from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


SCREAMING_SNAKE_CASE__		:       int       						=       logging.get_logger(__name__)







def      _a					(   lowercase__	:      Union[tf.Tensor, np.ndarray]			):







     '''simple docstring'''


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

     SCREAMING_SNAKE_CASE__					:					str      =		tf.shape(lowercase__			)

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

     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		tensor.shape.as_list()

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







def      _a					(   lowercase__	:      tf.Tensor  ,	lowercase__	:      Optional[int] = None  ,	lowercase__	:      Optional[str] = None			):







     '''simple docstring'''


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







def      _a					(   lowercase__	:      List[Any]  ,	lowercase__	:      Optional[int]  ,	lowercase__	:      Tuple  ,	lowercase__	:      Optional[Any]=1E-5  ,	lowercase__	:      Union[str, Any]=-1			):







     '''simple docstring'''


     if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowercase__  ,	lowercase__			):
          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__					:					Optional[int]      =		tf.nn.moments(lowercase__  ,	axes=[axis]  ,	keepdims=lowercase__			)

     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__					:					List[Any]      =		[1] * inputs.shape.rank
          SCREAMING_SNAKE_CASE__					:					Dict      =		shape_list(lowercase__			)[axis]
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		tf.reshape(lowercase__  ,	lowercase__			)
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		tf.reshape(lowercase__  ,	lowercase__			)

     # Compute layer normalization using the batch_normalization
     # function.
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		tf.nn.batch_normalization(
         lowercase__  ,	lowercase__  ,	lowercase__  ,	offset=lowercase__  ,	scale=lowercase__  ,	variance_epsilon=lowercase__  ,	)
     return outputs







def      _a					(   lowercase__	:      List[Any]  ,	lowercase__	:      Dict=0  ,	lowercase__	:      List[Any]=-1			):







     '''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__					:					str      =		tf.shape(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Dict      =		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]  ,	axis=0			)
     return tf.reshape(lowercase__  ,	lowercase__			)







def      _a					(   lowercase__	:      tf.Tensor			):







     '''simple docstring'''


     if not isinstance(lowercase__  ,	tf.Tensor			):
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		tf.convert_to_tensor(lowercase__			)  # Catches stray NumPy inputs
     if encoder_attention_mask.shape.rank == 3:
          SCREAMING_SNAKE_CASE__					:					str      =		encoder_attention_mask[:, None, :, :]
     if encoder_attention_mask.shape.rank == 2:
          SCREAMING_SNAKE_CASE__					:					Any      =		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__					:					Any      =		(
         tf.cast(1  ,	encoder_attention_mask.dtype			) - encoder_extended_attention_mask
     ) * encoder_extended_attention_mask.dtype.min

     return encoder_extended_attention_mask







def      _a					(   lowercase__	:      tf.Tensor  ,	lowercase__	:      int  ,	lowercase__	:      str = "input_ids"			):







     '''simple docstring'''


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







def      _a					(   lowercase__	:      Union[str, Any]  ,	lowercase__	:      Union[str, Any]  ,	lowercase__	:      Optional[Any]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Any      =		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__					:					str      =		[x for x in data if len(lowercase__			) > 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__					:					Any      =		np.asarray(lowercase__			)

     SCREAMING_SNAKE_CASE__					:					str      =		1
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		np.array_split(lowercase__  ,	lowercase__			)

     # 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__					:					Optional[Any]      =		np.array_split(lowercase__  ,	lowercase__			)

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







def      _a					(   lowercase__	:      List[Any]  ,	lowercase__	:      int			):







     '''simple docstring'''


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







def      _a					(   lowercase__	:      Optional[int]			):







     '''simple docstring'''


     def _expand_single_ad_tensor(lowercase__	:      Optional[int]			):
          if isinstance(lowercase__  ,	tf.Tensor			) and t.shape.rank == 1:
               return tf.expand_dims(lowercase__  ,	axis=-1			)
          return t

     return tf.nest.map_structure(_expand_single_ad_tensor  ,	lowercase__			)







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""simple docstring"""







	A             =					HfArgumentParser(UpperCamelCase__    )
	A             =					parser.parse_args_into_dataclasses()[0]
	A             =					TensorFlowBenchmark(args=UpperCamelCase__    )
	try:
		A             =					parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		A             =					"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
		A             =					""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
		A             =					""""""
		A             =					eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
		A             =					[]
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]    )
			else:
				wrong_args.append(UpperCamelCase__    )
		if len(UpperCamelCase__    ) > 0:
			A             =					full_error_msg + begin_error_msg + str(UpperCamelCase__    )
		raise ValueError(UpperCamelCase__    )
	benchmark.run()


if __name__ == "__main__":
				main()



