
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed

import numpy as np

import datasets

from .execute import check_correctness


_SCREAMING_SNAKE_CASE    :      Dict          =			"\\n@misc{chen2021evaluating,\n      title={Evaluating Large Language Models Trained on Code},\n      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n      year={2021},\n      eprint={2107.03374},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n"

_SCREAMING_SNAKE_CASE    :      Dict          =			"\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper \"Evaluating Large Language Models Trained on Code\"\n(https://arxiv.org/abs/2107.03374).\n"


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			"\nCalculates how good are predictions given some references, using certain scores\nArgs:\n    predictions: list of candidates to evaluate. Each candidates should be a list\n        of strings with several code candidates to solve the problem.\n    references: a list with a test for each prediction. Each test should evaluate the\n        correctness of a code candidate.\n    k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n    num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n    timeout:\nReturns:\n    pass_at_k: dict with pass rates for each k\n    results: dict with granular results of each unittest\nExamples:\n    >>> code_eval = datasets.load_metric(\"code_eval\")\n    >>> test_cases = [\"assert add(2,3)==5\"]\n    >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]\n    >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n    >>> print(pass_at_k)\n    {'pass@1': 0.5, 'pass@2': 1.0}\n"


_SCREAMING_SNAKE_CASE    :      List[Any]          =			"\n################################################################################\n                                  !!!WARNING!!!\n################################################################################\nThe \"code_eval\" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper \"Evaluating Large\nLanguage Models Trained on Code\" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"\n\n################################################################################\\n"

_SCREAMING_SNAKE_CASE    :      Any          =			"The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE."







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,			_KWARGS_DESCRIPTION   )
class  UpperCamelCase__		(    datasets.Metric   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					return datasets.MetricInfo(
					    # This is the description that will appear on the metrics page.
					    description=_DESCRIPTION				,       citation=_CITATION				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
					        {
					            '''predictions''': datasets.Sequence(datasets.Value('''string'''		)		),
					            '''references''': datasets.Value('''string'''		),
					        }		)				,       homepage='''https://github.com/openai/human-eval'''				,       codebase_urls=['''https://github.com/openai/human-eval''']				,       reference_urls=['''https://github.com/openai/human-eval''']				,       license=_LICENSE				,       )





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=[1, 10, 100]				,       UpperCamelCase__=4				,       UpperCamelCase__=3.0		):

					if os.getenv('''HF_ALLOW_CODE_EVAL'''				,       0		) != "1":
									raise ValueError(_WARNING		)

					if os.name == "nt":
									raise NotImplementedError('''This metric is currently not supported on Windows.'''		)

					with ThreadPoolExecutor(max_workers=lowercase_		) as executor:
									A__  :	Optional[Any]         =							[]
									A__  :	List[str]         =							Counter()
									A__  :	Union[str, Any]         =							0
									A__  :	List[str]         =							defaultdict(lowercase_		)

									for task_id, (candidates, test_case) in enumerate(zip(lowercase_				,       lowercase_		)		):
													for candidate in candidates:
																	A__  :	Any         =							candidate + """\n""" + test_case
																	A__  :	Dict         =							(test_program, timeout, task_id, completion_id[task_id])
																	A__  :	Union[str, Any]         =							executor.submit(lowercase_				,       *lowercase_		)
																	futures.append(lowercase_		)
																	completion_id[task_id] += 1
																	n_samples += 1

									for future in as_completed(lowercase_		):
													A__  :	Optional[Any]         =							future.result()
													results[result["task_id"]].append((result['''completion_id'''], result)		)

					A__  :	int         =							[], []
					for result in results.values():
									result.sort()
									A__  :	str         =							[r[1]["""passed"""] for r in result]
									total.append(len(lowercase_		)		)
									correct.append(sum(lowercase_		)		)
					A__  :	List[str]         =							np.array(lowercase_		)
					A__  :	List[Any]         =							np.array(lowercase_		)

					A__  :	Optional[int]         =							k
					A__  :	List[Any]         =							{F"pass@{k}": estimate_pass_at_k(lowercase_				,       lowercase_				,       lowercase_		).mean() for k in ks if (total >= k).all()}

					return pass_at_k, results




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

				"""simple docstring"""







				def estimator(__UpperCamelCase :						int							,						__UpperCamelCase :						int							,						__UpperCamelCase :						int       ) -> float:
								if n - c < k:
												return 1.0
								return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1							,						n + 1       )       )

				if isinstance(UpperCAmelCase__							,						UpperCAmelCase__       ):
								A__  :	Any         =							itertools.repeat(UpperCAmelCase__							,						len(UpperCAmelCase__       )       )
				else:
								assert len(UpperCAmelCase__       ) == len(UpperCAmelCase__       )
								A__  :	Union[str, Any]         =							iter(UpperCAmelCase__       )

				return np.array([estimator(int(UpperCAmelCase__       )							,						int(UpperCAmelCase__       )							,						UpperCAmelCase__       ) for n, c in zip(UpperCAmelCase__							,						UpperCAmelCase__       )]       )

