


'''simple docstring'''







import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


snake_case_       :    List[Any]          =					get_logger(__name__)

# Datasets
snake_case_       :    Union[str, Any]          =					'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
snake_case_       :    Dict          =					'https://cdn-datasets.huggingface.co/datasets/datasets'
snake_case_       :    str          =					'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'

# Metrics
snake_case_       :    Optional[int]          =					'https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'
snake_case_       :    Optional[Any]          =					'https://cdn-datasets.huggingface.co/datasets/metric'
snake_case_       :    Tuple          =					'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'

# Hub
snake_case_       :    int          =					os.environ.get("HF_ENDPOINT", "https://huggingface.co")
snake_case_       :    str          =					HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
snake_case_       :    Any          =					'main'

snake_case_       :    Tuple          =					version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
					import importlib_metadata
else:
					import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
snake_case_       :    List[str]          =					{'1', 'ON', 'YES', 'TRUE'}
snake_case_       :    List[Any]          =					ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
snake_case_       :    List[str]          =					version.parse(importlib_metadata.version("dill"))
snake_case_       :    int          =					version.parse(importlib_metadata.version("pandas"))
snake_case_       :    Optional[Any]          =					version.parse(importlib_metadata.version("pyarrow"))

snake_case_       :    Union[str, Any]          =					os.environ.get("USE_TF", "AUTO").upper()
snake_case_       :    List[Any]          =					os.environ.get("USE_TORCH", "AUTO").upper()
snake_case_       :    Optional[Any]          =					os.environ.get("USE_JAX", "AUTO").upper()

snake_case_       :    List[Any]          =					'N/A'
snake_case_       :    int          =					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
					snake_case_       :    List[str]          =					importlib.util.find_spec("torch") is not None
					if TORCH_AVAILABLE:
										try:
															snake_case_       :    Union[str, Any]          =					version.parse(importlib_metadata.version("torch"))
															logger.info(f'''PyTorch version {TORCH_VERSION} available.''')
										except importlib_metadata.PackageNotFoundError:
															pass
else:
					logger.info("Disabling PyTorch because USE_TF is set")

snake_case_       :    int          =					'N/A'
snake_case_       :    Optional[int]          =					False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
					snake_case_       :    Optional[int]          =					importlib.util.find_spec("tensorflow") is not None
					if TF_AVAILABLE:
										# For the metadata, we have to look for both tensorflow and tensorflow-cpu
										for package in [
										    "tensorflow",
										    "tensorflow-cpu",
										    "tensorflow-gpu",
										    "tf-nightly",
										    "tf-nightly-cpu",
										    "tf-nightly-gpu",
										    "intel-tensorflow",
										    "tensorflow-rocm",
										    "tensorflow-macos",
										]:
															try:
																				snake_case_       :    Any          =					version.parse(importlib_metadata.version(package))
															except importlib_metadata.PackageNotFoundError:
																				continue
															else:
																				break
										else:
															snake_case_       :    Tuple          =					False
					if TF_AVAILABLE:
										if TF_VERSION.major < 2:
															logger.info(f'''TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.''')
															snake_case_       :    Any          =					False
										else:
															logger.info(f'''TensorFlow version {TF_VERSION} available.''')
else:
					logger.info("Disabling Tensorflow because USE_TORCH is set")


snake_case_       :    Union[str, Any]          =					'N/A'
snake_case_       :    str          =					False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
					snake_case_       :    Union[str, Any]          =					importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
					if JAX_AVAILABLE:
										try:
															snake_case_       :    str          =					version.parse(importlib_metadata.version("jax"))
															logger.info(f'''JAX version {JAX_VERSION} available.''')
										except importlib_metadata.PackageNotFoundError:
															pass
else:
					logger.info("Disabling JAX because USE_JAX is set to False")


snake_case_       :    str          =					os.environ.get("USE_BEAM", "AUTO").upper()
snake_case_       :    List[Any]          =					'N/A'
snake_case_       :    Tuple          =					False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
					try:
										snake_case_       :    str          =					version.parse(importlib_metadata.version("apache_beam"))
										snake_case_       :    Any          =					True
										logger.info(f'''Apache Beam version {BEAM_VERSION} available.''')
					except importlib_metadata.PackageNotFoundError:
										pass
else:
					logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
snake_case_       :    Optional[int]          =					importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
snake_case_       :    List[str]          =					importlib.util.find_spec("PIL") is not None
snake_case_       :    Optional[Any]          =					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
snake_case_       :    Tuple          =					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
snake_case_       :    List[Any]          =					importlib.util.find_spec("rarfile") is not None
snake_case_       :    Union[str, Any]          =					importlib.util.find_spec("zstandard") is not None
snake_case_       :    Any          =					importlib.util.find_spec("lz4") is not None
snake_case_       :    int          =					importlib.util.find_spec("py7zr") is not None


