




'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_snake_case				:				Dict       							=		logging.get_logger(__name__)
def 		snake_case_   (UpperCamelCase	:  Union[tf.Tensor, np.ndarray]				):





							'''simple docstring'''


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

							_a     					=    tf.shape(UpperCamelCase				)

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

							_a     					=    tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase				)]
def 		snake_case_   (UpperCamelCase	:  tf.Tensor			,						UpperCamelCase	:  Optional[int] = None			,						UpperCamelCase	:  Optional[str] = None				):





							'''simple docstring'''


							return tf.nn.softmax(logits=logits + 1e-9			,						axis=UpperCamelCase			,						name=UpperCamelCase				)
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[str]			,						UpperCamelCase	:  Tuple=1e-5			,						UpperCamelCase	:  Tuple=-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
							_a					,							_a     					=    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
														_a     					=    [1] * inputs.shape.rank
														_a     					=    shape_list(UpperCamelCase				)[axis]
														_a     					=    tf.reshape(UpperCamelCase			,						UpperCamelCase				)
														_a     					=    tf.reshape(UpperCamelCase			,						UpperCamelCase				)

							# Compute layer normalization using the batch_normalization
							# function.
							_a     					=    tf.nn.batch_normalization(
							    UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						offset=UpperCamelCase			,						scale=UpperCamelCase			,						variance_epsilon=UpperCamelCase			,						)
							return outputs
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  List[str]=0			,						UpperCamelCase	:  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

							_a     					=    tf.shape(UpperCamelCase				)
							_a     					=    tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
							_a     					=    tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]			,						axis=0				)
							return tf.reshape(UpperCamelCase			,						UpperCamelCase				)
def 		snake_case_   (UpperCamelCase	:  tf.Tensor				):





							'''simple docstring'''


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

							return encoder_extended_attention_mask
def 		snake_case_   (UpperCamelCase	:  tf.Tensor			,						UpperCamelCase	:  int			,						UpperCamelCase	:  str = "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 		snake_case_   (UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							_a     					=    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.
							_a     					=    [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}'				)

							_a     					=    np.asarray(UpperCamelCase				)

							_a     					=    1
							_a     					=    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
														_a     					=    np.array_split(UpperCamelCase			,						UpperCamelCase				)

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(UpperCamelCase				):
																					_a     					=    chunk_data
							else:
														_a     					=    data
def 		snake_case_   (UpperCamelCase	:  Any			,						UpperCamelCase	:  Any				):





							'''simple docstring'''


							if name in group.attrs:
														_a     					=    [n.decode('''utf8'''				) if hasattr(UpperCamelCase			,						'''decode'''				) else n for n in group.attrs[name]]
							else:
														_a     					=    []
														_a     					=    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 		snake_case_   (UpperCamelCase	:  Tuple				):





							'''simple docstring'''


							def _expand_single_ad_tensor(UpperCamelCase	:  List[Any]				):
														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				)







'''simple docstring'''


import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_snake_case				:				str       							=		logging.get_logger(__name__)

_snake_case				:				Tuple       							=		{'vocab_file': 'spiece.model'}

_snake_case				:				Optional[int]       							=		{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

_snake_case				:				Tuple       							=		{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}

class       A   (		_a   ):
							lowercase_          =    VOCAB_FILES_NAMES
							lowercase_          =    PRETRAINED_VOCAB_FILES_MAP
							lowercase_          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_          =    ['input_ids', 'attention_mask']
							lowercase_          =    []







							def __init__(  self	:  Optional[Any]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[int]="<unk>"							,      lowerCAmelCase_	:  Tuple="<s>"							,      lowerCAmelCase_	:  List[str]="</s>"							,      lowerCAmelCase_	:  int="<pad>"							,      lowerCAmelCase_	:  Union[str, Any]="[SEP]"							,      lowerCAmelCase_	:  Dict="[MASK]"							,      lowerCAmelCase_	:  Optional[int]="[CLS]"							,      lowerCAmelCase_	:  Optional[Dict[str, Any]] = None							,      **lowerCAmelCase_	:  Union[str, Any]							,      )      ->							None:


														"""simple docstring"""


														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else bos_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else eos_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else unk_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else pad_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else cls_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else sep_token

														# Mask token behave like a normal word, i.e. include the space before it
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else mask_token

														_a     					=    {} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=lowerCAmelCase_							,      eos_token=lowerCAmelCase_							,      unk_token=lowerCAmelCase_							,      pad_token=lowerCAmelCase_							,      sep_token=lowerCAmelCase_							,      mask_token=lowerCAmelCase_							,      cls_token=lowerCAmelCase_							,      sp_model_kwargs=self.sp_model_kwargs							,      **lowerCAmelCase_							,      )

														_a     					=    vocab_file

														_a     					=    spm.SentencePieceProcessor(**self.sp_model_kwargs )
														self.sp_model.Load(lowerCAmelCase_ )







							@property
							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							int:


														"""simple docstring"""


														return self.sp_model.get_piece_size()







							def        __lowerCAmelCase      (  self	:  str )      ->							List[str]:


