




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    OpenAIGPTConfig,
							    OpenAIGPTDoubleHeadsModel,
							    OpenAIGPTForSequenceClassification,
							    OpenAIGPTLMHeadModel,
							    OpenAIGPTModel,
							)




class 		__A      :

						def __init__(		self   :Optional[Any]    , __snake_case   :Optional[int]    , __snake_case   :Union[str, Any]=13    , __snake_case   :List[Any]=7    , __snake_case   :Any=True    , __snake_case   :List[Any]=True    , __snake_case   :Any=True    , __snake_case   :Any=99    , __snake_case   :List[Any]=32    , __snake_case   :Tuple=5    , __snake_case   :List[Any]=4    , __snake_case   :Union[str, Any]=37    , __snake_case   :Union[str, Any]="gelu"    , __snake_case   :Union[str, Any]=0.1    , __snake_case   :Optional[int]=0.1    , __snake_case   :List[Any]=5_12    , __snake_case   :Any=16    , __snake_case   :Union[str, Any]=2    , __snake_case   :Optional[Any]=0.02    , __snake_case   :List[str]=3    , __snake_case   :List[Any]=4    , __snake_case   :Dict=None    , ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=parent
										__magic_name__	:	Any								=batch_size
										__magic_name__	:	Any								=seq_length
										__magic_name__	:	int								=is_training
										__magic_name__	:	Any								=use_token_type_ids
										__magic_name__	:	List[Any]								=use_labels
										__magic_name__	:	List[str]								=vocab_size
										__magic_name__	:	Optional[int]								=hidden_size
										__magic_name__	:	Tuple								=num_hidden_layers
										__magic_name__	:	Optional[Any]								=num_attention_heads
										__magic_name__	:	Dict								=intermediate_size
										__magic_name__	:	Optional[Any]								=hidden_act
										__magic_name__	:	str								=hidden_dropout_prob
										__magic_name__	:	List[str]								=attention_probs_dropout_prob
										__magic_name__	:	Optional[int]								=max_position_embeddings
										__magic_name__	:	Tuple								=type_vocab_size
										__magic_name__	:	Any								=type_sequence_label_size
										__magic_name__	:	Tuple								=initializer_range
										__magic_name__	:	Dict								=num_labels
										__magic_name__	:	List[Any]								=num_choices
										__magic_name__	:	Optional[Any]								=scope
										__magic_name__	:	List[Any]								=self.vocab_size - 1

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Dict								=ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

										__magic_name__	:	List[Any]								=None
										if self.use_token_type_ids:
														__magic_name__	:	Optional[Any]								=ids_tensor([self.batch_size, self.seq_length]    , self.type_vocab_size )

										__magic_name__	:	str								=None
										__magic_name__	:	Dict								=None
										__magic_name__	:	int								=None
										if self.use_labels:
														__magic_name__	:	Tuple								=ids_tensor([self.batch_size]    , self.type_sequence_label_size )
														__magic_name__	:	Tuple								=ids_tensor([self.batch_size, self.seq_length]    , self.num_labels )
														__magic_name__	:	List[Any]								=ids_tensor([self.batch_size]    , self.num_choices )

										__magic_name__	:	Dict								=OpenAIGPTConfig(
										    vocab_size=self.vocab_size    , n_embd=self.hidden_size    , n_layer=self.num_hidden_layers    , n_head=self.num_attention_heads    , n_positions=self.max_position_embeddings    , pad_token_id=self.pad_token_id    , )

										__magic_name__	:	Tuple								=ids_tensor([self.num_hidden_layers, self.num_attention_heads]    , 2 )

										return (
										    config,
										    input_ids,
										    head_mask,
										    token_type_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										)

						def     A__						(		self   :Optional[int]    , __snake_case   :Union[str, Any]    , __snake_case   :Dict    , __snake_case   :Optional[int]    , __snake_case   :Optional[int]    , *__snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								=OpenAIGPTModel(config=__snake_case )
										model.to(__snake_case )
										model.eval()

										__magic_name__	:	Optional[Any]								=model(__snake_case    , token_type_ids=__snake_case    , head_mask=__snake_case )
										__magic_name__	:	Dict								=model(__snake_case    , token_type_ids=__snake_case )
										__magic_name__	:	Optional[Any]								=model(__snake_case )

										self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size) )

						def     A__						(		self   :Union[str, Any]    , __snake_case   :int    , __snake_case   :int    , __snake_case   :List[str]    , __snake_case   :List[Any]    , *__snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=OpenAIGPTLMHeadModel(__snake_case )
										model.to(__snake_case )
										model.eval()

