






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      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=3						,_SCREAMING_SNAKE_CASE		:       Dict=3_2						,_SCREAMING_SNAKE_CASE		:       Optional[int]=3						,_SCREAMING_SNAKE_CASE		:       Tuple=1_0						,_SCREAMING_SNAKE_CASE		:       str=[1_0, 2_0, 3_0, 4_0]						,_SCREAMING_SNAKE_CASE		:       Tuple=[1, 1, 2, 1]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=True						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=True						,_SCREAMING_SNAKE_CASE		:       Any="relu"						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=3						,_SCREAMING_SNAKE_CASE		:       Dict=None						,)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      parent
						A	 =      batch_size
						A	 =      image_size
						A	 =      num_channels
						A	 =      embeddings_size
						A	 =      hidden_sizes
						A	 =      depths
						A	 =      is_training
						A	 =      use_labels
						A	 =      hidden_act
						A	 =      num_labels
						A	 =      scope
						A	 =      len(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       str		)    ->   int:


						'''simple docstring'''
						A	 =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

						A	 =      self.get_config()

						return config, pixel_values, labels
		def       A(  self		:       Optional[Any]		)    ->   Optional[int]:


						'''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		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Dict						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Any:


						'''simple docstring'''
						A	 =      RegNetModel(config=_SCREAMING_SNAKE_CASE		)
						model.to(_SCREAMING_SNAKE_CASE		)
						model.eval()
						A	 =      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 // 3_2, self.image_size // 3_2)						,)
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   List[str]:


						'''simple docstring'''
						A	 =      self.num_labels
						A	 =      RegNetForImageClassification(_SCREAMING_SNAKE_CASE		)
						model.to(_SCREAMING_SNAKE_CASE		)
						model.eval()
						A	 =      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]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      self.prepare_config_and_inputs()
						A ,		A ,		A	 =      config_and_inputs
						A	 =      {'pixel_values': pixel_values}
						return config, inputs_dict


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







		"""simple docstring"""



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

		snake_case    =   False
		snake_case    =   False
		snake_case    =   False
		snake_case    =   False
		def       A(  self		:       Optional[Any]		)    ->   str:


						'''simple docstring'''
						A	 =      RegNetModelTester(self		)
						A	 =      ConfigTester(self						,config_class=_SCREAMING_SNAKE_CASE						,has_text_modality=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[int]		)    ->   int:


						'''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		:       Dict		)    ->   int:


						'''simple docstring'''
						return
		@unittest.skip(reason='RegNet does not use inputs_embeds'		)
		def       A(  self		:       Optional[Any]		)    ->   int:


						'''simple docstring'''
						pass
		@unittest.skip(reason='RegNet does not support input and output embeddings'		)
		def       A(  self		:       Optional[Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						pass
		def       A(  self		:       List[str]		)    ->   List[str]:


						'''simple docstring'''
						A ,		A	 =      self.model_tester.prepare_config_and_inputs_for_common()

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

										A	 =      ['pixel_values']
										self.assertListEqual(arg_names[:1]						,_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[Any]		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Tuple		)    ->   List[str]:


						'''simple docstring'''
						A ,		A	 =      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										A	 =      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		)    ->   Optional[int]:


						'''simple docstring'''
						def check_hidden_states_output(_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       str		):
										A	 =      model_class(_SCREAMING_SNAKE_CASE		)
										model.to(_SCREAMING_SNAKE_CASE		)
										model.eval()

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

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

										A	 =      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]						,)

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

														check_hidden_states_output(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[Any]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      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]		)    ->   Optional[Any]:


						'''simple docstring'''
						for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										A	 =      RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
										self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)







def      snake_case		(							):
				A	 =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
				return image



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







		"""simple docstring"""



		@cached_property
		def       A(  self		:       int		)    ->   Optional[Any]:


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







		@slow
		def       A(  self		:       Dict		)    ->   int:


						'''simple docstring'''
						A	 =      RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_SCREAMING_SNAKE_CASE		)

						A	 =      self.default_image_processor
						A	 =      prepare_img()
						A	 =      image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors='pt'		).to(_SCREAMING_SNAKE_CASE		)

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

						# verify the logits
						A	 =      torch.Size((1, 1_0_0_0)		)
						self.assertEqual(outputs.logits.shape						,_SCREAMING_SNAKE_CASE		)

						A	 =      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		)		)













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





