

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 					lowercase							:




				def __init__(   self ,  snake_case ,  snake_case=3 ,  snake_case=32 ,  snake_case=3 ,  snake_case=10 ,  snake_case=[10, 20, 30, 40] ,  snake_case=[1, 1, 2, 1] ,  snake_case=True ,  snake_case=True ,  snake_case="relu" ,  snake_case=3 ,  snake_case=None ,  ):
											snake_case_     							=       parent
											snake_case_     							=       batch_size
											snake_case_     							=       image_size
											snake_case_     							=       num_channels
											snake_case_     							=       embeddings_size
											snake_case_     							=       hidden_sizes
											snake_case_     							=       depths
											snake_case_     							=       is_training
											snake_case_     							=       use_labels
											snake_case_     							=       hidden_act
											snake_case_     							=       num_labels
											snake_case_     							=       scope
											snake_case_     							=       len(snake_case			)




				def        a				(   self			):
											snake_case_     							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

											snake_case_     							=       self.get_config()

											return config, pixel_values, labels




				def        a				(   self			):
											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 ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       RegNetModel(config=snake_case			)
											model.to(snake_case			)
											model.eval()
											snake_case_     							=       model(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 ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       self.num_labels
											snake_case_     							=       RegNetForImageClassification(snake_case			)
											model.to(snake_case			)
											model.eval()
											snake_case_     							=       model(snake_case ,  labels=snake_case			)
											self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)			)




				def        a				(   self			):
											snake_case_     							=       self.prepare_config_and_inputs()
											snake_case_							,       snake_case_							,       snake_case_     							=       config_and_inputs
											snake_case_     							=       {'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class 					lowercase							(			lowercase_      ,    lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     (
				    {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__SCREAMING_SNAKE_CASE   :					Dict			     =     False
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     False
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     False
				__SCREAMING_SNAKE_CASE   :					int			     =     False




				def        a				(   self			):
											snake_case_     							=       RegNetModelTester(self			)
											snake_case_     							=       ConfigTester(self ,  config_class=snake_case ,  has_text_modality=snake_case			)




				def        a				(   self			):
											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			):
											return




				@unittest.skip(reason='RegNet does not use inputs_embeds'			)
				def        a				(   self			):
											pass




				@unittest.skip(reason='RegNet does not support input and output embeddings'			)
				def        a				(   self			):
											pass




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()

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

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




				def        a				(   self			):
											snake_case_     							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case			)




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		snake_case_     							=       model_class(config=snake_case			)
																		for name, module in model.named_modules():
																									if isinstance(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			):
											def check_hidden_states_output(snake_case ,  snake_case ,  snake_case			):
																		snake_case_     							=       model_class(snake_case			)
																		model.to(snake_case			)
																		model.eval()

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

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

																		snake_case_     							=       self.model_tester.num_stages
																		self.assertEqual(len(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] ,  )

											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_     							=       ['basic', 'bottleneck']
											for model_class in self.all_model_classes:
																		for layer_type in layers_type:
																									snake_case_     							=       layer_type
																									snake_case_     							=       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"]
																									snake_case_     							=       True

																									check_hidden_states_output(snake_case ,  snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case			)




				@slow
				def        a				(   self			):
											for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_     							=       RegNetModel.from_pretrained(snake_case			)
																		self.assertIsNotNone(snake_case			)





def    __lowerCamelCase      (					):







							'''simple docstring'''






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







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




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




				@slow
				def        a				(   self			):
											snake_case_     							=       RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(snake_case			)

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

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

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

											snake_case_     							=       torch.tensor([-0.41_80, -1.50_51, -3.48_36]			).to(snake_case			)

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







import argparse


_UpperCAmelCase						:  Union[str, Any]									=			"""docs/source/_static/js/custom.js"""





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							with open(UpperCamelCase__  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														snake_case_     							=       f.readlines()
							snake_case_     							=       0

							# First let's put the right version
							while not lines[index].startswith('const stableVersion =' ):
														index += 1
							snake_case_     							=       F'''const stableVersion = "v{version}"\n'''

							# Then update the dictionary
							while not lines[index].startswith('const versionMapping = {' ):
														index += 1

							# We go until the end
							while not lines[index].startswith('}' ):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

							with open(UpperCamelCase__  ,				'w'  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														f.writelines(UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  int									=			argparse.ArgumentParser()
					parser.add_argument("""--version""", help="""Release version.""")
					_UpperCAmelCase						:  str									=			parser.parse_args()
					update_custom_js(args.version)




