





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_lowerCamelCase   				=			logging.get_logger(__name__)


_lowerCamelCase   				=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCamelCase   				=			{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_lowerCamelCase   				=			{
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					VOCAB_FILES_NAMES
							lowerCamelCase_           =					PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase_           =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase_           =					["input_ids", "attention_mask"]
							lowerCamelCase_           =					BartTokenizer


							def __init__(							self    :str							,				__A    :Optional[Any]=None							,				__A    :List[Any]=None							,				__A    :Optional[int]=None							,				__A    :int="replace"							,				__A    :str="<s>"							,				__A    :Tuple="</s>"							,				__A    :Dict="</s>"							,				__A    :Union[str, Any]="<s>"							,				__A    :int="<unk>"							,				__A    :Tuple="<pad>"							,				__A    :Union[str, Any]="<mask>"							,				__A    :Any=False							,				__A    :Optional[int]=True							,				**__A    :List[str]							,				)   ->   str:



									"""simple docstring"""



									super().__init__(
									    __A							,				__A							,				tokenizer_file=__A							,				errors=__A							,				bos_token=__A							,				eos_token=__A							,				sep_token=__A							,				cls_token=__A							,				unk_token=__A							,				pad_token=__A							,				mask_token=__A							,				add_prefix_space=__A							,				trim_offsets=__A							,				**__A							,				)

									SCREAMING_SNAKE_CASE__			=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
									if pre_tok_state.get("""add_prefix_space"""							,				__A		) != add_prefix_space:
											SCREAMING_SNAKE_CASE__			=      getattr(__A							,				pre_tok_state.pop("""type"""		)		)
											SCREAMING_SNAKE_CASE__			=      add_prefix_space
											SCREAMING_SNAKE_CASE__			=      pre_tok_class(**__A		)

									SCREAMING_SNAKE_CASE__			=      add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									SCREAMING_SNAKE_CASE__			=      """post_processor"""
									SCREAMING_SNAKE_CASE__			=      getattr(self.backend_tokenizer							,				__A							,				__A		)
									if tokenizer_component_instance:
											SCREAMING_SNAKE_CASE__			=      json.loads(tokenizer_component_instance.__getstate__()		)

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
													SCREAMING_SNAKE_CASE__			=      tuple(state["""sep"""]		)
											if "cls" in state:
													SCREAMING_SNAKE_CASE__			=      tuple(state["""cls"""]		)

											SCREAMING_SNAKE_CASE__			=      False

											if state.get("""add_prefix_space"""							,				__A		) != add_prefix_space:
													SCREAMING_SNAKE_CASE__			=      add_prefix_space
													SCREAMING_SNAKE_CASE__			=      True

											if state.get("""trim_offsets"""							,				__A		) != trim_offsets:
													SCREAMING_SNAKE_CASE__			=      trim_offsets
													SCREAMING_SNAKE_CASE__			=      True

											if changes_to_apply:
													SCREAMING_SNAKE_CASE__			=      getattr(__A							,				state.pop("""type"""		)		)
													SCREAMING_SNAKE_CASE__			=      component_class(**__A		)
													setattr(self.backend_tokenizer							,				__A							,				__A		)


							@property
							def      _snake_case  (							self    :int		)   ->   str:



									"""simple docstring"""



									if self._mask_token is None:
											if self.verbose:
													logger.error("""Using mask_token, but it is not set yet."""		)
											return None
									return str(self._mask_token		)


							@mask_token.setter
							def      _snake_case  (							self    :List[Any]							,				__A    :List[str]		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      AddedToken(__A							,				lstrip=__A							,				rstrip=__A		) if isinstance(__A							,				__A		) else value
									SCREAMING_SNAKE_CASE__			=      value


							def      _snake_case  (							self    :Optional[Any]							,				*__A    :Any							,				**__A    :Tuple		)   ->   BatchEncoding:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      kwargs.get("""is_split_into_words"""							,				__A		)

									if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    """to use it with pretokenized inputs."""		)

									return super()._batch_encode_plus(*__A							,				**__A		)


							def      _snake_case  (							self    :str							,				*__A    :List[Any]							,				**__A    :str		)   ->   BatchEncoding:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      kwargs.get("""is_split_into_words"""							,				__A		)

									if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    """to use it with pretokenized inputs."""		)

									return super()._encode_plus(*__A							,				**__A		)


							def      _snake_case  (							self    :Optional[int]							,				__A    :str							,				__A    :Optional[str] = None		)   ->   Tuple[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self._tokenizer.model.save(__A							,				name=__A		)
									return tuple(__A		)


							def      _snake_case  (							self    :List[Any]							,				__A    :Dict							,				__A    :int=None		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
											return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


							def      _snake_case  (							self    :Any							,				__A    :List[int]							,				__A    :Optional[List[int]] = None		)   ->   List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      [self.sep_token_id]
									SCREAMING_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 + sep + token_ids_a + sep		) * [0]






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]    ):
		SCREAMING_SNAKE_CASE__			=      []
		for data in source_data:
				for i, el in enumerate(UpperCamelCase__    ):
						if len(UpperCamelCase__    ) < i + 1:
								data_lists.append([]    )
						data_lists[i].append(float(UpperCamelCase__    )    )
		return data_lists






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]					,   UpperCamelCase__:							list[int]    ):
		SCREAMING_SNAKE_CASE__			=      []
		for dlist, weight in zip(UpperCamelCase__					,   UpperCamelCase__    ):
				SCREAMING_SNAKE_CASE__			=      min(UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      max(UpperCamelCase__    )

				SCREAMING_SNAKE_CASE__			=      []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))    )
								except ZeroDivisionError:
										score.append(1    )

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)    )
								except ZeroDivisionError:
										score.append(0    )

        # weight not 0 or 1
				else:
						SCREAMING_SNAKE_CASE__			=      f'''Invalid weight of {weight:f} provided'''
						raise ValueError(UpperCamelCase__    )

				score_lists.append(UpperCamelCase__    )

		return score_lists






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]    ):
		SCREAMING_SNAKE_CASE__			=      [0 for i in range(len(score_lists[0]    )    )]

		for slist in score_lists:
				for j, ele in enumerate(UpperCamelCase__    ):
						SCREAMING_SNAKE_CASE__			=      final_scores[j] + ele

		return final_scores






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]					,   UpperCamelCase__:							list[int]    ):
		SCREAMING_SNAKE_CASE__			=      get_data(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      calculate_each_score(UpperCamelCase__					,   UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      generate_final_scores(UpperCamelCase__    )

		# append scores to source data
		for i, ele in enumerate(UpperCamelCase__    ):
				source_data[i].append(UpperCamelCase__    )

		return source_data