






import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
       from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DPTImageProcessor
class        a       :



      """simple docstring"""


      def __init__( self   :     Union[str, Any]						, __lowercase   :     Union[str, Any]						, __lowercase   :     Tuple=2						, __lowercase   :     Any=32						, __lowercase   :     Any=16						, __lowercase   :     str=3						, __lowercase   :     Dict=True						, __lowercase   :     Tuple=True						, __lowercase   :     Any=32						, __lowercase   :     Dict=4						, __lowercase   :     List[str]=[0, 1, 2, 3]						, __lowercase   :     Optional[int]=4						, __lowercase   :     Dict=37						, __lowercase   :     Union[str, Any]="gelu"						, __lowercase   :     str=0.1						, __lowercase   :     List[Any]=0.1						, __lowercase   :     List[Any]=0.02						, __lowercase   :     Dict=3						, __lowercase   :     Optional[int]=[1, 384, 24, 24]						, __lowercase   :     List[str]=True						, __lowercase   :     Any=None						, )				-> Union[str, Any]:
        __UpperCAmelCase				:      List[Any]	 =    parent
        __UpperCAmelCase				:      Dict	 =    batch_size
        __UpperCAmelCase				:      Optional[Any]	 =    image_size
        __UpperCAmelCase				:      Tuple	 =    patch_size
        __UpperCAmelCase				:      Union[str, Any]	 =    num_channels
        __UpperCAmelCase				:      Union[str, Any]	 =    is_training
        __UpperCAmelCase				:      List[str]	 =    use_labels
        __UpperCAmelCase				:      List[Any]	 =    hidden_size
        __UpperCAmelCase				:      Optional[int]	 =    num_hidden_layers
        __UpperCAmelCase				:      Tuple	 =    backbone_out_indices
        __UpperCAmelCase				:      Union[str, Any]	 =    num_attention_heads
        __UpperCAmelCase				:      str	 =    intermediate_size
        __UpperCAmelCase				:      List[str]	 =    hidden_act
        __UpperCAmelCase				:      List[str]	 =    hidden_dropout_prob
        __UpperCAmelCase				:      List[Any]	 =    attention_probs_dropout_prob
        __UpperCAmelCase				:      str	 =    initializer_range
        __UpperCAmelCase				:      Dict	 =    num_labels
        __UpperCAmelCase				:      List[str]	 =    backbone_featmap_shape
        __UpperCAmelCase				:      Dict	 =    scope
        __UpperCAmelCase				:      Optional[Any]	 =    is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        __UpperCAmelCase				:      List[Any]	 =    (image_size // patch_size) ** 2
        __UpperCAmelCase				:      Optional[int]	 =    num_patches + 1
      def       UpperCAmelCase ( self   :     List[str]   )				-> List[Any]:
        __UpperCAmelCase				:      Dict	 =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

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

        return config, pixel_values, labels
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> List[str]:
        __UpperCAmelCase				:      Optional[Any]	 =    {
            """global_padding""": """same""",
            """layer_type""": """bottleneck""",
            """depths""": [3, 4, 9],
            """out_features""": ["""stage1""", """stage2""", """stage3"""],
            """embedding_dynamic_padding""": True,
            """hidden_sizes""": [96, 192, 384, 768],
            """num_groups""": 2,
        }

