



"""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


__snake_case				:  Optional[int] =       logging.get_logger(__name__)


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

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



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





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

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

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

				__lowerCAmelCase :    str				=			add_prefix_space

				# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
				__lowerCAmelCase :    Dict				=			"post_processor"
				__lowerCAmelCase :    Optional[int]				=			getattr(self.backend_tokenizer			,     __a			,     __a)
				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 :    int				=			tuple(state["sep"])
							if "cls" in state:
										__lowerCAmelCase :    str				=			tuple(state["cls"])

							__lowerCAmelCase :    Any				=			False

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

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

							if changes_to_apply:
										__lowerCAmelCase :    List[Any]				=			getattr(__a			,     state.pop("type"))
										__lowerCAmelCase :    Any				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			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 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Dict)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			AddedToken(__a			,     lstrip=__a			,     rstrip=__a) if isinstance(__a			,     __a) else value
				__lowerCAmelCase :    List[str]				=			value

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     *_SCREAMING_SNAKE_CASE:						Optional[Any]			,     **_SCREAMING_SNAKE_CASE:						Optional[Any])							->			BatchEncoding:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     *_SCREAMING_SNAKE_CASE:						Tuple			,     **_SCREAMING_SNAKE_CASE:						Union[str, Any])							->			BatchEncoding:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Optional[str] = None)							->			Tuple[str]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			self._tokenizer.model.save(__a			,     name=__a)
				return tuple(__a)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Dict			,     _SCREAMING_SNAKE_CASE:						int=None)							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			[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 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						List[int]			,     _SCREAMING_SNAKE_CASE:						Optional[List[int]] = None)							->			List[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			[self.sep_token_id]
				__lowerCAmelCase :    Optional[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]







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Union[Dict[str, EncodedInput], BatchEncoding]			,     _SCREAMING_SNAKE_CASE:						Optional[int] = None			,     _SCREAMING_SNAKE_CASE:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD			,     _SCREAMING_SNAKE_CASE:						Optional[int] = None			,     _SCREAMING_SNAKE_CASE:						Optional[bool] = None			,     )							->			dict:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			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:
							__lowerCAmelCase :    Optional[int]				=			"attention_mask" in self.model_input_names

				if return_attention_mask and "global_attention_mask" in encoded_inputs:
							__lowerCAmelCase :    Union[str, Any]				=			encoded_inputs[self.model_input_names[0]]
							# `global_attention_mask` need to have the same length as other (sequential) inputs.
							__lowerCAmelCase :    List[Any]				=			len(encoded_inputs["global_attention_mask"]) != len(__a)

							if needs_to_be_padded:
										__lowerCAmelCase :    Tuple				=			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`
													__lowerCAmelCase :    Optional[Any]				=			(
													    encoded_inputs["global_attention_mask"] + [-1] * difference
													)
										elif self.padding_side == "left":
													__lowerCAmelCase :    List[Any]				=			[-1] * difference + encoded_inputs[
													    "global_attention_mask"
													]
										else:
													raise ValueError("Invalid padding strategy:" + str(self.padding_side))

				return encoded_inputs
'''simple docstring'''


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import (
					    ImageTextPipelineOutput,
					    UniDiffuserPipeline,
					)
else:
					from .modeling_text_decoder import UniDiffuserTextDecoder
					from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
					from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