import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import YolosForObjectDetection, YolosModel
					from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__=13				,       UpperCamelCase__=[30, 30]				,       UpperCamelCase__=2				,       UpperCamelCase__=3				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=32				,       UpperCamelCase__=5				,       UpperCamelCase__=4				,       UpperCamelCase__=37				,       UpperCamelCase__="gelu"				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=10				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=3				,       UpperCamelCase__=None				,       UpperCamelCase__=8				,       UpperCamelCase__=10				,       ):
					A__  :	Optional[int]         =							parent
					A__  :	List[Any]         =							batch_size
					A__  :	Dict         =							image_size
					A__  :	Any         =							patch_size
					A__  :	Dict         =							num_channels
					A__  :	List[Any]         =							is_training
					A__  :	int         =							use_labels
					A__  :	Any         =							hidden_size
					A__  :	List[str]         =							num_hidden_layers
					A__  :	Optional[int]         =							num_attention_heads
					A__  :	Optional[Any]         =							intermediate_size
					A__  :	str         =							hidden_act
					A__  :	str         =							hidden_dropout_prob
					A__  :	Optional[int]         =							attention_probs_dropout_prob
					A__  :	Optional[int]         =							type_sequence_label_size
					A__  :	Any         =							initializer_range
					A__  :	Optional[int]         =							num_labels
					A__  :	Union[str, Any]         =							scope
					A__  :	Union[str, Any]         =							n_targets
					A__  :	Dict         =							num_detection_tokens
					# we set the expected sequence length (which is used in several tests)
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
					A__  :	int         =							(image_size[1] // patch_size) * (image_size[0] // patch_size)
					A__  :	List[str]         =							num_patches + 1 + self.num_detection_tokens





	def   __snake_case				(			self		):
					A__  :	Dict         =							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

					A__  :	int         =							None
					if self.use_labels:
									# labels is a list of Dict (each Dict being the labels for a given example in the batch)
									A__  :	Tuple         =							[]
									for i in range(self.batch_size		):
													A__  :	List[Any]         =							{}
													A__  :	Tuple         =							torch.randint(
													    high=self.num_labels				,       size=(self.n_targets,)				,       device=UpperCamelCase__		)
													A__  :	Any         =							torch.rand(self.n_targets				,       4				,       device=UpperCamelCase__		)
													labels.append(UpperCamelCase__		)

					A__  :	Union[str, Any]         =							self.get_config()

					return config, pixel_values, labels





	def   __snake_case				(			self		):
					return YolosConfig(
					    image_size=self.image_size				,       patch_size=self.patch_size				,       num_channels=self.num_channels				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       is_decoder=UpperCamelCase__				,       initializer_range=self.initializer_range				,       num_detection_tokens=self.num_detection_tokens				,       num_labels=self.num_labels				,       )





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Tuple         =							YolosModel(config=UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()
					A__  :	Optional[Any]         =							model(UpperCamelCase__		)
					self.parent.assertEqual(
					    result.last_hidden_state.shape				,       (self.batch_size, self.expected_seq_len, self.hidden_size)		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Any         =							YolosForObjectDetection(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()

					A__  :	Union[str, Any]         =							model(pixel_values=UpperCamelCase__		)
					A__  :	Optional[int]         =							model(UpperCamelCase__		)

					self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
					self.parent.assertEqual(result.pred_boxes.shape				,       (self.batch_size, self.num_detection_tokens, 4)		)

					A__  :	Union[str, Any]         =							model(pixel_values=UpperCamelCase__				,       labels=UpperCamelCase__		)

					self.parent.assertEqual(result.loss.shape				,       ()		)
					self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
					self.parent.assertEqual(result.pred_boxes.shape				,       (self.batch_size, self.num_detection_tokens, 4)		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.prepare_config_and_inputs()
					A__       ,							A__       ,							A__  :	Optional[Any]         =							config_and_inputs
					A__  :	Optional[int]         =							{'''pixel_values''': pixel_values}
					return config, inputs_dict







@require_torch
class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
	_lowerCAmelCase							       =				(
	    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
	)

	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=False		):
					A__  :	Optional[int]         =							super()._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__				,       return_labels=UpperCamelCase__		)

					if return_labels:
									if model_class.__name__ == "YolosForObjectDetection":
													A__  :	str         =							[]
													for i in range(self.model_tester.batch_size		):
																	A__  :	int         =							{}
																	A__  :	Dict         =							torch.ones(
																	    size=(self.model_tester.n_targets,)				,       device=UpperCamelCase__				,       dtype=torch.long		)
																	A__  :	Dict         =							torch.ones(
																	    self.model_tester.n_targets				,       4				,       device=UpperCamelCase__				,       dtype=torch.float		)
																	labels.append(UpperCamelCase__		)
													A__  :	Dict         =							labels

					return inputs_dict





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							YolosModelTester(self		)
					A__  :	List[str]         =							ConfigTester(self				,       config_class=UpperCamelCase__				,       has_text_modality=UpperCamelCase__				,       hidden_size=37		)





	def   __snake_case				(			self		):
					self.config_tester.run_common_tests()





	def   __snake_case				(			self		):
					# YOLOS does not use inputs_embeds
					pass





	def   __snake_case				(			self		):
					A__       ,							A__  :	Dict         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__  :	Any         =							model_class(UpperCamelCase__		)
									self.assertIsInstance(model.get_input_embeddings()				,       (nn.Module)		)
									A__  :	str         =							model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(UpperCamelCase__				,       nn.Linear		)		)





	def   __snake_case				(			self		):
					A__       ,							A__  :	int         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__  :	List[str]         =							model_class(UpperCamelCase__		)
									A__  :	str         =							inspect.signature(model.forward		)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									A__  :	Optional[int]         =							[*signature.parameters.keys()]

