


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


__snake_case            =   logging.get_logger(__name__)


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

__snake_case            =   {
    "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",
    },
}

__snake_case            =   {
    "allenai/led-base-16384": 16_384,
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= PRETRAINED_VOCAB_FILES_MAP
			lowercase						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= LEDTokenizer
			lowercase						= ["""input_ids""", """attention_mask"""]




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







						"""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       ,       )

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

						UpperCamelCase         =					add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						UpperCamelCase         =					"""post_processor"""
						UpperCamelCase         =					getattr(self.backend_tokenizer       ,       __A       ,       __A						)
						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"""       ,       __A						) != add_prefix_space:
												UpperCamelCase         =					add_prefix_space
												UpperCamelCase         =					True

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

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




			@property
			# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
			def   lowerCamelCase_					(						self							:		List[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   lowerCamelCase_					(						self							:		List[Any]       ,       __magic_name__							:		Optional[int]						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		List[str]       ,       *__magic_name__							:		str       ,       **__magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		List[str]       ,       *__magic_name__							:		int       ,       **__magic_name__							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str] = None						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		int       ,       __magic_name__							:		Union[str, Any]=None						):







						"""simple docstring"""







						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							:		int       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						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							:		List[str]       ,       __magic_name__							:		Union[Dict[str, EncodedInput], BatchEncoding]       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		PaddingStrategy = PaddingStrategy.DO_NOT_PAD       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		Optional[bool] = None       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					super()._pad(
						    encoded_inputs=__A       ,       max_length=__A       ,       padding_strategy=__A       ,       pad_to_multiple_of=__A       ,       return_attention_mask=__A       ,       )

						# 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(__A						)

									if needs_to_be_padded:
												UpperCamelCase         =					len(__A						) - 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








import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= ["""image_processor""", """tokenizer"""]
			lowercase						= """FlavaImageProcessor"""
			lowercase						= ("""BertTokenizer""", """BertTokenizerFast""")




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		str=None       ,       **__magic_name__							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""       ,       __magic_name__       ,       )
									UpperCamelCase         =					kwargs.pop("""feature_extractor"""						)

						UpperCamelCase         =					image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""						)
						if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""						)

						super().__init__(__magic_name__       ,       __magic_name__						)
						UpperCamelCase         =					self.image_processor




			def __call__(						self							:		int       ,       __magic_name__							:		Optional[ImageInput] = None       ,       __magic_name__							:		Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Union[bool, str, PaddingStrategy] = False       ,       __magic_name__							:		Union[bool, str, TruncationStrategy] = False       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		int = 0       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Optional[Union[str, TensorType]] = None       ,       **__magic_name__							:		str       ,       ):







						"""simple docstring"""







						if text is None and images is None:
									raise ValueError("""You have to specify either text or images. Both cannot be none."""						)

						if text is not None:
									UpperCamelCase         =					self.tokenizer(
									    text=__magic_name__       ,       add_special_tokens=__magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       stride=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_token_type_ids=__magic_name__       ,       return_attention_mask=__magic_name__       ,       return_overflowing_tokens=__magic_name__       ,       return_special_tokens_mask=__magic_name__       ,       return_offsets_mapping=__magic_name__       ,       return_length=__magic_name__       ,       verbose=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )
						if images is not None:
									UpperCamelCase         =					self.image_processor(
									    __magic_name__       ,       return_image_mask=__magic_name__       ,       return_codebook_pixels=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )

						if text is not None and images is not None:
									encoding.update(__magic_name__						)
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**__magic_name__						)       ,       tensor_type=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Tuple       ,       *__magic_name__							:		Dict       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.tokenizer.batch_decode(*__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		int       ,       *__magic_name__							:		Tuple       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						return self.tokenizer.decode(*__magic_name__       ,       **__magic_name__						)




			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.tokenizer.model_input_names
						UpperCamelCase         =					self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,       __magic_name__       ,       )
						return self.image_processor_class





			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,       __magic_name__       ,       )
						return self.image_processor




