
import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
			    MODEL_MAPPING,
			    LevitForImageClassification,
			    LevitForImageClassificationWithTeacher,
			    LevitModel,
			)
			from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import LevitImageProcessor




class    SCREAMING_SNAKE_CASE__     (	A__					):





	'''simple docstring'''



	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			self.config_class(**self.inputs_dict	)
				self.parent.assertTrue(hasattr(UpperCamelCase_			,  'hidden_sizes'	)	)
				self.parent.assertTrue(hasattr(UpperCamelCase_			,  'num_attention_heads'	)	)




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    str			,  lowercase	:    Optional[int]			,  lowercase	:    List[str]=13			,  lowercase	:    List[str]=64			,  lowercase	:    List[Any]=3			,  lowercase	:    Any=3			,  lowercase	:    int=2			,  lowercase	:    str=1			,  lowercase	:    Optional[int]=16			,  lowercase	:    Union[str, Any]=[128, 256, 384]			,  lowercase	:    List[Any]=[4, 6, 8]			,  lowercase	:    List[str]=[2, 3, 4]			,  lowercase	:    str=[16, 16, 16]			,  lowercase	:    Optional[Any]=0			,  lowercase	:    Any=[2, 2, 2]			,  lowercase	:    Tuple=[2, 2, 2]			,  lowercase	:    Optional[Any]=0.02			,  lowercase	:    str=True			,  lowercase	:    Optional[Any]=True			,  lowercase	:    Tuple=2			,  ):


				'''simple docstring'''


				_snake_case		=			parent
				_snake_case		=			batch_size
				_snake_case		=			image_size
				_snake_case		=			num_channels
				_snake_case		=			kernel_size
				_snake_case		=			stride
				_snake_case		=			padding
				_snake_case		=			hidden_sizes
				_snake_case		=			num_attention_heads
				_snake_case		=			depths
				_snake_case		=			key_dim
				_snake_case		=			drop_path_rate
				_snake_case		=			patch_size
				_snake_case		=			attention_ratio
				_snake_case		=			mlp_ratio
				_snake_case		=			initializer_range
				_snake_case		=			[
				    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]
				_snake_case		=			is_training
				_snake_case		=			use_labels
				_snake_case		=			num_labels
				_snake_case		=			initializer_range



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				_snake_case		=			None
				if self.use_labels:
							_snake_case		=			ids_tensor([self.batch_size]			,  self.num_labels	)

				_snake_case		=			self.get_config()
				return config, pixel_values, labels



	def  A							(  self	:    int	):


				'''simple docstring'''


				return LevitConfig(
				    image_size=self.image_size			,  num_channels=self.num_channels			,  kernel_size=self.kernel_size			,  stride=self.stride			,  padding=self.padding			,  patch_size=self.patch_size			,  hidden_sizes=self.hidden_sizes			,  num_attention_heads=self.num_attention_heads			,  depths=self.depths			,  key_dim=self.key_dim			,  drop_path_rate=self.drop_path_rate			,  mlp_ratio=self.mlp_ratio			,  attention_ratio=self.attention_ratio			,  initializer_range=self.initializer_range			,  down_ops=self.down_ops			,  )



	def  A							(  self	:    Optional[Any]			,  lowercase	:    Any			,  lowercase	:    Union[str, Any]			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			LevitModel(config=UpperCamelCase_	)
				model.to(UpperCamelCase_	)
				model.eval()
				_snake_case		=			model(UpperCamelCase_	)
				_snake_case		=			(self.image_size, self.image_size)
				_snake_case		=			image_size[0], image_size[1]
				for _ in range(4	):
							_snake_case		=			floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1	)
							_snake_case		=			floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1	)
				self.parent.assertEqual(
				    result.last_hidden_state.shape			,  (self.batch_size, ceil(height / 4	) * ceil(width / 4	), self.hidden_sizes[-1])			,  )



	def  A							(  self	:    Optional[int]			,  lowercase	:    List[str]			,  lowercase	:    Optional[Any]			,  lowercase	:    Any	):


				'''simple docstring'''


				_snake_case		=			self.num_labels
				_snake_case		=			LevitForImageClassification(UpperCamelCase_	)
				model.to(UpperCamelCase_	)
				model.eval()
				_snake_case		=			model(UpperCamelCase_			,  labels=UpperCamelCase_	)
				self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)	)





	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			self.prepare_config_and_inputs()
				_snake_case		=			config_and_inputs
				_snake_case		=			{'''pixel_values''': pixel_values}
				return config, inputs_dict