										__magic_name__	:	Tuple								=model(__snake_case    , token_type_ids=__snake_case    , labels=__snake_case )
										self.parent.assertEqual(result.loss.shape    , () )
										self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size) )

						def     A__						(		self   :Any    , __snake_case   :Any    , __snake_case   :Tuple    , __snake_case   :int    , __snake_case   :Union[str, Any]    , *__snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	Dict								=OpenAIGPTDoubleHeadsModel(__snake_case )
										model.to(__snake_case )
										model.eval()

										__magic_name__	:	str								=model(__snake_case    , token_type_ids=__snake_case    , labels=__snake_case )
										self.parent.assertEqual(result.loss.shape    , () )
										self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size) )

						def     A__						(		self   :int    , __snake_case   :List[Any]    , __snake_case   :Optional[Any]    , __snake_case   :Tuple    , __snake_case   :Tuple    , *__snake_case   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.num_labels
										__magic_name__	:	List[str]								=OpenAIGPTForSequenceClassification(__snake_case )
										model.to(__snake_case )
										model.eval()

										__magic_name__	:	List[str]								=ids_tensor([self.batch_size]    , self.type_sequence_label_size )
										__magic_name__	:	Optional[Any]								=model(__snake_case    , token_type_ids=__snake_case    , labels=__snake_case )
										self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels) )


						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)				, (
										    __magic_name__
										)				, (
										    __magic_name__
										)				, (
										    __magic_name__
										)				, (
										    __magic_name__
										)				, (
										    __magic_name__
										)				, (
										    __magic_name__
										)				, 
										)	:	List[str]								=config_and_inputs
										__magic_name__	:	int								={
										    """input_ids""": input_ids,
										    """token_type_ids""": token_type_ids,
										    """head_mask""": head_mask,
										}

										return config, inputs_dict




@require_torch
class 		__A      (			UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					(
						    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase     						=					(
						    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
						UpperCamelCase     						=					(
						    {
						        """feature-extraction""": OpenAIGPTModel,
						        """text-classification""": OpenAIGPTForSequenceClassification,
						        """text-generation""": OpenAIGPTLMHeadModel,
						        """zero-shot""": OpenAIGPTForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)

						def     A__						(		self   :Any    , __snake_case   :List[Any]    , __snake_case   :Any    , __snake_case   :Any    , __snake_case   :Union[str, Any]    , __snake_case   :List[Any] ):
										'''simple docstring'''

										if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
														# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
														# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
														# tiny config could not be created.
														return True

										return False

						def     A__						(		self   :Any    , __snake_case   :Union[str, Any]    , __snake_case   :Optional[int]    , __snake_case   :Any=False ):
										'''simple docstring'''

										__magic_name__	:	int								=super()._prepare_for_class(__snake_case    , __snake_case    , return_labels=__snake_case )

										if return_labels:
														if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																		__magic_name__	:	Dict								=torch.zeros(
																		    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)    , dtype=torch.long    , device=__snake_case    , )
																		__magic_name__	:	List[Any]								=inputs_dict["""labels"""]
																		__magic_name__	:	Optional[Any]								=inputs_dict["""labels"""]
																		__magic_name__	:	Optional[int]								=torch.zeros(
																		    (self.model_tester.batch_size, self.model_tester.num_choices)    , dtype=torch.long    , device=__snake_case    , )
																		__magic_name__	:	Dict								=torch.zeros(
																		    self.model_tester.batch_size    , dtype=torch.long    , device=__snake_case )
										return inputs_dict

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=OpenAIGPTModelTester(self )
										__magic_name__	:	int								=ConfigTester(self    , config_class=__snake_case    , n_embd=37 )

						def     A__						(		self   :str ):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_model(*__snake_case )

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*__snake_case )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_double_lm_head_model(*__snake_case )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__snake_case )


						@slow
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__magic_name__	:	Optional[int]								=OpenAIGPTModel.from_pretrained(__snake_case )
														self.assertIsNotNone(__snake_case )




@require_torch
class 		__A      (			unittest.TestCase			):

						@slow
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
										model.to(__snake_case )
										__magic_name__	:	List[Any]								=torch.tensor([[4_81, 47_35, 5_44]]    , dtype=torch.long    , device=__snake_case )  # the president is
										__magic_name__	:	List[str]								=[
										    4_81,
										    47_35,
										    5_44,
										    2_46,
										    9_63,
										    8_70,
										    7_62,
										    2_39,
										    2_44,
										    4_04_77,
										    2_44,
										    2_49,
										    7_19,
										    8_81,
										    4_87,
										    5_44,
										    2_40,
										    2_44,
										    6_03,
										    4_81,
										]  # the president is a very good man. " \n " i\'m sure he is, " said the

