






'''simple docstring'''

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_		:

       @staticmethod
       def 						A__		(   *_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Any:



             """simple docstring"""






             pass







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      hashlib.mda(image.tobytes()					)
      return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        a_		(    unittest.TestCase       ):
  lowercase  	=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      DepthEstimationPipeline(model=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]

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



        """simple docstring"""






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

        UpperCamelCase       						=      datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""    ,	"""image"""    ,	split="""test"""   )
        UpperCamelCase       						=      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   )},
            ]    ,	_SCREAMING_SNAKE_CASE    ,	)

  @require_tf
  @unittest.skip("""Depth estimation is not implemented in TF"""   )
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        pass

  @slow
  @require_torch
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      """Intel/dpt-large"""
        UpperCamelCase       						=      pipeline("""depth-estimation"""    ,	model=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
        UpperCamelCase       						=      hashimage(outputs["""depth"""]   )

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()   )    ,	2_9.3_0_4   )
        self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()   )    ,	2.6_6_2   )


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



        """simple docstring"""






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








'''simple docstring'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     SCREAMING_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.',
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

     SCREAMING_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)
