


"""simple docstring"""




import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


snake_case_  :     Optional[int]							  =							get_logger(__name__)

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

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

snake_case_  :     Union[str, Any]							  =							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_  :     str							  =							ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
snake_case_  :     List[Any]							  =							version.parse(importlib_metadata.version('''dill'''))
snake_case_  :     List[Any]							  =							version.parse(importlib_metadata.version('''pandas'''))
snake_case_  :     List[Any]							  =							version.parse(importlib_metadata.version('''pyarrow'''))

snake_case_  :     Any							  =							os.environ.get('''USE_TF''', '''AUTO''').upper()
snake_case_  :     Dict							  =							os.environ.get('''USE_TORCH''', '''AUTO''').upper()
snake_case_  :     int							  =							os.environ.get('''USE_JAX''', '''AUTO''').upper()

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

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
							snake_case_  :     int							  =							importlib.util.find_spec('''torch''') is not None
							if TORCH_AVAILABLE:
														try:
																					snake_case_  :     str							  =							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_  :     Dict							  =							False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
							snake_case_  :     List[str]							  =							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_  :     Union[str, Any]							  =							version.parse(importlib_metadata.version(package))
																					except importlib_metadata.PackageNotFoundError:
																												continue
																					else:
																												break
														else:
																					snake_case_  :     Any							  =							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_  :     Optional[Any]							  =							False
														else:
																					logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
							logger.info('''Disabling Tensorflow because USE_TORCH is set''')


snake_case_  :     Tuple							  =							'''N/A'''
snake_case_  :     Any							  =							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_  :     Union[str, Any]							  =							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_  :     Union[str, Any]							  =							os.environ.get('''USE_BEAM''', '''AUTO''').upper()
snake_case_  :     str							  =							'''N/A'''
snake_case_  :     List[Any]							  =							False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
							try:
														snake_case_  :     str							  =							version.parse(importlib_metadata.version('''apache_beam'''))
														snake_case_  :     str							  =							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[Any]							  =							importlib.util.find_spec('''PIL''') is not None
snake_case_  :     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_  :     Dict							  =							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_  :     Any							  =							importlib.util.find_spec('''zstandard''') is not None
snake_case_  :     List[Any]							  =							importlib.util.find_spec('''lz4''') is not None
snake_case_  :     List[str]							  =							importlib.util.find_spec('''py7zr''') is not None


# Cache location
snake_case_  :     Optional[int]							  =							'''~/.cache'''
snake_case_  :     Any							  =							os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
snake_case_  :     List[Any]							  =							os.path.join(XDG_CACHE_HOME, '''huggingface''')
snake_case_  :     Dict							  =							os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))

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

snake_case_  :     Tuple							  =							os.path.join(HF_CACHE_HOME, '''metrics''')
snake_case_  :     Union[str, Any]							  =							Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

snake_case_  :     Optional[Any]							  =							os.path.join(HF_CACHE_HOME, '''modules''')
snake_case_  :     int							  =							Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

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

snake_case_  :     str							  =							'''extracted'''
snake_case_  :     str							  =							os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
snake_case_  :     List[Any]							  =							Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
snake_case_  :     Union[str, Any]							  =							(
    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_  :     Union[str, Any]							  =							10_00

# Size of the preloaded record batch in `Dataset.__iter__`
snake_case_  :     List[Any]							  =							10

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

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

# Parquet configuration
snake_case_  :     List[str]							  =							1_00
snake_case_  :     Union[str, Any]							  =							1_00
snake_case_  :     Optional[int]							  =							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_  :     Optional[int]							  =							0  # Disabled
snake_case_  :     Optional[int]							  =							float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

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

snake_case_  :     List[str]							  =							'''datasets_modules'''

snake_case_  :     str							  =							2_55

# Streaming
snake_case_  :     str							  =							20
snake_case_  :     str							  =							5

# Datasets without script
snake_case_  :     Union[str, Any]							  =							2_00
snake_case_  :     Any							  =							10
snake_case_  :     Optional[Any]							  =							2_00

