






'''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]      ):

				'''simple docstring'''





				assert "mock" in _fsspec_registry
				assert "bz2" in _fsspec_registry





def 		_UpperCAmelCase   (							):

				'''simple docstring'''





				assert "mock" not in _fsspec_registry
				assert "bz2" in _fsspec_registry





def 		_UpperCAmelCase   (							):

				'''simple docstring'''





				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]      ):

				'''simple docstring'''





				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]      ):

				'''simple docstring'''





				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]      ):

				'''simple docstring'''





				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_		:  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]      ):

				'''simple docstring'''





				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   (							):

				'''simple docstring'''





				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 random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				IFInpaintingSuperResolutionPipeline
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}       )
			snake_case__  =				PipelineTesterMixin.required_optional_params - {"latents"}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			List[str]:
							return self._get_superresolution_dummy_components()






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict=0					)			->			List[Any]:
							if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
											a_		:  Optional[int]											=      torch.manual_seed(__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      floats_tensor((1, 3, 16, 16)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, Any]											=      {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''original_image''': original_image,
							    '''mask_image''': mask_image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}

							return inputs






			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()  ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''  ,						)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			int:
							self._test_save_load_optional_components()






			@unittest.skipIf(torch_device != '''cuda'''  ,						reason='''float16 requires CUDA'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Optional[int]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Optional[Any]:
							self._test_save_load_local()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Optional[Any]:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2  ,						)