@require_torch
class    SCREAMING_SNAKE_CASE__     (	A__							,A__							,unittest.TestCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		int       =					(
	    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
	    if is_torch_available()
	    else ()
	)
	_UpperCAmelCase       :		List[str]       =					(
	    {
	        "feature-extraction": LevitModel,
	        "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	_UpperCAmelCase       :		Tuple       =					False
	_UpperCAmelCase       :		Any       =					False
	_UpperCAmelCase       :		List[str]       =					False
	_UpperCAmelCase       :		Any       =					False
	_UpperCAmelCase       :		List[Any]       =					False



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			LevitModelTester(self	)
				_snake_case		=			ConfigTester(self			,  config_class=UpperCamelCase_			,  has_text_modality=UpperCamelCase_			,  hidden_size=37	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				return



	@unittest.skip(reason='Levit does not use inputs_embeds'	)
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				pass



	@unittest.skip(reason='Levit does not support input and output embeddings'	)
	def  A							(  self	:    int	):


				'''simple docstring'''


				pass



	@unittest.skip(reason='Levit does not output attentions'	)
	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				pass



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							_snake_case		=			model_class(UpperCamelCase_	)
							_snake_case		=			inspect.signature(model.forward	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							_snake_case		=			[*signature.parameters.keys()]

							_snake_case		=			['''pixel_values''']
							self.assertListEqual(arg_names[:1]			,  UpperCamelCase_	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				def check_hidden_states_output(lowercase	:    Optional[int]			,  lowercase	:    Optional[Any]			,  lowercase	:    Union[str, Any]	):
							_snake_case		=			model_class(UpperCamelCase_	)
							model.to(UpperCamelCase_	)
							model.eval()

							with torch.no_grad():
										_snake_case		=			model(**self._prepare_for_class(UpperCamelCase_			,  UpperCamelCase_	)	)

							_snake_case		=			outputs.hidden_states

							_snake_case		=			len(self.model_tester.depths	) + 1
							self.assertEqual(len(UpperCamelCase_	)			,  UpperCamelCase_	)

							_snake_case		=			(self.model_tester.image_size, self.model_tester.image_size)
							_snake_case		=			image_size[0], image_size[1]
							for _ in range(4	):
										_snake_case		=			floor(
										    (
										        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
										        / self.model_tester.stride
										    )
										    + 1	)
										_snake_case		=			floor(
										    (
										        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
										        / self.model_tester.stride
										    )
										    + 1	)
							# verify the first hidden states (first block)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]	)			,  [
							        height * width,
							        self.model_tester.hidden_sizes[0],
							    ]			,  )

				_snake_case		=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							_snake_case		=			True
							check_hidden_states_output(UpperCamelCase_			,  UpperCamelCase_			,  UpperCamelCase_	)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							_snake_case		=			True

							check_hidden_states_output(UpperCamelCase_			,  UpperCamelCase_			,  UpperCamelCase_	)



	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				pass



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    List[str]			,  lowercase	:    List[str]			,  lowercase	:    Dict=False	):


				'''simple docstring'''


				_snake_case		=			super()._prepare_for_class(UpperCamelCase_			,  UpperCamelCase_			,  return_labels=UpperCamelCase_	)

				if return_labels:
							if model_class.__name__ == "LevitForImageClassificationWithTeacher":
										del inputs_dict["labels"]

				return inputs_dict



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCamelCase_	)



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_	)



	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				if not self.model_tester.is_training:
							return

				_snake_case		=			self.model_tester.prepare_config_and_inputs_for_common()
				_snake_case		=			True

				for model_class in self.all_model_classes:
							# LevitForImageClassificationWithTeacher supports inference-only
							if (
							    model_class in get_values(UpperCamelCase_	)
							    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
							):
										continue
							_snake_case		=			model_class(UpperCamelCase_	)
							model.to(UpperCamelCase_	)
							model.train()
							_snake_case		=			self._prepare_for_class(UpperCamelCase_			,  UpperCamelCase_			,  return_labels=UpperCamelCase_	)
							_snake_case		=			model(**UpperCamelCase_	).loss
							loss.backward()



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs_for_common()
				if not self.model_tester.is_training:
							return

				_snake_case		=			False
				_snake_case		=			True

				for model_class in self.all_model_classes:
							if model_class in get_values(UpperCamelCase_	) or not model_class.supports_gradient_checkpointing:
										continue
							# LevitForImageClassificationWithTeacher supports inference-only
							if model_class.__name__ == "LevitForImageClassificationWithTeacher":
										continue
							_snake_case		=			model_class(UpperCamelCase_	)
							model.gradient_checkpointing_enable()
							model.to(UpperCamelCase_	)
							model.train()
							_snake_case		=			self._prepare_for_class(UpperCamelCase_			,  UpperCamelCase_			,  return_labels=UpperCamelCase_	)
							_snake_case		=			model(**UpperCamelCase_	).loss
							loss.backward()