									A__  :	Optional[Any]         =							['''pixel_values''']
									self.assertListEqual(arg_names[:1]				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__       ,							A__  :	List[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()
					A__  :	Tuple         =							True

					# in YOLOS, the seq_len is different
					A__  :	List[Any]         =							self.model_tester.expected_seq_len
					for model_class in self.all_model_classes:
									A__  :	Any         =							True
									A__  :	Optional[int]         =							False
									A__  :	Optional[Any]         =							True
									A__  :	int         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	List[str]         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)
									A__  :	Optional[int]         =							outputs.attentions
									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									A__  :	Tuple         =							True
									A__  :	Optional[Any]         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	Tuple         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)
									A__  :	Tuple         =							outputs.attentions
									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]		)				,       [self.model_tester.num_attention_heads, seq_len, seq_len]				,       )
									A__  :	List[Any]         =							len(UpperCamelCase__		)

									# Check attention is always last and order is fine
									A__  :	List[str]         =							True
									A__  :	List[Any]         =							True
									A__  :	int         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	Tuple         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)

									A__  :	Tuple         =							1
									self.assertEqual(out_len + added_hidden_states				,       len(UpperCamelCase__		)		)

									A__  :	List[str]         =							outputs.attentions

									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]		)				,       [self.model_tester.num_attention_heads, seq_len, seq_len]				,       )





	def   __snake_case				(			self		):
					def check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	str         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()

									with torch.no_grad():
													A__  :	int         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)

									A__  :	Optional[Any]         =							outputs.hidden_states

									A__  :	int         =							getattr(
									    self.model_tester				,       '''expected_num_hidden_layers'''				,       self.model_tester.num_hidden_layers + 1		)
									self.assertEqual(len(UpperCamelCase__		)				,       UpperCamelCase__		)

									# YOLOS has a different seq_length
									A__  :	Union[str, Any]         =							self.model_tester.expected_seq_len

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]		)				,       [seq_length, self.model_tester.hidden_size]				,       )

					A__       ,							A__  :	Optional[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__  :	int         =							True
									check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									A__  :	Optional[int]         =							True

									check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_object_detection(*UpperCamelCase__		)





	@slow
	def   __snake_case				(			self		):
					for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									A__  :	Union[str, Any]         =							YolosModel.from_pretrained(UpperCamelCase__		)
									self.assertIsNotNone(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      ) ->       List[str]:

				"""simple docstring"""







				A__  :	int         =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
				return image







@require_torch
@require_vision
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	@cached_property
	def   __snake_case				(			self		):
					return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''		) if is_vision_available() else None





	@slow
	def   __snake_case				(			self		):
					A__  :	Tuple         =							YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(UpperCamelCase__		)

					A__  :	str         =							self.default_image_processor
					A__  :	Tuple         =							prepare_img()
					A__  :	Tuple         =							image_processor(images=UpperCamelCase__				,       return_tensors='''pt'''		).to(UpperCamelCase__		)

					# forward pass
					with torch.no_grad():
									A__  :	Any         =							model(inputs.pixel_values		)

					# verify outputs
					A__  :	List[Any]         =							torch.Size((1, 100, 92)		)
					self.assertEqual(outputs.logits.shape				,       UpperCamelCase__		)

					A__  :	Optional[int]         =							torch.tensor(
					    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]				,       device=UpperCamelCase__				,       )
					A__  :	Optional[int]         =							torch.tensor(
					    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]				,       device=UpperCamelCase__		)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,       UpperCamelCase__				,       atol=1e-4		)		)
					self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]				,       UpperCamelCase__				,       atol=1e-4		)		)

					# verify postprocessing
					A__  :	Dict         =							image_processor.post_process_object_detection(
					    UpperCamelCase__				,       threshold=0.3				,       target_sizes=[image.size[::-1]]		)[0]
					A__  :	int         =							torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]		).to(UpperCamelCase__		)
					A__  :	str         =							[75, 75, 17, 63, 17]
					A__  :	Tuple         =							torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]		).to(UpperCamelCase__		)

					self.assertEqual(len(results['''scores''']		)				,       5		)
					self.assertTrue(torch.allclose(results['''scores''']				,       UpperCamelCase__				,       atol=1e-4		)		)
					self.assertSequenceEqual(results['''labels'''].tolist()				,       UpperCamelCase__		)
					self.assertTrue(torch.allclose(results['''boxes'''][0, :]				,       UpperCamelCase__		)		)