



"""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  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	''''''
       UpperCamelCase		=	'''hf-legacy'''  # "hf://"" is reserved for hffs



       def __init__(				self     :    Optional[Any]						,							_UpperCAmelCase     :    Optional[DatasetInfo] = None						,							_UpperCAmelCase     :    Optional[str] = None						,							**_UpperCAmelCase     :    List[Any]						,							)					->		List[str]:


             '''simple docstring'''




             super().__init__(self						,							**_UpperCAmelCase       )
             UpperCAmelCase_     					=				repo_info
             UpperCAmelCase_     					=				token
             UpperCAmelCase_     					=				None



       def 	lowercase__							(				self     :    List[Any]       )					->		Any:


             '''simple docstring'''




             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(_UpperCAmelCase       ): {"name": str(_UpperCAmelCase       ), "size": None, "type": "directory"}
                                 for d in list(PurePosixPath(hf_file.rfilename       ).parents       )[:-1]
                             }       )



       def 	lowercase__							(				self     :    List[Any]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    str = "rb"						,							**_UpperCAmelCase     :    Optional[int]						,							)					->		str:


             '''simple docstring'''




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



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    List[str]						,							**_UpperCAmelCase     :    Tuple       )					->		Union[str, Any]:


             '''simple docstring'''




             self._get_dirs()
             UpperCAmelCase_     					=				self._strip_protocol(_UpperCAmelCase       )
             if path in self.dir_cache:
                   return self.dir_cache[path]
             else:
                   raise FileNotFoundError(_UpperCAmelCase       )







       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    Optional[Any]=False						,							**_UpperCAmelCase     :    str       )					->		str:


             '''simple docstring'''




             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       )










'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

@slow
@require_torch_gpu
class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    int   )     ->				Any:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-1







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_dpmpp_2m'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=7.5				,     num_inference_steps=15				,     output_type='''np'''				,     use_karras_sigmas=lowerCAmelCase_				,     )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array(
														    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

