
import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowercase        =  '''\
    Text data.
    Second line of data.'''

lowercase        =  '''file'''



@pytest.fixture(scope="""session"""       )
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]       )  ->    List[str]:
							lowerCamelCase_							  =					tmp_path_factory.mktemp("""data"""       ) / (FILE_PATH + """.zstd""")
							lowerCamelCase_							  =					bytes(UpperCAmelCase__			,				"""utf-8"""       )
							with zstd.open(UpperCAmelCase__			,				"""wb"""       ) as f:
														f.write(UpperCAmelCase__       )
							return path



@pytest.fixture
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]       )  ->    Dict:
							with open(os.path.join(tmpfs.local_root_dir			,				UpperCAmelCase__       )			,				"""w"""       ) as f:
														f.write(UpperCAmelCase__       )
							return FILE_PATH



@pytest.mark.parametrize("""compression_format"""			,				["""gzip""", """xz""", """zstd"""]       )
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      List[str]       )  ->    List[Any]:
							lowerCamelCase_							  =					{"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
							lowerCamelCase_							  =					input_paths[compression_format]
							lowerCamelCase_							  =					tmp_path / """cache"""
							lowerCamelCase_							  =					DownloadConfig(cache_dir=UpperCAmelCase__			,				extract_compressed_file=UpperCAmelCase__       )
							lowerCamelCase_							  =					cached_path(UpperCAmelCase__			,				download_config=UpperCAmelCase__       )
							with open(UpperCAmelCase__       ) as f:
														lowerCamelCase_							  =					f.read()
							with open(UpperCAmelCase__       ) as f:
														lowerCamelCase_							  =					f.read()
							assert extracted_file_content == expected_file_content



@pytest.mark.parametrize("""default_extracted"""			,				[True, False]       )
@pytest.mark.parametrize("""default_cache_dir"""			,				[True, False]       )
def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      int       )  ->    Union[str, Any]:
							lowerCamelCase_							  =					"""custom_cache"""
							lowerCamelCase_							  =					"""custom_extracted_dir"""
							lowerCamelCase_							  =					tmp_path / """custom_extracted_path"""
							if default_extracted:
														lowerCamelCase_							  =					("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
							else:
														monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR"""			,				UpperCAmelCase__       )
														monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""			,				str(UpperCAmelCase__       )       )
														lowerCamelCase_							  =					custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

							lowerCamelCase_							  =					xz_file
							lowerCamelCase_							  =					(
							    DownloadConfig(extract_compressed_file=UpperCAmelCase__       )
							    if default_cache_dir
							    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir			,				extract_compressed_file=UpperCAmelCase__       )
							)
							lowerCamelCase_							  =					cached_path(UpperCAmelCase__			,				download_config=UpperCAmelCase__       )
							assert Path(UpperCAmelCase__       ).parent.parts[-2:] == expected



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str       )  ->    List[Any]:
							# absolute path
							lowerCamelCase_							  =					str(Path(UpperCAmelCase__       ).resolve()       )
							assert cached_path(UpperCAmelCase__       ) == text_file
							# relative path
							lowerCamelCase_							  =					str(Path(UpperCAmelCase__       ).resolve().relative_to(Path(os.getcwd()       )       )       )
							assert cached_path(UpperCAmelCase__       ) == text_file



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple       )  ->    int:
							# absolute path
							lowerCamelCase_							  =					str(tmp_path.resolve() / """__missing_file__.txt"""       )
							with pytest.raises(UpperCAmelCase__       ):
														cached_path(UpperCAmelCase__       )
							# relative path
							lowerCamelCase_							  =					"""./__missing_file__.txt"""
							with pytest.raises(UpperCAmelCase__       ):
														cached_path(UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[Any]       )  ->    List[Any]:
							lowerCamelCase_							  =					get_from_cache(F'''tmp://{tmpfs_file}'''       )
							with open(UpperCAmelCase__       ) as f:
														lowerCamelCase_							  =					f.read()
							assert output_file_content == FILE_CONTENT



@patch("""datasets.config.HF_DATASETS_OFFLINE"""			,				UpperCAmelCase__       )
def      __lowerCAmelCase						(							)  ->    List[str]:
							with pytest.raises(UpperCAmelCase__       ):
														cached_path("""https://huggingface.co"""       )



@patch("""datasets.config.HF_DATASETS_OFFLINE"""			,				UpperCAmelCase__       )
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]       )  ->    int:
							lowerCamelCase_							  =					tmp_path_factory.mktemp("""data"""       ) / """file.html"""
							with pytest.raises(UpperCAmelCase__       ):
														http_get("""https://huggingface.co"""			,				temp_file=UpperCAmelCase__       )
							with pytest.raises(UpperCAmelCase__       ):
														http_head("""https://huggingface.co"""       )



