





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCAmelCase_      							=     logging.get_logger(__name__)





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



							'''simple docstring'''




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

							_snake_case									=				tf.shape(UpperCamelCase__						)

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

							_snake_case									=				tensor.shape.as_list()

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





def 						lowerCamelCase__     (     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 						lowerCamelCase__     (     UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       List[Any]=1e-5       ,    UpperCamelCase__			:       Optional[int]=-1						)			-> Optional[int]:



							'''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
							_snake_case							,     _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
														_snake_case									=				[1] * inputs.shape.rank
														_snake_case									=				shape_list(UpperCamelCase__						)[axis]
														_snake_case									=				tf.reshape(UpperCamelCase__       ,    UpperCamelCase__						)
														_snake_case									=				tf.reshape(UpperCamelCase__       ,    UpperCamelCase__						)

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       Optional[Any]=0       ,    UpperCamelCase__			:       int=-1						)			-> str:



							'''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

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       tf.Tensor						)			-> tf.Tensor:



							'''simple docstring'''




							if not isinstance(UpperCamelCase__       ,    tf.Tensor						):
														_snake_case									=				tf.convert_to_tensor(UpperCamelCase__						)  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														_snake_case									=				encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														_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))
							_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 						lowerCamelCase__     (     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 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       str						)			-> Union[str, Any]:



							'''simple docstring'''




							_snake_case									=				64_512
							# 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									=				[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}'''						)

							_snake_case									=				np.asarray(UpperCamelCase__						)

							_snake_case									=				1
							_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
														_snake_case									=				np.array_split(UpperCamelCase__       ,    UpperCamelCase__						)

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       List[str]						)			-> Tuple:



							'''simple docstring'''




							if name in group.attrs:
														_snake_case									=				[n.decode('utf8'						) if hasattr(UpperCamelCase__       ,    'decode'						) else n for n in group.attrs[name]]
							else:
														_snake_case									=				[]
														_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 						lowerCamelCase__     (     UpperCamelCase__			:       int						)			-> List[Any]:



							'''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__						)










def 						lowerCamelCase__     (     UpperCamelCase__			:       list[int]       ,    UpperCamelCase__			:       int						)			-> bool:



					'''simple docstring'''




					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				[[False] * (required_sum + 1) for _ in range(arr_len + 1						)]

					# for each arr value, a sum of zero(0) can be formed by not taking any element
					# hence True/1
					for i in range(arr_len + 1						):
										_snake_case									=				True

					# sum is not zero and set is empty then false
					for i in range(1       ,    required_sum + 1						):
										_snake_case									=				False

					for i in range(1       ,    arr_len + 1						):
										for j in range(1       ,    required_sum + 1						):
															if arr[i - 1] > j:
																				_snake_case									=				subset[i - 1][j]
															if arr[i - 1] <= j:
																				_snake_case									=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

					return subset[arr_len][required_sum]


if __name__ == "__main__":
					import doctest

					doctest.testmod()



