






'''simple docstring'''







import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard





def 		_UpperCAmelCase   (							__A							:   Optional[Any]      ):
				assert "mock" in _fsspec_registry
				assert "bz2" in _fsspec_registry





def 		_UpperCAmelCase   (							):
				assert "mock" not in _fsspec_registry
				assert "bz2" in _fsspec_registry





def 		_UpperCAmelCase   (							):
				a_		:  Any											=      '''mock-s3-bucket'''
				a_		:  List[Any]											=      f's3://{mock_bucket}'
				a_		:  List[Any]											=      extract_path_from_uri(__A      )
				assert dataset_path.startswith('''s3://'''      ) is False

				a_		:  int											=      '''./local/path'''
				a_		:  Dict											=      extract_path_from_uri(__A      )
				assert dataset_path == new_dataset_path





def 		_UpperCAmelCase   (							__A							:   Optional[int]      ):
				a_		:  List[str]											=      is_remote_filesystem(__A      )
				assert is_remote is True

				a_		:  str											=      fsspec.filesystem('''file'''      )

				a_		:  Union[str, Any]											=      is_remote_filesystem(__A      )
				assert is_remote is False





@pytest.mark.parametrize('''compression_fs_class'''						,		__A      )
def 		_UpperCAmelCase   (							__A							:   List[Any]						,		__A							:   Any						,		__A							:   List[Any]						,		__A							:   Dict						,		__A							:   Optional[Any]						,		__A							:   int						,		__A							:   Union[str, Any]      ):
				a_		:  str											=      {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
				a_		:  str											=      input_paths[compression_fs_class.protocol]
				if input_path is None:
								a_		:  List[str]											=      f'for \'{compression_fs_class.protocol}\' compression protocol, '
								if compression_fs_class.protocol == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_fs_class.protocol == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(__A      )
				a_		:  Tuple											=      fsspec.filesystem(compression_fs_class.protocol						,		fo=__A      )
				assert isinstance(__A						,		__A      )
				a_		:  List[str]											=      os.path.basename(__A      )
				a_		:  Optional[int]											=      expected_filename[: expected_filename.rindex('''.'''      )]
				assert fs.glob('''*'''      ) == [expected_filename]
				with fs.open(__A						,		'''r'''						,		encoding='''utf-8'''      ) as f, open(__A						,		encoding='''utf-8'''      ) as expected_file:
								assert f.read() == expected_file.read()





@pytest.mark.parametrize('''protocol'''						,		['''zip''', '''gzip''']      )
def 		_UpperCAmelCase   (							__A							:   List[str]						,		__A							:   Optional[Any]						,		__A							:   List[str]      ):
				a_		:  int											=      {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
				a_		:  List[Any]											=      compressed_file_paths[protocol]
				a_		:  Dict											=      '''dataset.jsonl'''
				a_		:  List[str]											=      f'{protocol}://{member_file_path}::{compressed_file_path}'
				a_				,			*a_		:  Optional[Any]											=      fsspec.get_fs_token_paths(__A      )
				assert fs.isfile(__A      )
				assert not fs.isfile('''non_existing_''' + member_file_path      )





@pytest.mark.integration
def 		_UpperCAmelCase   (							__A							:   int						,		__A							:   Optional[Any]						,		__A							:   str						,		__A							:   Optional[int]      ):
				a_		:  Dict											=      hf_api.dataset_info(__A						,		token=__A      )
				a_		:  int											=      HfFileSystem(repo_info=__A						,		token=__A      )
				assert sorted(hffs.glob('''*'''      )      ) == [".gitattributes", "data"]
				assert hffs.isdir('''data'''      )
				assert hffs.isfile('''.gitattributes'''      ) and hffs.isfile('''data/text_data.txt'''      )
				with open(__A      ) as f:
								assert hffs.open('''data/text_data.txt'''						,		'''r'''      ).read() == f.read()





def 		_UpperCAmelCase   (							):
				a_		:  str											=      '''bz2'''

				# Import module
				import datasets.filesystems

				# Overwrite protocol and reload
				register_implementation(__A						,		__A						,		clobber=__A      )
				with pytest.warns(__A      ) as warning_info:
								importlib.reload(datasets.filesystems      )

				assert len(__A      ) == 1
				assert (
				    str(warning_info[0].message      )
				    == f'A filesystem protocol was already set for {protocol} and will be overwritten.'
				)












'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			List[Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			List[Any]:
							torch.manual_seed(0					)
							a_		:  Tuple											=      UNetaDModel(
							    sample_size=(32, 64)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')  ,						)
							return model






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			List[Any]:
							torch.manual_seed(0					)
							a_		:  Any											=      UNetaDConditionModel(
							    sample_size=(64, 32)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')  ,						cross_attention_dim=10  ,						)
							return model






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							torch.manual_seed(0					)
							a_		:  List[Any]											=      AutoencoderKL(
							    sample_size=(128, 64)  ,						in_channels=1  ,						out_channels=1  ,						latent_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')  ,						up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')  ,						)
							a_		:  List[Any]											=      UNetaDModel(
							    sample_size=(64, 32)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')  ,						)
							return vqvae, unet







			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Union[str, Any]:
							a_		:  Dict											=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a_		:  Union[str, Any]											=      Mel(
							    x_res=self.dummy_unet.config.sample_size[1]  ,						y_res=self.dummy_unet.config.sample_size[0]  ,						)

							a_		:  Any											=      DDPMScheduler()
							a_		:  str											=      AudioDiffusionPipeline(vqvae=__SCREAMING_SNAKE_CASE  ,						unet=self.dummy_unet  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[int]											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4					)
							a_		:  List[Any]											=      output.audios[0]
							a_		:  Dict											=      output.images[0]

							a_		:  Dict											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  Optional[Any]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output[0][0]

							assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
							assert (
							    image.height == self.dummy_unet.config.sample_size[0]
							    and image.width == self.dummy_unet.config.sample_size[1]
							)
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  str											=      np.frombuffer(image_from_tuple.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

							a_		:  str											=      Mel(
							    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]  ,						y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]  ,						)

							a_		:  int											=      DDIMScheduler()
							a_		:  Dict											=      self.dummy_vqvae_and_unet
							a_		:  List[str]											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=dummy_vqvae_and_unet[1]  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  List[str]											=      np.random.uniform(-1  ,						1  ,						((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
							a_		:  int											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  int											=      pipe(raw_audio=__SCREAMING_SNAKE_CASE  ,						generator=__SCREAMING_SNAKE_CASE  ,						start_step=5  ,						steps=10					)
							a_		:  List[str]											=      output.images[0]

							assert (
							    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
							    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
							)
							a_		:  Optional[Any]											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

							a_		:  List[str]											=      self.dummy_unet_condition
							a_		:  Dict											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=__SCREAMING_SNAKE_CASE  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  Any											=      torch.rand((1, 1, 10)					)
							a_		:  Tuple											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						encoding=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.images[0]
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0





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






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Any:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Any:
							a_		:  Any											=      torch_device

							a_		:  Optional[int]											=      DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''					)
							a_		:  Dict											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.audios[0]
							a_		:  Tuple											=      output.images[0]

							assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
							assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
							a_		:  str											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  Tuple											=      np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0