# Progress bars
snake_case_  :     List[str]							  =							0.05  # 20 progress updates per sec



import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class   A__							:




						def __init__(    self   : List[Any]			,      _a   : List[str]			,      _a   : Optional[Any]=sys.maxsize					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''bilinear'''
										_SCREAMING_SNAKE_CASE     					=max_size
										_SCREAMING_SNAKE_CASE     					=short_edge_length




						def __call__(    self   : Any			,      _a   : Tuple					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=[]
										for img in imgs:
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=img.shape[:2]
														# later: provide list and randomly choose index for resize
														_SCREAMING_SNAKE_CASE     					=np.random.randint(self.short_edge_length[0]			,      self.short_edge_length[1] + 1					)
														if size == 0:
																		return img
														_SCREAMING_SNAKE_CASE     					=size * 1.0 / min(_a			,      _a					)
														if h < w:
																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=size, scale * w
														else:
																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=scale * h, size
														if max(_a			,      _a					) > self.max_size:
																		_SCREAMING_SNAKE_CASE     					=self.max_size * 1.0 / max(_a			,      _a					)
																		_SCREAMING_SNAKE_CASE     					=newh * scale
																		_SCREAMING_SNAKE_CASE     					=neww * scale
														_SCREAMING_SNAKE_CASE     					=int(neww + 0.5					)
														_SCREAMING_SNAKE_CASE     					=int(newh + 0.5					)

														if img.dtype == np.uinta:
																		_SCREAMING_SNAKE_CASE     					=Image.fromarray(_a					)
																		_SCREAMING_SNAKE_CASE     					=pil_image.resize((neww, newh)			,      PILImageResampling.BILINEAR					)
																		_SCREAMING_SNAKE_CASE     					=np.asarray(_a					)
														else:
																		_SCREAMING_SNAKE_CASE     					=img.permute(2			,      0			,      1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																		_SCREAMING_SNAKE_CASE     					=nn.functional.interpolate(
																		    _a			,      (newh, neww)			,      mode=self.interp_method			,      align_corners=_a					).squeeze(0					)
														img_augs.append(_a					)

										return img_augs

class   A__							:




						def __init__(    self   : Any			,      _a   : Tuple					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,      cfg.INPUT.MAX_SIZE_TEST					)
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.FORMAT
										_SCREAMING_SNAKE_CASE     					=cfg.SIZE_DIVISIBILITY
										_SCREAMING_SNAKE_CASE     					=cfg.PAD_VALUE
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.MAX_SIZE_TEST
										_SCREAMING_SNAKE_CASE     					=cfg.MODEL.DEVICE
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=lambda _a					: (x - self.pixel_mean) / self.pixel_std




						def 							__UpperCamelCase			(    self   : int			,      _a   : Optional[Any]					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=tuple(max(_a					) for s in zip(*[img.shape for img in images]					)					)
										_SCREAMING_SNAKE_CASE     					=[im.shape[-2:] for im in images]
										_SCREAMING_SNAKE_CASE     					=[
										    nn.functional.pad(
										        _a			,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,      value=self.pad_value			,      )
										    for size, im in zip(_a			,      _a					)
										]

										return torch.stack(_a					), torch.tensor(_a					)




						def __call__(    self   : str			,      _a   : Union[str, Any]			,      _a   : Any=False					)							->       Tuple:


										"""simple docstring"""





										with torch.no_grad():
														if not isinstance(_a			,      _a					):
																		_SCREAMING_SNAKE_CASE     					=[images]
														if single_image:
																		assert len(_a					) == 1
														for i in range(len(_a					)					):
																		if isinstance(images[i]			,      torch.Tensor					):
																						images.insert(_a			,      images.pop(_a					).to(self.device					).float()					)
																		elif not isinstance(images[i]			,      torch.Tensor					):
																						images.insert(
																						    _a			,      torch.as_tensor(img_tensorize(images.pop(_a					)			,      input_format=self.input_format					)					)
																						    .to(self.device					)
																						    .float()			,      )
            # resize smallest edge
														_SCREAMING_SNAKE_CASE     					=torch.tensor([im.shape[:2] for im in images]					)
														_SCREAMING_SNAKE_CASE     					=self.aug(_a					)
														# transpose images and convert to torch tensors
														# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
														# now normalize before pad to avoid useless arithmetic
														_SCREAMING_SNAKE_CASE     					=[self.normalizer(_a					) for x in images]
														# now pad them to do the following operations
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.pad(_a					)
														# Normalize

														if self.size_divisibility > 0:
																		raise NotImplementedError()
														# pad
														_SCREAMING_SNAKE_CASE     					=torch.true_divide(_a			,      _a					)
														if single_image:
																		return images[0], sizes[0], scales_yx[0]
														else:
																		return images, sizes, scales_yx







def   lowerCamelCase(				a__		,a__):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def   lowerCamelCase(				a__		,a__):
				assert torch.isfinite(a__).all(), "Box tensor contains infinite or NaN!"
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=box_size
				tensor[:, 0].clamp_(min=0		,max=a__)
				tensor[:, 1].clamp_(min=0		,max=a__)
				tensor[:, 2].clamp_(min=0		,max=a__)
				tensor[:, 3].clamp_(min=0		,max=a__)