


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCamelCase											=       logging.get_logger(__name__)




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[tf.Tensor, np.ndarray]     ):
       '''simple docstring'''





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

       _lowerCamelCase       :		str		=tf.shape(SCREAMING_SNAKE_CASE__     )

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

       _lowerCamelCase       :		Optional[int]		=tensor.shape.as_list()

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




def 	a_      (    SCREAMING_SNAKE_CASE__					:      tf.Tensor		,			SCREAMING_SNAKE_CASE__					:      Optional[int] = None		,			SCREAMING_SNAKE_CASE__					:      Optional[str] = None     ):
       '''simple docstring'''





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




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Dict		,			SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      List[Any]		,			SCREAMING_SNAKE_CASE__					:      List[Any]=1e-5		,			SCREAMING_SNAKE_CASE__					:      str=-1     ):
       '''simple docstring'''





       if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     ):
              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
       _lowerCamelCase	,     _lowerCamelCase       :		Union[str, Any]		=tf.nn.moments(SCREAMING_SNAKE_CASE__		,			axes=[axis]		,			keepdims=SCREAMING_SNAKE_CASE__     )

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

       # Compute layer normalization using the batch_normalization
       # function.
       _lowerCamelCase       :		Any		=tf.nn.batch_normalization(
           SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			offset=SCREAMING_SNAKE_CASE__		,			scale=SCREAMING_SNAKE_CASE__		,			variance_epsilon=SCREAMING_SNAKE_CASE__		,			)
       return outputs




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      Any=0		,			SCREAMING_SNAKE_CASE__					:      str=-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

       _lowerCamelCase       :		List[str]		=tf.shape(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		int		=tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]     )
       _lowerCamelCase       :		Optional[Any]		=tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]		,			axis=0     )
       return tf.reshape(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      tf.Tensor     ):
       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE__		,			tf.Tensor     ):
              _lowerCamelCase       :		str		=tf.convert_to_tensor(SCREAMING_SNAKE_CASE__     )  # Catches stray NumPy inputs
       if encoder_attention_mask.shape.rank == 3:
              _lowerCamelCase       :		List[str]		=encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.shape.rank == 2:
              _lowerCamelCase       :		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))
       _lowerCamelCase       :		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_      (    SCREAMING_SNAKE_CASE__					:      tf.Tensor		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      str = "input_ids"     ):
       '''simple docstring'''





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




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[Any]		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Dict     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[int]		=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.
       _lowerCamelCase       :		Any		=[x for x in data if len(SCREAMING_SNAKE_CASE__     ) > 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}'''     )

       _lowerCamelCase       :		int		=np.asarray(SCREAMING_SNAKE_CASE__     )

       _lowerCamelCase       :		Optional[int]		=1
       _lowerCamelCase       :		Dict		=np.array_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       # 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
              _lowerCamelCase       :		List[str]		=np.array_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       if num_chunks > 1:
              for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE__     ):
                     _lowerCamelCase       :		List[Any]		=chunk_data
       else:
              _lowerCamelCase       :		List[str]		=data




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Any		,			SCREAMING_SNAKE_CASE__					:      Any     ):
       '''simple docstring'''





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




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[Any]     ):
       '''simple docstring'''





       def _expand_single_ad_tensor(SCREAMING_SNAKE_CASE__					:      Union[str, Any]     ):
              if isinstance(SCREAMING_SNAKE_CASE__		,			tf.Tensor     ) and t.shape.rank == 1:
                     return tf.expand_dims(SCREAMING_SNAKE_CASE__		,			axis=-1     )
              return t

       return tf.nest.map_structure(_expand_single_ad_tensor		,			SCREAMING_SNAKE_CASE__     )






# Lint as: python3
import itertools
import os
import re


lowerCamelCase											=       re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase											=       re.compile(R'([a-z\d])([A-Z])')

lowerCamelCase											=       re.compile(R'(?<!_)_(?!_)')
lowerCamelCase											=       re.compile(R'(_{2,})')

lowerCamelCase											=       R'^\w+(\.\w+)*$'

lowerCamelCase											=       R'<>:/\|?*'




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[str]		=_uppercase_uppercase_re.sub(r'\1_\2'		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		List[Any]		=_lowercase_uppercase_re.sub(r'\1_\2'		,			SCREAMING_SNAKE_CASE__     )
       return name.lower()




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=_single_underscore_re.split(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Tuple		=[_multiple_underscores_re.split(SCREAMING_SNAKE_CASE__     ) for n in name]
       return "".join(n.capitalize() for n in itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__     ) if n != ''     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[Any]     ):
       '''simple docstring'''





       if os.path.basename(SCREAMING_SNAKE_CASE__     ) != name:
              raise ValueError(F'''Should be a dataset name, not a path: {name}'''     )
       return camelcase_to_snakecase(SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]     ):
       '''simple docstring'''





       if os.path.basename(SCREAMING_SNAKE_CASE__     ) != name:
              raise ValueError(F'''Should be a dataset name, not a path: {name}'''     )
       if not re.match(_split_re		,			SCREAMING_SNAKE_CASE__     ):
              raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''     )
       return F'''{filename_prefix_for_name(SCREAMING_SNAKE_CASE__     )}-{split}'''




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      List[Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      int=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=filename_prefix_for_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if filetype_suffix:
              prefix += F'''.{filetype_suffix}'''
       _lowerCamelCase       :		Optional[Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return F'''{filepath}*'''




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      List[str]=None		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		=filename_prefix_for_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Union[str, Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       if shard_lengths:
              _lowerCamelCase       :		Union[str, Any]		=len(SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Optional[int]		=[F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(SCREAMING_SNAKE_CASE__     )]
              if filetype_suffix:
                     _lowerCamelCase       :		List[Any]		=[filename + F'''.{filetype_suffix}''' for filename in filenames]
              return filenames
       else:
              _lowerCamelCase       :		Any		=prefix
              if filetype_suffix:
                     filename += F'''.{filetype_suffix}'''
              return [filename]


