
"""simple docstring"""



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 		a_						( unittest.TestCase	):







    @property
    def      _snake_case					(			self			:       Dict     )		->int:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   UNetaDModel(
            block_out_channels=(32, 64)     ,					layers_per_block=2     ,					sample_size=32     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")     ,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")     ,					)
        return model







    @property
    def      _snake_case					(			self			:       Optional[Any]     )		->str:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   VQModel(
            block_out_channels=[32, 64]     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,					up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,					latent_channels=3     ,					)
        return model







    @property
    def      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   CLIPTextConfig(
            bos_token_id=0     ,					eos_token_id=2     ,					hidden_size=32     ,					intermediate_size=37     ,					layer_norm_eps=1e-05     ,					num_attention_heads=4     ,					num_hidden_layers=5     ,					pad_token_id=1     ,					vocab_size=10_00     ,					)
        return CLIPTextModel(__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.dummy_uncond_unet
        _UpperCAmelCase          =   DDIMScheduler()
        _UpperCAmelCase          =   self.dummy_vq_model

        _UpperCAmelCase          =   LDMPipeline(unet=__UpperCamelCase     ,					vqvae=__UpperCamelCase     ,					scheduler=__UpperCamelCase     )
        ldm.to(__UpperCamelCase     )
        ldm.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""numpy"""     ).images

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""numpy"""     ,					return_dict=__UpperCamelCase     )[0]

        _UpperCAmelCase          =   image[0, -3:, -3:, -1]
        _UpperCAmelCase          =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _UpperCAmelCase          =   np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]     )
        _UpperCAmelCase          =   1e-2 if torch_device != """mps""" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < tolerance







@slow
@require_torch
class 		a_						( unittest.TestCase	):







    def      _snake_case					(			self			:       List[Any]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""     )
        ldm.to(__UpperCamelCase     )
        ldm.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=5     ,					output_type="""numpy"""     ).images

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

        assert image.shape == (1, 2_56, 2_56, 3)
        _UpperCAmelCase          =   np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]     )
        _UpperCAmelCase          =   1e-2 if torch_device != """mps""" else 3e-2

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

"""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_						( _UpperCAmelCase	):
    a      :				List[Any]     =				''
    a      :				Union[str, Any]     =				'hf-legacy'  # "hf://"" is reserved for hffs







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






        '''simple docstring'''






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







    def      _snake_case					(			self			:       List[str]     )		->List[str]:






        '''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      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       str = "rb"     ,					**__UpperCamelCase			:       Any     ,					)		->List[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      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Dict     )		->Tuple:






        '''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      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Tuple=False     ,					**__UpperCamelCase			:       List[str]     )		->Optional[Any]:






        '''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     )