

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_big_bird import BigBirdTokenizer
else:
		A									=  None

A									=  logging.get_logger(__name__)
A									=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A									=  {
    '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'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

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


A									=  '▁'


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	VOCAB_FILES_NAMES
			__A    	=	PRETRAINED_VOCAB_FILES_MAP
			__A    	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A    	=	BigBirdTokenizer
			__A    	=	["""input_ids""", """attention_mask"""]
			__A    	=	[]


			def __init__(     self			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase="<unk>"			,					__UpperCamelCase="<s>"			,					__UpperCamelCase="</s>"			,					__UpperCamelCase="<pad>"			,					__UpperCamelCase="[SEP]"			,					__UpperCamelCase="[MASK]"			,					__UpperCamelCase="[CLS]"			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				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

				super().__init__(
				    __UpperCamelCase			,					tokenizer_file=__UpperCamelCase			,					bos_token=__UpperCamelCase			,					eos_token=__UpperCamelCase			,					unk_token=__UpperCamelCase			,					sep_token=__UpperCamelCase			,					pad_token=__UpperCamelCase			,					cls_token=__UpperCamelCase			,					mask_token=__UpperCamelCase			,					**__UpperCamelCase			,					)

				snake_case_   		=			vocab_file
				snake_case_   		=			False if not self.vocab_file else True


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None			,					__UpperCamelCase = False				):

				"""simple docstring"""


				if already_has_special_tokens:
					if token_ids_a is not None:
						raise ValueError(
						    'You should not supply a second sequence if the provided sequence of '
						    'ids is already formatted with special tokens for the model.'				)
					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None				):

				"""simple docstring"""


				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]







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None				):

				"""simple docstring"""


				if not self.can_save_slow_tokenizer:
					raise ValueError(
					    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
					    'tokenizer.'				)

				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				):
					copyfile(self.vocab_file			,					__UpperCamelCase				)

				return (out_vocab_file,)



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A									=  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MraForMaskedLM',
		    'MraForMultipleChoice',
		    'MraForQuestionAnswering',
		    'MraForSequenceClassification',
		    'MraForTokenClassification',
		    'MraLayer',
		    'MraModel',
		    'MraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure)
