






'''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


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 					lowercase						(  __lowercase ):
							_lowerCamelCase		:      Union[str, Any]=   VOCAB_FILES_NAMES
							_lowerCamelCase		:      Union[str, Any]=   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:      List[str]=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:      Optional[int]=   ["input_ids", "attention_mask"]
							_lowerCamelCase		:      str=   BartTokenizer


							def __init__(			self    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case="replace"    ,			_snake_case="<s>"    ,			_snake_case="</s>"    ,			_snake_case="</s>"    ,			_snake_case="<s>"    ,			_snake_case="<unk>"    ,			_snake_case="<pad>"    ,			_snake_case="<mask>"    ,			_snake_case=False    ,			_snake_case=True    ,			**_snake_case    ,			)							->				Any:
								super().__init__(
								    _snake_case    ,			_snake_case    ,			tokenizer_file=_snake_case    ,			errors=_snake_case    ,			bos_token=_snake_case    ,			eos_token=_snake_case    ,			sep_token=_snake_case    ,			cls_token=_snake_case    ,			unk_token=_snake_case    ,			pad_token=_snake_case    ,			mask_token=_snake_case    ,			add_prefix_space=_snake_case    ,			trim_offsets=_snake_case    ,			**_snake_case    ,			)

								UpperCAmelCase_		:      List[str]       	=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
								if pre_tok_state.get('add_prefix_space'    ,			_snake_case) != add_prefix_space:
									UpperCAmelCase_		:      Dict       	=					getattr(_snake_case    ,			pre_tok_state.pop('type'))
									UpperCAmelCase_		:      Optional[Any]       	=					add_prefix_space
									UpperCAmelCase_		:      List[str]       	=					pre_tok_class(**_snake_case)

								UpperCAmelCase_		:      Any       	=					add_prefix_space

								# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
								UpperCAmelCase_		:      Tuple       	=					"post_processor"
								UpperCAmelCase_		:      Tuple       	=					getattr(self.backend_tokenizer    ,			_snake_case    ,			_snake_case)
								if tokenizer_component_instance:
									UpperCAmelCase_		:      Dict       	=					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:
										UpperCAmelCase_		:      Optional[int]       	=					tuple(state['sep'])
									if "cls" in state:
										UpperCAmelCase_		:      Dict       	=					tuple(state['cls'])

									UpperCAmelCase_		:      List[str]       	=					False

									if state.get('add_prefix_space'    ,			_snake_case) != add_prefix_space:
										UpperCAmelCase_		:      Tuple       	=					add_prefix_space
										UpperCAmelCase_		:      List[Any]       	=					True

									if state.get('trim_offsets'    ,			_snake_case) != trim_offsets:
										UpperCAmelCase_		:      Union[str, Any]       	=					trim_offsets
										UpperCAmelCase_		:      Any       	=					True

									if changes_to_apply:
										UpperCAmelCase_		:      List[str]       	=					getattr(_snake_case    ,			state.pop('type'))
										UpperCAmelCase_		:      str       	=					component_class(**_snake_case)
										setattr(self.backend_tokenizer    ,			_snake_case    ,			_snake_case)


							@property
							def 						_snake_case							(			self)							->				str:
								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    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_		:      Optional[int]       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else value
								UpperCAmelCase_		:      List[Any]       	=					value


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				BatchEncoding:
								UpperCAmelCase_		:      Tuple       	=					kwargs.get('is_split_into_words'    ,			_snake_case)

								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(*_snake_case    ,			**_snake_case)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				BatchEncoding:
								UpperCAmelCase_		:      Union[str, Any]       	=					kwargs.get('is_split_into_words'    ,			_snake_case)

								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(*_snake_case    ,			**_snake_case)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				Tuple[str]:
								UpperCAmelCase_		:      int       	=					self._tokenizer.model.save(_snake_case    ,			name=_snake_case)
								return tuple(_snake_case)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=None)							->				Any:
								UpperCAmelCase_		:      str       	=					[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    ,			_snake_case    ,			_snake_case = None)							->				List[int]:
								UpperCAmelCase_		:      int       	=					[self.sep_token_id]
								UpperCAmelCase_		:      Union[str, Any]       	=					[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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "tanreinama/GPTSAN-2.8B-spout_is_uniform": (
        "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
    ),
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   "gptsan-japanese"
							_lowerCamelCase		:      List[Any]=   [
							    "past_key_values",
							]
							_lowerCamelCase		:      Tuple=   {
							    "hidden_size": "d_model",
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(			self    ,			_snake_case=3_6000    ,			_snake_case=1280    ,			_snake_case=1024    ,			_snake_case=8192    ,			_snake_case=4096    ,			_snake_case=128    ,			_snake_case=10    ,			_snake_case=0    ,			_snake_case=16    ,			_snake_case=16    ,			_snake_case=128    ,			_snake_case=0.0    ,			_snake_case=1e-5    ,			_snake_case=False    ,			_snake_case=0.0    ,			_snake_case="float32"    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=0.002    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=3_5998    ,			_snake_case=3_5995    ,			_snake_case=3_5999    ,			**_snake_case    ,			)							->				Optional[int]:
								UpperCAmelCase_		:      Dict       	=					vocab_size
								UpperCAmelCase_		:      Optional[int]       	=					max_position_embeddings
								UpperCAmelCase_		:      Any       	=					d_model
								UpperCAmelCase_		:      int       	=					d_ff
								UpperCAmelCase_		:      Tuple       	=					d_ext
								UpperCAmelCase_		:      Tuple       	=					d_spout
								UpperCAmelCase_		:      List[Any]       	=					num_switch_layers
								UpperCAmelCase_		:      Tuple       	=					num_ext_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_switch_layers + num_ext_layers
								UpperCAmelCase_		:      Tuple       	=					num_heads
								UpperCAmelCase_		:      List[str]       	=					num_experts
								UpperCAmelCase_		:      List[str]       	=					expert_capacity
								UpperCAmelCase_		:      Optional[int]       	=					dropout_rate
								UpperCAmelCase_		:      List[Any]       	=					layer_norm_epsilon
								UpperCAmelCase_		:      Any       	=					router_bias
								UpperCAmelCase_		:      Optional[Any]       	=					router_jitter_noise
								UpperCAmelCase_		:      str       	=					router_dtype
								UpperCAmelCase_		:      List[str]       	=					router_ignore_padding_tokens
								UpperCAmelCase_		:      Optional[int]       	=					output_hidden_states
								UpperCAmelCase_		:      Union[str, Any]       	=					output_attentions
								UpperCAmelCase_		:      Optional[int]       	=					initializer_factor
								UpperCAmelCase_		:      Optional[int]       	=					output_router_logits
								UpperCAmelCase_		:      List[str]       	=					use_cache

								super().__init__(
								    separator_token_id=_snake_case    ,			pad_token_id=_snake_case    ,			eos_token_id=_snake_case    ,			**_snake_case    ,			)






