


'''simple docstring'''




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  lowerCAmelCase				:



	def __init__(					self ,					snake_case__ ,					snake_case__=13 ,					snake_case__=[30, 30] ,					snake_case__=2 ,					snake_case__=3 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=32 ,					snake_case__=5 ,					snake_case__=4 ,					snake_case__=37 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=10 ,					snake_case__=0.0_2 ,					snake_case__=3 ,					snake_case__=None ,					snake_case__=8 ,					snake_case__=10 ,					):
		lowerCAmelCase					:    str      							= parent
		lowerCAmelCase					:    Tuple      							= batch_size
		lowerCAmelCase					:    List[Any]      							= image_size
		lowerCAmelCase					:    List[Any]      							= patch_size
		lowerCAmelCase					:    Dict      							= num_channels
		lowerCAmelCase					:    int      							= is_training
		lowerCAmelCase					:    Optional[Any]      							= use_labels
		lowerCAmelCase					:    Optional[int]      							= hidden_size
		lowerCAmelCase					:    List[Any]      							= num_hidden_layers
		lowerCAmelCase					:    int      							= num_attention_heads
		lowerCAmelCase					:    Any      							= intermediate_size
		lowerCAmelCase					:    Union[str, Any]      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Optional[int]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= type_sequence_label_size
		lowerCAmelCase					:    Any      							= initializer_range
		lowerCAmelCase					:    str      							= num_labels
		lowerCAmelCase					:    Any      							= scope
		lowerCAmelCase					:    Dict      							= n_targets
		lowerCAmelCase					:    Tuple      							= 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
		lowerCAmelCase					:    List[Any]      							= (image_size[1] // patch_size) * (image_size[0] // patch_size)
		lowerCAmelCase					:    int      							= num_patches + 1 + self.num_detection_tokens



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]    )

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

		lowerCAmelCase					:    Any      							= self.get_config()

		return config, pixel_values, labels



	def 				lowercase     (					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=snake_case__ ,					initializer_range=self.initializer_range ,					num_detection_tokens=self.num_detection_tokens ,					num_labels=self.num_labels ,					)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Any      							= YolosModel(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    List[Any]      							= model(snake_case__    )
		self.parent.assertEqual(
		    result.last_hidden_state.shape ,					(self.batch_size, self.expected_seq_len, self.hidden_size)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[int]      							= YolosForObjectDetection(snake_case__    )
		model.to(snake_case__    )
		model.eval()

		lowerCAmelCase					:    Union[str, Any]      							= model(pixel_values=snake_case__    )
		lowerCAmelCase					:    Dict      							= model(snake_case__    )

		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)    )

		lowerCAmelCase					:    str      							= model(pixel_values=snake_case__ ,					labels=snake_case__    )

		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 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= self.prepare_config_and_inputs()
		lowerCAmelCase					:    Optional[int]      							= config_and_inputs
		lowerCAmelCase					:    Dict      							= {'pixel_values': pixel_values}
		return config, inputs_dict



@require_torch
class  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					str			      = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
	_lowerCamelCase   :					str			      = (
	    {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
	)

	_lowerCamelCase   :					Optional[int]			      = False
	_lowerCamelCase   :					Dict			      = False
	_lowerCamelCase   :					Optional[Any]			      = False
	_lowerCamelCase   :					Union[str, Any]			      = False



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__=False    ):
		lowerCAmelCase					:    Any      							= super()._prepare_for_class(snake_case__ ,					snake_case__ ,					return_labels=snake_case__    )

		if return_labels:
			if model_class.__name__ == "YolosForObjectDetection":
				lowerCAmelCase					:    Optional[Any]      							= []
				for i in range(self.model_tester.batch_size    ):
					lowerCAmelCase					:    List[str]      							= {}
					lowerCAmelCase					:    Tuple      							= torch.ones(
					    size=(self.model_tester.n_targets,) ,					device=snake_case__ ,					dtype=torch.long    )
					lowerCAmelCase					:    Any      							= torch.ones(
					    self.model_tester.n_targets ,					4 ,					device=snake_case__ ,					dtype=torch.float    )
					labels.append(snake_case__    )
				lowerCAmelCase					:    str      							= labels

		return inputs_dict



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= YolosModelTester(self    )
		lowerCAmelCase					:    Optional[Any]      							= ConfigTester(self ,					config_class=snake_case__ ,					has_text_modality=snake_case__ ,					hidden_size=37    )



