






import inspect
import unittest

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

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


if is_torch_available():
							import torch
							from torch import nn

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


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class    _UpperCAmelCase      :

				"""simple docstring"""




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

								'''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        lowercase__     (  self		:					List[str]						):

								'''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=lowerCamelCase						)
																lowercase__     				=    torch.rand(self.n_targets,						4,						device=lowerCamelCase						)
																labels.append(lowerCamelCase						)

								lowercase__     				=    self.get_config()

								return config, pixel_values, labels




				def        lowercase__     (  self		:					Tuple						):

								'''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=lowerCamelCase,						initializer_range=self.initializer_range,						num_detection_tokens=self.num_detection_tokens,						num_labels=self.num_labels,						)




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					List[str],						lowerCamelCase		:					Any,						lowerCamelCase		:					Tuple						):

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




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					Dict,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    YolosForObjectDetection(lowerCamelCase						)
								model.to(lowerCamelCase						)
								model.eval()

								lowercase__     				=    model(pixel_values=lowerCamelCase						)
								lowercase__     				=    model(lowerCamelCase						)

								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=lowerCamelCase,						labels=lowerCamelCase						)

								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		:					Optional[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    _UpperCAmelCase      ( A__	,A__	,unittest.TestCase			):

				"""simple docstring"""




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

				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					Optional[Any]=False						):

								'''simple docstring'''
								lowercase__     				=    super()._prepare_for_class(lowerCamelCase,						lowerCamelCase,						return_labels=lowerCamelCase						)

								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=lowerCamelCase,						dtype=torch.long						)
																				lowercase__     				=    torch.ones(
																				    self.model_tester.n_targets,						4,						device=lowerCamelCase,						dtype=torch.float						)
																				labels.append(lowerCamelCase						)
																lowercase__     				=    labels

								return inputs_dict




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    YolosModelTester(self						)
								lowercase__     				=    ConfigTester(self,						config_class=lowerCamelCase,						has_text_modality=lowerCamelCase,						hidden_size=37						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# YOLOS does not use inputs_embeds
								pass




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

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




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__     				=    model_class(lowerCamelCase						)
												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],						lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase						)




				def        lowercase__     (  self		:					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(lowerCamelCase						)
												model.to(lowerCamelCase						)
												model.eval()
												with torch.no_grad():
																lowercase__     				=    model(**self._prepare_for_class(lowerCamelCase,						lowerCamelCase						)						)
												lowercase__     				=    outputs.attentions
												self.assertEqual(len(lowerCamelCase						),						self.model_tester.num_hidden_layers						)

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												lowercase__     				=    True
												lowercase__     				=    model_class(lowerCamelCase						)
												model.to(lowerCamelCase						)
												model.eval()
												with torch.no_grad():
																lowercase__     				=    model(**self._prepare_for_class(lowerCamelCase,						lowerCamelCase						)						)
												lowercase__     				=    outputs.attentions
												self.assertEqual(len(lowerCamelCase						),						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(lowerCamelCase						)

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

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

												lowercase__     				=    outputs.attentions

												self.assertEqual(len(lowerCamelCase						),						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		:					List[str]						):

								'''simple docstring'''
								def check_hidden_states_output(lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					str,						lowerCamelCase		:					Optional[int]						):
												lowercase__     				=    model_class(lowerCamelCase						)
												model.to(lowerCamelCase						)
												model.eval()

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

												lowercase__     				=    outputs.hidden_states

												lowercase__     				=    getattr(
												    self.model_tester,						'''expected_num_hidden_layers''',						self.model_tester.num_hidden_layers + 1						)
												self.assertEqual(len(lowerCamelCase						),						lowerCamelCase						)

												# 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(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)

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

												check_hidden_states_output(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_object_detection(*lowerCamelCase						)




				@slow
				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__     				=    YolosModel.from_pretrained(lowerCamelCase						)
												self.assertIsNotNone(lowerCamelCase						)






def 						a    (		):






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




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

				"""simple docstring"""




				@cached_property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''						) if is_vision_available() else None




				@slow
				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''						).to(lowerCamelCase						)

								lowercase__     				=    self.default_image_processor
								lowercase__     				=    prepare_img()
								lowercase__     				=    image_processor(images=lowerCamelCase,						return_tensors='''pt'''						).to(lowerCamelCase						)

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

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

								lowercase__     				=    torch.tensor(
								    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]],						device=lowerCamelCase,						)
								lowercase__     				=    torch.tensor(
								    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]],						device=lowerCamelCase						)
								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3],						lowerCamelCase,						atol=1E-4						)						)
								self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3],						lowerCamelCase,						atol=1E-4						)						)

								# verify postprocessing
								lowercase__     				=    image_processor.post_process_object_detection(
								    lowerCamelCase,						threshold=0.3,						target_sizes=[image.size[::-1]]						)[0]
								lowercase__     				=    torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]						).to(lowerCamelCase						)
								lowercase__     				=    [75, 75, 17, 63, 17]
								lowercase__     				=    torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]						).to(lowerCamelCase						)

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












from __future__ import annotations






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    0.00
				lowercase__     				=    0
				for resistor in resistors:
								if resistor <= 0:
												lowercase__     				=    F"""Resistor at index {index} has a negative or zero value!"""
												raise ValueError(lowerCamelCase_       )
								first_sum += 1 / float(lowerCamelCase_       )
								index += 1
				return 1 / first_sum






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    0.00
				lowercase__     				=    0
				for resistor in resistors:
								sum_r += resistor
								if resistor < 0:
												lowercase__     				=    F"""Resistor at index {index} has a negative value!"""
												raise ValueError(lowerCamelCase_       )
								index += 1
				return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()




