

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image
else:







  class        a__       :







    """simple docstring"""




    @staticmethod
    def  _lowercase      (			*UpperCAmelCase__    : str				,				**UpperCAmelCase__    : Dict		)					->List[Any]:
          """simple docstring"""







          pass






def 					__lowercase      (     _A			)     ->						str:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      List[Any]       =   hashlib.mda(image.tobytes()			)
      return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Dict			       =MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : int		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   DepthEstimationPipeline(model=__UpperCamelCase				,				image_processor=__UpperCamelCase		)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]


  def  _lowercase      (			self    : Any				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Union[str, Any]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
        self.assertEqual({"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)}				,				__UpperCamelCase		)
        import datasets

        SCREAMING_SNAKE_CASE :      List[Any]       =   datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""				,				"""image"""				,				split="""test"""		)
        SCREAMING_SNAKE_CASE :      str       =   depth_estimator(
            [
                Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		),
                """http://images.cocodataset.org/val2017/000000039769.jpg""",
                # RGBA
                dataset[0]["""file"""],
                # LA
                dataset[1]["""file"""],
                # L
                dataset[2]["""file"""],
            ]		)
        self.assertEqual(
            [
                {"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)},
                {"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)},
                {"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)},
                {"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)},
                {"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)},
            ]				,				__UpperCamelCase				,				)


  @require_tf
  @unittest.skip("""Depth estimation is not implemented in TF"""		)
  def  _lowercase      (			self    : Optional[int]		)					->List[Any]:
        """simple docstring"""







        pass


  @slow
  @require_torch
  def  _lowercase      (			self    : List[Any]		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   """Intel/dpt-large"""
        SCREAMING_SNAKE_CASE :      Dict       =   pipeline("""depth-estimation"""				,				model=__UpperCamelCase		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""		)
        SCREAMING_SNAKE_CASE :      int       =   hashimage(outputs["""depth"""]		)

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()		)				,				29.3_04		)
        self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()		)				,				2.6_62		)




  @require_torch
  def  _lowercase      (			self    : Dict		)					->List[Any]:
        """simple docstring"""







        self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""		)







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__ :     Optional[Any]				    =    False
UpperCAmelCase__ :     Union[str, Any]				    =    True
UpperCAmelCase__ :     int				    =    False


if __name__ == "__main__":
  UpperCAmelCase__ :     List[Any]				    =    argparse.ArgumentParser()

  parser.add_argument(
      """--repo_path""",
      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.""")

  UpperCAmelCase__ :     List[str]				    =    parser.parse_args()

  UpperCAmelCase__ :     Dict				    =    {
      """image_size""": """sample_size""",
      """num_res_blocks""": """layers_per_block""",
      """block_channels""": """block_out_channels""",
      """down_blocks""": """down_block_types""",
      """up_blocks""": """up_block_types""",
      """downscale_freq_shift""": """freq_shift""",
      """resnet_num_groups""": """norm_num_groups""",
      """resnet_act_fn""": """act_fn""",
      """resnet_eps""": """norm_eps""",
      """num_head_channels""": """attention_head_dim""",
  }

  UpperCAmelCase__ :     str				    =    {
      """time_steps""": """time_proj""",
      """mid""": """mid_block""",
      """downsample_blocks""": """down_blocks""",
      """upsample_blocks""": """up_blocks""",
  }

  UpperCAmelCase__ :     Union[str, Any]				    =    """""" if has_file(args.repo_path, """config.json""") else """unet"""

  with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
    UpperCAmelCase__ :     Union[str, Any]				    =    reader.read()
    UpperCAmelCase__ :     Any				    =    json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, """config.json"""):
    UpperCAmelCase__ :     int				    =    UNetaDModel(**config)
  else:
    UpperCAmelCase__ :     Optional[Any]				    =    UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
    UpperCAmelCase__ :     int				    =    class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  UpperCAmelCase__ :     Optional[Any]				    =    dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        UpperCAmelCase__ :     str				    =    config[key]
        del config[key]

    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

  if do_only_weights:
    UpperCAmelCase__ :     Dict				    =    torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

    UpperCAmelCase__ :     Optional[Any]				    =    {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
        continue
      UpperCAmelCase__ :     Union[str, Any]				    =    False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(""".""")[0] == key:
          UpperCAmelCase__ :     List[Any]				    =    param_value
          UpperCAmelCase__ :     List[str]				    =    True
      if not has_changed:
        UpperCAmelCase__ :     Any				    =    param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))




