

import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
a_						     =    'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
a_						     =    direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
a_						     =    re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a_						     =    re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
a_						     =    re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a_						     =    [
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,			_a)
	return [m.group(0) for m in matches]

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	SCREAMING_SNAKE_CASE   :      str				     =       {
	    config.replace("Config"							,			""): model_type for model_type, config in config_maping_names.items()
	}

	# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       collections.defaultdict(_a)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       collections.defaultdict(_a)
	SCREAMING_SNAKE_CASE   :      Any				     =       collections.defaultdict(_a)

	# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
	for attr_name in dir(_a):
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       None
		if _re_tf_models.match(_a) is not None:
			SCREAMING_SNAKE_CASE   :      List[str]				     =       tf_models
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       _re_tf_models.match(_a).groups()[0]
		elif _re_flax_models.match(_a) is not None:
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       flax_models
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       _re_flax_models.match(_a).groups()[0]
		elif _re_pt_models.match(_a) is not None:
			SCREAMING_SNAKE_CASE   :      Any				     =       pt_models
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       _re_pt_models.match(_a).groups()[0]

		if lookup_dict is not None:
			while len(_a) > 0:
				if attr_name in model_prefix_to_model_type:
					SCREAMING_SNAKE_CASE   :      Dict				     =       True
					break
				# Try again after removing the last word in the name
				SCREAMING_SNAKE_CASE   :      str				     =       "".join(camel_case_split(_a)[:-1])

	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       set(list(pt_models.keys()) + list(tf_models.keys()) + list(flax_models.keys()))
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       list(_a)
	all_models.sort()

	SCREAMING_SNAKE_CASE   :      Dict				     =       {"model_type": all_models}
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [pt_models[t] for t in all_models]
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       [tf_models[t] for t in all_models]
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [flax_models[t] for t in all_models]

	# Now let's use the auto-mapping names to make sure
	SCREAMING_SNAKE_CASE   :      Any				     =       {}
	for t in all_models:
		if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
			SCREAMING_SNAKE_CASE   :      List[str]				     =       "AutoProcessor"
		elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "AutoTokenizer"
		elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "AutoFeatureExtractor"
		else:
			# Default to AutoTokenizer if a model has nothing, for backward compatibility.
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "AutoTokenizer"

	SCREAMING_SNAKE_CASE   :      List[Any]				     =       [processors[t] for t in all_models]

	return pd.DataFrame(_a)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Dict				     =       [
	    transformers_module.models.auto.modeling_auto,
	    transformers_module.models.auto.modeling_tf_auto,
	    transformers_module.models.auto.modeling_flax_auto,
	]
	for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
		SCREAMING_SNAKE_CASE   :      Dict				     =       [model_mapping, f"TF_{model_mapping}", f"FLAX_{model_mapping}"]
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       [auto_class, f"TF_{auto_class}", f"Flax_{auto_class}"]
		# Loop through all three frameworks
		for module, cls, mapping in zip(_a							,			_a							,			_a):
			# The type of pipeline may not exist in this framework
			if not hasattr(_a							,			_a):
				continue
			# First extract all model_names
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       []
			for name in getattr(_a							,			_a).values():
				if isinstance(_a							,			_a):
					model_names.append(_a)
				else:
					model_names.extend(list(_a))

            # Add pipeline tag and auto model class for those models
			table.update({model_name: (pipeline_tag, cls) for model_name in model_names})

	return table

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       get_frameworks_table()
	SCREAMING_SNAKE_CASE   :      Any				     =       Dataset.from_pandas(_a)

	SCREAMING_SNAKE_CASE   :      int				     =       hf_hub_download(
	    "huggingface/transformers-metadata"							,			"pipeline_tags.json"							,			repo_type="dataset"							,			token=_a)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       Dataset.from_json(_a)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {
	    tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
	    for i in range(len(_a))
	}
	SCREAMING_SNAKE_CASE   :      Dict				     =       update_pipeline_and_auto_class_table(_a)

