





'''simple docstring'''




import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
  from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=32		,      _UpperCAmelCase=2		,      _UpperCAmelCase=3		,      _UpperCAmelCase=16		,      _UpperCAmelCase=[1, 2, 1]		,      _UpperCAmelCase=[2, 2, 4]		,      _UpperCAmelCase=2		,      _UpperCAmelCase=2.0		,      _UpperCAmelCase=True		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-5		,      _UpperCAmelCase=True		,      _UpperCAmelCase=None		,      _UpperCAmelCase=True		,      _UpperCAmelCase=10		,      _UpperCAmelCase=8		,      ):

          '''simple docstring'''






          __A		:					str						=			parent
          __A		:					int						=			batch_size
          __A		:					Optional[Any]						=			image_size
          __A		:					Dict						=			patch_size
          __A		:					List[str]						=			num_channels
          __A		:					List[str]						=			embed_dim
          __A		:					Any						=			depths
          __A		:					str						=			num_heads
          __A		:					List[str]						=			window_size
          __A		:					Union[str, Any]						=			mlp_ratio
          __A		:					Union[str, Any]						=			qkv_bias
          __A		:					int						=			hidden_dropout_prob
          __A		:					Any						=			attention_probs_dropout_prob
          __A		:					Optional[int]						=			drop_path_rate
          __A		:					List[Any]						=			hidden_act
          __A		:					Optional[Any]						=			use_absolute_embeddings
          __A		:					Optional[int]						=			patch_norm
          __A		:					List[Any]						=			layer_norm_eps
          __A		:					int						=			initializer_range
          __A		:					str						=			is_training
          __A		:					Dict						=			scope
          __A		:					Dict						=			use_labels
          __A		:					int						=			type_sequence_label_size
          __A		:					Union[str, Any]						=			encoder_stride


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          __A		:					Dict						=			None
          if self.use_labels:
               __A		:					Dict						=			ids_tensor([self.batch_size]		,      self.type_sequence_label_size)

          __A		:					int						=			self.get_config()

          return config, pixel_values, labels


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return SwinvaConfig(
              image_size=self.image_size		,      patch_size=self.patch_size		,      num_channels=self.num_channels		,      embed_dim=self.embed_dim		,      depths=self.depths		,      num_heads=self.num_heads		,      window_size=self.window_size		,      mlp_ratio=self.mlp_ratio		,      qkv_bias=self.qkv_bias		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      drop_path_rate=self.drop_path_rate		,      hidden_act=self.hidden_act		,      use_absolute_embeddings=self.use_absolute_embeddings		,      path_norm=self.patch_norm		,      layer_norm_eps=self.layer_norm_eps		,      initializer_range=self.initializer_range		,      encoder_stride=self.encoder_stride		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			SwinvaModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					int						=			model(_UpperCAmelCase)

          __A		:					List[Any]						=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
          __A		:					Union[str, Any]						=			int(config.embed_dim * 2 ** (len(config.depths) - 1))

          self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, expected_seq_len, expected_dim))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[Any]						=			SwinvaForMaskedImageModeling(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					Tuple						=			model(_UpperCAmelCase)
          self.parent.assertEqual(
              result.logits.shape		,      (self.batch_size, self.num_channels, self.image_size, self.image_size))

          # test greyscale images
          __A		:					Dict						=			1
          __A		:					Any						=			SwinvaForMaskedImageModeling(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()

          __A		:					List[Any]						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
          __A		:					List[Any]						=			model(_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, 1, self.image_size, self.image_size))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			self.type_sequence_label_size
          __A		:					Union[str, Any]						=			SwinvaForImageClassification(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					Optional[int]						=			model(_UpperCAmelCase		,      labels=_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			self.prepare_config_and_inputs()
          __A					,__A					,__A		:					Tuple						=			config_and_inputs
          __A		:					Union[str, Any]						=			{'pixel_values': pixel_values}
          return config, inputs_dict




@require_torch
class 					SCREAMING_SNAKE_CASE       (a__   ,						a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			(
         (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
     )
     lowerCAmelCase						     =			(
         {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
         if is_torch_available()
         else {}
     )

     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			SwinvaModelTester(self)
          __A		:					Any						=			ConfigTester(self		,      config_class=_UpperCAmelCase		,      embed_dim=37)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     @unittest.skip(reason='Swinv2 does not use inputs_embeds')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					Union[str, Any]						=			model_class(_UpperCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()		,      (nn.Module))
               __A		:					Union[str, Any]						=			model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_UpperCAmelCase		,      nn.Linear))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					str						=			model_class(_UpperCAmelCase)
               __A		:					int						=			inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A		:					List[Any]						=			[*signature.parameters.keys()]

               __A		:					int						=			['pixel_values']
               self.assertListEqual(arg_names[:1]		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Optional[int]						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					List[Any]						=			True

          for model_class in self.all_model_classes:
               __A		:					Optional[Any]						=			True
               __A		:					int						=			False
               __A		:					List[Any]						=			True
               __A		:					List[Any]						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					List[str]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))
               __A		:					Any						=			outputs.attentions
               __A		:					Any						=			len(self.model_tester.depths)
               self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               __A		:					str						=			True
               __A		:					Union[str, Any]						=			config.window_size**2
               __A		:					List[Any]						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					int						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))
               __A		:					str						=			outputs.attentions
               self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

               self.assertListEqual(
                   list(attentions[0].shape[-3:])		,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,      )
               __A		:					int						=			len(_UpperCAmelCase)

