


'''simple docstring'''





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


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__  			=				{
    '''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'''
        ),
    },
}
UpperCAmelCase__  			=				{
    '''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'''
        ),
    },
}
UpperCAmelCase__  			=				{
    '''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'''
        ),
    },
}

UpperCAmelCase__  			=				{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
UpperCAmelCase__  			=				{
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
UpperCAmelCase__  			=				{
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


UpperCAmelCase__  			=				{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
UpperCAmelCase__  			=				{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
UpperCAmelCase__  			=				{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Tuple					      =			VOCAB_FILES_NAMES
			A						:				Optional[Any]					      =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			A						:				List[Any]					      =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A						:				Any					      =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
			A						:				List[str]					      =			DPRContextEncoderTokenizer


class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Optional[int]					      =			VOCAB_FILES_NAMES
			A						:				List[str]					      =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			A						:				Optional[int]					      =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A						:				str					      =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
			A						:				Any					      =			DPRQuestionEncoderTokenizer


UpperCAmelCase__  			=				collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

UpperCAmelCase__  			=				collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


UpperCAmelCase__  			=				r'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''
@add_start_docstrings(a_   )
class 						a__					:





			'''simple docstring'''

			def __call__(  self							:			List[Any]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			Optional[str] = None			,    lowerCAmelCase_							:			Optional[str] = None			,    lowerCAmelCase_							:			Union[bool, str] = False			,    lowerCAmelCase_							:			Union[bool, str] = False			,    lowerCAmelCase_							:			Optional[int] = None			,    lowerCAmelCase_							:			Optional[Union[str, TensorType]] = None			,    lowerCAmelCase_							:			Optional[bool] = None			,    **lowerCAmelCase_							:			Tuple			,    )      ->		BatchEncoding:
						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:
									__A=	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_			,    )
						__A=	titles if not isinstance(lowerCAmelCase_			,    lowerCAmelCase_	) else [titles]
						__A=	texts if not isinstance(lowerCAmelCase_			,    lowerCAmelCase_	) else [texts]
						__A=	len(lowerCAmelCase_	)
						__A=	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."""
						__A=	super().__call__(lowerCAmelCase_			,    lowerCAmelCase_			,    padding=lowerCAmelCase_			,    truncation=lowerCAmelCase_	)['input_ids']
						__A=	super().__call__(lowerCAmelCase_			,    add_special_tokens=lowerCAmelCase_			,    padding=lowerCAmelCase_			,    truncation=lowerCAmelCase_	)['input_ids']
						__A=	{
						    '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:
									__A=	[]
									for input_ids in encoded_inputs["input_ids"]:
												attention_mask.append([int(input_id != self.pad_token_id	) for input_id in input_ids]	)
									__A=	attention_mask
						return self.pad(lowerCAmelCase_			,    padding=lowerCAmelCase_			,    max_length=lowerCAmelCase_			,    return_tensors=lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			List[str]			,    lowerCAmelCase_							:			BatchEncoding			,    lowerCAmelCase_							:			DPRReaderOutput			,    lowerCAmelCase_							:			int = 16			,    lowerCAmelCase_							:			int = 64			,    lowerCAmelCase_							:			int = 4			,    )      ->		List[DPRSpanPrediction]:
						__A=	reader_input['input_ids']
						__A,	__A,	__A=	reader_output[:3]
						__A=	len(lowerCAmelCase_	)
						__A=	sorted(range(lowerCAmelCase_	)			,    reverse=lowerCAmelCase_			,    key=relevance_logits.__getitem__	)
						__A=	[]
						for doc_id in sorted_docs:
									__A=	list(input_ids[doc_id]	)
									# assuming question & title information is at the beginning of the sequence
									__A=	sequence_ids.index(self.sep_token_id			,    2	) + 1  # second sep id
									if sequence_ids[-1] == self.pad_token_id:
												__A=	sequence_ids.index(self.pad_token_id	)
									else:
												__A=	len(lowerCAmelCase_	)

									__A=	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 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			List[int]			,    lowerCAmelCase_							:			List[int]			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			int			,    )      ->		List[DPRSpanPrediction]:
						__A=	[]
						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)	)
						__A=	sorted(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: x[1]			,    reverse=lowerCAmelCase_	)
						__A=	[]
						for (start_index, end_index), score in scores:
									assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
									__A=	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(a_   )
class 						a__					(					a_      ,  a_   ):





			'''simple docstring'''

			A						:				Union[str, Any]					      =			VOCAB_FILES_NAMES
			A						:				Dict					      =			READER_PRETRAINED_VOCAB_FILES_MAP
			A						:				Tuple					      =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A						:				Optional[Any]					      =			READER_PRETRAINED_INIT_CONFIGURATION
			A						:				str					      =			['''input_ids''', '''attention_mask''']
			A						:				Optional[int]					      =			DPRReaderTokenizer







'''simple docstring'''





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import BridgeTowerImageProcessor
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			def __init__(  self							:			str			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Dict[str, int] = None			,    lowerCAmelCase_							:			int = 32			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Union[int, float] = 1 / 255			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			str=7			,    lowerCAmelCase_							:			Union[str, Any]=30			,    lowerCAmelCase_							:			str=400			,    lowerCAmelCase_							:			int=3			,    )      ->		str:
						__A=	parent
						__A=	do_resize
						__A=	size if size is not None else {'shortest_edge': 288}
						__A=	size_divisor
						__A=	do_rescale
						__A=	rescale_factor
						__A=	do_normalize
						__A=	do_center_crop
						__A=	image_mean
						__A=	image_std
						__A=	do_pad
						__A=	batch_size
						__A=	num_channels
						__A=	min_resolution
						__A=	max_resolution
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "size_divisor": self.size_divisor,
						}

			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			str=False	)      ->		str:
						if not batched:
									__A=	self.size['shortest_edge']
									__A=	image_inputs[0]
									if isinstance(lowerCAmelCase_			,    Image.Image	):
												__A,	__A=	image.size
									else:
												__A,	__A=	image.shape[1], image.shape[2]
									__A=	size / min(lowerCAmelCase_			,    lowerCAmelCase_	)
									if h < w:
												__A,	__A=	size, scale * w
									else:
												__A,	__A=	scale * h, size

									__A=	int((1_333 / 800) * size	)
									if max(lowerCAmelCase_			,    lowerCAmelCase_	) > max_size:
												__A=	max_size / max(lowerCAmelCase_			,    lowerCAmelCase_	)
												__A=	newh * scale
												__A=	neww * scale

									__A,	__A=	int(newh + 0.5	), int(neww + 0.5	)
									__A,	__A=	(
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

						else:
									__A=	[]
									for image in image_inputs:
												__A,	__A=	self.get_expected_values([image]	)
												expected_values.append((expected_height, expected_width)	)
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[0]	)[0]
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[1]	)[1]

						return expected_height, expected_width


@require_torch
@require_vision
class 						a__					(					a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				int					      =			BridgeTowerImageProcessor if is_vision_available() else None
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Optional[int]:
						__A=	BridgeTowerImageProcessingTester(self	)
			@property
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Union[str, Any]:
						return self.image_processor_tester.prepare_image_processor_dict()
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		List[Any]:
						__A=	self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_mean'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_std'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_normalize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_resize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size_divisor'	)	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						pass
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    Image.Image	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    numpify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    np.ndarray	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )

			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Tuple:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    torchify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    torch.Tensor	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )



