



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  snake_case__:

					"""simple docstring"""






					def __init__(				self   :      Any ,  SCREAMING_SNAKE_CASE   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      Tuple=3 ,  SCREAMING_SNAKE_CASE   :      Optional[int]=32 ,  SCREAMING_SNAKE_CASE   :      List[Any]=3 ,  SCREAMING_SNAKE_CASE   :      List[Any]=10 ,  SCREAMING_SNAKE_CASE   :      str=[10, 20, 30, 40] ,  SCREAMING_SNAKE_CASE   :      Dict=[1, 1, 2, 1] ,  SCREAMING_SNAKE_CASE   :      Dict=True ,  SCREAMING_SNAKE_CASE   :      str=True ,  SCREAMING_SNAKE_CASE   :      Dict="relu" ,  SCREAMING_SNAKE_CASE   :      Tuple=3 ,  SCREAMING_SNAKE_CASE   :      List[Any]=None ,  ):
									lowercase__	:       List[str]						=	parent
									lowercase__	:       Tuple						=	batch_size
									lowercase__	:       Optional[Any]						=	image_size
									lowercase__	:       List[Any]						=	num_channels
									lowercase__	:       List[Any]						=	embeddings_size
									lowercase__	:       Tuple						=	hidden_sizes
									lowercase__	:       Dict						=	depths
									lowercase__	:       Any						=	is_training
									lowercase__	:       str						=	use_labels
									lowercase__	:       Tuple						=	hidden_act
									lowercase__	:       Tuple						=	num_labels
									lowercase__	:       Optional[Any]						=	scope
									lowercase__	:       Optional[int]						=	len(__lowerCAmelCase	)







					def 					snake_case	(				self   :      Dict	):
									lowercase__	:       Dict						=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

									lowercase__	:       int						=	None
									if self.use_labels:
													lowercase__	:       str						=	ids_tensor([self.batch_size] ,  self.num_labels	)

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

									return config, pixel_values, labels







					def 					snake_case	(				self   :      Any	):
									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 					snake_case	(				self   :      List[str] ,  SCREAMING_SNAKE_CASE   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      List[str] ,  SCREAMING_SNAKE_CASE   :      int	):
									lowercase__	:       Optional[Any]						=	RegNetModel(config=__lowerCAmelCase	)
									model.to(__lowerCAmelCase	)
									model.eval()
									lowercase__	:       Optional[Any]						=	model(__lowerCAmelCase	)
									# 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 					snake_case	(				self   :      int ,  SCREAMING_SNAKE_CASE   :      List[str] ,  SCREAMING_SNAKE_CASE   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      Any	):
									lowercase__	:       Optional[Any]						=	self.num_labels
									lowercase__	:       Optional[int]						=	RegNetForImageClassification(__lowerCAmelCase	)
									model.to(__lowerCAmelCase	)
									model.eval()
									lowercase__	:       Tuple						=	model(__lowerCAmelCase ,  labels=__lowerCAmelCase	)
									self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)	)







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






@require_torch
class  snake_case__(_UpperCamelCase							,     _UpperCamelCase							,     unittest.TestCase     ):

					"""simple docstring"""






					lowercase_								=						(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
					lowercase_								=						(
					    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowercase_								=						False
					lowercase_								=						False
					lowercase_								=						False
					lowercase_								=						False







					def 					snake_case	(				self   :      Any	):
									lowercase__	:       List[str]						=	RegNetModelTester(self	)
									lowercase__	:       Tuple						=	ConfigTester(self ,  config_class=__lowerCAmelCase ,  has_text_modality=__lowerCAmelCase	)







					def 					snake_case	(				self   :      Dict	):
									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 					snake_case	(				self   :      List[str]	):
									return







					@unittest.skip(reason="RegNet does not use inputs_embeds"	)
					def 					snake_case	(				self   :      List[Any]	):
									pass