        return DPTConfig(
            image_size=self.image_size						, patch_size=self.patch_size						, num_channels=self.num_channels						, hidden_size=self.hidden_size						, num_hidden_layers=self.num_hidden_layers						, backbone_out_indices=self.backbone_out_indices						, num_attention_heads=self.num_attention_heads						, intermediate_size=self.intermediate_size						, hidden_act=self.hidden_act						, hidden_dropout_prob=self.hidden_dropout_prob						, attention_probs_dropout_prob=self.attention_probs_dropout_prob						, is_decoder=__lowercase						, initializer_range=self.initializer_range						, is_hybrid=self.is_hybrid						, backbone_config=__lowercase						, backbone_featmap_shape=self.backbone_featmap_shape						, )
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     Union[str, Any]						, __lowercase   :     str						, __lowercase   :     Optional[int]   )				-> Dict:
        __UpperCAmelCase				:      Union[str, Any]	 =    DPTModel(config=__lowercase   )
        model.to(__lowercase   )
        model.eval()
        __UpperCAmelCase				:      Optional[Any]	 =    model(__lowercase   )
        self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)   )
      def       UpperCAmelCase ( self   :     str						, __lowercase   :     List[Any]						, __lowercase   :     str						, __lowercase   :     str   )				-> int:
        __UpperCAmelCase				:      Union[str, Any]	 =    self.num_labels
        __UpperCAmelCase				:      Optional[int]	 =    DPTForDepthEstimation(__lowercase   )
        model.to(__lowercase   )
        model.eval()
        __UpperCAmelCase				:      Tuple	 =    model(__lowercase   )
        self.parent.assertEqual(result.predicted_depth.shape						, (self.batch_size, self.image_size, self.image_size)   )
      def       UpperCAmelCase ( self   :     int						, __lowercase   :     Any						, __lowercase   :     Dict						, __lowercase   :     List[str]   )				-> str:
        __UpperCAmelCase				:      Dict	 =    self.num_labels
        __UpperCAmelCase				:      Any	 =    DPTForSemanticSegmentation(__lowercase   )
        model.to(__lowercase   )
        model.eval()
        __UpperCAmelCase				:      Dict	 =    model(__lowercase						, labels=__lowercase   )
        self.parent.assertEqual(
            result.logits.shape						, (self.batch_size, self.num_labels, self.image_size, self.image_size)   )




      def       UpperCAmelCase ( self   :     Optional[int]   )				-> List[str]:
        __UpperCAmelCase				:      Union[str, Any]	 =    self.prepare_config_and_inputs()
        __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Tuple	 =    config_and_inputs
        __UpperCAmelCase				:      Optional[int]	 =    {"""pixel_values""": pixel_values}
        return config, inputs_dict


@require_torch
class        a       (					lowercase__				,			lowercase__				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					List[str]							=      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      a    :					List[str]							=      (
          {
              'depth-estimation': DPTForDepthEstimation,
              'feature-extraction': DPTModel,
              'image-segmentation': DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      a    :					Optional[Any]							=      False
      a    :					Dict							=      False
      a    :					Dict							=      False
      def       UpperCAmelCase ( self   :     List[Any]   )				-> str:
        __UpperCAmelCase				:      Union[str, Any]	 =    DPTModelTester(self   )
        __UpperCAmelCase				:      Optional[Any]	 =    ConfigTester(self						, config_class=__lowercase						, has_text_modality=__lowercase						, hidden_size=37   )
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Union[str, Any]:
        self.config_tester.run_common_tests()
      @unittest.skip(reason="""DPT does not use inputs_embeds"""   )
      def       UpperCAmelCase ( self   :     str   )				-> Optional[int]:
        pass
      def       UpperCAmelCase ( self   :     int   )				-> Optional[int]:
        __UpperCAmelCase ,					__UpperCAmelCase				:      int	 =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase				:      Any	 =    model_class(__lowercase   )
          self.assertIsInstance(model.get_input_embeddings()						, (nn.Module)   )
          __UpperCAmelCase				:      Tuple	 =    model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(__lowercase						, nn.Linear   )   )
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> str:
        __UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    self.model_tester.prepare_config_and_inputs_for_common()

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

          __UpperCAmelCase				:      Any	 =    ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]						, __lowercase   )
      def       UpperCAmelCase ( self   :     List[str]   )				-> Optional[Any]:
        __UpperCAmelCase				:      Union[str, Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase   )
      def       UpperCAmelCase ( self   :     Tuple   )				-> Any:
        __UpperCAmelCase				:      Union[str, Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*__lowercase   )
      def       UpperCAmelCase ( self   :     Tuple   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Dict	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase   )
      def       UpperCAmelCase ( self   :     Tuple   )				-> List[str]:
        for model_class in self.all_model_classes:
          if model_class.__name__ == "DPTForDepthEstimation":
            continue

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

          if model_class in get_values(__lowercase   ):
            continue

          __UpperCAmelCase				:      List[str]	 =    model_class(__lowercase   )
          model.to(__lowercase   )
          model.train()
          __UpperCAmelCase				:      int	 =    self._prepare_for_class(__lowercase						, __lowercase						, return_labels=__lowercase   )
          __UpperCAmelCase				:      List[str]	 =    model(**__lowercase   ).loss
          loss.backward()
      def       UpperCAmelCase ( self   :     Any   )				-> List[str]:
        for model_class in self.all_model_classes:
          if model_class.__name__ == "DPTForDepthEstimation":
            continue

