



import inspect
import unittest

from transformers import RegNetConfig
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 torch import nn

						from transformers import RegNetForImageClassification, RegNetModel
						from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor


class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=32 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=10 ,					__UpperCAmelCase=[10, 20, 30, 40] ,					__UpperCAmelCase=[1, 1, 2, 1] ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase="relu" ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=None ,					):







											"""simple docstring"""



											a__	:							Any      						= parent
											a__	:							Union[str, Any]      						= batch_size
											a__	:							Any      						= image_size
											a__	:							Optional[Any]      						= num_channels
											a__	:							List[str]      						= embeddings_size
											a__	:							Any      						= hidden_sizes
											a__	:							Union[str, Any]      						= depths
											a__	:							List[Any]      						= is_training
											a__	:							Optional[Any]      						= use_labels
											a__	:							Dict      						= hidden_act
											a__	:							Tuple      						= num_labels
											a__	:							int      						= scope
											a__	:							Any      						= len(__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

											a__	:							List[str]      						= None
											if self.use_labels:
															a__	:							Any      						= ids_tensor([self.batch_size] ,					self.num_labels				)

											a__	:							List[Any]      						= self.get_config()

											return config, pixel_values, labels



							def 		_A	(							self				):







											"""simple docstring"""



											return RegNetConfig(
											    num_channels=self.num_channels ,					embeddings_size=self.embeddings_size ,					hidden_sizes=self.hidden_sizes ,					depths=self.depths ,					hidden_act=self.hidden_act ,					num_labels=self.num_labels ,					)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Dict      						= RegNetModel(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							str      						= model(__UpperCAmelCase				)
											# expected last hidden states: B, C, H // 32, W // 32
											self.parent.assertEqual(
											    result.last_hidden_state.shape ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,					)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.num_labels
											a__	:							List[str]      						= RegNetForImageClassification(__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Dict      						= model(__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.prepare_config_and_inputs()
											a__	:							Dict      						= config_and_inputs
											a__	:							Union[str, Any]      						= {"pixel_values": pixel_values}
											return config, inputs_dict





@require_torch
class 				_a						(       SCREAMING_SNAKE_CASE       ,  SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Optional[Any] =       (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
							A				:Union[str, Any] =       (
							    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
							    if is_torch_available()
							    else {}
							)

							A				:Optional[int] =       False
							A				:int =       False
							A				:str =       False
							A				:Dict =       False



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= RegNetModelTester(self				)
											a__	:							int      						= ConfigTester(self ,					config_class=__UpperCAmelCase ,					has_text_modality=__UpperCAmelCase				)



							def 		_A	(							self				):







											"""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				):







											"""simple docstring"""



											return



							@unittest.skip(reason="RegNet does not use inputs_embeds"				)
							def 		_A	(							self				):







											"""simple docstring"""



											pass



							@unittest.skip(reason="RegNet does not support input and output embeddings"				)
							def 		_A	(							self				):







											"""simple docstring"""



											pass



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															a__	:							Optional[int]      						= model_class(__UpperCAmelCase				)
															a__	:							Dict      						= inspect.signature(model.forward				)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															a__	:							str      						= [*signature.parameters.keys()]

															a__	:							Dict      						= ["pixel_values"]
															self.assertListEqual(arg_names[:1] ,					__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															a__	:							List[Any]      						= model_class(config=__UpperCAmelCase				)
															for name, module in model.named_modules():
																			if isinstance(__UpperCAmelCase ,					(nn.BatchNormad, nn.GroupNorm)				):
																							self.assertTrue(
																							    torch.all(module.weight == 1				) ,					msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,					)
																							self.assertTrue(
																							    torch.all(module.bias == 0				) ,					msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,					)



							def 		_A	(							self				):







											"""simple docstring"""



											def check_hidden_states_output(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):
															a__	:							Optional[Any]      						= model_class(__UpperCAmelCase				)
															model.to(__UpperCAmelCase				)
															model.eval()

															with torch.no_grad():
																			a__	:							List[str]      						= model(**self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase				)				)

															a__	:							Tuple      						= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															a__	:							List[Any]      						= self.model_tester.num_stages
															self.assertEqual(len(__UpperCAmelCase				) ,					expected_num_stages + 1				)

															# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]				) ,					[self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,					)

											a__	:							Optional[Any]      						= self.model_tester.prepare_config_and_inputs_for_common()
											a__	:							Union[str, Any]      						= ["basic", "bottleneck"]
											for model_class in self.all_model_classes:
															for layer_type in layers_type:
																			a__	:							int      						= layer_type
																			a__	:							Optional[int]      						= True
																			check_hidden_states_output(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			a__	:							Optional[Any]      						= True

																			check_hidden_states_output(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a__	:							List[Any]      						= RegNetModel.from_pretrained(__UpperCAmelCase				)
															self.assertIsNotNone(__UpperCAmelCase				)






def     SCREAMING_SNAKE_CASE(      )	->						int:
				a__	:							Any      						= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
				return image






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





							'''simple docstring'''

							@cached_property
							def 		_A	(							self				):







											"""simple docstring"""



											return (
											    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
											    if is_vision_available()
											    else None
											)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(__UpperCAmelCase				)

											a__	:							Optional[Any]      						= self.default_image_processor
											a__	:							Optional[int]      						= prepare_img()
											a__	:							Tuple      						= image_processor(images=__UpperCAmelCase ,					return_tensors="pt"				).to(__UpperCAmelCase				)

											# forward pass
											with torch.no_grad():
															a__	:							str      						= model(**__UpperCAmelCase				)

											# verify the logits
											a__	:							Tuple      						= torch.Size((1, 1000)				)
											self.assertEqual(outputs.logits.shape ,					__UpperCAmelCase				)

											a__	:							str      						= torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]				).to(__UpperCAmelCase				)

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





"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						float:
 return 1_0 - x * x



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 # Bolzano theory in order to find if there is a root between a and b
 if equation(lowercase__   ) * equation(lowercase__   ) >= 0:
  raise ValueError("Wrong space!"   )

 lowerCAmelCase__   :				Union[str, Any] 			=				a
 while (b - a) >= 0.01:
  # Find middle point
  lowerCAmelCase__   :				int 			=				(a + b) / 2
  # Check if middle point is root
  if equation(lowercase__   ) == 0.0:
   break
  # Decide the side to repeat the steps
  if equation(lowercase__   ) * equation(lowercase__   ) < 0:
   lowerCAmelCase__   :				str 			=				c
  else:
   lowerCAmelCase__   :				List[str] 			=				c
 return c


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(bisection(-2, 5))
    print(bisection(0, 6))






