






'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=1					)->				Tuple:
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split("""."""					)[n_shave_prefix_segments:]					)
      else:
            return ".".join(path.split("""."""					)[:n_shave_prefix_segments]					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				Dict:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item.replace("""in_layers.0"""	,		"""norm1"""					)
            UpperCamelCase       						=      new_item.replace("""in_layers.2"""	,		"""conv1"""					)

            UpperCamelCase       						=      new_item.replace("""out_layers.0"""	,		"""norm2"""					)
            UpperCamelCase       						=      new_item.replace("""out_layers.3"""	,		"""conv2"""					)

            UpperCamelCase       						=      new_item.replace("""emb_layers.1"""	,		"""time_emb_proj"""					)
            UpperCamelCase       						=      new_item.replace("""skip_connection"""	,		"""conv_shortcut"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

            mapping.append({"""old""": old_item, """new""": new_item}					)

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				List[str]:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item

            UpperCamelCase       						=      new_item.replace("""norm.weight"""	,		"""group_norm.weight"""					)
            UpperCamelCase       						=      new_item.replace("""norm.bias"""	,		"""group_norm.bias"""					)

            UpperCamelCase       						=      new_item.replace("""proj_out.weight"""	,		"""proj_attn.weight"""					)
            UpperCamelCase       						=      new_item.replace("""proj_out.bias"""	,		"""proj_attn.bias"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

            mapping.append({"""old""": old_item, """new""": new_item}					)

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				str:
      assert isinstance(__UpperCamelCase	,		__UpperCamelCase					), "Paths should be a list of dicts containing 'old' and 'new' keys."

      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
            for path, path_map in attention_paths_to_split.items():
                  UpperCamelCase       						=      old_checkpoint[path]
                  UpperCamelCase       						=      old_tensor.shape[0] // 3

                  UpperCamelCase       						=      (-1, channels) if len(old_tensor.shape					) == 3 else (-1)

                  UpperCamelCase       						=      old_tensor.shape[0] // config["""num_head_channels"""] // 3

                  UpperCamelCase       						=      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
                  UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      old_tensor.split(channels // num_heads	,		dim=1					)

                  UpperCamelCase       						=      query.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      key.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      value.reshape(__UpperCamelCase					)

      for path in paths:
            UpperCamelCase       						=      path["""new"""]

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            # Global renaming happens here
            UpperCamelCase       						=      new_path.replace("""middle_block.0"""	,		"""mid_block.resnets.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.1"""	,		"""mid_block.attentions.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.2"""	,		"""mid_block.resnets.1"""					)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        UpperCamelCase       						=      new_path.replace(replacement["""old"""]	,		replacement["""new"""]					)

        # proj_attn.weight has to be converted from conv 1D to linear
            if "proj_attn.weight" in new_path:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]][:, :, 0]
            else:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]]







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Optional[int]:
      UpperCamelCase       						=      {}

      UpperCamelCase       						=      checkpoint["""time_embed.0.weight"""]
      UpperCamelCase       						=      checkpoint["""time_embed.0.bias"""]
      UpperCamelCase       						=      checkpoint["""time_embed.2.weight"""]
      UpperCamelCase       						=      checkpoint["""time_embed.2.bias"""]

      UpperCamelCase       						=      checkpoint["""input_blocks.0.0.weight"""]
      UpperCamelCase       						=      checkpoint["""input_blocks.0.0.bias"""]

      UpperCamelCase       						=      checkpoint["""out.0.weight"""]
      UpperCamelCase       						=      checkpoint["""out.0.bias"""]
      UpperCamelCase       						=      checkpoint["""out.2.weight"""]
      UpperCamelCase       						=      checkpoint["""out.2.bias"""]

      # Retrieves the keys for the input blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """input_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the middle blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """middle_block""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the output blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """output_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      for i in range(1	,		__UpperCamelCase					):
            UpperCamelCase       						=      (i - 1) // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      (i - 1) % (config["""num_res_blocks"""] + 1)

            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

            if F"input_blocks.{i}.0.op.weight" in checkpoint:
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.weight"
                  ]
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.bias"
                  ]
                  continue

            UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
            UpperCamelCase       						=      {"""old""": F"input_blocks.{i}.0", """new""": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
            UpperCamelCase       						=      {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
            assign_to_checkpoint(
                __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path, resnet_op]	,		config=__UpperCamelCase					)

            if len(__UpperCamelCase					):
                  UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      {
                      """old""": F"input_blocks.{i}.1",
                      """new""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
                  }
                  UpperCamelCase       						=      {
                      F"input_blocks.{i}.1.qkv.bias": {
                          """key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                          """query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                          """value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                      },
                      F"input_blocks.{i}.1.qkv.weight": {
                          """key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                          """query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                          """value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                      },
                  }
                  assign_to_checkpoint(
                      __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase	,		)

      UpperCamelCase       						=      middle_blocks[0]
      UpperCamelCase       						=      middle_blocks[1]
      UpperCamelCase       						=      middle_blocks[2]

