
import os
import warnings
from typing import List, Optional

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


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







class  UpperCamelCase__		:







	'''simple docstring'''





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





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





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

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

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

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





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





	def   __snake_case				(			self				,       *UpperCamelCase__				,       **UpperCamelCase__		):
					return self.generator.batch_decode(*UpperCamelCase__				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       *UpperCamelCase__				,       **UpperCamelCase__		):
					return self.generator.decode(*UpperCamelCase__				,       **UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__         =							self.question_encoder





	def   __snake_case				(			self		):
					A__         =							self.generator





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = "longest"				,       UpperCamelCase__ = None				,       UpperCamelCase__ = True				,       **UpperCamelCase__				,       ):
					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'''				,       UpperCamelCase__				,       )
					if max_length is None:
									A__         =							self.current_tokenizer.model_max_length
					A__         =							self(
					    UpperCamelCase__				,       add_special_tokens=UpperCamelCase__				,       return_tensors=UpperCamelCase__				,       max_length=UpperCamelCase__				,       padding=UpperCamelCase__				,       truncation=UpperCamelCase__				,       **UpperCamelCase__				,       )
					if tgt_texts is None:
									return model_inputs
					# Process tgt_texts
					if max_target_length is None:
									A__         =							self.current_tokenizer.model_max_length
					A__         =							self(
					    text_target=UpperCamelCase__				,       add_special_tokens=UpperCamelCase__				,       return_tensors=UpperCamelCase__				,       padding=UpperCamelCase__				,       max_length=UpperCamelCase__				,       truncation=UpperCamelCase__				,       **UpperCamelCase__				,       )
					A__         =							labels['''input_ids''']
					return model_inputs

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE    :      int          =			{
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			['BertTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			[
					    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'BertForMaskedLM',
					    'BertForMultipleChoice',
					    'BertForNextSentencePrediction',
					    'BertForPreTraining',
					    'BertForQuestionAnswering',
					    'BertForSequenceClassification',
					    'BertForTokenClassification',
					    'BertLayer',
					    'BertLMHeadModel',
					    'BertModel',
					    'BertPreTrainedModel',
					    'load_tf_weights_in_bert',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Tuple          =			[
					    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFBertEmbeddings',
					    'TFBertForMaskedLM',
					    'TFBertForMultipleChoice',
					    'TFBertForNextSentencePrediction',
					    'TFBertForPreTraining',
					    'TFBertForQuestionAnswering',
					    'TFBertForSequenceClassification',
					    'TFBertForTokenClassification',
					    'TFBertLMHeadModel',
					    'TFBertMainLayer',
					    'TFBertModel',
					    'TFBertPreTrainedModel',
					]
try:
					if not is_tensorflow_text_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Dict          =			['TFBertTokenizer']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Optional[int]          =			[
					    'FlaxBertForCausalLM',
					    'FlaxBertForMaskedLM',
					    'FlaxBertForMultipleChoice',
					    'FlaxBertForNextSentencePrediction',
					    'FlaxBertForPreTraining',
					    'FlaxBertForQuestionAnswering',
					    'FlaxBertForSequenceClassification',
					    'FlaxBertForTokenClassification',
					    'FlaxBertModel',
					    'FlaxBertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
					from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bert_fast import BertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bert import (
										    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BertForMaskedLM,
										    BertForMultipleChoice,
										    BertForNextSentencePrediction,
										    BertForPreTraining,
										    BertForQuestionAnswering,
										    BertForSequenceClassification,
										    BertForTokenClassification,
										    BertLayer,
										    BertLMHeadModel,
										    BertModel,
										    BertPreTrainedModel,
										    load_tf_weights_in_bert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_bert import (
										    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBertEmbeddings,
										    TFBertForMaskedLM,
										    TFBertForMultipleChoice,
										    TFBertForNextSentencePrediction,
										    TFBertForPreTraining,
										    TFBertForQuestionAnswering,
										    TFBertForSequenceClassification,
										    TFBertForTokenClassification,
										    TFBertLMHeadModel,
										    TFBertMainLayer,
										    TFBertModel,
										    TFBertPreTrainedModel,
										)

					try:
										if not is_tensorflow_text_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bert_tf import TFBertTokenizer

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_bert import (
										    FlaxBertForCausalLM,
										    FlaxBertForMaskedLM,
										    FlaxBertForMultipleChoice,
										    FlaxBertForNextSentencePrediction,
										    FlaxBertForPreTraining,
										    FlaxBertForQuestionAnswering,
										    FlaxBertForSequenceClassification,
										    FlaxBertForTokenClassification,
										    FlaxBertModel,
										    FlaxBertPreTrainedModel,
										)

else:
					import sys

					_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)