




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_   		=     False

class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      pass







@nightly
@require_torch_gpu
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      def       snake_case	(     self					: Optional[Any]				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' ,       torch_dtype=torch.floataa				)
          pipe.to(snake_case				)
          pipe.set_progress_bar_config(disable=snake_case				)

          __UpperCamelCase  		=						load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

          __UpperCamelCase  		=						torch.manual_seed(0				)
          __UpperCamelCase  		=						pipe.dual_guided(
              prompt='''first prompt''' ,       image=snake_case ,       text_to_image_strength=0.75 ,       generator=snake_case ,       guidance_scale=7.5 ,       num_inference_steps=2 ,       output_type='''numpy''' ,       ).images

          with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(snake_case				)
              __UpperCamelCase  		=						VersatileDiffusionPipeline.from_pretrained(snake_case ,       torch_dtype=torch.floataa				)
          pipe.to(snake_case				)
          pipe.set_progress_bar_config(disable=snake_case				)

          __UpperCamelCase  		=						generator.manual_seed(0				)
          __UpperCamelCase  		=						pipe.dual_guided(
              prompt='''first prompt''' ,       image=snake_case ,       text_to_image_strength=0.75 ,       generator=snake_case ,       guidance_scale=7.5 ,       num_inference_steps=2 ,       output_type='''numpy''' ,       ).images

          assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"







      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' ,       torch_dtype=torch.floataa				)
          pipe.to(snake_case				)
          pipe.set_progress_bar_config(disable=snake_case				)

          __UpperCamelCase  		=						'''cyberpunk 2077'''
          __UpperCamelCase  		=						load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
          __UpperCamelCase  		=						torch.manual_seed(0				)
          __UpperCamelCase  		=						pipe.dual_guided(
              prompt=snake_case ,       image=snake_case ,       text_to_image_strength=0.75 ,       generator=snake_case ,       guidance_scale=7.5 ,       num_inference_steps=50 ,       output_type='''numpy''' ,       ).images

          __UpperCamelCase  		=						image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          __UpperCamelCase  		=						np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

          __UpperCamelCase  		=						'''A painting of a squirrel eating a burger '''
          __UpperCamelCase  		=						torch.manual_seed(0				)
          __UpperCamelCase  		=						pipe.text_to_image(
              prompt=snake_case ,       generator=snake_case ,       guidance_scale=7.5 ,       num_inference_steps=50 ,       output_type='''numpy'''				).images

          __UpperCamelCase  		=						image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          __UpperCamelCase  		=						np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

          __UpperCamelCase  		=						pipe.image_variation(snake_case ,       generator=snake_case ,       output_type='''numpy'''				).images

          __UpperCamelCase  		=						image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          __UpperCamelCase  		=						np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1












from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Any    =					""
      lowerCAmelCase__			: Any    =					"hf-legacy"  # "hf://"" is reserved for hffs







      def __init__(     self					: str ,       snake_case					: Optional[DatasetInfo] = None ,       snake_case					: Optional[str] = None ,       **snake_case					: List[Any] ,       ):
          super().__init__(self ,       **snake_case				)
          __UpperCamelCase  		=						repo_info
          __UpperCamelCase  		=						token
          __UpperCamelCase  		=						None







      def       snake_case	(     self					: List[Any]				):
          if self.dir_cache is None:
              __UpperCamelCase  		=						{}
              for hf_file in self.repo_info.siblings:
                  # TODO(QL): add sizes
                  __UpperCamelCase  		=						{
                      '''name''': hf_file.rfilename,
                      '''size''': None,
                      '''type''': '''file''',
                  }
                  self.dir_cache.update(
                      {
                          str(snake_case				): {'''name''': str(snake_case				), '''size''': None, '''type''': '''directory'''}
                          for d in list(PurePosixPath(hf_file.rfilename				).parents				)[:-1]
                      }				)







      def       snake_case	(     self					: Dict ,       snake_case					: str ,       snake_case					: str = "rb" ,       **snake_case					: Union[str, Any] ,       ):
          if not isinstance(self.repo_info ,       snake_case				):
              raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"				)
          __UpperCamelCase  		=						hf_hub_url(self.repo_info.id ,       snake_case ,       revision=self.repo_info.sha				)
          return fsspec.open(
              snake_case ,       mode=snake_case ,       headers=get_authentication_headers_for_url(snake_case ,       use_auth_token=self.token				) ,       client_kwargs={'''trust_env''': True} ,       ).open()







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: Tuple ,       **snake_case					: List[Any]				):
          self._get_dirs()
          __UpperCamelCase  		=						self._strip_protocol(snake_case				)
          if path in self.dir_cache:
              return self.dir_cache[path]
          else:
              raise FileNotFoundError(snake_case				)







      def       snake_case	(     self					: List[str] ,       snake_case					: int ,       snake_case					: Tuple=False ,       **snake_case					: Dict				):
          self._get_dirs()
          __UpperCamelCase  		=						PurePosixPath(path.strip('''/'''				)				)
          __UpperCamelCase  		=						{}
          for p, f in self.dir_cache.items():
              __UpperCamelCase  		=						PurePosixPath(p.strip('''/'''				)				)
              __UpperCamelCase  		=						p.parent
              if root == path:
                  __UpperCamelCase  		=						f
          __UpperCamelCase  		=						list(paths.values()				)
          if detail:
              return out
          else:
              return sorted(f['''name'''] for f in out				)






