"""simple docstring"""




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      :		int           =   False
class 						__A							( unittest.TestCase		):
     pass







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






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""				,     torch_dtype=torch.floataa  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

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

       _lowerCAmelCase						:       Any  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Union[str, Any]  			=     pipe.dual_guided(
           prompt="""first prompt"""				,     image=a__				,     text_to_image_strength=0.7_5				,     generator=a__				,     guidance_scale=7.5				,     num_inference_steps=2				,     output_type="""numpy"""				,     ).images

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

       _lowerCAmelCase						:       Optional[int]  			=     generator.manual_seed(0  )
       _lowerCAmelCase						:       Dict  			=     pipe.dual_guided(
           prompt="""first prompt"""				,     image=a__				,     text_to_image_strength=0.7_5				,     generator=a__				,     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     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""				,     torch_dtype=torch.floataa  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Dict  			=     """cyberpunk 2077"""
       _lowerCAmelCase						:       int  			=     load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""  )
       _lowerCAmelCase						:       Optional[Any]  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Tuple  			=     pipe.dual_guided(
           prompt=a__				,     image=a__				,     text_to_image_strength=0.7_5				,     generator=a__				,     guidance_scale=7.5				,     num_inference_steps=50				,     output_type="""numpy"""				,     ).images

       _lowerCAmelCase						:       List[str]  			=     image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:       Optional[int]  			=     np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]  )

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

       _lowerCAmelCase						:       str  			=     """A painting of a squirrel eating a burger """
       _lowerCAmelCase						:       List[Any]  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       str  			=     pipe.text_to_image(
           prompt=a__				,     generator=a__				,     guidance_scale=7.5				,     num_inference_steps=50				,     output_type="""numpy"""  ).images

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

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:       Dict  			=     np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]  )

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

       _lowerCAmelCase						:       str  			=     pipe.image_variation(a__				,     generator=a__				,     output_type="""numpy"""  ).images

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

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:       Optional[int]  			=     np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]  )

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





"""simple docstring"""




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 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Union[str, Any]							=		""
     _UpperCamelCase							:			str							=		"hf-legacy"  # "hf://"" is reserved for hffs






     def __init__(		self				,     a__ = None				,     a__ = None				,     **a__				,     ):
       super().__init__(self				,     **a__  )
       _lowerCAmelCase						:       Any  			=     repo_info
       _lowerCAmelCase						:       Optional[Any]  			=     token
       _lowerCAmelCase						:       Optional[int]  			=     None






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






     def     __A  (		self				,     a__				,     a__ = "rb"				,     **a__				,     ):
       if not isinstance(self.repo_info				,     a__  ):
         raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"  )
       _lowerCAmelCase						:       Tuple  			=     hf_hub_url(self.repo_info.id				,     a__				,     revision=self.repo_info.sha  )
       return fsspec.open(
           a__				,     mode=a__				,     headers=get_authentication_headers_for_url(a__				,     use_auth_token=self.token  )				,     client_kwargs={"""trust_env""": True}				,     ).open()






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






     def     __A  (		self				,     a__				,     a__=False				,     **a__  ):
       self._get_dirs()
       _lowerCAmelCase						:       Any  			=     PurePosixPath(path.strip("""/"""  )  )
       _lowerCAmelCase						:       List[str]  			=     {}
       for p, f in self.dir_cache.items():
         _lowerCAmelCase						:       Any  			=     PurePosixPath(p.strip("""/"""  )  )
         _lowerCAmelCase						:       Optional[int]  			=     p.parent
         if root == path:
           _lowerCAmelCase						:       Dict  			=     f
       _lowerCAmelCase						:       Union[str, Any]  			=     list(paths.values()  )
       if detail:
         return out
       else:
         return sorted(f["""name"""] for f in out  )