               # Check attention is always last and order is fine
               __A		:					int						=			True
               __A		:					List[Any]						=			True
               __A		:					Dict						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					List[Any]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               if hasattr(self.model_tester		,      'num_hidden_states_types'):
                    __A		:					List[Any]						=			self.model_tester.num_hidden_states_types
               else:
                    # also another +1 for reshaped_hidden_states
                    __A		:					str						=			2
               self.assertEqual(out_len + added_hidden_states		,      len(_UpperCAmelCase))

               __A		:					Tuple						=			outputs.attentions

               self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

               self.assertListEqual(
                   list(self_attentions[0].shape[-3:])		,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			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.hidden_states

          __A		:					str						=			getattr(
              self.model_tester		,      'expected_num_hidden_layers'		,      len(self.model_tester.depths) + 1)
          self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

          # Swinv2 has a different seq_length
          __A		:					Any						=			(
              config.patch_size
              if isinstance(config.patch_size		,      collections.abc.Iterable)
              else (config.patch_size, config.patch_size)
          )

          __A		:					Any						=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

          self.assertListEqual(
              list(hidden_states[0].shape[-2:])		,      [num_patches, self.model_tester.embed_dim]		,      )

          __A		:					Tuple						=			outputs.reshaped_hidden_states
          self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

          __A					,__A					,__A					,__A		:					Any						=			reshaped_hidden_states[0].shape
          __A		:					Tuple						=			(
              reshaped_hidden_states[0].view(_UpperCAmelCase		,      _UpperCAmelCase		,      height * width).permute(0		,      2		,      1)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:])		,      [num_patches, self.model_tester.embed_dim]		,      )


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					int						=			self.model_tester.prepare_config_and_inputs_for_common()

          __A		:					Optional[int]						=			(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size		,      collections.abc.Iterable)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
               __A		:					int						=			True
               self.check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

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

               self.check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					Optional[int]						=			3

          __A		:					Tuple						=			(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size		,      collections.abc.Iterable)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          __A		:					Any						=			(
              config.patch_size
              if isinstance(config.patch_size		,      collections.abc.Iterable)
              else (config.patch_size, config.patch_size)
          )

          __A		:					Any						=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          __A		:					str						=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
               __A		:					Any						=			True
               self.check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      (padded_height, padded_width))

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __A		:					Any						=			True
               self.check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      (padded_height, padded_width))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A		:					str						=			SwinvaModel.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          __A		:					str						=			_config_zero_init(_UpperCAmelCase)
          for model_class in self.all_model_classes:
               __A		:					List[str]						=			model_class(config=_UpperCAmelCase)
               for name, param in model.named_parameters():
                    if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                         self.assertIn(
                             ((param.data.mean() * 1e9).round() / 1e9).item()		,      [0.0, 1.0]		,      msg=F'Parameter {name} of model {model_class} seems not properly initialized'		,      )




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


     @cached_property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return (
              AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256')
              if is_vision_available()
              else None
          )


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256').to(
              _UpperCAmelCase)
          __A		:					Any						=			self.default_image_processor

          __A		:					Optional[Any]						=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
          __A		:					Any						=			image_processor(images=_UpperCAmelCase		,      return_tensors='pt').to(_UpperCAmelCase)

          # forward pass
          with torch.no_grad():
               __A		:					Tuple						=			model(**_UpperCAmelCase)

          # verify the logits
          __A		:					Optional[int]						=			torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape		,      _UpperCAmelCase)
          __A		:					Optional[Any]						=			torch.tensor([-0.3947, -0.4306, 0.0026]).to(_UpperCAmelCase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]		,      _UpperCAmelCase		,      atol=1e-4))






'''simple docstring'''




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					int						=			RobertaPreLayerNormConfig.from_pretrained(
         __snake_case					,				architectures=['RobertaPreLayerNormForMaskedLM']     )

     # convert state_dict
     __A		:					Tuple						=			torch.load(hf_hub_download(repo_id=__snake_case					,				filename='pytorch_model.bin'     )     )
     __A		:					str						=			{}
     for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith('roberta.'     ):
               __A		:					Dict						=			'roberta_prelayernorm.' + tensor_key[len('roberta.'     ) :]

          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith('.self.LayerNorm.weight'     ) or tensor_key.endswith('.self.LayerNorm.bias'     ):
               continue

          __A		:					str						=			tensor_value

     __A		:					Union[str, Any]						=			RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__snake_case					,				config=__snake_case					,				state_dict=__snake_case     )
     model.save_pretrained(__snake_case     )

     # convert tokenizer
     __A		:					List[Any]						=			AutoTokenizer.from_pretrained(__snake_case     )
     tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint-repo''',
      default=None,
      type=str,
      required=True,
      help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)