
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		,	lowerCamelCase		,	lowerCamelCase=13		,	lowerCamelCase=32		,	lowerCamelCase=2		,	lowerCamelCase=3		,	lowerCamelCase=16		,	lowerCamelCase=[1, 2, 1]		,	lowerCamelCase=[2, 2, 4]		,	lowerCamelCase=2		,	lowerCamelCase=2.0		,	lowerCamelCase=True		,	lowerCamelCase=0.0		,	lowerCamelCase=0.0		,	lowerCamelCase=0.1		,	lowerCamelCase="gelu"		,	lowerCamelCase=False		,	lowerCamelCase=True		,	lowerCamelCase=0.0_2		,	lowerCamelCase=1e-5		,	lowerCamelCase=True		,	lowerCamelCase=None		,	lowerCamelCase=True		,	lowerCamelCase=10		,	lowerCamelCase=8		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				image_size
          snake_case__						=				patch_size
          snake_case__						=				num_channels
          snake_case__						=				embed_dim
          snake_case__						=				depths
          snake_case__						=				num_heads
          snake_case__						=				window_size
          snake_case__						=				mlp_ratio
          snake_case__						=				qkv_bias
          snake_case__						=				hidden_dropout_prob
          snake_case__						=				attention_probs_dropout_prob
          snake_case__						=				drop_path_rate
          snake_case__						=				hidden_act
          snake_case__						=				use_absolute_embeddings
          snake_case__						=				patch_norm
          snake_case__						=				layer_norm_eps
          snake_case__						=				initializer_range
          snake_case__						=				is_training
          snake_case__						=				scope
          snake_case__						=				use_labels
          snake_case__						=				type_sequence_label_size
          snake_case__						=				encoder_stride
      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.type_sequence_label_size )

          snake_case__						=				self.get_config()

          return config, pixel_values, labels
      def A_			(			self ):
          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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				SwinvaModel(config=lowercase__ )
          model.to(lowercase__ )
          model.eval()
          snake_case__						=				model(lowercase__ )

          snake_case__						=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
          snake_case__						=				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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				SwinvaForMaskedImageModeling(config=lowercase__ )
          model.to(lowercase__ )
          model.eval()
          snake_case__						=				model(lowercase__ )
          self.parent.assertEqual(
              result.logits.shape		,	(self.batch_size, self.num_channels, self.image_size, self.image_size) )

          # test greyscale images
          snake_case__						=				1
          snake_case__						=				SwinvaForMaskedImageModeling(lowercase__ )
          model.to(lowercase__ )
          model.eval()

          snake_case__						=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
          snake_case__						=				model(lowercase__ )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, 1, self.image_size, self.image_size) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.type_sequence_label_size
          snake_case__						=				SwinvaForImageClassification(lowercase__ )
          model.to(lowercase__ )
          model.eval()
          snake_case__						=				model(lowercase__		,	labels=lowercase__ )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size) )



      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          snake_case__						=				config_and_inputs
          snake_case__						=				{"""pixel_values""": pixel_values}
          return config, inputs_dict





@require_torch
class 					_SCREAMING_SNAKE_CASE  (      _UpperCAmelCase       ,					_UpperCAmelCase       ,					unittest.TestCase ):
      _A						:		str    	=     (
          (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
      )
      _A						:		Optional[Any]    	=     (
          {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
          if is_torch_available()
          else {}
      )

      _A						:		List[Any]    	=     False
      _A						:		Union[str, Any]    	=     False
      _A						:		Optional[Any]    	=     False
      _A						:		Tuple    	=     False
      def A_			(			self ):
          snake_case__						=				SwinvaModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=lowercase__		,	embed_dim=37 )
      def A_			(			self ):
          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 ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase__ )
      @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
      def A_			(			self ):
          pass
      @unittest.skip(reason="""Swinv2 does not use inputs_embeds""" )
      def A_			(			self ):
          pass
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              snake_case__						=				model_class(lowercase__ )
              self.assertIsInstance(model.get_input_embeddings()		,	(nn.Module) )
              snake_case__						=				model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowercase__		,	nn.Linear ) )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              snake_case__						=				model_class(lowercase__ )
              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]		,	lowercase__ )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__						=				True

          for model_class in self.all_model_classes:
              snake_case__						=				True
              snake_case__						=				False
              snake_case__						=				True
              snake_case__						=				model_class(lowercase__ )
              model.to(lowercase__ )
              model.eval()
              with torch.no_grad():
                  snake_case__						=				model(**self._prepare_for_class(lowercase__		,	lowercase__ ) )
              snake_case__						=				outputs.attentions
              snake_case__						=				len(self.model_tester.depths )
              self.assertEqual(len(lowercase__ )		,	lowercase__ )