# Cache location
snake_case_       :    Tuple          =					'~/.cache'
snake_case_       :    Dict          =					os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
snake_case_       :    Dict          =					os.path.join(XDG_CACHE_HOME, "huggingface")
snake_case_       :    Tuple          =					os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

snake_case_       :    str          =					os.path.join(HF_CACHE_HOME, "datasets")
snake_case_       :    List[Any]          =					Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

snake_case_       :    Optional[int]          =					os.path.join(HF_CACHE_HOME, "metrics")
snake_case_       :    Optional[int]          =					Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

snake_case_       :    List[str]          =					os.path.join(HF_CACHE_HOME, "modules")
snake_case_       :    List[Any]          =					Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

snake_case_       :    Optional[int]          =					'downloads'
snake_case_       :    Optional[int]          =					os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
snake_case_       :    int          =					Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

snake_case_       :    Optional[int]          =					'extracted'
snake_case_       :    Union[str, Any]          =					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
snake_case_       :    Tuple          =					Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
snake_case_       :    List[str]          =					(
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
snake_case_       :    List[Any]          =					10_00

# Size of the preloaded record batch in `Dataset.__iter__`
snake_case_       :    int          =					10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
snake_case_       :    str          =					4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
snake_case_       :    Dict          =					'500MB'

# Parquet configuration
snake_case_       :    Optional[Any]          =					1_00
snake_case_       :    List[Any]          =					1_00
snake_case_       :    Tuple          =					1_00

# Offline mode
snake_case_       :    Optional[Any]          =					os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
snake_case_       :    Union[str, Any]          =					0  # Disabled
snake_case_       :    Tuple          =					float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
snake_case_       :    str          =					'dataset.arrow'
snake_case_       :    List[str]          =					'indices.arrow'
snake_case_       :    List[Any]          =					'state.json'
snake_case_       :    int          =					'dataset_info.json'
snake_case_       :    Tuple          =					'dataset_infos.json'
snake_case_       :    int          =					'LICENSE'
snake_case_       :    Union[str, Any]          =					'metric_info.json'
snake_case_       :    List[Any]          =					'dataset_dict.json'

snake_case_       :    Optional[Any]          =					'datasets_modules'

snake_case_       :    Dict          =					2_55

# Streaming
snake_case_       :    Optional[Any]          =					20
snake_case_       :    str          =					5

# Datasets without script
snake_case_       :    Optional[int]          =					2_00
snake_case_       :    Optional[Any]          =					10
snake_case_       :    Any          =					2_00

# Progress bars
snake_case_       :    Any          =					0.05  # 20 progress updates per sec












'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

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


class    __a (lowerCamelCase       ):
							__a   :				UNetaDModel
							__a   :				PNDMScheduler

							def __init__(							self							:       Optional[Any]    ,       __magic_name__							:       UNetaDModel    ,       __magic_name__							:       PNDMScheduler				)   ->    Tuple:







													"""simple docstring"""
													super().__init__()

													UpperCAmelCase_     : Optional[int]         =					PNDMScheduler.from_config(scheduler.config				)

													self.register_modules(unet=__magic_name__    ,       scheduler=__magic_name__				)



							@torch.no_grad()
							def __call__(							self							:       Union[str, Any]    ,       __magic_name__							:       int = 1    ,       __magic_name__							:       int = 50    ,       __magic_name__							:       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,       __magic_name__							:       Optional[str] = "pil"    ,       __magic_name__							:       bool = True    ,       **__magic_name__							:       Dict    ,       )   ->    Union[ImagePipelineOutput, Tuple]:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					randn_tensor(
													    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,       generator=__magic_name__    ,       device=self.device    ,       )

													self.scheduler.set_timesteps(__magic_name__				)
													for t in self.progress_bar(self.scheduler.timesteps				):
																			UpperCAmelCase_     : Optional[int]         =					self.unet(__magic_name__    ,       __magic_name__				).sample

																			UpperCAmelCase_     : str         =					self.scheduler.step(__magic_name__    ,       __magic_name__    ,       __magic_name__				).prev_sample

													UpperCAmelCase_     : int         =					(image / 2 + 0.5).clamp(0    ,       1				)
													UpperCAmelCase_     : Any         =					image.cpu().permute(0    ,       2    ,       3    ,       1				).numpy()
													if output_type == "pil":
																			UpperCAmelCase_     : Optional[int]         =					self.numpy_to_pil(__magic_name__				)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=__magic_name__				)





