



"""simple docstring"""





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowercase  							=					logging.get_logger(__name__)


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

lowercase  							=					{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

lowercase  							=					{
    '''allenai/led-base-16384''': 1_63_84,
}




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					VOCAB_FILES_NAMES
							_UpperCAmelCase      				=					PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase      				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase      				=					LEDTokenizer
							_UpperCAmelCase      				=					['''input_ids''', '''attention_mask''']






							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	,       )			->							Optional[int]:
									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							 =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
									if pre_tok_state.get('add_prefix_space'	,       snake_case						) != add_prefix_space:
											_UpperCAmelCase							 =		getattr(snake_case	,       pre_tok_state.pop('type'						)						)
											_UpperCAmelCase							 =		add_prefix_space
											_UpperCAmelCase							 =		pre_tok_class(**snake_case						)

									_UpperCAmelCase							 =		add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									_UpperCAmelCase							 =		'post_processor'
									_UpperCAmelCase							 =		getattr(self.backend_tokenizer	,       snake_case	,       snake_case						)
									if tokenizer_component_instance:
											_UpperCAmelCase							 =		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							 =		tuple(state['sep']						)
											if "cls" in state:
													_UpperCAmelCase							 =		tuple(state['cls']						)

											_UpperCAmelCase							 =		False

											if state.get('add_prefix_space'	,       snake_case						) != add_prefix_space:
													_UpperCAmelCase							 =		add_prefix_space
													_UpperCAmelCase							 =		True

											if state.get('trim_offsets'	,       snake_case						) != trim_offsets:
													_UpperCAmelCase							 =		trim_offsets
													_UpperCAmelCase							 =		True

											if changes_to_apply:
													_UpperCAmelCase							 =		getattr(snake_case	,       state.pop('type'						)						)
													_UpperCAmelCase							 =		component_class(**snake_case						)
													setattr(self.backend_tokenizer	,       snake_case	,       snake_case						)






							@property
							# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
							def        lowerCamelCase_   (  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        lowerCamelCase_   (  self	,       snake_case						)			->							str:
									_UpperCAmelCase							 =		AddedToken(snake_case	,       lstrip=snake_case	,       rstrip=snake_case						) if isinstance(snake_case	,       snake_case						) else value
									_UpperCAmelCase							 =		value






							def        lowerCamelCase_   (  self	,       *snake_case	,       **snake_case						)			->							BatchEncoding:
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self	,       *snake_case	,       **snake_case						)			->							BatchEncoding:
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self	,       snake_case	,       snake_case = None						)			->							Tuple[str]:
									_UpperCAmelCase							 =		self._tokenizer.model.save(snake_case	,       name=snake_case						)
									return tuple(snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=None						)			->							List[Any]:
									_UpperCAmelCase							 =		[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        lowerCamelCase_   (  self	,       snake_case	,       snake_case = None						)			->							List[int]:
									_UpperCAmelCase							 =		[self.sep_token_id]
									_UpperCAmelCase							 =		[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        lowerCamelCase_   (  self	,       snake_case	,       snake_case = None	,       snake_case = PaddingStrategy.DO_NOT_PAD	,       snake_case = None	,       snake_case = None	,       )			->							dict:
									_UpperCAmelCase							 =		super()._pad(
									    encoded_inputs=snake_case	,       max_length=snake_case	,       padding_strategy=snake_case	,       pad_to_multiple_of=snake_case	,       return_attention_mask=snake_case	,       )

									# Load from model defaults
									if return_attention_mask is None:
											_UpperCAmelCase							 =		'attention_mask' in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
											_UpperCAmelCase							 =		encoded_inputs[self.model_input_names[0]]
											# `global_attention_mask` need to have the same length as other (sequential) inputs.
											_UpperCAmelCase							 =		len(encoded_inputs['global_attention_mask']						) != len(snake_case						)

											if needs_to_be_padded:
													_UpperCAmelCase							 =		len(snake_case						) - len(encoded_inputs['global_attention_mask']						)

													if self.padding_side == "right":
															# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
															_UpperCAmelCase							 =		(
															    encoded_inputs['global_attention_mask'] + [-1] * difference
															)
													elif self.padding_side == "left":
															_UpperCAmelCase							 =		[-1] * difference + encoded_inputs[
															    'global_attention_mask'
															]
													else:
															raise ValueError('Invalid padding strategy:' + str(self.padding_side						)						)

									return encoded_inputs







"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


lowercase  							=					logging.get_logger(__name__)




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       *snake_case	,       **snake_case						)			->							None:
									warnings.warn(
									    'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use YolosImageProcessor instead.'	,       snake_case	,       )
									super().__init__(*snake_case	,       **snake_case						)


