


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 				A       :
					'''simple docstring'''






					def __init__(self       :		str ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Union[str, Any]=13 ,   _UpperCAmelCase       :		Dict=[30, 30] ,   _UpperCAmelCase       :		Optional[int]=2 ,   _UpperCAmelCase       :		Optional[Any]=3 ,   _UpperCAmelCase       :		Union[str, Any]=True ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		Optional[int]=32 ,   _UpperCAmelCase       :		List[Any]=5 ,   _UpperCAmelCase       :		int=4 ,   _UpperCAmelCase       :		Optional[Any]=37 ,   _UpperCAmelCase       :		Union[str, Any]="gelu" ,   _UpperCAmelCase       :		Tuple=0.1 ,   _UpperCAmelCase       :		int=0.1 ,   _UpperCAmelCase       :		List[str]=10 ,   _UpperCAmelCase       :		List[str]=0.02 ,   _UpperCAmelCase       :		str=3 ,   _UpperCAmelCase       :		int=None ,   _UpperCAmelCase       :		List[Any]=8 ,   _UpperCAmelCase       :		Dict=10 ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						parent
							lowercase__	     =						batch_size
							lowercase__	     =						image_size
							lowercase__	     =						patch_size
							lowercase__	     =						num_channels
							lowercase__	     =						is_training
							lowercase__	     =						use_labels
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_act
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						type_sequence_label_size
							lowercase__	     =						initializer_range
							lowercase__	     =						num_labels
							lowercase__	     =						scope
							lowercase__	     =						n_targets
							lowercase__	     =						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
							lowercase__	     =						(image_size[1] // patch_size) * (image_size[0] // patch_size)
							lowercase__	     =						num_patches + 1 + self.num_detection_tokens

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)

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

							lowercase__	     =						self.get_config()

							return config, pixel_values, labels

					def     lowerCamelCase__	(self       :		Tuple						)		->  List[Any]:




							"""simple docstring"""


							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     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						YolosModel(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(
							    result.last_hidden_state.shape ,   (self.batch_size, self.expected_seq_len, self.hidden_size)						)

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		int						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						YolosForObjectDetection(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(pixel_values=_UpperCAmelCase						)
							lowercase__	     =						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)						)

							lowercase__	     =						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     lowerCamelCase__	(self       :		str						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.prepare_config_and_inputs()
							lowercase__     ,      lowercase__     ,      lowercase__	     =						config_and_inputs
							lowercase__	     =						{"""pixel_values""": pixel_values}
							return config, inputs_dict






@require_torch
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
					A__     =					(
					    {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
					)

					A__     =					False
					A__     =					False
					A__     =					False
					A__     =					False

					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		int=False						)		->  str:




							"""simple docstring"""


							lowercase__	     =						super()._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase ,   return_labels=_UpperCAmelCase						)

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

							return inputs_dict

					def     lowerCamelCase__	(self       :		Dict						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						YolosModelTester(self						)
							lowercase__	     =						ConfigTester(self ,   config_class=_UpperCAmelCase ,   has_text_modality=_UpperCAmelCase ,   hidden_size=37						)

					def     lowerCamelCase__	(self       :		Dict						)		->  str:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  List[str]:




							"""simple docstring"""


							pass

					def     lowerCamelCase__	(self       :		Tuple						)		->  str:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()

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

					def     lowerCamelCase__	(self       :		Dict						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase__	     =						["""pixel_values"""]
									self.assertListEqual(arg_names[:1] ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Dict						)		->  List[str]:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()
							lowercase__	     =						True

							# in YOLOS, the seq_len is different
							lowercase__	     =						self.model_tester.expected_seq_len
							for model_class in self.all_model_classes:
									lowercase__	     =						True
									lowercase__	     =						False
									lowercase__	     =						True
									lowercase__	     =						model_class(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()
									with torch.no_grad():
											lowercase__	     =						model(**self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)						)
									lowercase__	     =						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"]
									lowercase__	     =						True
									lowercase__	     =						model_class(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()
									with torch.no_grad():
											lowercase__	     =						model(**self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)						)
									lowercase__	     =						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] ,   )
									lowercase__	     =						len(_UpperCAmelCase						)

