
'''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


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{'vocab_file': 'spiece.model'}

__lowerCAmelCase												=	{
    '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'
        ),
    }
}

__lowerCAmelCase												=	{
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						VOCAB_FILES_NAMES
							lowerCAmelCase_ 						=						PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_ 						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_ 						=						["input_ids", "attention_mask"]
							lowerCAmelCase_ 						=						[]

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase="<unk>"  ,  UpperCAmelCase="<s>"  ,  UpperCAmelCase="</s>"  ,  UpperCAmelCase="<pad>"  ,  UpperCAmelCase="[SEP]"  ,  UpperCAmelCase="[MASK]"  ,  UpperCAmelCase="[CLS]"  ,  UpperCAmelCase = None  ,  **UpperCAmelCase  ,  )					->       None:
														_snake_case       =  AddedToken(UpperCAmelCase  ,  lstrip=UpperCAmelCase  ,  rstrip=UpperCAmelCase  ) if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ) else bos_token
														_snake_case       =  AddedToken(UpperCAmelCase  ,  lstrip=UpperCAmelCase  ,  rstrip=UpperCAmelCase  ) if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ) else eos_token
														_snake_case       =  AddedToken(UpperCAmelCase  ,  lstrip=UpperCAmelCase  ,  rstrip=UpperCAmelCase  ) if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ) else unk_token
														_snake_case       =  AddedToken(UpperCAmelCase  ,  lstrip=UpperCAmelCase  ,  rstrip=UpperCAmelCase  ) if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ) else pad_token
														_snake_case       =  AddedToken(UpperCAmelCase  ,  lstrip=UpperCAmelCase  ,  rstrip=UpperCAmelCase  ) if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ) else cls_token
														_snake_case       =  AddedToken(UpperCAmelCase  ,  lstrip=UpperCAmelCase  ,  rstrip=UpperCAmelCase  ) if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ) else sep_token

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

														_snake_case       =  {} if sp_model_kwargs is None else sp_model_kwargs

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

														_snake_case       =  vocab_file

														_snake_case       =  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
														self.sp_model.Load(UpperCAmelCase  )

							@property
							def 	lowercase							(self  )					->       str:
														return self.sp_model.get_piece_size()

							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  {self.convert_ids_to_tokens(UpperCAmelCase  ): i for i in range(self.vocab_size  )}
														vocab.update(self.added_tokens_encoder  )
														return vocab

							def __getstate__(self  )					->       Tuple:
														_snake_case       =  self.__dict__.copy()
														_snake_case       =  None
														return state

							def __setstate__(self  ,  UpperCAmelCase  )					->       Tuple:
														_snake_case       =  d

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

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

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       List[str]:
														return self.sp_model.encode(UpperCAmelCase  ,  out_type=UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return self.sp_model.piece_to_id(UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  self.sp_model.IdToPiece(UpperCAmelCase  )
														return token

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Union[str, Any]:
														_snake_case       =  []
														_snake_case       =  """"""
														_snake_case       =  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(UpperCAmelCase  ) + token
																												_snake_case       =  True
																												_snake_case       =  []
																					else:
																												current_sub_tokens.append(UpperCAmelCase  )
																												_snake_case       =  False
														out_string += self.sp_model.decode(UpperCAmelCase  )
														return out_string.strip()

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = False  ,  UpperCAmelCase = None  ,  UpperCAmelCase = True  ,  **UpperCAmelCase  ,  )					->       str:
														_snake_case       =  kwargs.pop("""use_source_tokenizer"""  ,  UpperCAmelCase  )

														_snake_case       =  self.convert_ids_to_tokens(UpperCAmelCase  ,  skip_special_tokens=UpperCAmelCase  )

														# 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
														_snake_case       =  []
														_snake_case       =  []
														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(UpperCAmelCase  )  )
																																			_snake_case       =  []
																												sub_texts.append(UpperCAmelCase  )
																					else:
																												current_sub_text.append(UpperCAmelCase  )
														if current_sub_text:
																					sub_texts.append(self.convert_tokens_to_string(UpperCAmelCase  )  )

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

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

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Tuple[str]:
														if not os.path.isdir(UpperCAmelCase  ):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
																					return
														_snake_case       =  os.path.join(
														    UpperCAmelCase  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

														return (out_vocab_file,)

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       List[int]:
														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														_snake_case       =  [self.cls_token_id]
														_snake_case       =  [self.sep_token_id]
														return cls + token_ids_a + sep + token_ids_a + sep

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = False  )					->       List[int]:
														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=UpperCAmelCase  ,  token_ids_a=UpperCAmelCase  ,  already_has_special_tokens=UpperCAmelCase  )

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





							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       List[int]:
														_snake_case       =  [self.sep_token_id]
														_snake_case       =  [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]

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase												=	{
    'configuration_efficientformer': [
        'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientFormerConfig',
    ]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	['EfficientFormerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'EfficientFormerForImageClassification',
				    'EfficientFormerForImageClassificationWithTeacher',
				    'EfficientFormerModel',
				    'EfficientFormerPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFEfficientFormerForImageClassification',
				    'TFEfficientFormerForImageClassificationWithTeacher',
				    'TFEfficientFormerModel',
				    'TFEfficientFormerPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_efficientformer import EfficientFormerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_efficientformer import (
								    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EfficientFormerForImageClassification,
								    EfficientFormerForImageClassificationWithTeacher,
								    EfficientFormerModel,
								    EfficientFormerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_efficientformer import (
								    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFEfficientFormerForImageClassification,
								    TFEfficientFormerForImageClassificationWithTeacher,
								    TFEfficientFormerModel,
								    TFEfficientFormerPreTrainedModel,
								)

else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)