




import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


SCREAMING_SNAKE_CASE__     :     int		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     Tuple		    =       {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__     :     int		    =       {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
SCREAMING_SNAKE_CASE__     :     Any		    =       {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       {
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
SCREAMING_SNAKE_CASE__     :     Dict		    =       {
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
SCREAMING_SNAKE_CASE__     :     Tuple		    =       {
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


SCREAMING_SNAKE_CASE__     :     Tuple		    =       {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE__     :     Dict		    =       {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= VOCAB_FILES_NAMES
	A    							= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	A    							= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A    							= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	A    							= DPRContextEncoderTokenizer




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= VOCAB_FILES_NAMES
	A    							= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	A    							= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A    							= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	A    							= DPRQuestionEncoderTokenizer




SCREAMING_SNAKE_CASE__     :     Dict		    =       collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

SCREAMING_SNAKE_CASE__     :     Tuple		    =       collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


SCREAMING_SNAKE_CASE__     :     List[Any]		    =       r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "



@add_start_docstrings(SCREAMING_SNAKE_CASE__     )
class       a_       :





	def __call__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					BatchEncoding:




				"""simple docstring"""



				if titles is None and texts is None:
							return super().__call__(
							    lowerCamelCase__      ,     padding=lowerCamelCase__      ,     truncation=lowerCamelCase__      ,     max_length=lowerCamelCase__      ,     return_tensors=lowerCamelCase__      ,     return_attention_mask=lowerCamelCase__      ,     **lowerCamelCase__      ,     )
				elif titles is None or texts is None:
							SCREAMING_SNAKE_CASE_								=						titles if texts is None else texts
							return super().__call__(
							    lowerCamelCase__      ,     lowerCamelCase__      ,     padding=lowerCamelCase__      ,     truncation=lowerCamelCase__      ,     max_length=lowerCamelCase__      ,     return_tensors=lowerCamelCase__      ,     return_attention_mask=lowerCamelCase__      ,     **lowerCamelCase__      ,     )
				SCREAMING_SNAKE_CASE_								=						titles if not isinstance(lowerCamelCase__      ,     lowerCamelCase__      ) else [titles]
				SCREAMING_SNAKE_CASE_								=						texts if not isinstance(lowerCamelCase__      ,     lowerCamelCase__      ) else [texts]
				SCREAMING_SNAKE_CASE_								=						len(lowerCamelCase__      )
				SCREAMING_SNAKE_CASE_								=						questions if not isinstance(lowerCamelCase__      ,     lowerCamelCase__      ) else [questions] * n_passages
				assert len(lowerCamelCase__      ) == len(
				    lowerCamelCase__      ), f'There should be as many titles than texts but got {len(lowerCamelCase__      )} titles and {len(lowerCamelCase__      )} texts.'
				SCREAMING_SNAKE_CASE_								=						super().__call__(lowerCamelCase__      ,     lowerCamelCase__      ,     padding=lowerCamelCase__      ,     truncation=lowerCamelCase__      )['input_ids']
				SCREAMING_SNAKE_CASE_								=						super().__call__(lowerCamelCase__      ,     add_special_tokens=lowerCamelCase__      ,     padding=lowerCamelCase__      ,     truncation=lowerCamelCase__      )['input_ids']
				SCREAMING_SNAKE_CASE_								=						{
				    'input_ids': [
				        (encoded_question_and_title + encoded_text)[:max_length]
				        if max_length is not None and truncation
				        else encoded_question_and_title + encoded_text
				        for encoded_question_and_title, encoded_text in zip(lowerCamelCase__      ,     lowerCamelCase__      )
				    ]
				}
				if return_attention_mask is not False:
							SCREAMING_SNAKE_CASE_								=						[]
							for input_ids in encoded_inputs["input_ids"]:
										attention_mask.append([int(input_id != self.pad_token_id      ) for input_id in input_ids]      )
							SCREAMING_SNAKE_CASE_								=						attention_mask
				return self.pad(lowerCamelCase__      ,     padding=lowerCamelCase__      ,     max_length=lowerCamelCase__      ,     return_tensors=lowerCamelCase__      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = 16      ,     SCREAMING_SNAKE_CASE = 64      ,     SCREAMING_SNAKE_CASE = 4      ,     )  ->					List[DPRSpanPrediction]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						reader_input['input_ids']
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						reader_output[:3]
				SCREAMING_SNAKE_CASE_								=						len(lowerCamelCase__      )
				SCREAMING_SNAKE_CASE_								=						sorted(range(lowerCamelCase__      )      ,     reverse=lowerCamelCase__      ,     key=relevance_logits.__getitem__      )
				SCREAMING_SNAKE_CASE_								=						[]
				for doc_id in sorted_docs:
							SCREAMING_SNAKE_CASE_								=						list(input_ids[doc_id]      )
							# assuming question & title information is at the beginning of the sequence
							SCREAMING_SNAKE_CASE_								=						sequence_ids.index(self.sep_token_id      ,     2      ) + 1  # second sep id
							if sequence_ids[-1] == self.pad_token_id:
										SCREAMING_SNAKE_CASE_								=						sequence_ids.index(self.pad_token_id      )
							else:
										SCREAMING_SNAKE_CASE_								=						len(lowerCamelCase__      )