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
      UpperCamelCase       						=      {
          """middle_block.1.qkv.bias""": {
              """key""": """mid_block.attentions.0.key.bias""",
              """query""": """mid_block.attentions.0.query.bias""",
              """value""": """mid_block.attentions.0.value.bias""",
          },
          """middle_block.1.qkv.weight""": {
              """key""": """mid_block.attentions.0.key.weight""",
              """query""": """mid_block.attentions.0.query.weight""",
              """value""": """mid_block.attentions.0.value.weight""",
          },
      }
      assign_to_checkpoint(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase					)

      for i in range(__UpperCamelCase					):
            UpperCamelCase       						=      i // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      i % (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      [shave_segments(__UpperCamelCase	,		2					) for name in output_blocks[i]]
            UpperCamelCase       						=      {}

            for layer in output_block_layers:
                  UpperCamelCase						,UpperCamelCase       						=      layer.split("""."""					)[0], shave_segments(__UpperCamelCase	,		1					)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(__UpperCamelCase					)
                  else:
                        UpperCamelCase       						=      [layer_name]

            if len(__UpperCamelCase					) > 1:
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)

                  UpperCamelCase       						=      {"""old""": F"output_blocks.{i}.0", """new""": F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
                  assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		config=__UpperCamelCase					)

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        UpperCamelCase       						=      list(output_block_list.values()					).index(["""conv.weight""", """conv.bias"""]					)
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.weight"
                        ]
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.bias"
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(__UpperCamelCase					) == 2:
                              UpperCamelCase       						=      []

                  if len(__UpperCamelCase					):
                        UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                        UpperCamelCase       						=      {
                            """old""": F"output_blocks.{i}.1",
                            """new""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                        }
                        UpperCamelCase       						=      {
                            F"output_blocks.{i}.1.qkv.bias": {
                                """key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                                """query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                                """value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                            },
                            F"output_blocks.{i}.1.qkv.weight": {
                                """key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                                """query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                                """value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                            },
                        }
                        assign_to_checkpoint(
                            __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions					) else None	,		config=__UpperCamelCase	,		)
            else:
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase	,		n_shave_prefix_segments=1					)
                  for path in resnet_0_paths:
                        UpperCamelCase       						=      """.""".join(["""output_blocks""", str(__UpperCamelCase					), path["""old"""]]					)
                        UpperCamelCase       						=      """.""".join(["""up_blocks""", str(__UpperCamelCase					), """resnets""", str(__UpperCamelCase					), path["""new"""]]					)

                        UpperCamelCase       						=      checkpoint[old_path]

      return new_checkpoint


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

     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
     )

     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The config json file corresponding to the architecture.',
     )

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          SCREAMING_SNAKE_CASE__											=     json.loads(f.read())

     SCREAMING_SNAKE_CASE__											=     convert_ldm_checkpoint(checkpoint, config)

     if "ldm" in config:
          del config["ldm"]

     SCREAMING_SNAKE_CASE__											=     UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          SCREAMING_SNAKE_CASE__											=     DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
          SCREAMING_SNAKE_CASE__											=     VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

          SCREAMING_SNAKE_CASE__											=     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)








'''simple docstring'''

import inspect
import unittest

from transformers import MobileNetVaConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor






class        a_		(    lowerCamelCase       ):

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.config_class(**self.inputs_dict   )
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""tf_padding"""   )   )
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""depth_multiplier"""   )   )






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=13    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE=0.2_5    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE="relu6"    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=10    ,	_SCREAMING_SNAKE_CASE=None    ,	)						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      image_size
        UpperCamelCase       						=      depth_multiplier
        UpperCamelCase       						=      min_depth
        UpperCamelCase       						=      tf_padding
        UpperCamelCase       						=      int(last_hidden_size * depth_multiplier   )
        UpperCamelCase       						=      output_stride
        UpperCamelCase       						=      hidden_act
        UpperCamelCase       						=      classifier_dropout_prob
        UpperCamelCase       						=      use_labels
        UpperCamelCase       						=      is_training
        UpperCamelCase       						=      num_labels
        UpperCamelCase       						=      initializer_range
        UpperCamelCase       						=      scope

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

        UpperCamelCase       						=      self.get_config()

        return config, pixel_values, labels, pixel_labels

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        return MobileNetVaConfig(
            num_channels=self.num_channels    ,	image_size=self.image_size    ,	depth_multiplier=self.depth_multiplier    ,	min_depth=self.min_depth    ,	tf_padding=self.tf_padding    ,	hidden_act=self.hidden_act    ,	classifier_dropout_prob=self.classifier_dropout_prob    ,	initializer_range=self.initializer_range    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaModel(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,	(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.num_labels
        UpperCamelCase       						=      MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)   )


  def 						A__		(   self   )						->   Any:



        """simple docstring"""






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






@require_torch
class        a_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
  lowercase  	=   (
      {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
      if is_torch_available()
      else {}
  )

  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaModelTester(self   )
        UpperCamelCase       						=      MobileNetVaConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	has_text_modality=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        self.config_tester.run_common_tests()

  @unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV1 does not output attentions"""   )
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCamelCase       						=      ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        def check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
              model.to(_SCREAMING_SNAKE_CASE   )
              model.eval()

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

              UpperCamelCase       						=      outputs.hidden_states

              UpperCamelCase       						=      26
              self.assertEqual(len(_SCREAMING_SNAKE_CASE   )    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      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"]
              UpperCamelCase       						=      True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE   )


  @slow
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase       						=      MobileNetVaModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def 					lowercase__				(   )->				Optional[Any]:
      UpperCamelCase       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
      return image






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

  @cached_property
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        return (
            MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""   ) if is_vision_available() else None
        )


  @slow
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""   ).to(_SCREAMING_SNAKE_CASE   )

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

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

        # verify the logits
        UpperCamelCase       						=      torch.Size((1, 1001)   )
        self.assertEqual(outputs.logits.shape    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]   ).to(_SCREAMING_SNAKE_CASE   )

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