREAMING_SNAKE_CASE__    							=   [x for x in data if len(__UpperCamelCase			) > HDF5_OBJECT_HEADER_LIMIT]

  # Expecting this to never be true.
  if bad_attributes:
    raise RuntimeError(
        """The following attributes cannot be saved to HDF5 file because """
        f"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
        f"""bytes: {bad_attributes}"""			)

  SCREAMING_SNAKE_CASE__    							=   np.asarray(__UpperCamelCase			)

  SCREAMING_SNAKE_CASE__    							=   1
  SCREAMING_SNAKE_CASE__    							=   np.array_split(__UpperCamelCase				,  __UpperCamelCase			)

  # This will never loop forever thanks to the test above.
  while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data			):
    num_chunks += 1
    SCREAMING_SNAKE_CASE__    							=   np.array_split(__UpperCamelCase				,  __UpperCamelCase			)

  if num_chunks > 1:
    for chunk_id, chunk_data in enumerate(__UpperCamelCase			):
      SCREAMING_SNAKE_CASE__    							=   chunk_data
  else:
    SCREAMING_SNAKE_CASE__    							=   data



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int				,  __UpperCamelCase					: Any			)							->      Union[str, Any]:







  """simple docstring"""





  if name in group.attrs:
    SCREAMING_SNAKE_CASE__    							=   [n.decode("""utf8"""			) if hasattr(__UpperCamelCase				,  """decode"""			) else n for n in group.attrs[name]]
  else:
    SCREAMING_SNAKE_CASE__    							=   []
    SCREAMING_SNAKE_CASE__    							=   0
    while "%s%d" % (name, chunk_id) in group.attrs:
      data.extend(
          [n.decode("""utf8"""			) if hasattr(__UpperCamelCase				,  """decode"""			) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]			)
      chunk_id += 1
  return data






def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Optional[int]:







  """simple docstring"""





  def _expand_single_ad_tensor(__UpperCamelCase					: str			):
    if isinstance(__UpperCamelCase				,  tf.Tensor			) and t.shape.rank == 1:
      return tf.expand_dims(__UpperCamelCase				,  axis=-1			)
    return t

  return tf.nest.map_structure(_expand_single_ad_tensor				,  __UpperCamelCase			)

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.nn.Linear(10 ,			10   )
     SCREAMING_SNAKE_CASE__    							=   torch.optim.SGD(model.parameters() ,			0.1   )
     SCREAMING_SNAKE_CASE__    							=   Accelerator()
     SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(_lowercase   )
     try:
       pickle.loads(pickle.dumps(_lowercase   )   )
     except Exception as e:
       self.fail(f"""Accelerated optimizer pickling failed with {e}"""   )
     AcceleratorState._reset_state()
