





'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


A   : Optional[int]             =			logging.get_logger(__name__)





def       lowercase_      (		lowercase__						)   ->List[int]:
						if isinstance(lowercase__				,  np.ndarray						):
												return list(tensor.shape						)

						_snake_case:   str  					=		tf.shape(lowercase__						)

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

						_snake_case:   List[Any]  					=		tensor.shape.as_list()

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





def       lowercase_      (		lowercase__				,  lowercase__ = None				,  lowercase__ = None						)   ->tf.Tensor:
						return tf.nn.softmax(logits=logits + 1e-9				,  axis=lowercase__				,  name=lowercase__						)





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__=1e-5				,  lowercase__=-1						)   ->Optional[int]:
						# This is a very simplified functional layernorm, designed to duplicate
						# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
						# models in Transformers.

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

						# Compute layer normalization using the batch_normalization
						# function.
						_snake_case:   str  					=		tf.nn.batch_normalization(
						    lowercase__				,  lowercase__				,  lowercase__				,  offset=lowercase__				,  scale=lowercase__				,  variance_epsilon=lowercase__				,  )
						return outputs





def       lowercase_      (		lowercase__				,  lowercase__=0				,  lowercase__=-1						)   ->Union[str, Any]:
						# Replicates the behavior of torch.flatten in TF

						# If end_dim or start_dim is negative, count them from the end
						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

						_snake_case:   Tuple  					=		tf.shape(lowercase__						)
						_snake_case:   str  					=		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]						)
						_snake_case:   Dict  					=		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]				,  axis=0						)
						return tf.reshape(lowercase__				,  lowercase__						)





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

						return encoder_extended_attention_mask





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__ = "input_ids"						)   ->None:
						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       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__						)   ->Union[str, Any]:
						_snake_case:   Union[str, Any]  					=		6_4512
						# 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.
						_snake_case:   Union[str, Any]  					=		[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}'''						)

						_snake_case:   List[Any]  					=		np.asarray(lowercase__						)

						_snake_case:   Optional[int]  					=		1
						_snake_case:   Tuple  					=		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
												_snake_case:   List[str]  					=		np.array_split(lowercase__				,  lowercase__						)

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





def       lowercase_      (		lowercase__				,  lowercase__						)   ->Optional[int]:
						if name in group.attrs:
												_snake_case:   Dict  					=		[n.decode('utf8'						) if hasattr(lowercase__				,  'decode'						) else n for n in group.attrs[name]]
						else:
												_snake_case:   Dict  					=		[]
												_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       lowercase_      (		lowercase__						)   ->Dict:

						def _expand_single_ad_tensor(lowercase__						):
												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__						)












'''simple docstring'''






from ..utils import DummyObject, requires_backends







class 			lowerCamelCase					(						metaclass=__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["torch", "scipy"]


					def __init__(					self	:  Tuple       ,     *__snake_case	:  List[Any]       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





											requires_backends(self       ,     ['torch', 'scipy']       )


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Optional[int]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											requires_backends(cls       ,     ['torch', 'scipy']       )




					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  List[Any]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  str       ):





											'''simple docstring'''





											requires_backends(cls       ,     ['torch', 'scipy']       )