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

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

              # Check attention is always last and order is fine
              snake_case__						=				True
              snake_case__						=				True
              snake_case__						=				model_class(lowercase__ )
              model.to(lowercase__ )
              model.eval()
              with torch.no_grad():
                  snake_case__						=				model(**self._prepare_for_class(lowercase__		,	lowercase__ ) )

              if hasattr(self.model_tester		,	"""num_hidden_states_types""" ):
                  snake_case__						=				self.model_tester.num_hidden_states_types
              else:
                  # also another +1 for reshaped_hidden_states
                  snake_case__						=				2
              self.assertEqual(out_len + added_hidden_states		,	len(lowercase__ ) )

              snake_case__						=				outputs.attentions

              self.assertEqual(len(lowercase__ )		,	lowercase__ )

              self.assertListEqual(
                  list(self_attentions[0].shape[-3:] )		,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,	)
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				model_class(lowercase__ )
          model.to(lowercase__ )
          model.eval()

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

          snake_case__						=				outputs.hidden_states

          snake_case__						=				getattr(
              self.model_tester		,	"""expected_num_hidden_layers"""		,	len(self.model_tester.depths ) + 1 )
          self.assertEqual(len(lowercase__ )		,	lowercase__ )

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

          snake_case__						=				(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]		,	)

          snake_case__						=				outputs.reshaped_hidden_states
          self.assertEqual(len(lowercase__ )		,	lowercase__ )

          snake_case__						=				reshaped_hidden_states[0].shape
          snake_case__						=				(
              reshaped_hidden_states[0].view(lowercase__		,	lowercase__		,	height * width ).permute(0		,	2		,	1 )
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:] )		,	[num_patches, self.model_tester.embed_dim]		,	)
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__						=				(
              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:
              snake_case__						=				True
              self.check_hidden_states_output(lowercase__		,	lowercase__		,	lowercase__		,	lowercase__ )

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

              self.check_hidden_states_output(lowercase__		,	lowercase__		,	lowercase__		,	lowercase__ )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__						=				3

          snake_case__						=				(
              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)
          )
          snake_case__						=				(
              config.patch_size
              if isinstance(config.patch_size		,	collections.abc.Iterable )
              else (config.patch_size, config.patch_size)
          )

          snake_case__						=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          snake_case__						=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
              snake_case__						=				True
              self.check_hidden_states_output(lowercase__		,	lowercase__		,	lowercase__		,	(padded_height, padded_width) )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              snake_case__						=				True
              self.check_hidden_states_output(lowercase__		,	lowercase__		,	lowercase__		,	(padded_height, padded_width) )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__ )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase__ )
      @slow
      def A_			(			self ):
          for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__						=				SwinvaModel.from_pretrained(lowercase__ )
              self.assertIsNotNone(lowercase__ )



      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__						=				_config_zero_init(lowercase__ )
          for model_class in self.all_model_classes:
              snake_case__						=				model_class(config=lowercase__ )
              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 A_			(			self ):
          return (
              AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" )
              if is_vision_available()
              else None
          )



      @slow
      def A_			(			self ):
          snake_case__						=				SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" ).to(
              lowercase__ )
          snake_case__						=				self.default_image_processor

          snake_case__						=				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
          snake_case__						=				image_processor(images=lowercase__		,	return_tensors="""pt""" ).to(lowercase__ )

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

          # verify the logits
          snake_case__						=				torch.Size((1, 10_00) )
          self.assertEqual(outputs.logits.shape		,	lowercase__ )
          snake_case__						=				torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6] ).to(lowercase__ )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]		,	lowercase__		,	atol=1e-4 ) )





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )
     parser.add_argument(
         '''--original_config_file''',
         type=str,
         required=True,
         help='''The YAML config file corresponding to the original architecture.''',
     )
     parser.add_argument(
         '''--num_in_channels''',
         default=None,
         type=int,
         help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
     )
     parser.add_argument(
         '''--image_size''',
         default=512,
         type=int,
         help=(
             '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
             ''' Base. Use 768 for Stable Diffusion v2.'''
         ),
     )
     parser.add_argument(
         '''--extract_ema''',
         action='''store_true''',
         help=(
             '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
             ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
             ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
         ),
     )
     parser.add_argument(
         '''--upcast_attention''',
         action='''store_true''',
         help=(
             '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
             ''' diffusion 2.1.'''
         ),
     )
     parser.add_argument(
         '''--from_safetensors''',
         action='''store_true''',
         help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
     )
     parser.add_argument(
         '''--to_safetensors''',
         action='''store_true''',
         help='''Whether to store pipeline in safetensors format or not.''',
     )
     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')





     def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
         if string == "True":
             return True
         elif string == "False":
             return False
         else:
             raise ValueError(F"""could not parse string as bool {string}"""							)

     parser.add_argument(
         '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
     )

     parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

     __magic_name__            =  parser.parse_args()

     __magic_name__            =  download_controlnet_from_original_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         extract_ema=args.extract_ema,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         use_linear_projection=args.use_linear_projection,
         cross_attention_dim=args.cross_attention_dim,
     )





     controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