														"""simple docstring"""


														_a     					=    {self.convert_ids_to_tokens(lowerCAmelCase_ ): i for i in range(self.vocab_size )}
														vocab.update(self.added_tokens_encoder )
														return vocab







							def __getstate__(  self	:  str )      ->							Tuple:


														"""simple docstring"""


														_a     					=    self.__dict__.copy()
														_a     					=    None
														return state







							def __setstate__(  self	:  List[str]							,      lowerCAmelCase_	:  Any )      ->							Dict:


														"""simple docstring"""


														_a     					=    d

														# for backward compatibility
														if not hasattr(self							,      '''sp_model_kwargs''' ):
																					_a     					=    {}

														_a     					=    spm.SentencePieceProcessor(**self.sp_model_kwargs )
														self.sp_model.Load(self.vocab_file )







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  str )      ->							List[str]:


														"""simple docstring"""


														return self.sp_model.encode(lowerCAmelCase_							,      out_type=lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  List[str] )      ->							int:


														"""simple docstring"""


														return self.sp_model.piece_to_id(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    self.sp_model.IdToPiece(lowerCAmelCase_ )
														return token







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  Union[str, Any] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    []
														_a     					=    ''''''
														_a     					=    False
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												if not prev_is_special:
																																			out_string += " "
																												out_string += self.sp_model.decode(lowerCAmelCase_ ) + token
																												_a     					=    True
																												_a     					=    []
																					else:
																												current_sub_tokens.append(lowerCAmelCase_ )
																												_a     					=    False
														out_string += self.sp_model.decode(lowerCAmelCase_ )
														return out_string.strip()







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  bool = False							,      lowerCAmelCase_	:  bool = None							,      lowerCAmelCase_	:  bool = True							,      **lowerCAmelCase_	:  Tuple							,      )      ->							str:


														"""simple docstring"""


														_a     					=    kwargs.pop('''use_source_tokenizer'''							,      lowerCAmelCase_ )

														_a     					=    self.convert_ids_to_tokens(lowerCAmelCase_							,      skip_special_tokens=lowerCAmelCase_ )

														# To avoid mixing byte-level and unicode for byte-level BPT
														# we need to build string separately for added tokens and byte-level tokens
														# cf. https://github.com/huggingface/transformers/issues/1133
														_a     					=    []
														_a     					=    []
														for token in filtered_tokens:
																					if skip_special_tokens and token in self.all_special_ids:
																												continue
																					if token in self.added_tokens_encoder:
																												if current_sub_text:
																																			sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_ ) )
																																			_a     					=    []
																												sub_texts.append(lowerCAmelCase_ )
																					else:
																												current_sub_text.append(lowerCAmelCase_ )
														if current_sub_text:
																					sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_ ) )

														# Mimic the behavior of the Rust tokenizer:
														# No space before [MASK] and [SEP]
														if spaces_between_special_tokens:
																					_a     					=    re.sub(R''' (\[(MASK|SEP)\])'''							,      R'''\1'''							,      ''' '''.join(lowerCAmelCase_ ) )
														else:
																					_a     					=    ''''''.join(lowerCAmelCase_ )

														_a     					=    (
														    clean_up_tokenization_spaces
														    if clean_up_tokenization_spaces is not None
														    else self.clean_up_tokenization_spaces
														)
														if clean_up_tokenization_spaces:
																					_a     					=    self.clean_up_tokenization(lowerCAmelCase_ )
																					return clean_text
														else:
																					return text







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[str] = None )      ->							Tuple[str]:


														"""simple docstring"""


														if not os.path.isdir(lowerCAmelCase_ ):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
																					return
														_a     					=    os.path.join(
														    lowerCAmelCase_							,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

														if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase_ ) and os.path.isfile(self.vocab_file ):
																					copyfile(self.vocab_file							,      lowerCAmelCase_ )
														elif not os.path.isfile(self.vocab_file ):
																					with open(lowerCAmelCase_							,      '''wb''' ) as fi:
																												_a     					=    self.sp_model.serialized_model_proto()
																												fi.write(lowerCAmelCase_ )

														return (out_vocab_file,)







							def        __lowerCAmelCase      (  self	:  int							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None )      ->							List[int]:


														"""simple docstring"""


														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														_a     					=    [self.cls_token_id]
														_a     					=    [self.sep_token_id]
														return cls + token_ids_a + sep + token_ids_a + sep







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None							,      lowerCAmelCase_	:  bool = False )      ->							List[int]:


														"""simple docstring"""


														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowerCAmelCase_							,      token_ids_a=lowerCAmelCase_							,      already_has_special_tokens=lowerCAmelCase_ )

														if token_ids_a is None:
																					return [1] + ([0] * len(lowerCAmelCase_ )) + [1]
														return [1] + ([0] * len(lowerCAmelCase_ )) + [1] + ([0] * len(lowerCAmelCase_ )) + [1]







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None )      ->							List[int]:


														"""simple docstring"""


														_a     					=    [self.sep_token_id]
														_a     					=    [self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep ) * [0]
														return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]

