

"""simple docstring"""


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


__A						       =      logging.get_logger(__name__)


__A						       =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
__A						       =      {
    "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",
    },
}

__A						       =      {
    "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    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Any       				=  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :				str       				=  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_       :				Any       				=  BartTokenizer

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



						'''simple docstring'''


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

						__lowerCAmelCase:	Tuple   				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
						if pre_tok_state.get("add_prefix_space"					,  UpperCamelCase__) != add_prefix_space:
								__lowerCAmelCase:	Any   				=      getattr(UpperCamelCase__					,  pre_tok_state.pop("type"))
								__lowerCAmelCase:	Any   				=      add_prefix_space
								__lowerCAmelCase:	Union[str, Any]   				=      pre_tok_class(**UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						__lowerCAmelCase:	Tuple   				=      "post_processor"
						__lowerCAmelCase:	str   				=      getattr(self.backend_tokenizer					,  UpperCamelCase__					,  UpperCamelCase__)
						if tokenizer_component_instance:
								__lowerCAmelCase:	Tuple   				=      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:
										__lowerCAmelCase:	Dict   				=      tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:	Union[str, Any]   				=      tuple(state["cls"])

								__lowerCAmelCase:	Optional[Any]   				=      False

								if state.get("add_prefix_space"					,  UpperCamelCase__) != add_prefix_space:
										__lowerCAmelCase:	Dict   				=      add_prefix_space
										__lowerCAmelCase:	Optional[int]   				=      True

								if state.get("trim_offsets"					,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:	int   				=      trim_offsets
										__lowerCAmelCase:	Dict   				=      True

								if changes_to_apply:
										__lowerCAmelCase:	int   				=      getattr(UpperCamelCase__					,  state.pop("type"))
										__lowerCAmelCase:	List[str]   				=      component_class(**UpperCamelCase__)
										setattr(self.backend_tokenizer					,  UpperCamelCase__					,  UpperCamelCase__)

				@property
				def 							lowercase_					(     self							:    List[Any])->       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 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    int)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      AddedToken(UpperCamelCase__					,  lstrip=UpperCamelCase__					,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__					,  UpperCamelCase__) else value
						__lowerCAmelCase:	Union[str, Any]   				=      value

				def 							lowercase_					(     self							:    str					,  *UpperCamelCase__							:    Union[str, Any]					,  **UpperCamelCase__							:    Any)->       BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      kwargs.get("is_split_into_words"					,  UpperCamelCase__)

						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(*UpperCamelCase__					,  **UpperCamelCase__)

				def 							lowercase_					(     self							:    Optional[Any]					,  *UpperCamelCase__							:    Any					,  **UpperCamelCase__							:    Optional[int])->       BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      kwargs.get("is_split_into_words"					,  UpperCamelCase__)

						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(*UpperCamelCase__					,  **UpperCamelCase__)

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Optional[str] = None)->       Tuple[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self._tokenizer.model.save(UpperCamelCase__					,  name=UpperCamelCase__)
						return tuple(UpperCamelCase__)

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    List[Any]=None)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      [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 							lowercase_					(     self							:    Dict					,  UpperCamelCase__							:    List[int]					,  UpperCamelCase__							:    Optional[List[int]] = None)->       List[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      [self.sep_token_id]
						__lowerCAmelCase:	int   				=      [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]





"""simple docstring"""


from math import ceil






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Tuple   				=      list(range(0 ,			__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	Optional[Any]   				=      [item for sublist in list(device_map.values()			) for item in sublist]

		# Duplicate check
		__lowerCAmelCase:	List[Any]   				=      []
		for i in device_map_blocks:
				if device_map_blocks.count(__SCREAMING_SNAKE_CASE			) > 1 and i not in duplicate_blocks:
						duplicate_blocks.append(__SCREAMING_SNAKE_CASE			)
    # Missing blocks
		__lowerCAmelCase:	Optional[Any]   				=      [i for i in blocks if i not in device_map_blocks]
		__lowerCAmelCase:	List[Any]   				=      [i for i in device_map_blocks if i not in blocks]

		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
				    " These attention blocks were specified more than once: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
				    "blocks to a device on the device_map: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
				    + str(__SCREAMING_SNAKE_CASE			)			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	List[Any]   				=      list(range(__SCREAMING_SNAKE_CASE			)			)
		__lowerCAmelCase:	Dict   				=      int(ceil(n_layers / len(__SCREAMING_SNAKE_CASE			)			)			)
		__lowerCAmelCase:	Union[str, Any]   				=      [layers[i : i + n_blocks] for i in range(0 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)]

		return dict(zip(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)