							SCREAMING_SNAKE_CASE_								=						self._get_best_spans(
							    start_logits=start_logits[doc_id][passage_offset:sequence_len]      ,     end_logits=end_logits[doc_id][passage_offset:sequence_len]      ,     max_answer_length=lowerCamelCase__      ,     top_spans=lowerCamelCase__      ,     )
							for start_index, end_index in best_spans:
										start_index += passage_offset
										end_index += passage_offset
										nbest_spans_predictions.append(
										    DPRSpanPrediction(
										        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]      ,     relevance_score=relevance_logits[doc_id]      ,     doc_id=lowerCamelCase__      ,     start_index=lowerCamelCase__      ,     end_index=lowerCamelCase__      ,     text=self.decode(sequence_ids[start_index : end_index + 1]      )      ,     )      )
							if len(lowerCamelCase__      ) >= num_spans:
										break
				return nbest_spans_predictions[:num_spans]





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     )  ->					List[DPRSpanPrediction]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[]
				for start_index, start_score in enumerate(lowerCamelCase__      ):
							for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]      ):
										scores.append(((start_index, start_index + answer_length), start_score + end_score)      )
				SCREAMING_SNAKE_CASE_								=						sorted(lowerCamelCase__      ,     key=lambda SCREAMING_SNAKE_CASE      : x[1]      ,     reverse=lowerCamelCase__      )
				SCREAMING_SNAKE_CASE_								=						[]
				for (start_index, end_index), score in scores:
							assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
							SCREAMING_SNAKE_CASE_								=						end_index - start_index + 1
							assert length <= max_answer_length, f'Span is too long: {length} > {max_answer_length}'
							if any(
							    start_index <= prev_start_index <= prev_end_index <= end_index
							    or prev_start_index <= start_index <= end_index <= prev_end_index
							    for (prev_start_index, prev_end_index) in chosen_span_intervals      ):
										continue
							chosen_span_intervals.append((start_index, end_index)      )

							if len(lowerCamelCase__      ) == top_spans:
										break
				return chosen_span_intervals




@add_end_docstrings(SCREAMING_SNAKE_CASE__     )
class       a_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__     ):
	A    							= VOCAB_FILES_NAMES
	A    							= READER_PRETRAINED_VOCAB_FILES_MAP
	A    							= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A    							= READER_PRETRAINED_INIT_CONFIGURATION
	A    							= ['''input_ids''', '''attention_mask''']
	A    							= DPRReaderTokenizer






import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			['''image_processor''', '''tokenizer''']
			snake_case_  						=			'''OwlViTImageProcessor'''
			snake_case_  						=			('''CLIPTokenizer''', '''CLIPTokenizerFast''')


			def __init__( self     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							__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.'     ,		lowerCamelCase__     ,		)
											__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__(lowerCamelCase__     ,		lowerCamelCase__				)


			def __call__( self     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__="max_length"     ,		lowerCamelCase__="np"     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''

							if text is None and query_images is None and images is None:
											raise ValueError(
											    'You have to specify at least one text or query image or image. All three cannot be none.'				)

							if text is not None:
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				) or (isinstance(lowerCamelCase__     ,		lowerCamelCase__				) and not isinstance(text[0]     ,		lowerCamelCase__				)):
															__lowerCamelCase									=       [self.tokenizer(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)]

											elif isinstance(lowerCamelCase__     ,		lowerCamelCase__				) and isinstance(text[0]     ,		lowerCamelCase__				):
															__lowerCamelCase									=       []

															# Maximum number of queries across batch
															__lowerCamelCase									=       max([len(lowerCamelCase__				) for t in text]				)

															# Pad all batch samples to max number of text queries
															for t in text:
																			if len(lowerCamelCase__				) != max_num_queries:
																							__lowerCamelCase									=       t + [' '] * (max_num_queries - len(lowerCamelCase__				))

																			__lowerCamelCase									=       self.tokenizer(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)
																			encodings.append(lowerCamelCase__				)
											else:
															raise TypeError('Input text should be a string, a list of strings or a nested list of strings'				)

											if return_tensors == "np":
															__lowerCamelCase									=       np.concatenate([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       np.concatenate([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

											elif return_tensors == "jax" and is_flax_available():
															import jax.numpy as jnp

															__lowerCamelCase									=       jnp.concatenate([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       jnp.concatenate([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

											elif return_tensors == "pt" and is_torch_available():
															import torch

															__lowerCamelCase									=       torch.cat([encoding['input_ids'] for encoding in encodings]     ,		dim=0				)
															__lowerCamelCase									=       torch.cat([encoding['attention_mask'] for encoding in encodings]     ,		dim=0				)

											elif return_tensors == "tf" and is_tf_available():
															import tensorflow as tf

															__lowerCamelCase									=       tf.stack([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       tf.stack([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

											else:
															raise ValueError('Target return tensor type could not be returned'				)

											__lowerCamelCase									=       BatchEncoding()
											__lowerCamelCase									=       input_ids
											__lowerCamelCase									=       attention_mask

							if query_images is not None:
											__lowerCamelCase									=       BatchEncoding()
											__lowerCamelCase									=       self.image_processor(
											    lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				).pixel_values
											__lowerCamelCase									=       query_pixel_values

							if images is not None:
											__lowerCamelCase									=       self.image_processor(lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)

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


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							return self.image_processor.post_process(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							return self.image_processor.post_process_object_detection(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.image_processor.post_process_image_guided_detection(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


			@property
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,		lowerCamelCase__     ,		)
							return self.image_processor_class






			@property
			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,		lowerCamelCase__     ,		)
							return self.image_processor


