

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 import BertTokenizer


_UpperCAmelCase	:     Optional[int]		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Optional[Any]		       = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase	:     Optional[Any]		       = {
    """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	:     Optional[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"""
        ),
    },
}
_UpperCAmelCase	:     Any		       = {
    """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	:     str		       = {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_UpperCAmelCase	:     int		       = {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_UpperCAmelCase	:     Any		       = {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


_UpperCAmelCase	:     List[str]		       = {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_UpperCAmelCase	:     Tuple		       = {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_UpperCAmelCase	:     str		       = {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				List[str]     =    VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE      :				Any     =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE      :				Dict     =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE      :				Dict     =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE      :				Dict     =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE      :				str     =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



_UpperCAmelCase	:     Optional[Any]		       = collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_UpperCAmelCase	:     str		       = collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_UpperCAmelCase	:     Optional[int]		       = 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)

    Returns:
        `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(lowercase_    )
class 				lowercase       :




				def __call__(      self   ,     snake_case   ,     snake_case = None   ,     snake_case = None   ,     snake_case = False   ,     snake_case = False   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     **snake_case   ,     ):
											if titles is None and texts is None:
																		return super().__call__(
																		    snake_case   ,     padding=snake_case   ,     truncation=snake_case   ,     max_length=snake_case   ,     return_tensors=snake_case   ,     return_attention_mask=snake_case   ,     **snake_case   ,     )
											elif titles is None or texts is None:
																		snake_case_           =					titles if texts is None else texts
																		return super().__call__(
																		    snake_case   ,     snake_case   ,     padding=snake_case   ,     truncation=snake_case   ,     max_length=snake_case   ,     return_tensors=snake_case   ,     return_attention_mask=snake_case   ,     **snake_case   ,     )
											snake_case_           =					titles if not isinstance(snake_case   ,     snake_case ) else [titles]
											snake_case_           =					texts if not isinstance(snake_case   ,     snake_case ) else [texts]
											snake_case_           =					len(snake_case )
											snake_case_           =					questions if not isinstance(snake_case   ,     snake_case ) else [questions] * n_passages
											if len(snake_case ) != len(snake_case ):
																		raise ValueError(
																		    F'''There should be as many titles than texts but got {len(snake_case )} titles and {len(snake_case )} texts.''' )
											snake_case_           =					super().__call__(snake_case   ,     snake_case   ,     padding=snake_case   ,     truncation=snake_case )['input_ids']
											snake_case_           =					super().__call__(snake_case   ,     add_special_tokens=snake_case   ,     padding=snake_case   ,     truncation=snake_case )['input_ids']
											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(snake_case   ,     snake_case )
											    ]
											}
											if return_attention_mask is not False:
																		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] )
																		snake_case_           =					attention_mask
											return self.pad(snake_case   ,     padding=snake_case   ,     max_length=snake_case   ,     return_tensors=snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case = 16   ,     snake_case = 64   ,     snake_case = 4   ,     ):
											snake_case_           =					reader_input['input_ids']
											snake_case_       ,					snake_case_       ,					snake_case_           =					reader_output[:3]
											snake_case_           =					len(snake_case )
											snake_case_           =					sorted(range(snake_case )   ,     reverse=snake_case   ,     key=relevance_logits.__getitem__ )
											snake_case_           =					[]
											for doc_id in sorted_docs:
																		snake_case_           =					list(input_ids[doc_id] )
																		# assuming question & title information is at the beginning of the sequence
																		snake_case_           =					sequence_ids.index(self.sep_token_id   ,     2 ) + 1  # second sep id
																		if sequence_ids[-1] == self.pad_token_id:
																									snake_case_           =					sequence_ids.index(self.pad_token_id )
																		else:
																									snake_case_           =					len(snake_case )

																		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=snake_case   ,     top_spans=snake_case   ,     )
																		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=snake_case   ,     start_index=snake_case   ,     end_index=snake_case   ,     text=self.decode(sequence_ids[start_index : end_index + 1] )   ,     ) )
																		if len(snake_case ) >= num_spans:
																									break
											return nbest_spans_predictions[:num_spans]




