



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			:




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




				def 							A__      (						self					:						str						):
							lowercase__					  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

							lowercase__					  =				self.get_config()

							return config, pixel_values, labels




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




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				self.prepare_config_and_inputs()
							lowercase__  ,       lowercase__  ,       lowercase__					  =				config_and_inputs
							lowercase__					  =				{"pixel_values": pixel_values}
							return config, inputs_dict
@require_torch
class 				_snake_case			(					snake_case__    ,	snake_case__    ,	unittest.TestCase):
				UpperCamelCase__				:					Any     		=(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
				UpperCamelCase__				:					Optional[Any]     		=(
				    {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
				    if is_torch_available()
				    else {}
				)

				UpperCamelCase__				:					int     		=False
				UpperCamelCase__				:					Union[str, Any]     		=False
				UpperCamelCase__				:					str     		=False
				UpperCamelCase__				:					Union[str, Any]     		=False




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				RegNetModelTester(self						)
							lowercase__					  =				ConfigTester(self,					config_class=_SCREAMING_SNAKE_CASE,					has_text_modality=_SCREAMING_SNAKE_CASE						)




				def 							A__      (						self					:						Optional[int]						):
							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					:						Dict						):
							return




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




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




				def 							A__      (						self					:						List[str]						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

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




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE						)




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

							for model_class in self.all_model_classes:
										lowercase__					  =				model_class(config=_SCREAMING_SNAKE_CASE						)
										for name, module in model.named_modules():
													if isinstance(_SCREAMING_SNAKE_CASE,					(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					:						int						):
							def check_hidden_states_output(__lowercase					:						Union[str, Any],					__lowercase					:						str,					__lowercase					:						str						):
										lowercase__					  =				model_class(_SCREAMING_SNAKE_CASE						)
										model.to(_SCREAMING_SNAKE_CASE						)
										model.eval()

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

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

										lowercase__					  =				self.model_tester.num_stages
										self.assertEqual(len(_SCREAMING_SNAKE_CASE						),					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__					  =				self.model_tester.prepare_config_and_inputs_for_common()
							lowercase__					  =				["basic", "bottleneck"]
							for model_class in self.all_model_classes:
										for layer_type in layers_type:
													lowercase__					  =				layer_type
													lowercase__					  =				True
													check_hidden_states_output(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE						)

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

													check_hidden_states_output(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE						)




				def 							A__      (						self					:						List[Any]						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE						)




				@slow
				def 							A__      (						self					:						Optional[int]						):
							for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__					  =				RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE						)
										self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)



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




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




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




				@slow
				def 							A__      (						self					:						Dict						):
							lowercase__					  =				RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(_SCREAMING_SNAKE_CASE						)

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

							# forward pass
							with torch.no_grad():
										lowercase__					  =				model(**_SCREAMING_SNAKE_CASE						)

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

							lowercase__					  =				torch.tensor([-0.4180, -1.5051, -3.4836]						).to(_SCREAMING_SNAKE_CASE						)

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









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				0
			for ch in input_str:
						lowercase__					  =				ord(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				pow(2						,  SCREAMING_SNAKE_CASE_  )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()




