
"""simple docstring"""






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


_lowercase							=				logging.get_logger(__name__)

class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase       =       question_encoder
										__lowercase       =       generator
										__lowercase       =       self.question_encoder
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										if os.path.isfile(lowerCamelCase_       ):
													raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''       )
										os.makedirs(lowerCamelCase_  ,    exist_ok=lowerCamelCase_       )
										__lowercase       =       os.path.join(lowerCamelCase_  ,    "question_encoder_tokenizer"       )
										__lowercase       =       os.path.join(lowerCamelCase_  ,    "generator_tokenizer"       )
										self.question_encoder.save_pretrained(lowerCamelCase_       )
										self.generator.save_pretrained(lowerCamelCase_       )
							@classmethod
							def   SCREAMING_SNAKE_CASE						(     cls  ,    _lowerCamelCase  ,    **_lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										# dynamically import AutoTokenizer
										from ..auto.tokenization_auto import AutoTokenizer

										__lowercase       =       kwargs.pop("config"  ,    lowerCamelCase_       )

										if config is None:
													__lowercase       =       RagConfig.from_pretrained(lowerCamelCase_       )

										__lowercase       =       AutoTokenizer.from_pretrained(
										    lowerCamelCase_  ,    config=config.question_encoder  ,    subfolder="question_encoder_tokenizer"       )
										__lowercase       =       AutoTokenizer.from_pretrained(
										    lowerCamelCase_  ,    config=config.generator  ,    subfolder="generator_tokenizer"       )
										return cls(question_encoder=lowerCamelCase_  ,    generator=lowerCamelCase_       )
							def __call__(     self  ,    *_lowerCamelCase  ,    **_lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										return self.current_tokenizer(*lowerCamelCase_  ,    **lowerCamelCase_       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    *_lowerCamelCase  ,    **_lowerCamelCase       )			->							int:




										'''simple docstring'''
										return self.generator.batch_decode(*lowerCamelCase_  ,    **lowerCamelCase_       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    *_lowerCamelCase  ,    **_lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										return self.generator.decode(*lowerCamelCase_  ,    **lowerCamelCase_       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										__lowercase       =       self.question_encoder
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										__lowercase       =       self.generator





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = "longest"  ,    _lowerCamelCase = None  ,    _lowerCamelCase = True  ,    **_lowerCamelCase  ,    )			->							Any:




										'''simple docstring'''
										warnings.warn(
										    "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
										    "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
										    "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
										    "details"  ,    lowerCamelCase_  ,    )
										if max_length is None:
													__lowercase       =       self.current_tokenizer.model_max_length
										__lowercase       =       self(
										    lowerCamelCase_  ,    add_special_tokens=lowerCamelCase_  ,    return_tensors=lowerCamelCase_  ,    max_length=lowerCamelCase_  ,    padding=lowerCamelCase_  ,    truncation=lowerCamelCase_  ,    **lowerCamelCase_  ,    )
										if tgt_texts is None:
													return model_inputs
										# Process tgt_texts
										if max_target_length is None:
													__lowercase       =       self.current_tokenizer.model_max_length
										__lowercase       =       self(
										    text_target=lowerCamelCase_  ,    add_special_tokens=lowerCamelCase_  ,    return_tensors=lowerCamelCase_  ,    padding=lowerCamelCase_  ,    max_length=lowerCamelCase_  ,    truncation=lowerCamelCase_  ,    **lowerCamelCase_  ,    )
										__lowercase       =       labels['input_ids']
										return model_inputs












from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase:	List[Any] = logging.get_logger(__name__)

__UpperCamelCase:	Dict = {
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"luke"

	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						str=50267,					lowerCamelCase_:						Dict=500000,					lowerCamelCase_:						Union[str, Any]=768,					lowerCamelCase_:						Dict=256,					lowerCamelCase_:						int=12,					lowerCamelCase_:						Union[str, Any]=12,					lowerCamelCase_:						str=3072,					lowerCamelCase_:						List[Any]="gelu",					lowerCamelCase_:						int=0.1,					lowerCamelCase_:						Optional[Any]=0.1,					lowerCamelCase_:						Any=512,					lowerCamelCase_:						List[str]=2,					lowerCamelCase_:						str=0.0_2,					lowerCamelCase_:						Union[str, Any]=1E-12,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Optional[Any]=1,					lowerCamelCase_:						Dict=0,					lowerCamelCase_:						List[Any]=2,					**lowerCamelCase_:						Optional[Any],					):
			super().__init__(pad_token_id=lowerCamelCase_,					bos_token_id=lowerCamelCase_,					eos_token_id=lowerCamelCase_,					**lowerCamelCase_			)

			lowercase__					:      Tuple      =		vocab_size
			lowercase__					:      Any      =		entity_vocab_size
			lowercase__					:      Tuple      =		hidden_size
			lowercase__					:      List[str]      =		entity_emb_size
			lowercase__					:      Tuple      =		num_hidden_layers
			lowercase__					:      Optional[Any]      =		num_attention_heads
			lowercase__					:      Dict      =		hidden_act
			lowercase__					:      Optional[int]      =		intermediate_size
			lowercase__					:      List[str]      =		hidden_dropout_prob
			lowercase__					:      Dict      =		attention_probs_dropout_prob
			lowercase__					:      Union[str, Any]      =		max_position_embeddings
			lowercase__					:      int      =		type_vocab_size
			lowercase__					:      Optional[Any]      =		initializer_range
			lowercase__					:      Dict      =		layer_norm_eps
			lowercase__					:      List[Any]      =		use_entity_aware_attention
			lowercase__					:      Optional[Any]      =		classifier_dropout