@patch("""datasets.config.HF_DATASETS_OFFLINE"""			,				UpperCAmelCase__       )
def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[Any]       )  ->    Any:
							lowerCamelCase_							  =					tmp_path_factory.mktemp("""data"""       ) / """file.html"""
							with pytest.raises(UpperCAmelCase__       ):
														ftp_get("""ftp://huggingface.co"""			,				temp_file=UpperCAmelCase__       )
							with pytest.raises(UpperCAmelCase__       ):
														ftp_head("""ftp://huggingface.co"""       )



@patch("""datasets.config.HF_DATASETS_OFFLINE"""			,				UpperCAmelCase__       )
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]       )  ->    Any:
							lowerCamelCase_							  =					tmp_path_factory.mktemp("""data"""       ) / """file.html"""
							with pytest.raises(UpperCAmelCase__       ):
														fsspec_get("""s3://huggingface.co"""			,				temp_file=UpperCAmelCase__       )
							with pytest.raises(UpperCAmelCase__       ):
														fsspec_head("""s3://huggingface.co"""       )







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''pixel_values''']

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BILINEAR   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[int, float] = 1 / 2_5_5   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					**__UpperCamelCase					:     Optional[int]   ,					):
												super().__init__(**__UpperCamelCase   )
												lowerCamelCase_							  =					size if size is not None else {"""shortest_edge""": 3_8_4}
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					do_resize
												lowerCamelCase_							  =					size
												# Default value set here for backwards compatibility where the value in config is None
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
												lowerCamelCase_							  =					resample
												lowerCamelCase_							  =					do_rescale
												lowerCamelCase_							  =					rescale_factor
												lowerCamelCase_							  =					do_normalize
												lowerCamelCase_							  =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowerCamelCase_							  =					image_std if image_std is not None else IMAGENET_STANDARD_STD

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Dict[str, int]   ,					__UpperCamelCase					:     float   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BICUBIC   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     Tuple   ,					):
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
												if "shortest_edge" not in size:
																			raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''   )
												lowerCamelCase_							  =					size["""shortest_edge"""]

												if shortest_edge < 3_8_4:
																			# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																			lowerCamelCase_							  =					int(shortest_edge / crop_pct   )
																			lowerCamelCase_							  =					get_resize_output_image_size(__UpperCamelCase   ,					size=__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
																			lowerCamelCase_							  =					resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
																			# then crop to (shortest_edge, shortest_edge)
																			return center_crop(image=__UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
												else:
																			# warping (no cropping) when evaluated at 384 or larger
																			return resize(
																			    __UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[int, float]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     int   ,					):
												return rescale(__UpperCamelCase   ,					scale=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[float, List[float]]   ,					__UpperCamelCase					:     Union[float, List[float]]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     List[str]   ,					):
												return normalize(__UpperCamelCase   ,					mean=__UpperCamelCase   ,					std=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     ImageInput   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					__UpperCamelCase					:     ChannelDimension = ChannelDimension.FIRST   ,					**__UpperCamelCase					:     List[str]   ,					):
												lowerCamelCase_							  =					do_resize if do_resize is not None else self.do_resize
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else self.crop_pct
												lowerCamelCase_							  =					resample if resample is not None else self.resample
												lowerCamelCase_							  =					do_rescale if do_rescale is not None else self.do_rescale
												lowerCamelCase_							  =					rescale_factor if rescale_factor is not None else self.rescale_factor
												lowerCamelCase_							  =					do_normalize if do_normalize is not None else self.do_normalize
												lowerCamelCase_							  =					image_mean if image_mean is not None else self.image_mean
												lowerCamelCase_							  =					image_std if image_std is not None else self.image_std

												lowerCamelCase_							  =					size if size is not None else self.size
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					make_list_of_images(__UpperCamelCase   )

												if not valid_images(__UpperCamelCase   ):
																			raise ValueError(
																			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																			    """torch.Tensor, tf.Tensor or jax.ndarray."""   )

												if do_resize and size is None or resample is None:
																			raise ValueError("""Size and resample must be specified if do_resize is True."""   )

												if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
																			raise ValueError("""crop_pct must be specified if size < 384."""   )

												if do_rescale and rescale_factor is None:
																			raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

												if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError("""Image mean and std must be specified if do_normalize is True."""   )

												# All transformations expect numpy arrays.
												lowerCamelCase_							  =					[to_numpy_array(__UpperCamelCase   ) for image in images]

												if do_resize:
																			lowerCamelCase_							  =					[self.resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					crop_pct=__UpperCamelCase   ,					resample=__UpperCamelCase   ) for image in images]

												if do_rescale:
																			lowerCamelCase_							  =					[self.rescale(image=__UpperCamelCase   ,					scale=__UpperCamelCase   ) for image in images]

												if do_normalize:
																			lowerCamelCase_							  =					[self.normalize(image=__UpperCamelCase   ,					mean=__UpperCamelCase   ,					std=__UpperCamelCase   ) for image in images]

												lowerCamelCase_							  =					[to_channel_dimension_format(__UpperCamelCase   ,					__UpperCamelCase   ) for image in images]

												lowerCamelCase_							  =					{"""pixel_values""": images}
												return BatchFeature(data=__UpperCamelCase   ,					tensor_type=__UpperCamelCase   )