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     ):
											snake_case_           =					[]
											for start_index, start_score in enumerate(snake_case ):
																		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) )
											snake_case_           =					sorted(snake_case   ,     key=lambda snake_case : x[1]   ,     reverse=snake_case )
											snake_case_           =					[]
											for (start_index, end_index), score in scores:
																		if start_index > end_index:
																									raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]''' )
																		snake_case_           =					end_index - start_index + 1
																		if length > max_answer_length:
																									raise ValueError(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(snake_case ) == top_spans:
																									break
											return chosen_span_intervals







@add_end_docstrings(lowercase_    )
class 				lowercase       ( lowercase_			,						lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE      :				List[str]     =    READER_PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE      :				str     =    READER_PRETRAINED_INIT_CONFIGURATION
				__SCREAMING_SNAKE_CASE      :				str     =    ['''input_ids''', '''attention_mask''']







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor







class 				lowercase       ( unittest.TestCase    ):




				def __init__(      self   ,     snake_case   ,     snake_case=7   ,     snake_case=3   ,     snake_case=30   ,     snake_case=400   ,     snake_case=True   ,     snake_case=None   ,     snake_case=True   ,     snake_case=1 / 255   ,     snake_case=True   ,     snake_case=[0.5, 0.5, 0.5]   ,     snake_case=[0.5, 0.5, 0.5]   ,     snake_case=True   ,     ):
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											snake_case_           =					size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
											snake_case_           =					parent
											snake_case_           =					batch_size
											snake_case_           =					num_channels
											snake_case_           =					min_resolution
											snake_case_           =					max_resolution
											snake_case_           =					do_resize
											snake_case_           =					size
											snake_case_           =					do_rescale
											snake_case_           =					rescale_factor
											snake_case_           =					do_normalize
											snake_case_           =					image_mean
											snake_case_           =					image_std
											snake_case_           =					do_pad




				def 					a							(      self ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_pad": self.do_pad,
											}




				def 					a							(      self   ,     snake_case   ,     snake_case=False ):
											if not batched:
																		snake_case_           =					image_inputs[0]
																		if isinstance(snake_case   ,     Image.Image ):
																									snake_case_       ,					snake_case_           =					image.size
																		else:
																									snake_case_       ,					snake_case_           =					image.shape[1], image.shape[2]
																		if w < h:
																									snake_case_           =					int(self.size['shortest_edge'] * h / w )
																									snake_case_           =					self.size['shortest_edge']
																		elif w > h:
																									snake_case_           =					self.size['shortest_edge']
																									snake_case_           =					int(self.size['shortest_edge'] * w / h )
																		else:
																									snake_case_           =					self.size['shortest_edge']
																									snake_case_           =					self.size['shortest_edge']

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

											return expected_height, expected_width







@require_torch
@require_vision
class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				str     =    DetrImageProcessor if is_vision_available() else None




				def 					a							(      self ):
											snake_case_           =					DetrImageProcessingTester(self )




				@property
				def 					a							(      self ):
											return self.image_processor_tester.prepare_image_processor_dict()




				def 					a							(      self ):
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											self.assertTrue(hasattr(snake_case   ,     'image_mean' ) )
											self.assertTrue(hasattr(snake_case   ,     'image_std' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_normalize' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_rescale' ) )
											self.assertTrue(hasattr(snake_case   ,     'rescale_factor' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_resize' ) )
											self.assertTrue(hasattr(snake_case   ,     'size' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_pad' ) )




				def 					a							(      self ):
											snake_case_           =					self.image_processing_class.from_dict(self.image_processor_dict )
											self.assertEqual(image_processor.size   ,     {'shortest_edge': 18, 'longest_edge': 1333} )
											self.assertEqual(image_processor.do_pad   ,     snake_case )

											snake_case_           =					self.image_processing_class.from_dict(
											    self.image_processor_dict   ,     size=42   ,     max_size=84   ,     pad_and_return_pixel_mask=snake_case )
											self.assertEqual(image_processor.size   ,     {'shortest_edge': 42, 'longest_edge': 84} )
											self.assertEqual(image_processor.do_pad   ,     snake_case )




				def 					a							(      self ):
											pass




				def 					a							(      self ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random PIL images
											snake_case_           =					prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     Image.Image )