	# Sort the model classes to avoid some nondeterministic updates to create false update commits.
	SCREAMING_SNAKE_CASE   :      str				     =       sorted(table.keys())
	SCREAMING_SNAKE_CASE   :      Tuple				     =       pd.DataFrame(
	    {
	        "model_class": model_classes,
	        "pipeline_tag": [table[m][0] for m in model_classes],
	        "auto_class": [table[m][1] for m in model_classes],
	    })
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       Dataset.from_pandas(_a)

	with tempfile.TemporaryDirectory() as tmp_dir:
		frameworks_dataset.to_json(os.path.join(_a							,			"frameworks.json"))
		tags_dataset.to_json(os.path.join(_a							,			"pipeline_tags.json"))

		if commit_sha is not None:
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (
			    f"Update with commit {commit_sha}\n\nSee: "
			    f"https://github.com/huggingface/transformers/commit/{commit_sha}"
			)
		else:
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       "Update"

		upload_folder(
		    repo_id="huggingface/transformers-metadata"							,			folder_path=_a							,			repo_type="dataset"							,			token=_a							,			commit_message=_a							,			)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       transformers_module.pipelines.SUPPORTED_TASKS
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
	for key in pipeline_tasks:
		if key not in in_table:
			SCREAMING_SNAKE_CASE   :      Any				     =       pipeline_tasks[key]["pt"]
			if isinstance(_a							,			(list, tuple)):
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model[0]
			SCREAMING_SNAKE_CASE   :      int				     =       model.__name__
			if model not in in_table.values():
				missing.append(_a)

	if len(_a) > 0:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ", ".join(_a)
		raise ValueError(
		    "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
		    f"`utils/update_metadata.py`: {msg}. Please add them!")


if __name__ == "__main__":
			a_						     =    argparse.ArgumentParser()
			parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
			parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
			parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
			a_						     =    parser.parse_args()

			if args.check_only:
						check_pipeline_tags()
			else:
						update_metadata(args.token, args.commit_sha)


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 DetaImageProcessor






class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def __init__(			self							: Any      ,      a							: int      ,      a							: Optional[int]=7      ,      a							: List[Any]=3      ,      a							: Any=30      ,      a							: Dict=400      ,      a							: str=True      ,      a							: List[Any]=None      ,      a							: List[str]=True      ,      a							: Optional[Any]=[0.5, 0.5, 0.5]      ,      a							: Optional[int]=[0.5, 0.5, 0.5]      ,      a							: Union[str, Any]=True      ,      a							: Tuple=1 / 255      ,      a							: Optional[int]=True      ,      )							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
				SCREAMING_SNAKE_CASE   :      Any				     =       parent
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      str				     =       num_channels
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       min_resolution
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       max_resolution
				SCREAMING_SNAKE_CASE   :      str				     =       do_resize
				SCREAMING_SNAKE_CASE   :      Any				     =       size
				SCREAMING_SNAKE_CASE   :      str				     =       do_normalize
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_mean
				SCREAMING_SNAKE_CASE   :      Dict				     =       image_std
				SCREAMING_SNAKE_CASE   :      Any				     =       do_rescale
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       rescale_factor
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       do_pad
			def 							__UpperCamelCase (			self							: str	)							->       List[str]:

				"""simple docstring"""

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






			def 							__UpperCamelCase (			self							: Any      ,      a							: Union[str, Any]      ,      a							: int=False	)							->       int:

				"""simple docstring"""

				if not batched:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       image_inputs[0]
					if isinstance(a      ,      Image.Image	):
						SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       image.size
					else:
						SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       image.shape[1], image.shape[2]
					if w < h:
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       int(self.size["shortest_edge"] * h / w	)
						SCREAMING_SNAKE_CASE   :      str				     =       self.size["shortest_edge"]
					elif w > h:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.size["shortest_edge"]
						SCREAMING_SNAKE_CASE   :      str				     =       int(self.size["shortest_edge"] * w / h	)
					else:
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.size["shortest_edge"]
						SCREAMING_SNAKE_CASE   :      Dict				     =       self.size["shortest_edge"]

				else:
					SCREAMING_SNAKE_CASE   :      str				     =       []
					for image in image_inputs:
						SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.get_expected_values([image]	)
						expected_values.append((expected_height, expected_width)	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       max(a      ,      key=lambda a	: item[0]	)[0]
					SCREAMING_SNAKE_CASE   :      str				     =       max(a      ,      key=lambda a	: item[1]	)[1]

				return expected_height, expected_width






@require_torch
@require_vision
class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =DetaImageProcessor if is_vision_available() else None
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       DetaImageProcessingTester(self	)
			@property
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(a      ,      "image_mean"	)	)
				self.assertTrue(hasattr(a      ,      "image_std"	)	)
				self.assertTrue(hasattr(a      ,      "do_normalize"	)	)
				self.assertTrue(hasattr(a      ,      "do_resize"	)	)
				self.assertTrue(hasattr(a      ,      "do_rescale"	)	)
				self.assertTrue(hasattr(a      ,      "do_pad"	)	)
				self.assertTrue(hasattr(a      ,      "size"	)	)
			def 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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      ,      a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				SCREAMING_SNAKE_CASE   :      Any				     =       prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=a	)
				for image in image_inputs:
					self.assertIsInstance(a      ,      Image.Image	)

