'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_lowercase		:				Tuple	       =		logging.get_logger(__name__)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[int]				)    ->   Dict:
							if isinstance(_lowerCAmelCase   ,   np.ndarray				):
														return list(tensor.shape				)

							lowercase_      :    Union[str, Any]       		= tf.shape(_lowerCAmelCase				)

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

							lowercase_      :    Any       		= tensor.shape.as_list()

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




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      List[Any] = None   ,   UpperCAmelCase__ :      int = None				)    ->   Optional[int]:
							return tf.nn.softmax(logits=logits + 1e-9   ,   axis=_lowerCAmelCase   ,   name=_lowerCAmelCase				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Dict   ,   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      str=1e-5   ,   UpperCAmelCase__ :      Union[str, Any]=-1				)    ->   Optional[Any]:
							# 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(_lowerCAmelCase   ,   _lowerCAmelCase				):
														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_      :    int       		= tf.nn.moments(_lowerCAmelCase   ,   axes=[axis]   ,   keepdims=_lowerCAmelCase				)

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

							# Compute layer normalization using the batch_normalization
							# function.
							lowercase_      :    List[Any]       		= tf.nn.batch_normalization(
							    _lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   offset=_lowerCAmelCase   ,   scale=_lowerCAmelCase   ,   variance_epsilon=_lowerCAmelCase   ,   )
							return outputs




def 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      Optional[Any]=0   ,   UpperCAmelCase__ :      Optional[int]=-1				)    ->   str:
							# 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

							lowercase_      :    int       		= tf.shape(_lowerCAmelCase				)
							lowercase_      :    Dict       		= tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
							lowercase_      :    Union[str, Any]       		= tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]   ,   axis=0				)
							return tf.reshape(_lowerCAmelCase   ,   _lowerCAmelCase				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[Any]				)    ->   List[str]:
							if not isinstance(_lowerCAmelCase   ,   tf.Tensor				):
														lowercase_      :    Union[str, Any]       		= tf.convert_to_tensor(_lowerCAmelCase				)  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														lowercase_      :    List[Any]       		= encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														lowercase_      :    Optional[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))
							lowercase_      :    Dict       		= (
							    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__ :      Optional[Any]   ,   UpperCAmelCase__ :      Dict   ,   UpperCAmelCase__ :      int = "input_ids"				)    ->   Union[str, Any]:
							tf.debugging.assert_less(
							    _lowerCAmelCase   ,   tf.cast(_lowerCAmelCase   ,   dtype=tensor.dtype				)   ,   message=(
							        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(_lowerCAmelCase				)}) 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__ :      Tuple   ,   UpperCAmelCase__ :      Dict   ,   UpperCAmelCase__ :      Optional[int]				)    ->   int:
							lowercase_      :    Dict       		= 64512
							# 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_      :    Tuple       		= [x for x in data if len(_lowerCAmelCase				) > 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_      :    Union[str, Any]       		= np.asarray(_lowerCAmelCase				)

							lowercase_      :    Optional[Any]       		= 1
							lowercase_      :    List[str]       		= np.array_split(_lowerCAmelCase   ,   _lowerCAmelCase				)

							# 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_      :    List[Any]       		= np.array_split(_lowerCAmelCase   ,   _lowerCAmelCase				)

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(_lowerCAmelCase				):
																					lowercase_      :    Optional[int]       		= chunk_data
							else:
														lowercase_      :    Dict       		= data




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Optional[int]				)    ->   Tuple:
							if name in group.attrs:
														lowercase_      :    Any       		= [n.decode("""utf8"""				) if hasattr(_lowerCAmelCase   ,   """decode"""				) else n for n in group.attrs[name]]
							else:
														lowercase_      :    str       		= []
														lowercase_      :    str       		= 0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("""utf8"""				) if hasattr(_lowerCAmelCase   ,   """decode"""				) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]				)
																					chunk_id += 1
							return data




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[Any]				)    ->   List[str]:

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

							return tf.nest.map_structure(_expand_single_ad_tensor   ,   _lowerCAmelCase				)







'''simple docstring'''




_lowercase		:				int	       =		"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"




def 							lowerCamelCase			(   UpperCAmelCase__ :      bytes				)    ->   bytes:
							# Make sure the supplied data is a bytes-like object
							if not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    Union[str, Any]       		= F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
														raise TypeError(UpperCAmelCase__				)

							lowercase_      :    Dict       		= """""".join(bin(UpperCAmelCase__				)[2:].zfill(8				) for byte in data				)

							lowercase_      :    Union[str, Any]       		= len(UpperCAmelCase__				) % 6 != 0

							if padding_needed:
														# The padding that will be added later
														lowercase_      :    List[Any]       		= b"""=""" * ((6 - len(UpperCAmelCase__				) % 6) // 2)

														# Append binary_stream with arbitrary binary digits (0's by default) to make its
														# length a multiple of 6.
														binary_stream += "0" * (6 - len(UpperCAmelCase__				) % 6)
							else:
														lowercase_      :    Union[str, Any]       		= b""""""

							# Encode every 6 binary digits to their corresponding Base64 character
							return (
							    "".join(
							        B64_CHARSET[int(binary_stream[index : index + 6]   ,   2				)]
							        for index in range(0   ,   len(UpperCAmelCase__				)   ,   6				)				).encode()
							    + padding
							)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str				)    ->   bytes:
							# Make sure encoded_data is either a string or a bytes-like object
							if not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				) and not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    List[str]       		= (
														    """argument should be a bytes-like object or ASCII string, """
														    F'''not \'{encoded_data.__class__.__name__}\''''
														)
														raise TypeError(UpperCAmelCase__				)

							# In case encoded_data is a bytes-like object, make sure it contains only
							# ASCII characters so we convert it to a string object
							if isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														try:
																					lowercase_      :    Optional[int]       		= encoded_data.decode("""utf-8"""				)
														except UnicodeDecodeError:
																					raise ValueError("""base64 encoded data should only contain ASCII characters"""				)

							lowercase_      :    Any       		= encoded_data.count("""="""				)

							# Check if the encoded string contains non base64 characters
							if padding:
														assert all(
														    char in B64_CHARSET for char in encoded_data[:-padding]				), "Invalid base64 character(s) found."
							else:
														assert all(
														    char in B64_CHARSET for char in encoded_data				), "Invalid base64 character(s) found."

							# Check the padding
							assert len(UpperCAmelCase__				) % 4 == 0 and padding < 3, "Incorrect padding"

							if padding:
														# Remove padding if there is one
														lowercase_      :    Optional[int]       		= encoded_data[:-padding]

														lowercase_      :    Any       		= """""".join(
														    bin(B64_CHARSET.index(UpperCAmelCase__				)				)[2:].zfill(6				) for char in encoded_data				)[: -padding * 2]
							else:
														lowercase_      :    int       		= """""".join(
														    bin(B64_CHARSET.index(UpperCAmelCase__				)				)[2:].zfill(6				) for char in encoded_data				)

							lowercase_      :    Optional[int]       		= [
							    int(binary_stream[index : index + 8]   ,   2				)
							    for index in range(0   ,   len(UpperCAmelCase__				)   ,   8				)
							]

							return bytes(UpperCAmelCase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