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs_for_common()

				_snake_case		=			[
				    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
				    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
				    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
				]

				for model_class in self.all_model_classes:
							if (
							    model_class
							    not in [
							        *get_values(UpperCamelCase_	),
							    ]
							    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
							):
										continue

							for problem_type in problem_types:
										with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}'''	):
													_snake_case		=			problem_type['''title''']
													_snake_case		=			problem_type['''num_labels''']

													_snake_case		=			model_class(UpperCamelCase_	)
													model.to(UpperCamelCase_	)
													model.train()

													_snake_case		=			self._prepare_for_class(UpperCamelCase_			,  UpperCamelCase_			,  return_labels=UpperCamelCase_	)

													if problem_type["num_labels"] > 1:
																_snake_case		=			inputs['''labels'''].unsqueeze(1	).repeat(1			,  problem_type['num_labels']	)

													_snake_case		=			inputs['''labels'''].to(problem_type['dtype']	)

													# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
													# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
													# they have the same size." which is a symptom something in wrong for the regression problem.
													# See https://github.com/huggingface/transformers/issues/11780
													with warnings.catch_warnings(record=UpperCamelCase_	) as warning_list:
																_snake_case		=			model(**UpperCamelCase_	).loss
													for w in warning_list:
																if "Using a target size that is different to the input size" in str(w.message	):
																			raise ValueError(
																			    f'''Something is going wrong in the regression problem: intercepted {w.message}'''	)

													loss.backward()





	@slow
	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							_snake_case		=			LevitModel.from_pretrained(UpperCamelCase_	)
							self.assertIsNotNone(UpperCamelCase_	)






def        a_		(       ) ->  int:
			_snake_case		=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
			return image




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





	'''simple docstring'''



	@cached_property
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)





	@slow
	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(
				    UpperCamelCase_	)

				_snake_case		=			self.default_image_processor
				_snake_case		=			prepare_img()
				_snake_case		=			image_processor(images=UpperCamelCase_			,  return_tensors='pt'	).to(UpperCamelCase_	)

				# forward pass
				with torch.no_grad():
							_snake_case		=			model(**UpperCamelCase_	)

				# verify the logits
				_snake_case		=			torch.Size((1, 1_000)	)
				self.assertEqual(outputs.logits.shape			,  UpperCamelCase_	)

				_snake_case		=			torch.tensor([1.0448, -0.3745, -1.8317]	).to(UpperCamelCase_	)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]			,  UpperCamelCase_			,  atol=1E-4	)	)



'''simple docstring'''
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 ConditionalDetrImageProcessor
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=7    ,   UpperCamelCase_=3    ,   UpperCamelCase_=30    ,   UpperCamelCase_=400    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   UpperCamelCase_=True    ,   UpperCamelCase_=[0.5, 0.5, 0.5]    ,   UpperCamelCase_=[0.5, 0.5, 0.5]    ,   UpperCamelCase_=True    ,   UpperCamelCase_=1 / 255    ,   UpperCamelCase_=True    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
							UpperCamelCase__   :str												= parent
							UpperCamelCase__   :List[Any]												= batch_size
							UpperCamelCase__   :Dict												= num_channels
							UpperCamelCase__   :str												= min_resolution
							UpperCamelCase__   :Optional[Any]												= max_resolution
							UpperCamelCase__   :int												= do_resize
							UpperCamelCase__   :Optional[Any]												= size
							UpperCamelCase__   :Tuple												= do_normalize
							UpperCamelCase__   :List[Any]												= image_mean
							UpperCamelCase__   :Dict												= image_std
							UpperCamelCase__   :Union[str, Any]												= do_rescale
							UpperCamelCase__   :Union[str, Any]												= rescale_factor
							UpperCamelCase__   :Union[str, Any]												= do_pad




		def     lowerCAmelCase__				(		self							):

							'''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     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=False							):

							'''simple docstring'''


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

							else:
												UpperCamelCase__   :Any												= []
												for image in image_inputs:
																	UpperCamelCase__					,				UpperCamelCase__   :Dict												= self.get_expected_values([image]							)
																	expected_values.append((expected_height, expected_width)							)
												UpperCamelCase__   :List[Any]												= max(UpperCamelCase_    ,   key=lambda UpperCamelCase_							: item[0]							)[0]
												UpperCamelCase__   :Optional[int]												= max(UpperCamelCase_    ,   key=lambda UpperCamelCase_							: item[1]							)[1]

							return expected_height, expected_width



@require_torch
@require_vision
class    lowercase	(							A__	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				ConditionalDetrImageProcessor if is_vision_available() else None




