




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__		:Union[str, Any]			  =   logging.get_logger(__name__)







class     lowercase		:




							def __init__(    self       ,A__       ,A__):
												lowercase						     =						question_encoder
												lowercase						     =						generator
												lowercase						     =						self.question_encoder




							def 			A__				(    self       ,A__):
												if os.path.isfile(A__):
																	raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file')
												os.makedirs(A__       ,exist_ok=A__)
												lowercase						     =						os.path.join(A__       ,'''question_encoder_tokenizer''')
												lowercase						     =						os.path.join(A__       ,'''generator_tokenizer''')
												self.question_encoder.save_pretrained(A__)
												self.generator.save_pretrained(A__)




							@classmethod
							def 			A__				(    cls       ,A__       ,**A__):
												# dynamically import AutoTokenizer
												from ..auto.tokenization_auto import AutoTokenizer

												lowercase						     =						kwargs.pop('''config'''       ,A__)

												if config is None:
																	lowercase						     =						RagConfig.from_pretrained(A__)

												lowercase						     =						AutoTokenizer.from_pretrained(
												    A__       ,config=config.question_encoder       ,subfolder='''question_encoder_tokenizer''')
												lowercase						     =						AutoTokenizer.from_pretrained(
												    A__       ,config=config.generator       ,subfolder='''generator_tokenizer''')
												return cls(question_encoder=A__       ,generator=A__)




							def __call__(    self       ,*A__       ,**A__):
												return self.current_tokenizer(*A__       ,**A__)




							def 			A__				(    self       ,*A__       ,**A__):
												return self.generator.batch_decode(*A__       ,**A__)




							def 			A__				(    self       ,*A__       ,**A__):
												return self.generator.decode(*A__       ,**A__)




							def 			A__				(    self):
												lowercase						     =						self.question_encoder




							def 			A__				(    self):
												lowercase						     =						self.generator





							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = "longest"       ,A__ = None       ,A__ = True       ,**A__       ,):
												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'''       ,A__       ,)
												if max_length is None:
																	lowercase						     =						self.current_tokenizer.model_max_length
												lowercase						     =						self(
												    A__       ,add_special_tokens=A__       ,return_tensors=A__       ,max_length=A__       ,padding=A__       ,truncation=A__       ,**A__       ,)
												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=A__       ,add_special_tokens=A__       ,return_tensors=A__       ,padding=A__       ,max_length=A__       ,truncation=A__       ,**A__       ,)
												lowercase						     =						labels['input_ids']
												return model_inputs






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

lowercase__		:List[Any]			  =   {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[Any]	 ='''camembert'''




							def __init__(    self       ,A__=3_0_5_2_2       ,A__=7_6_8       ,A__=1_2       ,A__=1_2       ,A__=3_0_7_2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=5_1_2       ,A__=2       ,A__=0.02       ,A__=1E-12       ,A__=1       ,A__=0       ,A__=2       ,A__="absolute"       ,A__=True       ,A__=None       ,**A__       ,):
												super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__)

												lowercase						     =						vocab_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						hidden_act
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						type_vocab_size
												lowercase						     =						initializer_range
												lowercase						     =						layer_norm_eps
												lowercase						     =						position_embedding_type
												lowercase						     =						use_cache
												lowercase						     =						classifier_dropout







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							@property
							def 			A__				(    self):
												if self.task == "multiple-choice":
																	lowercase						     =						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	lowercase						     =						{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ])




