





"""simple docstring"""





import inspect
import unittest

from transformers import MobileViTConfig
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 MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
  from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						self.config_class(**self.inputs_dict )
        self.parent.assertTrue(hasattr(__lowercase						,				'''hidden_sizes''' ) )
        self.parent.assertTrue(hasattr(__lowercase						,				'''neck_hidden_sizes''' ) )
        self.parent.assertTrue(hasattr(__lowercase						,				'''num_attention_heads''' ) )




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      Tuple						,				__lowercase       :      str						,				__lowercase       :      int=13						,				__lowercase       :      List[Any]=32						,				__lowercase       :      Any=2						,				__lowercase       :      Union[str, Any]=3						,				__lowercase       :      Optional[Any]=640						,				__lowercase       :      List[str]=4						,				__lowercase       :      List[str]="silu"						,				__lowercase       :      int=3						,				__lowercase       :      Optional[Any]=32						,				__lowercase       :      List[str]=0.1						,				__lowercase       :      Dict=0.1						,				__lowercase       :      Optional[int]=0.1						,				__lowercase       :      Tuple=0.0_2						,				__lowercase       :      Tuple=True						,				__lowercase       :      Tuple=True						,				__lowercase       :      str=10						,				__lowercase       :      Dict=None						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						parent
        __UpperCAmelCase    :						str       =						batch_size
        __UpperCAmelCase    :						Optional[Any]       =						image_size
        __UpperCAmelCase    :						Optional[Any]       =						patch_size
        __UpperCAmelCase    :						str       =						num_channels
        __UpperCAmelCase    :						Union[str, Any]       =						last_hidden_size
        __UpperCAmelCase    :						Tuple       =						num_attention_heads
        __UpperCAmelCase    :						List[Any]       =						hidden_act
        __UpperCAmelCase    :						Optional[int]       =						conv_kernel_size
        __UpperCAmelCase    :						Tuple       =						output_stride
        __UpperCAmelCase    :						List[str]       =						hidden_dropout_prob
        __UpperCAmelCase    :						Optional[int]       =						attention_probs_dropout_prob
        __UpperCAmelCase    :						int       =						classifier_dropout_prob
        __UpperCAmelCase    :						List[str]       =						use_labels
        __UpperCAmelCase    :						int       =						is_training
        __UpperCAmelCase    :						Optional[Any]       =						num_labels
        __UpperCAmelCase    :						Optional[Any]       =						initializer_range
        __UpperCAmelCase    :						List[str]       =						scope

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        __UpperCAmelCase    :						Any       =						None
        __UpperCAmelCase    :						Union[str, Any]       =						None
        if self.use_labels:
          __UpperCAmelCase    :						List[str]       =						ids_tensor([self.batch_size]						,				self.num_labels )
          __UpperCAmelCase    :						Any       =						ids_tensor([self.batch_size, self.image_size, self.image_size]						,				self.num_labels )

        __UpperCAmelCase    :						Optional[int]       =						self.get_config()

        return config, pixel_values, labels, pixel_labels

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        return MobileViTConfig(
            image_size=self.image_size						,				patch_size=self.patch_size						,				num_channels=self.num_channels						,				num_attention_heads=self.num_attention_heads						,				hidden_act=self.hidden_act						,				conv_kernel_size=self.conv_kernel_size						,				output_stride=self.output_stride						,				hidden_dropout_prob=self.hidden_dropout_prob						,				attention_probs_dropout_prob=self.attention_probs_dropout_prob						,				classifier_dropout_prob=self.classifier_dropout_prob						,				initializer_range=self.initializer_range						,				)

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      List[Any]						,				__lowercase       :      List[str]						,				__lowercase       :      List[str]						,				__lowercase       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						MobileViTModel(config=__lowercase )
        model.to(__lowercase )
        model.eval()
        __UpperCAmelCase    :						Dict       =						model(__lowercase )
        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       :      int						,				__lowercase       :      Optional[int]						,				__lowercase       :      Tuple						,				__lowercase       :      Union[str, Any]						,				__lowercase       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						self.num_labels
        __UpperCAmelCase    :						Optional[Any]       =						MobileViTForImageClassification(__lowercase )
        model.to(__lowercase )
        model.eval()
        __UpperCAmelCase    :						str       =						model(__lowercase						,				labels=__lowercase )
        self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels) )

