
'''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


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   :




				def __init__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										snake_case       :     Dict =    question_encoder
										snake_case       :     Optional[Any] =    generator
										snake_case       :     List[Any] =    self.question_encoder




				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										if os.path.isfile(SCREAMING_SNAKE_CASE_      ):
																raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
										os.makedirs(SCREAMING_SNAKE_CASE_,    exist_ok=SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    os.path.join(SCREAMING_SNAKE_CASE_,    '''question_encoder_tokenizer'''      )
										snake_case       :     Union[str, Any] =    os.path.join(SCREAMING_SNAKE_CASE_,    '''generator_tokenizer'''      )
										self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE_      )
										self.generator.save_pretrained(SCREAMING_SNAKE_CASE_      )




				@classmethod
				def 	__snake_case							(							cls	:				List[str],    SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				str      ):
										# dynamically import AutoTokenizer
										from ..auto.tokenization_auto import AutoTokenizer

										snake_case       :     List[str] =    kwargs.pop('''config''',    SCREAMING_SNAKE_CASE_      )

										if config is None:
																snake_case       :     int =    RagConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )

										snake_case       :     List[Any] =    AutoTokenizer.from_pretrained(
										    SCREAMING_SNAKE_CASE_,    config=config.question_encoder,    subfolder='''question_encoder_tokenizer'''      )
										snake_case       :     Optional[int] =    AutoTokenizer.from_pretrained(
										    SCREAMING_SNAKE_CASE_,    config=config.generator,    subfolder='''generator_tokenizer'''      )
										return cls(question_encoder=SCREAMING_SNAKE_CASE_,    generator=SCREAMING_SNAKE_CASE_      )




				def __call__(							self	:				Any,    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										return self.current_tokenizer(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				str,    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										return self.generator.batch_decode(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				str,    *SCREAMING_SNAKE_CASE_	:				List[str],    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										return self.generator.decode(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Tuple =    self.question_encoder




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     List[Any] =    self.generator




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[List[str]] = None,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    SCREAMING_SNAKE_CASE_	:				str = "longest",    SCREAMING_SNAKE_CASE_	:				str = None,    SCREAMING_SNAKE_CASE_	:				bool = True,    **SCREAMING_SNAKE_CASE_	:				str,    ):
										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''',    SCREAMING_SNAKE_CASE_,    )
										if max_length is None:
																snake_case       :     List[str] =    self.current_tokenizer.model_max_length
										snake_case       :     Optional[int] =    self(
										    SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_,    return_tensors=SCREAMING_SNAKE_CASE_,    max_length=SCREAMING_SNAKE_CASE_,    padding=SCREAMING_SNAKE_CASE_,    truncation=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )
										if tgt_texts is None:
																return model_inputs
										# Process tgt_texts
										if max_target_length is None:
																snake_case       :     int =    self.current_tokenizer.model_max_length
										snake_case       :     Optional[int] =    self(
										    text_target=SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_,    return_tensors=SCREAMING_SNAKE_CASE_,    padding=SCREAMING_SNAKE_CASE_,    max_length=SCREAMING_SNAKE_CASE_,    truncation=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )
										snake_case       :     Optional[int] =    labels['''input_ids''']
										return model_inputs



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   ["ConvBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvBertForMaskedLM",
						    "ConvBertForMultipleChoice",
						    "ConvBertForQuestionAnswering",
						    "ConvBertForSequenceClassification",
						    "ConvBertForTokenClassification",
						    "ConvBertLayer",
						    "ConvBertModel",
						    "ConvBertPreTrainedModel",
						    "load_tf_weights_in_convbert",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFConvBertForMaskedLM",
						    "TFConvBertForMultipleChoice",
						    "TFConvBertForQuestionAnswering",
						    "TFConvBertForSequenceClassification",
						    "TFConvBertForTokenClassification",
						    "TFConvBertLayer",
						    "TFConvBertModel",
						    "TFConvBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
						from .tokenization_convbert import ConvBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_convbert_fast import ConvBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convbert import (
												    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvBertForMaskedLM,
												    ConvBertForMultipleChoice,
												    ConvBertForQuestionAnswering,
												    ConvBertForSequenceClassification,
												    ConvBertForTokenClassification,
												    ConvBertLayer,
												    ConvBertModel,
												    ConvBertPreTrainedModel,
												    load_tf_weights_in_convbert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_convbert import (
												    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFConvBertForMaskedLM,
												    TFConvBertForMultipleChoice,
												    TFConvBertForQuestionAnswering,
												    TFConvBertForSequenceClassification,
												    TFConvBertForTokenClassification,
												    TFConvBertLayer,
												    TFConvBertModel,
												    TFConvBertPreTrainedModel,
												)


else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