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



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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowerCAmelCase					:    Union[str, Any]      							= model_class(snake_case__    )
			self.assertIsInstance(model.get_input_embeddings() ,					(nn.Module)    )
			lowerCAmelCase					:    Optional[Any]      							= model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(snake_case__ ,					nn.Linear    )    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= self.model_tester.prepare_config_and_inputs_for_common()

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

			lowerCAmelCase					:    Any      							= ['pixel_values']
			self.assertListEqual(arg_names[:1] ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.model_tester.prepare_config_and_inputs_for_common()
		lowerCAmelCase					:    Union[str, Any]      							= True

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

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

			self.assertListEqual(
			    list(attentions[0].shape[-3:]    ) ,					[self.model_tester.num_attention_heads, seq_len, seq_len] ,					)
			lowerCAmelCase					:    Optional[int]      							= len(snake_case__    )

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

			lowerCAmelCase					:    Dict      							= 1
			self.assertEqual(out_len + added_hidden_states ,					len(snake_case__    )    )

			lowerCAmelCase					:    Optional[Any]      							= outputs.attentions

			self.assertEqual(len(snake_case__    ) ,					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 				lowercase     (					self    ):
		def check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    Optional[int]      							= model_class(snake_case__    )
			model.to(snake_case__    )
			model.eval()

			with torch.no_grad():
				lowerCAmelCase					:    str      							= model(**self._prepare_for_class(snake_case__ ,					snake_case__    )    )

			lowerCAmelCase					:    Optional[Any]      							= outputs.hidden_states

			lowerCAmelCase					:    Dict      							= getattr(
			    self.model_tester ,					'expected_num_hidden_layers' ,					self.model_tester.num_hidden_layers + 1    )
			self.assertEqual(len(snake_case__    ) ,					snake_case__    )

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

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

		lowerCAmelCase					:    Dict      							= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowerCAmelCase					:    Any      							= True
			check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    )

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			lowerCAmelCase					:    Dict      							= True

			check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_object_detection(*snake_case__    )



	@slow
	def 				lowercase     (					self    ):
		for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowerCAmelCase					:    List[Any]      							= YolosModel.from_pretrained(snake_case__    )
			self.assertIsNotNone(snake_case__    )


def 			__UpperCamelCase    (    )    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image






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



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



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= YolosForObjectDetection.from_pretrained('hustvl/yolos-small'    ).to(snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= self.default_image_processor
		lowerCAmelCase					:    List[Any]      							= prepare_img()
		lowerCAmelCase					:    List[Any]      							= image_processor(images=snake_case__ ,					return_tensors='pt'    ).to(snake_case__    )

		# forward pass
		with torch.no_grad():
			lowerCAmelCase					:    Optional[int]      							= model(inputs.pixel_values    )

		# verify outputs
		lowerCAmelCase					:    Optional[Any]      							= torch.Size((1, 100, 92)    )
		self.assertEqual(outputs.logits.shape ,					snake_case__    )

		lowerCAmelCase					:    Any      							= torch.tensor(
		    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] ,					device=snake_case__ ,					)
		lowerCAmelCase					:    Dict      							= 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=snake_case__    )
		self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,					snake_case__ ,					atol=1e-4    )    )
		self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] ,					snake_case__ ,					atol=1e-4    )    )

		# verify postprocessing
		lowerCAmelCase					:    Tuple      							= image_processor.post_process_object_detection(
		    snake_case__ ,					threshold=0.3 ,					target_sizes=[image.size[::-1]]    )[0]
		lowerCAmelCase					:    Dict      							= 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(snake_case__    )
		lowerCAmelCase					:    int      							= [75, 75, 17, 63, 17]
		lowerCAmelCase					:    str      							= torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]    ).to(snake_case__    )

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




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase  :		int		     =	{
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		Tuple		     =	[
				    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'NezhaForNextSentencePrediction',
				    'NezhaForMaskedLM',
				    'NezhaForPreTraining',
				    'NezhaForMultipleChoice',
				    'NezhaForQuestionAnswering',
				    'NezhaForSequenceClassification',
				    'NezhaForTokenClassification',
				    'NezhaModel',
				    'NezhaPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nezha import (
								    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NezhaForMaskedLM,
								    NezhaForMultipleChoice,
								    NezhaForNextSentencePrediction,
								    NezhaForPreTraining,
								    NezhaForQuestionAnswering,
								    NezhaForSequenceClassification,
								    NezhaForTokenClassification,
								    NezhaModel,
								    NezhaPreTrainedModel,
								)


else:
				import sys

				_lowerCAmelCase  :		str		     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