										__magic_name__	:	Tuple								=model.generate(__snake_case    , do_sample=__snake_case )
										self.assertListEqual(output_ids[0].tolist()    , __snake_case )










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 		__A      (			unittest.TestCase			):

						def __init__(		self   :List[str]    , __snake_case   :str    , __snake_case   :List[Any]=7    , __snake_case   :List[str]=3    , __snake_case   :int=30    , __snake_case   :int=4_00    , __snake_case   :List[str]=True    , __snake_case   :List[str]=None    , __snake_case   :Tuple=True    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Union[str, Any]=True    , __snake_case   :str=1 / 2_55    , __snake_case   :int=True    , ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
										__magic_name__	:	str								=parent
										__magic_name__	:	Tuple								=batch_size
										__magic_name__	:	Optional[Any]								=num_channels
										__magic_name__	:	Optional[int]								=min_resolution
										__magic_name__	:	Optional[Any]								=max_resolution
										__magic_name__	:	Optional[Any]								=do_resize
										__magic_name__	:	Any								=size
										__magic_name__	:	str								=do_normalize
										__magic_name__	:	str								=image_mean
										__magic_name__	:	List[Any]								=image_std
										__magic_name__	:	Optional[int]								=do_rescale
										__magic_name__	:	List[str]								=rescale_factor
										__magic_name__	:	Optional[int]								=do_pad

						def     A__						(		self   :Optional[int] ):
										'''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     A__						(		self   :List[str]    , __snake_case   :Any    , __snake_case   :List[Any]=False ):
										'''simple docstring'''

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

										else:
														__magic_name__	:	List[str]								=[]
														for image in image_inputs:
																		__magic_name__				, __magic_name__	:	Tuple								=self.get_expected_values([image] )
																		expected_values.append((expected_height, expected_width) )
														__magic_name__	:	Optional[int]								=max(__snake_case    , key=lambda __snake_case : item[0] )[0]
														__magic_name__	:	Tuple								=max(__snake_case    , key=lambda __snake_case : item[1] )[1]

										return expected_height, expected_width




@require_torch
@require_vision
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					DetaImageProcessor if is_vision_available() else None

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	int								=DetaImageProcessingTester(self )

						@property
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=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_resize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_rescale""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_pad""" ) )
										self.assertTrue(hasattr(__snake_case    , """size""" ) )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.image_processing_class.from_dict(self.image_processor_dict )
										self.assertEqual(image_processor.size    , {"""shortest_edge""": 18, """longest_edge""": 13_33} )
										self.assertEqual(image_processor.do_pad    , __snake_case )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										pass

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										__magic_name__	:	Tuple								=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
										__magic_name__	:	Tuple								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	str								=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
										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										__magic_name__	:	Dict								=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   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	int								=self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										__magic_name__	:	Optional[Any]								=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
										__magic_name__	:	str								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	int								=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
										__magic_name__	:	List[str]								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Any								=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   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										__magic_name__	:	Dict								=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
										__magic_name__	:	Tuple								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	int								=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
										__magic_name__	:	Dict								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Tuple								=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   :str ):
										'''simple docstring'''

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

										__magic_name__	:	str								={"""image_id""": 3_97_69, """annotations""": target}

										# encode them
										__magic_name__	:	Optional[Any]								=DetaImageProcessor()
										__magic_name__	:	Any								=image_processing(images=__snake_case    , annotations=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Tuple								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

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

										# verify area
										__magic_name__	:	Dict								=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Optional[int]								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	int								=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Any								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	Optional[Any]								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[str]								=torch.tensor([75, 75, 63, 65, 17, 17] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify orig_size
										__magic_name__	:	Optional[Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	Tuple								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )


						@slow
						def     A__						(		self   :int ):
										'''simple docstring'''

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

										__magic_name__	:	Optional[Any]								={"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

										__magic_name__	:	List[str]								=pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )

										# encode them
										__magic_name__	:	int								=DetaImageProcessor(format="""coco_panoptic""" )
										__magic_name__	:	Tuple								=image_processing(images=__snake_case    , annotations=__snake_case    , masks_path=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Union[str, Any]								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	str								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	str								=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Dict								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	Optional[Any]								=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Dict								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	int								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[Any]								=torch.tensor([17, 17, 63, 75, 75, 93] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify masks
										__magic_name__	:	List[Any]								=82_28_73
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    , __snake_case )
										# verify orig_size
										__magic_name__	:	Union[str, Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	List[Any]								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )




