


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


snake_case				:    List[Any]       				=     False







class 		_snake_case  (      unittest.TestCase							):
 pass







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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,  torch_dtype=torch.floataa    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

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

       a						:Optional[int] 							=       torch.manual_seed(0    )
       a						:str 							=       pipe.dual_guided(
           prompt='''first prompt'''					,  image=_lowerCamelCase					,  text_to_image_strength=0.75					,  generator=_lowerCamelCase					,  guidance_scale=7.5					,  num_inference_steps=2					,  output_type='''numpy'''					,  ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(_lowerCamelCase    )
             a						:Optional[Any] 							=       VersatileDiffusionPipeline.from_pretrained(_lowerCamelCase					,  torch_dtype=torch.floataa    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:int 							=       generator.manual_seed(0    )
       a						:Optional[int] 							=       pipe.dual_guided(
           prompt='''first prompt'''					,  image=_lowerCamelCase					,  text_to_image_strength=0.75					,  generator=_lowerCamelCase					,  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        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,  torch_dtype=torch.floataa    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

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

       a						:Dict 							=       image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       a						:Union[str, Any] 							=       np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]    )

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

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

       a						:Tuple 							=       image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       a						:Union[str, Any] 							=       np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]    )

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

       a						:Any 							=       pipe.image_variation(_lowerCamelCase					,  generator=_lowerCamelCase					,  output_type='''numpy'''    ).images

       a						:Any 							=       image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       a						:str 							=       np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]    )

       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 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ''
 SCREAMING_SNAKE_CASE__      =       'hf-legacy'  # "hf://"" is reserved for hffs




 def __init__(		self					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       super().__init__(self					,  **_lowerCamelCase    )
       a						:Union[str, Any] 							=       repo_info
       a						:int 							=       token
       a						:int 							=       None




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




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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:Union[str, Any] 							=       self._strip_protocol(_lowerCamelCase    )
       if path in self.dir_cache:
             return self.dir_cache[path]
       else:
             raise FileNotFoundError(_lowerCamelCase    )




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