					@unittest.skip(reason="RegNet does not support input and output embeddings"	)
					def 					snake_case	(				self   :      Union[str, Any]	):
									pass







					def 					snake_case	(				self   :      Optional[Any]	):
									lowercase__				,				lowercase__	:       int						=	self.model_tester.prepare_config_and_inputs_for_common()

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

													lowercase__	:       Optional[int]						=	["pixel_values"]
													self.assertListEqual(arg_names[:1] ,  __lowerCAmelCase	)







					def 					snake_case	(				self   :      Any	):
									lowercase__	:       List[str]						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCAmelCase	)







					def 					snake_case	(				self   :      Tuple	):
									lowercase__				,				lowercase__	:       str						=	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowercase__	:       List[Any]						=	model_class(config=__lowerCAmelCase	)
													for name, module in model.named_modules():
																	if isinstance(__lowerCAmelCase ,  (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 					snake_case	(				self   :      Union[str, Any]	):



									def check_hidden_states_output(SCREAMING_SNAKE_CASE   :      Union[str, Any] ,  SCREAMING_SNAKE_CASE   :      List[str] ,  SCREAMING_SNAKE_CASE   :      str	):
													lowercase__	:       Tuple						=	model_class(__lowerCAmelCase	)
													model.to(__lowerCAmelCase	)
													model.eval()

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

													lowercase__	:       str						=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													lowercase__	:       List[Any]						=	self.model_tester.num_stages
													self.assertEqual(len(__lowerCAmelCase	) ,  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] ,  )

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

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

																	check_hidden_states_output(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase	)







					def 					snake_case	(				self   :      int	):
									lowercase__	:       Any						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase	)







					@slow
					def 					snake_case	(				self   :      Union[str, Any]	):



									for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase__	:       Optional[int]						=	RegNetModel.from_pretrained(__lowerCAmelCase	)
													self.assertIsNotNone(__lowerCAmelCase	)






def    __lowerCamelCase   (  ):

				"""simple docstring"""


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

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

					"""simple docstring"""






					@cached_property
					def 					snake_case	(				self   :      int	):
									return (
									    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
									    if is_vision_available()
									    else None
									)







					@slow
					def 					snake_case	(				self   :      str	):
									lowercase__	:       Optional[int]						=	RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(__lowerCAmelCase	)

									lowercase__	:       Any						=	self.default_image_processor
									lowercase__	:       List[Any]						=	prepare_img()
									lowercase__	:       str						=	image_processor(images=__lowerCAmelCase ,  return_tensors="pt"	).to(__lowerCAmelCase	)

									# forward pass
									with torch.no_grad():
													lowercase__	:       List[str]						=	model(**__lowerCAmelCase	)

									# verify the logits
									lowercase__	:       int						=	torch.Size((1, 1_000)	)
									self.assertEqual(outputs.logits.shape ,  __lowerCAmelCase	)

									lowercase__	:       Any						=	torch.tensor([-0.4_180, -1.5_051, -3.4_836]	).to(__lowerCAmelCase	)

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





"""simple docstring"""






import math


def      UpperCAmelCase__			(							lowerCAmelCase__	:int						)					->       bool:
      '''simple docstring'''


      lowercase								=      math.loga(math.sqrt(4 * positive_integer + 1						) / 2 + 1 / 2						)

      return exponent == int(lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:float = 1 / 1_2_3_4_5						)					->       int:
      '''simple docstring'''


      lowercase								=      0
      lowercase								=      0

      lowercase								=      3
      while True:
            lowercase								=      (integer**2 - 1) / 4
            # if candidate is an integer, then there is a partition for k
            if partition_candidate == int(lowerCAmelCase__						):
                  lowercase								=      int(lowerCAmelCase__						)
                  total_partitions += 1
                  if check_partition_perfect(lowerCAmelCase__						):
                        perfect_partitions += 1
            if perfect_partitions > 0:
                  if perfect_partitions / total_partitions < max_proportion:
                        return int(lowerCAmelCase__						)
            integer += 1


if __name__ == "__main__":
       print(F"""{solution() = }""")



