


"""simple docstring"""





import math




def 	UpperCAmelCase     (		A	:						float     ,				A	:						float       ):

		'''simple docstring'''


		if (
		    not isinstance(A     ,				(int, float)       )
		    or power_factor < -1
		    or power_factor > 1
		):
				raise ValueError('power_factor must be a valid float value between -1 and 1.'       )
		return apparent_power * power_factor




def 	UpperCAmelCase     (		A	:						float     ,				A	:						float       ):

		'''simple docstring'''


		if (
		    not isinstance(A     ,				(int, float)       )
		    or power_factor < -1
		    or power_factor > 1
		):
				raise ValueError('power_factor must be a valid float value between -1 and 1.'       )
		return apparent_power * math.sqrt(1 - power_factor**2       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







"""simple docstring"""





import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 CvtForImageClassification, CvtModel
					from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		self.config_class(**self.inputs_dict						)
									self.parent.assertTrue(hasattr(snake_case	,       'embed_dim'						)						)
									self.parent.assertTrue(hasattr(snake_case	,       'num_heads'						)						)







class 			lowercase__			:
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case=13	,       snake_case=64	,       snake_case=3	,       snake_case=[16, 48, 96]	,       snake_case=[1, 3, 6]	,       snake_case=[1, 2, 10]	,       snake_case=[7, 3, 3]	,       snake_case=[4, 2, 2]	,       snake_case=[2, 1, 1]	,       snake_case=[2, 2, 2]	,       snake_case=[False, False, True]	,       snake_case=[0.0, 0.0, 0.0]	,       snake_case=0.02	,       snake_case=1E-12	,       snake_case=True	,       snake_case=True	,       snake_case=2	,       )			->							Tuple:
									_UpperCAmelCase							 =		parent
									_UpperCAmelCase							 =		batch_size
									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_sizes
									_UpperCAmelCase							 =		patch_stride
									_UpperCAmelCase							 =		patch_padding
									_UpperCAmelCase							 =		is_training
									_UpperCAmelCase							 =		use_labels
									_UpperCAmelCase							 =		num_labels
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		embed_dim
									_UpperCAmelCase							 =		num_heads
									_UpperCAmelCase							 =		stride_kv
									_UpperCAmelCase							 =		depth
									_UpperCAmelCase							 =		cls_token
									_UpperCAmelCase							 =		attention_drop_rate
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

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






							def        lowerCamelCase_   (  self						)			->							List[str]:
									return CvtConfig(
									    image_size=self.image_size	,       num_labels=self.num_labels	,       num_channels=self.num_channels	,       embed_dim=self.embed_dim	,       num_heads=self.num_heads	,       patch_sizes=self.patch_sizes	,       patch_padding=self.patch_padding	,       patch_stride=self.patch_stride	,       stride_kv=self.stride_kv	,       depth=self.depth	,       cls_token=self.cls_token	,       attention_drop_rate=self.attention_drop_rate	,       initializer_range=self.initializer_range	,       )






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[int]:
									_UpperCAmelCase							 =		CvtModel(config=snake_case						)
									model.to(snake_case						)
									model.eval()
									_UpperCAmelCase							 =		model(snake_case						)
									_UpperCAmelCase							 =		(self.image_size, self.image_size)
									_UpperCAmelCase ,					_UpperCAmelCase							 =		image_size[0], image_size[1]
									for i in range(len(self.depth						)						):
											_UpperCAmelCase							 =		floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
											_UpperCAmelCase							 =		floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
									self.parent.assertEqual(result.last_hidden_state.shape	,       (self.batch_size, self.embed_dim[-1], height, width)						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[Any]:
									_UpperCAmelCase							 =		self.num_labels
									_UpperCAmelCase							 =		CvtForImageClassification(snake_case						)
									model.to(snake_case						)
									model.eval()
									_UpperCAmelCase							 =		model(snake_case	,       labels=snake_case						)
									self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_labels)						)






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		self.prepare_config_and_inputs()
									_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		config_and_inputs
									_UpperCAmelCase							 =		{'pixel_values': pixel_values}
									return config, inputs_dict







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







							_UpperCAmelCase      				=					(CvtModel, CvtForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase      				=					(
							    {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		CvtModelTester(self						)
									_UpperCAmelCase							 =		ConfigTester(self	,       config_class=snake_case	,       has_text_modality=snake_case	,       hidden_size=37						)






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									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        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									return






							@unittest.skip(reason='Cvt does not output attentions'						)
							def        lowerCamelCase_   (  self						)			->							str:
									pass






							@unittest.skip(reason='Cvt does not use inputs_embeds'						)
							def        lowerCamelCase_   (  self						)			->							int:
									pass






							@unittest.skip(reason='Cvt does not support input and output embeddings'						)
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									pass






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()

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

											_UpperCAmelCase							 =		['pixel_values']
											self.assertListEqual(arg_names[:1]	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									def check_hidden_states_output(snake_case	,       snake_case	,       snake_case						):
											_UpperCAmelCase							 =		model_class(snake_case						)
											model.to(snake_case						)
											model.eval()

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

											_UpperCAmelCase							 =		outputs.hidden_states

											_UpperCAmelCase							 =		len(self.model_tester.depth						)
											self.assertEqual(len(snake_case						)	,       snake_case						)

											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-3:]						)	,       [
											        self.model_tester.embed_dim[0],
											        self.model_tester.image_size // 4,
											        self.model_tester.image_size // 4,
											    ]	,       )

									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_UpperCAmelCase							 =		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"]
											_UpperCAmelCase							 =		True

											check_hidden_states_output(snake_case	,       snake_case	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case						)






							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
							def        lowerCamelCase_   (  self						)			->							Dict:
									pass






							@slow
							def        lowerCamelCase_   (  self						)			->							Dict:
									for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase							 =		CvtModel.from_pretrained(snake_case						)
											self.assertIsNotNone(snake_case						)







def 	UpperCAmelCase     (		):

		'''simple docstring'''


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






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







							@cached_property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)






							@slow
							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(snake_case						)

									_UpperCAmelCase							 =		self.default_image_processor
									_UpperCAmelCase							 =		prepare_img()
									_UpperCAmelCase							 =		image_processor(images=snake_case	,       return_tensors='pt'						).to(snake_case						)

									# forward pass
									with torch.no_grad():
											_UpperCAmelCase							 =		model(**snake_case						)

									# verify the logits
									_UpperCAmelCase							 =		torch.Size((1, 1000)						)
									self.assertEqual(outputs.logits.shape	,       snake_case						)

									_UpperCAmelCase							 =		torch.tensor([0.9285, 0.9015, -0.3150]						).to(snake_case						)

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


