






"""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      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any]      )		->List[str]:
						assert "mock" in _fsspec_registry
						assert "bz2" in _fsspec_registry




def      _lowerCAmelCase		(							)		->Union[str, Any]:
						assert "mock" not in _fsspec_registry
						assert "bz2" in _fsspec_registry




def      _lowerCAmelCase		(							)		->Any:
						A__						:  str		  =  """mock-s3-bucket"""
						A__						:  Tuple		  =  f's3://{mock_bucket}'
						A__						:  Union[str, Any]		  =  extract_path_from_uri(UpperCAmelCase__      )
						assert dataset_path.startswith("""s3://"""      ) is False

						A__						:  Optional[int]		  =  """./local/path"""
						A__						:  Tuple		  =  extract_path_from_uri(UpperCAmelCase__      )
						assert dataset_path == new_dataset_path




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple      )		->List[str]:
						A__						:  Dict		  =  is_remote_filesystem(UpperCAmelCase__      )
						assert is_remote is True

						A__						:  List[str]		  =  fsspec.filesystem("""file"""      )

						A__						:  Any		  =  is_remote_filesystem(UpperCAmelCase__      )
						assert is_remote is False




@pytest.mark.parametrize("""compression_fs_class""",						UpperCAmelCase__      )
def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Any      )		->Optional[int]:
						A__						:  Union[str, Any]		  =  {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_file, """bz2""": bza_file, """lz4""": lza_file}
						A__						:  List[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(UpperCAmelCase__      )
						A__						:  int		  =  fsspec.filesystem(compression_fs_class.protocol,						fo=UpperCAmelCase__      )
						assert isinstance(UpperCAmelCase__,						UpperCAmelCase__      )
						A__						:  List[str]		  =  os.path.basename(UpperCAmelCase__      )
						A__						:  int		  =  expected_filename[: expected_filename.rindex("""."""      )]
						assert fs.glob("""*"""      ) == [expected_filename]
						with fs.open(UpperCAmelCase__,						"""r""",						encoding="""utf-8"""      ) as f, open(UpperCAmelCase__,						encoding="""utf-8"""      ) as expected_file:
												assert f.read() == expected_file.read()




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




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




def      _lowerCAmelCase		(							)		->Tuple:
						A__						:  str		  =  """bz2"""

						# Import module
						import datasets.filesystems

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

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














"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							42
							snake_case_     =							42


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						UNetaDModel			,    snake_case      :						KarrasVeScheduler							):
													'''simple docstring'''





													super().__init__()
													self.register_modules(unet=snake_case			,    scheduler=snake_case							)


							@torch.no_grad()
							def __call__(			self      :						Any			,    snake_case      :						int = 1			,    snake_case      :						int = 50			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.unet.config.sample_size
													A__						:  Dict		  =  (batch_size, 3, img_size, img_size)

													A__						:  Tuple		  =  self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													A__						:  Union[str, Any]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=self.device							) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(snake_case							)

													for t in self.progress_bar(self.scheduler.timesteps							):
																			# here sigma_t == t_i from the paper
																			A__						:  Optional[Any]		  =  self.scheduler.schedule[t]
																			A__						:  Union[str, Any]		  =  self.scheduler.schedule[t - 1] if t > 0 else 0

																			# 1. Select temporarily increased noise level sigma_hat
																			# 2. Add new noise to move from sample_i to sample_hat
																			A__  ,  A__						:  Dict		  =  self.scheduler.add_noise_to_input(snake_case			,    snake_case			,    generator=snake_case							)

																			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			A__						:  int		  =  (sigma_hat / 2) * model((sample_hat + 1) / 2			,    sigma_hat / 2							).sample

																			# 4. Evaluate dx/dt at sigma_hat
																			# 5. Take Euler step from sigma to sigma_prev
																			A__						:  Tuple		  =  self.scheduler.step(snake_case			,    snake_case			,    snake_case			,    snake_case							)

																			if sigma_prev != 0:
																									# 6. Apply 2nd order correction
																									# The model inputs and output are adjusted by following eq. (213) in [1].
																									A__						:  Dict		  =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,    sigma_prev / 2							).sample
																									A__						:  Tuple		  =  self.scheduler.step_correct(
																									    snake_case			,    snake_case			,    snake_case			,    snake_case			,    step_output.prev_sample			,    step_output["""derivative"""]			,    )
																			A__						:  Tuple		  =  step_output.prev_sample

													A__						:  Dict		  =  (sample / 2 + 0.5).clamp(0			,    1							)
													A__						:  Dict		  =  sample.cpu().permute(0			,    2			,    3			,    1							).numpy()
													if output_type == "pil":
																			A__						:  Optional[Any]		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)