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      List[str]						,				__lowercase       :      List[Any]						,				__lowercase       :      List[Any]						,				__lowercase       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						self.num_labels
        __UpperCAmelCase    :						str       =						MobileViTForSemanticSegmentation(__lowercase )
        model.to(__lowercase )
        model.eval()
        __UpperCAmelCase    :						Union[str, Any]       =						model(__lowercase )
        self.parent.assertEqual(
            result.logits.shape						,				(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )						,				)
        __UpperCAmelCase    :						List[Any]       =						model(__lowercase						,				labels=__lowercase )
        self.parent.assertEqual(
            result.logits.shape						,				(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )						,				)







      def       A_					(							self       :      Any ):


        '''simple docstring'''




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




@require_torch
class 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       Dict         =       (
          (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      _A     :       Tuple         =       (
          {
              'feature-extraction': MobileViTModel,
              'image-classification': MobileViTForImageClassification,
              'image-segmentation': MobileViTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      _A     :       List[Any]         =       False
      _A     :       List[str]         =       False
      _A     :       int         =       False
      _A     :       str         =       False

      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						MobileViTModelTester(self )
        __UpperCAmelCase    :						Union[str, Any]       =						MobileViTConfigTester(self						,				config_class=__lowercase						,				has_text_modality=__lowercase )

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        self.config_tester.run_common_tests()

      @unittest.skip(reason='''MobileViT does not use inputs_embeds''' )
      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        pass

      @unittest.skip(reason='''MobileViT does not support input and output embeddings''' )
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        pass

      @unittest.skip(reason='''MobileViT does not output attentions''' )
      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        pass

      def       A_					(							self       :      Dict ):


        '''simple docstring'''




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

        for model_class in self.all_model_classes:
          __UpperCAmelCase    :						int       =						model_class(__lowercase )
          __UpperCAmelCase    :						Union[str, Any]       =						inspect.signature(model.forward )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          __UpperCAmelCase    :						Tuple       =						[*signature.parameters.keys()]

          __UpperCAmelCase    :						List[Any]       =						['''pixel_values''']
          self.assertListEqual(arg_names[:1]						,				__lowercase )

      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
      def       A_					(							self       :      int ):


        '''simple docstring'''




        pass

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase )

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        def check_hidden_states_output(__lowercase       :      int						,				__lowercase       :      str						,				__lowercase       :      List[str] ):
          __UpperCAmelCase    :						Tuple       =						model_class(__lowercase )
          model.to(__lowercase )
          model.eval()

          with torch.no_grad():
            __UpperCAmelCase    :						Dict       =						model(**self._prepare_for_class(__lowercase						,				__lowercase ) )

          __UpperCAmelCase    :						Dict       =						outputs.hidden_states

          __UpperCAmelCase    :						str       =						5
          self.assertEqual(len(__lowercase )						,				__lowercase )

          # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
          # with the width and height being successively divided by 2.
          __UpperCAmelCase    :						List[str]       =						2
          for i in range(len(__lowercase ) ):
            self.assertListEqual(
                list(hidden_states[i].shape[-2:] )						,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]						,				)
            divisor *= 2

          self.assertEqual(self.model_tester.output_stride						,				divisor // 2 )

        __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase    :						List[Any]       =						True
          check_hidden_states_output(__lowercase						,				__lowercase						,				__lowercase )

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

          check_hidden_states_output(__lowercase						,				__lowercase						,				__lowercase )

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowercase )

      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase )







      @slow
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __UpperCAmelCase    :						Optional[Any]       =						MobileViTModel.from_pretrained(__lowercase )
          self.assertIsNotNone(__lowercase )


def       lowerCamelCase_       (   )						->List[Any]:





  """simple docstring"""


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







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

      '''simple docstring'''



      @cached_property
      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''' ) if is_vision_available() else None

      @slow
      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''' ).to(__lowercase )