									# Check attention is always last and order is fine
									lowercase__	     =						True
									lowercase__	     =						True
									lowercase__	     =						model_class(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()
									with torch.no_grad():
											lowercase__	     =						model(**self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)						)

									lowercase__	     =						1
									self.assertEqual(out_len + added_hidden_states ,   len(_UpperCAmelCase						)						)

									lowercase__	     =						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     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Tuple:




							"""simple docstring"""


							def check_hidden_states_output(_UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Any						):
									lowercase__	     =						model_class(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()

									with torch.no_grad():
											lowercase__	     =						model(**self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)						)

									lowercase__	     =						outputs.hidden_states

									lowercase__	     =						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
									lowercase__	     =						self.model_tester.expected_seq_len

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

							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase__	     =						True
									check_hidden_states_output(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

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

									check_hidden_states_output(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Any						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase						)


					@slow
					def     lowerCamelCase__	(self       :		str						)		->  Dict:




							"""simple docstring"""


							for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__	     =						YolosModel.from_pretrained(_UpperCAmelCase						)
									self.assertIsNotNone(_UpperCAmelCase						)






def 					UpperCamelCase   (      )  ->    List[str]:
		"""simple docstring"""




		lowercase__	     =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
		return image







@require_torch
@require_vision
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@cached_property
					def     lowerCamelCase__	(self       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""						) if is_vision_available() else None


					@slow
					def     lowerCamelCase__	(self       :		List[str]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""						).to(_UpperCAmelCase						)

							lowercase__	     =						self.default_image_processor
							lowercase__	     =						prepare_img()
							lowercase__	     =						image_processor(images=_UpperCAmelCase ,   return_tensors="""pt"""						).to(_UpperCAmelCase						)

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

							# verify outputs
							lowercase__	     =						torch.Size((1, 100, 92)						)
							self.assertEqual(outputs.logits.shape ,   _UpperCAmelCase						)

							lowercase__	     =						torch.tensor(
							    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] ,   device=_UpperCAmelCase ,   )
							lowercase__	     =						torch.tensor(
							    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] ,   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
							lowercase__	     =						image_processor.post_process_object_detection(
							    _UpperCAmelCase ,   threshold=0.3 ,   target_sizes=[image.size[::-1]]						)[0]
							lowercase__	     =						torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]						).to(_UpperCAmelCase						)
							lowercase__	     =						[75, 75, 17, 63, 17]
							lowercase__	     =						torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]						).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						)						)








"""simple docstring"""





from __future__ import annotations



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list[int | str] ):


							'''simple docstring'''







							create_state_space_tree(SCREAMING_SNAKE_CASE     ,    []     ,    0     ,    [0 for i in range(len(SCREAMING_SNAKE_CASE ) )] )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list[int | str]     ,    SCREAMING_SNAKE_CASE							:				list[int | str]     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				list[int]     ,    ):


							'''simple docstring'''







							if index == len(SCREAMING_SNAKE_CASE ):
														print(SCREAMING_SNAKE_CASE )
														return

							for i in range(len(SCREAMING_SNAKE_CASE ) ):
														if not index_used[i]:
																					current_sequence.append(sequence[i] )
																					__lowerCamelCase   :   List[Any]       =True
																					create_state_space_tree(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    index + 1     ,    SCREAMING_SNAKE_CASE )
																					current_sequence.pop()
																					__lowerCamelCase   :   List[Any]       =False
_UpperCamelCase					      =   [3, 1, 2, 4]
generate_all_permutations(sequence)

_UpperCamelCase					      =   ["A", "B", "C"]
generate_all_permutations(sequence_a)




