


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


a : Any		=				logging.get_logger(__name__)


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







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

			__lowercase 					=   tf.shape(_UpperCamelCase  )

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

			__lowercase 					=   tensor.shape.as_list()

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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = None       ,			_UpperCamelCase = None  ):


			'''simple docstring'''







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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase=1E-5       ,			_UpperCamelCase=-1  ):


			'''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
			__lowercase					, __lowercase 					=   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
						__lowercase 					=   [1] * inputs.shape.rank
						__lowercase 					=   shape_list(_UpperCamelCase  )[axis]
						__lowercase 					=   tf.reshape(_UpperCamelCase       ,			_UpperCamelCase  )
						__lowercase 					=   tf.reshape(_UpperCamelCase       ,			_UpperCamelCase  )

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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=0       ,			_UpperCamelCase=-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

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


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase       ,			tf.Tensor  ):
						__lowercase 					=   tf.convert_to_tensor(_UpperCamelCase  )  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						__lowercase 					=   encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						__lowercase 					=   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))
			__lowercase 					=   (
			    tf.cast(1       ,			encoder_attention_mask.dtype  ) - encoder_extended_attention_mask
			) * encoder_extended_attention_mask.dtype.min

			return encoder_extended_attention_mask


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase = "input_ids"  ):


			'''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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   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.
			__lowercase 					=   [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}'  )

			__lowercase 					=   np.asarray(_UpperCamelCase  )

			__lowercase 					=   1
			__lowercase 					=   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
						__lowercase 					=   np.array_split(_UpperCamelCase       ,			_UpperCamelCase  )

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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			if name in group.attrs:
						__lowercase 					=   [n.decode('''utf8'''  ) if hasattr(_UpperCamelCase       ,			'''decode'''  ) else n for n in group.attrs[name]]
			else:
						__lowercase 					=   []
						__lowercase 					=   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 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			def _expand_single_ad_tensor(_UpperCamelCase  ):
						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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return price * (1 + tax_rate)


if __name__ == "__main__":
	print(f'''{price_plus_tax(100, 0.25) = }''')
	print(f'''{price_plus_tax(125.50, 0.05) = }''')