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

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

											# Test batched
											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case   ,     batched=snake_case )

											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values
											self.assertEqual(
											    encoded_images.shape   ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )   ,     )




				def 					a							(      self ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random numpy tensors
											snake_case_           =					prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=snake_case   ,     numpify=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     np.ndarray )

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

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

											# Test batched
											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case   ,     batched=snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )   ,     )




				def 					a							(      self ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random PyTorch tensors
											snake_case_           =					prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=snake_case   ,     torchify=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     torch.Tensor )

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

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

											# Test batched
											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case   ,     batched=snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )   ,     )




				@slow
				def 					a							(      self ):
											# prepare image and target
											snake_case_           =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,     'r' ) as f:
																		snake_case_           =					json.loads(f.read() )

											snake_case_           =					{'image_id': 3_9769, 'annotations': target}

											# encode them
											snake_case_           =					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50' )
											snake_case_           =					image_processing(images=snake_case   ,     annotations=snake_case   ,     return_tensors='pt' )

											# verify pixel values
											snake_case_           =					torch.Size([1, 3, 800, 1066] )
											self.assertEqual(encoding['pixel_values'].shape   ,     snake_case )

											snake_case_           =					torch.tensor([0.27_96, 0.31_38, 0.34_81] )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											# verify area
											snake_case_           =					torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,     snake_case ) )
											# verify boxes
											snake_case_           =					torch.Size([6, 4] )
											self.assertEqual(encoding['labels'][0]['boxes'].shape   ,     snake_case )
											snake_case_           =					torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,     snake_case   ,     atol=1e-3 ) )
											# verify image_id
											snake_case_           =					torch.tensor([3_9769] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,     snake_case ) )
											# verify is_crowd
											snake_case_           =					torch.tensor([0, 0, 0, 0, 0, 0] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,     snake_case ) )
											# verify class_labels
											snake_case_           =					torch.tensor([75, 75, 63, 65, 17, 17] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,     snake_case ) )
											# verify orig_size
											snake_case_           =					torch.tensor([480, 640] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,     snake_case ) )
											# verify size
											snake_case_           =					torch.tensor([800, 1066] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,     snake_case ) )




				@slow
				def 					a							(      self ):
											# prepare image, target and masks_path
											snake_case_           =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'   ,     'r' ) as f:
																		snake_case_           =					json.loads(f.read() )

											snake_case_           =					{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

											snake_case_           =					pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )

											# encode them
											snake_case_           =					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic' )
											snake_case_           =					image_processing(images=snake_case   ,     annotations=snake_case   ,     masks_path=snake_case   ,     return_tensors='pt' )

											# verify pixel values
											snake_case_           =					torch.Size([1, 3, 800, 1066] )
											self.assertEqual(encoding['pixel_values'].shape   ,     snake_case )

											snake_case_           =					torch.tensor([0.27_96, 0.31_38, 0.34_81] )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											# verify area
											snake_case_           =					torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,     snake_case ) )
											# verify boxes
											snake_case_           =					torch.Size([6, 4] )
											self.assertEqual(encoding['labels'][0]['boxes'].shape   ,     snake_case )
											snake_case_           =					torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,     snake_case   ,     atol=1e-3 ) )
											# verify image_id
											snake_case_           =					torch.tensor([3_9769] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,     snake_case ) )
											# verify is_crowd
											snake_case_           =					torch.tensor([0, 0, 0, 0, 0, 0] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,     snake_case ) )
											# verify class_labels
											snake_case_           =					torch.tensor([17, 17, 63, 75, 75, 93] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,     snake_case ) )
											# verify masks
											snake_case_           =					82_2873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,     snake_case )
											# verify orig_size
											snake_case_           =					torch.tensor([480, 640] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,     snake_case ) )
											# verify size
											snake_case_           =					torch.tensor([800, 1066] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,     snake_case ) )