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= ConditionalDetrImageProcessingTester(self							)




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''image_mean'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''image_std'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''do_normalize'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''do_resize'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''size'''							)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= 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    ,   UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= self.image_processing_class.from_dict(
							    self.image_processor_dict    ,   size=42    ,   max_size=84    ,   pad_and_return_pixel_mask=UpperCamelCase_							)
							self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 42, '''longest_edge''': 84}							)
							self.assertEqual(image_processor.do_pad    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							pass




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__   :List[str]												= prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_    ,   Image.Image							)

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

							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_							)

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

							# Test batched
							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= image_processing(UpperCamelCase_    ,   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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							# Test not batched input
							UpperCamelCase__   :Union[str, Any]												= image_processing(image_inputs[0]    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= self.image_processor_tester.get_expected_values(UpperCamelCase_							)

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

							# Test batched
							UpperCamelCase__   :Union[str, Any]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

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

							UpperCamelCase__					,				UpperCamelCase__   :Dict												= self.image_processor_tester.get_expected_values(UpperCamelCase_							)

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

							# Test batched
							UpperCamelCase__   :List[str]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :Optional[int]												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

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




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							UpperCamelCase__   :Any												= {'''image_id''': 39769, '''annotations''': target}

							# encode them
							UpperCamelCase__   :str												= ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''							)
							UpperCamelCase__   :List[Any]												= image_processing(images=UpperCamelCase_    ,   annotations=UpperCamelCase_    ,   return_tensors='''pt'''							)

							# verify pixel values
							UpperCamelCase__   :List[str]												= torch.Size([1, 3, 800, 1066]							)
							self.assertEqual(encoding['''pixel_values'''].shape    ,   UpperCamelCase_							)

							UpperCamelCase__   :str												= torch.tensor([0.2796, 0.3138, 0.3481]							)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   UpperCamelCase_    ,   atol=1e-4							)							)

							# verify area
							UpperCamelCase__   :str												= torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   UpperCamelCase_							)							)
							# verify boxes
							UpperCamelCase__   :Optional[Any]												= torch.Size([6, 4]							)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   UpperCamelCase_    ,   atol=1e-3							)							)
							# verify image_id
							UpperCamelCase__   :List[Any]												= torch.tensor([39769]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   UpperCamelCase_							)							)
							# verify is_crowd
							UpperCamelCase__   :int												= torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   UpperCamelCase_							)							)
							# verify class_labels
							UpperCamelCase__   :List[str]												= torch.tensor([75, 75, 63, 65, 17, 17]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   UpperCamelCase_							)							)
							# verify orig_size
							UpperCamelCase__   :Tuple												= torch.tensor([480, 640]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   UpperCamelCase_							)							)
							# verify size
							UpperCamelCase__   :Union[str, Any]												= torch.tensor([800, 1066]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   UpperCamelCase_							)							)




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :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:
												UpperCamelCase__   :Tuple												= json.loads(f.read()							)

							UpperCamelCase__   :List[str]												= {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

							UpperCamelCase__   :Any												= pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

							# encode them
							UpperCamelCase__   :List[Any]												= ConditionalDetrImageProcessor(format='''coco_panoptic'''							)
							UpperCamelCase__   :Dict												= image_processing(images=UpperCamelCase_    ,   annotations=UpperCamelCase_    ,   masks_path=UpperCamelCase_    ,   return_tensors='''pt'''							)

							# verify pixel values
							UpperCamelCase__   :str												= torch.Size([1, 3, 800, 1066]							)
							self.assertEqual(encoding['''pixel_values'''].shape    ,   UpperCamelCase_							)

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

							# verify area
							UpperCamelCase__   :Tuple												= torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   UpperCamelCase_							)							)
							# verify boxes
							UpperCamelCase__   :Any												= torch.Size([6, 4]							)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :List[Any]												= torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   UpperCamelCase_    ,   atol=1e-3							)							)
							# verify image_id
							UpperCamelCase__   :List[str]												= torch.tensor([39769]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   UpperCamelCase_							)							)
							# verify is_crowd
							UpperCamelCase__   :Union[str, Any]												= torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   UpperCamelCase_							)							)
							# verify class_labels
							UpperCamelCase__   :str												= torch.tensor([17, 17, 63, 75, 75, 93]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   UpperCamelCase_							)							)
							# verify masks
							UpperCamelCase__   :Optional[Any]												= 822873
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,   UpperCamelCase_							)
							# verify orig_size
							UpperCamelCase__   :List[str]												= torch.tensor([480, 640]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   UpperCamelCase_							)							)
							# verify size
							UpperCamelCase__   :List[Any]												= torch.tensor([800, 1066]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   UpperCamelCase_							)							)