        __UpperCAmelCase    :						Dict       =						self.default_image_processor
        __UpperCAmelCase    :						Any       =						prepare_img()
        __UpperCAmelCase    :						Optional[Any]       =						image_processor(images=__lowercase						,				return_tensors='''pt''' ).to(__lowercase )

        # forward pass
        with torch.no_grad():
          __UpperCAmelCase    :						str       =						model(**__lowercase )

        # verify the logits
        __UpperCAmelCase    :						Optional[int]       =						torch.Size((1, 1_000) )
        self.assertEqual(outputs.logits.shape						,				__lowercase )

        __UpperCAmelCase    :						Dict       =						torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] ).to(__lowercase )

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

      @slow
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
        __UpperCAmelCase    :						Union[str, Any]       =						model.to(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )

        __UpperCAmelCase    :						List[Any]       =						prepare_img()
        __UpperCAmelCase    :						Tuple       =						image_processor(images=__lowercase						,				return_tensors='''pt''' ).to(__lowercase )

        # forward pass
        with torch.no_grad():
          __UpperCAmelCase    :						int       =						model(**__lowercase )
        __UpperCAmelCase    :						Any       =						outputs.logits

        # verify the logits
        __UpperCAmelCase    :						Union[str, Any]       =						torch.Size((1, 21, 32, 32) )
        self.assertEqual(logits.shape						,				__lowercase )

        __UpperCAmelCase    :						List[Any]       =						torch.tensor(
            [
                [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
                [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
                [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
            ]						,				device=__lowercase						,				)

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







      @slow
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
        __UpperCAmelCase    :						Dict       =						model.to(__lowercase )

        __UpperCAmelCase    :						Any       =						MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )

        __UpperCAmelCase    :						Optional[Any]       =						prepare_img()
        __UpperCAmelCase    :						Dict       =						image_processor(images=__lowercase						,				return_tensors='''pt''' ).to(__lowercase )

        # forward pass
        with torch.no_grad():
          __UpperCAmelCase    :						Tuple       =						model(**__lowercase )

        __UpperCAmelCase    :						Tuple       =						outputs.logits.detach().cpu()

        __UpperCAmelCase    :						Optional[int]       =						image_processor.post_process_semantic_segmentation(outputs=__lowercase						,				target_sizes=[(50, 60)] )
        __UpperCAmelCase    :						Union[str, Any]       =						torch.Size((50, 60) )
        self.assertEqual(segmentation[0].shape						,				__lowercase )

        __UpperCAmelCase    :						Any       =						image_processor.post_process_semantic_segmentation(outputs=__lowercase )
        __UpperCAmelCase    :						Tuple       =						torch.Size((32, 32) )
        self.assertEqual(segmentation[0].shape						,				__lowercase )






"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						OmegaConf.load(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						torch.load(UpperCAmelCase_  ,			map_location='''cpu'''  )['''model''']
  __UpperCAmelCase    :						List[Any]       =						list(state_dict.keys()  )

  # extract state_dict for VQVAE
  __UpperCAmelCase    :						Dict       =						{}
  __UpperCAmelCase    :						Tuple       =						'''first_stage_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						state_dict[key]

    # extract state_dict for UNetLDM
  __UpperCAmelCase    :						Union[str, Any]       =						{}
  __UpperCAmelCase    :						Any       =						'''model.diffusion_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						int       =						state_dict[key]

  __UpperCAmelCase    :						Any       =						config.model.params.first_stage_config.params
  __UpperCAmelCase    :						Dict       =						config.model.params.unet_config.params

  __UpperCAmelCase    :						Tuple       =						VQModel(**UpperCAmelCase_  ).eval()
  vqvae.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						UNetLDMModel(**UpperCAmelCase_  ).eval()
  unet.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						DDIMScheduler(
      timesteps=config.model.params.timesteps  ,			beta_schedule='''scaled_linear'''  ,			beta_start=config.model.params.linear_start  ,			beta_end=config.model.params.linear_end  ,			clip_sample=UpperCAmelCase_  ,			)

  __UpperCAmelCase    :						Tuple       =						LDMPipeline(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  pipeline.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:str				   =       argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  lowercase__				:Dict				   =       parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)