          __UpperCAmelCase ,					__UpperCAmelCase				:      List[str]	 =    self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCAmelCase				:      Any	 =    False
          __UpperCAmelCase				:      str	 =    True

          if model_class in get_values(__lowercase   ) or not model_class.supports_gradient_checkpointing:
            continue
          __UpperCAmelCase				:      Optional[int]	 =    model_class(__lowercase   )
          model.to(__lowercase   )
          model.gradient_checkpointing_enable()
          model.train()
          __UpperCAmelCase				:      Optional[Any]	 =    self._prepare_for_class(__lowercase						, __lowercase						, return_labels=__lowercase   )
          __UpperCAmelCase				:      Tuple	 =    model(**__lowercase   ).loss
          loss.backward()
      def       UpperCAmelCase ( self   :     Dict   )				-> int:
        __UpperCAmelCase ,					__UpperCAmelCase				:      str	 =    self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCAmelCase				:      Union[str, Any]	 =    _config_zero_init(__lowercase   )
        for model_class in self.all_model_classes:
          __UpperCAmelCase				:      List[Any]	 =    model_class(config=__lowercase   )
          # Skip the check for the backbone
          __UpperCAmelCase				:      Union[str, Any]	 =    []
          for name, module in model.named_modules():
            if module.__class__.__name__ == "DPTViTHybridEmbeddings":
              __UpperCAmelCase				:      Any	 =    [f"""{name}.{key}""" for key in module.state_dict().keys()]
              break

          for name, param in model.named_parameters():
            if param.requires_grad:
              if name in backbone_params:
                continue
              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"""						, )
      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
      def       UpperCAmelCase ( self   :     List[Any]   )				-> Tuple:
        pass
      @slow
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> List[Any]:
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
          __UpperCAmelCase				:      Tuple	 =    DPTModel.from_pretrained(__lowercase   )
          self.assertIsNotNone(__lowercase   )




      def       UpperCAmelCase ( self   :     int   )				-> int:
        # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
        __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCAmelCase				:      str	 =    """add"""
        with self.assertRaises(__lowercase   ):
          __UpperCAmelCase				:      Union[str, Any]	 =    DPTForDepthEstimation(__lowercase   )






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







@require_torch
@require_vision
@slow
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     Optional[int]   )				-> str:
        __UpperCAmelCase				:      Dict	 =    DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""   )
        __UpperCAmelCase				:      Union[str, Any]	 =    DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""   ).to(__lowercase   )

        __UpperCAmelCase				:      Optional[int]	 =    prepare_img()
        __UpperCAmelCase				:      Optional[int]	 =    image_processor(images=__lowercase						, return_tensors="""pt"""   ).to(__lowercase   )

        # forward pass
        with torch.no_grad():
          __UpperCAmelCase				:      Tuple	 =    model(**__lowercase   )
          __UpperCAmelCase				:      List[Any]	 =    outputs.predicted_depth

        # verify the predicted depth
        __UpperCAmelCase				:      Dict	 =    torch.Size((1, 384, 384)   )
        self.assertEqual(predicted_depth.shape						, __lowercase   )

        __UpperCAmelCase				:      int	 =    torch.tensor(
            [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]   ).to(__lowercase   )

        self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100						, __lowercase						, atol=1e-4   )   )






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

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

    parser.add_argument(
        '''--txt2img_unclip''',
        default='''kakaobrain/karlo-v1-alpha''',
        type=str,
        required=False,
        help='''The pretrained txt2img unclip.''',
    )

    __snake_case   							=						parser.parse_args()

    __snake_case   							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    __snake_case   							=						CLIPImageProcessor()
    __snake_case   							=						CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

    __snake_case   							=						UnCLIPImageVariationPipeline(
        decoder=txtaimg.decoder,
        text_encoder=txtaimg.text_encoder,
        tokenizer=txtaimg.tokenizer,
        text_proj=txtaimg.text_proj,
        feature_extractor=feature_extractor,
        image_encoder=image_encoder,
        super_res_first=txtaimg.super_res_first,
        super_res_last=txtaimg.super_res_last,
        decoder_scheduler=txtaimg.decoder_scheduler,
        super_res_scheduler=txtaimg.super_res_scheduler,
    )

    imgaimg.save_pretrained(args.dump_path)


