






'''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   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										=question_encoder
								_lowerCAmelCase										=generator
								_lowerCAmelCase										=self.question_encoder






		def 		UpperCamelCase__       (   self		,						__A				) ->     Optional[int]:
								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				)
								_lowerCAmelCase										=os.path.join(__A		,						'question_encoder_tokenizer'				)
								_lowerCAmelCase										=os.path.join(__A		,						'generator_tokenizer'				)
								self.question_encoder.save_pretrained(__A				)
								self.generator.save_pretrained(__A				)






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A		,						**__A				) ->     Optional[int]:
								# dynamically import AutoTokenizer
								from ..auto.tokenization_auto import AutoTokenizer

								_lowerCAmelCase										=kwargs.pop('config'		,						__A				)

								if config is None:
														_lowerCAmelCase										=RagConfig.from_pretrained(__A				)

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






		def __call__(   self		,						*__A		,						**__A				) ->     Union[str, Any]:
								return self.current_tokenizer(*__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Optional[int]:
								return self.generator.batch_decode(*__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     List[Any]:
								return self.generator.decode(*__A		,						**__A				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.question_encoder






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=self.generator






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None		,						__A = None		,						__A = None		,						__A = "longest"		,						__A = None		,						__A = True		,						**__A		,						) ->     BatchEncoding:
								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:
														_lowerCAmelCase										=self.current_tokenizer.model_max_length
								_lowerCAmelCase										=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:
														_lowerCAmelCase										=self.current_tokenizer.model_max_length
								_lowerCAmelCase										=self(
								    text_target=__A		,						add_special_tokens=__A		,						return_tensors=__A		,						padding=__A		,						max_length=__A		,						truncation=__A		,						**__A		,						)
								_lowerCAmelCase										=labels['input_ids']
								return model_inputs






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Any       =							['image_processor', 'tokenizer']
		lowercase   :      Any       =							'CLIPImageProcessor'
		lowercase   :      int       =							('CLIPTokenizer', 'CLIPTokenizerFast')






		def __init__(   self		,						__A=None		,						__A=None		,						**__A				) ->     str:
								_lowerCAmelCase										=None
								if "feature_extractor" in kwargs:
														warnings.warn(
														    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
														    ' instead.'		,						__A		,						)
														_lowerCAmelCase										=kwargs.pop('feature_extractor'				)

								_lowerCAmelCase										=image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
														raise ValueError('You need to specify an `image_processor`.'				)
								if tokenizer is None:
														raise ValueError('You need to specify a `tokenizer`.'				)

								super().__init__(__A		,						__A				)






		def __call__(   self		,						__A=None		,						__A=None		,						__A=None		,						**__A				) ->     Optional[int]:

								if text is None and images is None:
														raise ValueError('You have to specify either text or images. Both cannot be none.'				)

								if text is not None:
														_lowerCAmelCase										=self.tokenizer(__A		,						return_tensors=__A		,						**__A				)

								if images is not None:
														_lowerCAmelCase										=self.image_processor(__A		,						return_tensors=__A		,						**__A				)

								if text is not None and images is not None:
														_lowerCAmelCase										=image_features.pixel_values
														return encoding
								elif text is not None:
														return encoding
								else:
														return BatchEncoding(data=dict(**__A				)		,						tensor_type=__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Any:
								return self.tokenizer.batch_decode(*__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Optional[int]:
								return self.tokenizer.decode(*__A		,						**__A				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=self.tokenizer.model_input_names
								_lowerCAmelCase										=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,						__A		,						)
								return self.image_processor_class






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,						__A		,						)
								return self.image_processor





