





'''simple docstring'''


import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


_SCREAMING_SNAKE_CASE									=				"\\n    Text data.\n    Second line of data."

_SCREAMING_SNAKE_CASE									=				"file"


@pytest.fixture(scope="session"     )
def 						__a(SCREAMING_SNAKE_CASE_	:       Union[str, Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   tmp_path_factory.mktemp("data"     ) / (FILE_PATH + ".zstd")
    _lowerCAmelCase						      =   bytes(SCREAMING_SNAKE_CASE_		,							"utf-8"     )
    with zstd.open(SCREAMING_SNAKE_CASE_		,							"wb"     ) as f:
        f.write(SCREAMING_SNAKE_CASE_     )
    return path


@pytest.fixture
def 						__a(SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    with open(os.path.join(tmpfs.local_root_dir		,							SCREAMING_SNAKE_CASE_     )		,							"w"     ) as f:
        f.write(SCREAMING_SNAKE_CASE_     )
    return FILE_PATH


@pytest.mark.parametrize("compression_format"		,							["gzip", "xz", "zstd"]     )
def 						__a(SCREAMING_SNAKE_CASE_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   {"gzip": gz_file, "xz": xz_file, "zstd": zstd_path}
    _lowerCAmelCase						      =   input_paths[compression_format]
    _lowerCAmelCase						      =   tmp_path / "cache"
    _lowerCAmelCase						      =   DownloadConfig(cache_dir=SCREAMING_SNAKE_CASE_		,							extract_compressed_file=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   cached_path(SCREAMING_SNAKE_CASE_		,							download_config=SCREAMING_SNAKE_CASE_     )
    with open(SCREAMING_SNAKE_CASE_     ) as f:
        _lowerCAmelCase						      =   f.read()
    with open(SCREAMING_SNAKE_CASE_     ) as f:
        _lowerCAmelCase						      =   f.read()
    assert extracted_file_content == expected_file_content


@pytest.mark.parametrize("default_extracted"		,							[True, False]     )
@pytest.mark.parametrize("default_cache_dir"		,							[True, False]     )
def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       List[Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   "custom_cache"
    _lowerCAmelCase						      =   "custom_extracted_dir"
    _lowerCAmelCase						      =   tmp_path / "custom_extracted_path"
    if default_extracted:
        _lowerCAmelCase						      =   ("downloads" if default_cache_dir else custom_cache_dir, "extracted")
    else:
        monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_DIR"		,							SCREAMING_SNAKE_CASE_     )
        monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"		,							str(SCREAMING_SNAKE_CASE_     )     )
        _lowerCAmelCase						      =   custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

    _lowerCAmelCase						      =   xz_file
    _lowerCAmelCase						      =   (
        DownloadConfig(extract_compressed_file=SCREAMING_SNAKE_CASE_     )
        if default_cache_dir
        else DownloadConfig(cache_dir=tmp_path / custom_cache_dir		,							extract_compressed_file=SCREAMING_SNAKE_CASE_     )
    )
    _lowerCAmelCase						      =   cached_path(SCREAMING_SNAKE_CASE_		,							download_config=SCREAMING_SNAKE_CASE_     )
    assert Path(SCREAMING_SNAKE_CASE_     ).parent.parts[-2:] == expected


def 						__a(SCREAMING_SNAKE_CASE_	:       Union[str, Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   str(Path(SCREAMING_SNAKE_CASE_     ).resolve()     )
    assert cached_path(SCREAMING_SNAKE_CASE_     ) == text_file
    # relative path
    _lowerCAmelCase						      =   str(Path(SCREAMING_SNAKE_CASE_     ).resolve().relative_to(Path(os.getcwd()     )     )     )
    assert cached_path(SCREAMING_SNAKE_CASE_     ) == text_file


def 						__a(SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   str(tmp_path.resolve() / "__missing_file__.txt"     )
    with pytest.raises(SCREAMING_SNAKE_CASE_     ):
        cached_path(SCREAMING_SNAKE_CASE_     )
    # relative path
    _lowerCAmelCase						      =   "./__missing_file__.txt"
    with pytest.raises(SCREAMING_SNAKE_CASE_     ):
        cached_path(SCREAMING_SNAKE_CASE_     )


def 						__a(SCREAMING_SNAKE_CASE_	:       Any     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   get_from_cache(F'''tmp://{tmpfs_file}'''     )
    with open(SCREAMING_SNAKE_CASE_     ) as f:
        _lowerCAmelCase						      =   f.read()
    assert output_file_content == FILE_CONTENT


@patch("datasets.config.HF_DATASETS_OFFLINE"		,							SCREAMING_SNAKE_CASE_     )
def 						__a():



    '''simple docstring'''





    with pytest.raises(SCREAMING_SNAKE_CASE_     ):
        cached_path("https://huggingface.co"     )


@patch("datasets.config.HF_DATASETS_OFFLINE"		,							SCREAMING_SNAKE_CASE_     )
def 						__a(SCREAMING_SNAKE_CASE_	:       Union[str, Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   tmp_path_factory.mktemp("data"     ) / "file.html"
    with pytest.raises(SCREAMING_SNAKE_CASE_     ):
        http_get("https://huggingface.co"		,							temp_file=SCREAMING_SNAKE_CASE_     )
    with pytest.raises(SCREAMING_SNAKE_CASE_     ):
        http_head("https://huggingface.co"     )


@patch("datasets.config.HF_DATASETS_OFFLINE"		,							SCREAMING_SNAKE_CASE_     )
def 						__a(SCREAMING_SNAKE_CASE_	:       Union[str, Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   tmp_path_factory.mktemp("data"     ) / "file.html"
    with pytest.raises(SCREAMING_SNAKE_CASE_     ):
        ftp_get("ftp://huggingface.co"		,							temp_file=SCREAMING_SNAKE_CASE_     )
    with pytest.raises(SCREAMING_SNAKE_CASE_     ):
        ftp_head("ftp://huggingface.co"     )





@patch("datasets.config.HF_DATASETS_OFFLINE"		,							SCREAMING_SNAKE_CASE_     )
def 						__a(SCREAMING_SNAKE_CASE_	:       Union[str, Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   tmp_path_factory.mktemp("data"     ) / "file.html"
    with pytest.raises(SCREAMING_SNAKE_CASE_     ):
        fsspec_get("s3://huggingface.co"		,							temp_file=SCREAMING_SNAKE_CASE_     )
    with pytest.raises(SCREAMING_SNAKE_CASE_     ):
        fsspec_head("s3://huggingface.co"     )






"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class      A(  unittest.TestCase		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	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





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					:List[Any]					     =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

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






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

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					_UpperCamelCase					:Any					     =	np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

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