				# Test not batched input
				SCREAMING_SNAKE_CASE   :      Dict				     =       image_processing(image_inputs[0]      ,      return_tensors="pt"	).pixel_values

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       self.image_processor_tester.get_expected_values(a	)

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

				# Test batched
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.image_processor_tester.get_expected_values(a      ,      batched=a	)

				SCREAMING_SNAKE_CASE   :      str				     =       image_processing(a      ,      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 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE   :      str				     =       prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=a      ,      numpify=a	)
				for image in image_inputs:
					self.assertIsInstance(a      ,      np.ndarray	)

				# Test not batched input
				SCREAMING_SNAKE_CASE   :      List[str]				     =       image_processing(image_inputs[0]      ,      return_tensors="pt"	).pixel_values

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.image_processor_tester.get_expected_values(a	)

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

				# Test batched
				SCREAMING_SNAKE_CASE   :      List[str]				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       self.image_processor_tester.get_expected_values(a      ,      batched=a	)

				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,      )
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=a      ,      torchify=a	)
				for image in image_inputs:
					self.assertIsInstance(a      ,      torch.Tensor	)

				# Test not batched input
				SCREAMING_SNAKE_CASE   :      str				     =       image_processing(image_inputs[0]      ,      return_tensors="pt"	).pixel_values

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.image_processor_tester.get_expected_values(a	)

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

				# Test batched
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       self.image_processor_tester.get_expected_values(a      ,      batched=a	)

				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,      )
			@slow
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
				with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"      ,      "r"	) as f:
					SCREAMING_SNAKE_CASE   :      int				     =       json.loads(f.read()	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       {"image_id": 3_9769, "annotations": target}

				# encode them
				SCREAMING_SNAKE_CASE   :      int				     =       DetaImageProcessor()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       image_processing(images=a      ,      annotations=a      ,      return_tensors="pt"	)

				# verify pixel values
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.Size([1, 3, 800, 1066]	)
				self.assertEqual(encoding["pixel_values"].shape      ,      a	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.tensor([0.2796, 0.3138, 0.3481]	)
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,      a      ,      atol=1e-4	)	)

				# verify area
				SCREAMING_SNAKE_CASE   :      Any				     =       torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,      a	)	)
				# verify boxes
				SCREAMING_SNAKE_CASE   :      str				     =       torch.Size([6, 4]	)
				self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      a      ,      atol=1e-3	)	)
				# verify image_id
				SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor([3_9769]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      a	)	)
				# verify is_crowd
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.tensor([0, 0, 0, 0, 0, 0]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      a	)	)
				# verify class_labels
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.tensor([75, 75, 63, 65, 17, 17]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      a	)	)
				# verify orig_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.tensor([480, 640]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      a	)	)
				# verify size
				SCREAMING_SNAKE_CASE   :      int				     =       torch.tensor([800, 1066]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      a	)	)






			@slow
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
				with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"      ,      "r"	) as f:
					SCREAMING_SNAKE_CASE   :      Any				     =       json.loads(f.read()	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

				# encode them
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       DetaImageProcessor(format="coco_panoptic"	)
				SCREAMING_SNAKE_CASE   :      str				     =       image_processing(images=a      ,      annotations=a      ,      masks_path=a      ,      return_tensors="pt"	)

				# verify pixel values
				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.Size([1, 3, 800, 1066]	)
				self.assertEqual(encoding["pixel_values"].shape      ,      a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.tensor([0.2796, 0.3138, 0.3481]	)
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,      a      ,      atol=1e-4	)	)

				# verify area
				SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,      a	)	)
				# verify boxes
				SCREAMING_SNAKE_CASE   :      int				     =       torch.Size([6, 4]	)
				self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      a      ,      atol=1e-3	)	)
				# verify image_id
				SCREAMING_SNAKE_CASE   :      int				     =       torch.tensor([3_9769]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      a	)	)
				# verify is_crowd
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.tensor([0, 0, 0, 0, 0, 0]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      a	)	)
				# verify class_labels
				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.tensor([17, 17, 63, 75, 75, 93]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      a	)	)
				# verify masks
				SCREAMING_SNAKE_CASE   :      Tuple				     =       82_2873
				self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,      a	)
				# verify orig_size
				SCREAMING_SNAKE_CASE   :      Any				     =       torch.tensor([480, 640]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      a	)	)
				# verify size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.tensor([800, 1066]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      a	)	)