





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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_ 							=       logging.get_logger(__name__)


if is_vision_available():
					import PIL
class      UpperCamelCase_						(					_lowerCamelCase   ):






					"""simple docstring"""




					lowerCAmelCase_							=		['''pixel_values''']




					def __init__(						self			,  lowerCAmelCase_ = True			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = PILImageResampling.BICUBIC			,  lowerCAmelCase_ = True			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = True			,  lowerCAmelCase_ = 1 / 255			,  lowerCAmelCase_ = True			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = True			,  **lowerCAmelCase_			,  )						->							None:
										super().__init__(**lowerCAmelCase_					)
										_snake_case													=			size if size is not None else {'shortest_edge': 224}
										_snake_case													=			get_size_dict(lowerCAmelCase_			,  default_to_square=lowerCAmelCase_					)
										_snake_case													=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
										_snake_case													=			get_size_dict(lowerCAmelCase_			,  default_to_square=lowerCAmelCase_			,  param_name='crop_size'					)

										_snake_case													=			do_resize
										_snake_case													=			size
										_snake_case													=			resample
										_snake_case													=			do_center_crop
										_snake_case													=			crop_size
										_snake_case													=			do_rescale
										_snake_case													=			rescale_factor
										_snake_case													=			do_normalize
										_snake_case													=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										_snake_case													=			image_std if image_std is not None else OPENAI_CLIP_STD
										_snake_case													=			do_convert_rgb




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = PILImageResampling.BICUBIC			,  lowerCAmelCase_ = None			,  **lowerCAmelCase_			,  )						->							np.ndarray:
										_snake_case													=			get_size_dict(lowerCAmelCase_			,  default_to_square=lowerCAmelCase_					)
										if "shortest_edge" not in size:
															raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
										_snake_case													=			get_resize_output_image_size(lowerCAmelCase_			,  size=size['shortest_edge']			,  default_to_square=lowerCAmelCase_					)
										return resize(lowerCAmelCase_			,  size=lowerCAmelCase_			,  resample=lowerCAmelCase_			,  data_format=lowerCAmelCase_			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  **lowerCAmelCase_			,  )						->							np.ndarray:
										_snake_case													=			get_size_dict(lowerCAmelCase_					)
										if "height" not in size or "width" not in size:
															raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
										return center_crop(lowerCAmelCase_			,  size=(size['height'], size['width'])			,  data_format=lowerCAmelCase_			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  **lowerCAmelCase_			,  )						->							Dict:
										return rescale(lowerCAmelCase_			,  scale=lowerCAmelCase_			,  data_format=lowerCAmelCase_			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  **lowerCAmelCase_			,  )						->							np.ndarray:
										return normalize(lowerCAmelCase_			,  mean=lowerCAmelCase_			,  std=lowerCAmelCase_			,  data_format=lowerCAmelCase_			,  **lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = ChannelDimension.FIRST			,  **lowerCAmelCase_			,  )						->							PIL.Image.Image:
										_snake_case													=			do_resize if do_resize is not None else self.do_resize
										_snake_case													=			size if size is not None else self.size
										_snake_case													=			get_size_dict(lowerCAmelCase_			,  param_name='size'			,  default_to_square=lowerCAmelCase_					)
										_snake_case													=			resample if resample is not None else self.resample
										_snake_case													=			do_center_crop if do_center_crop is not None else self.do_center_crop
										_snake_case													=			crop_size if crop_size is not None else self.crop_size
										_snake_case													=			get_size_dict(lowerCAmelCase_			,  param_name='crop_size'			,  default_to_square=lowerCAmelCase_					)
										_snake_case													=			do_rescale if do_rescale is not None else self.do_rescale
										_snake_case													=			rescale_factor if rescale_factor is not None else self.rescale_factor
										_snake_case													=			do_normalize if do_normalize is not None else self.do_normalize
										_snake_case													=			image_mean if image_mean is not None else self.image_mean
										_snake_case													=			image_std if image_std is not None else self.image_std
										_snake_case													=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										_snake_case													=			make_list_of_images(lowerCAmelCase_					)

										if not valid_images(lowerCAmelCase_					):
															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:
															raise ValueError('Size must be specified if do_resize is True.'					)

										if do_center_crop and crop_size is None:
															raise ValueError('Crop size must be specified if do_center_crop is True.'					)

										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.'					)

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
															_snake_case													=			[convert_to_rgb(lowerCAmelCase_					) for image in images]

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

										if do_resize:
															_snake_case													=			[self.resize(image=lowerCAmelCase_			,  size=lowerCAmelCase_			,  resample=lowerCAmelCase_					) for image in images]

										if do_center_crop:
															_snake_case													=			[self.center_crop(image=lowerCAmelCase_			,  size=lowerCAmelCase_					) for image in images]

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

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

										_snake_case													=			[to_channel_dimension_format(lowerCAmelCase_			,  lowerCAmelCase_					) for image in images]

										_snake_case													=			{'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase_			,  tensor_type=lowerCAmelCase_					)










import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase_ 							=       [
    {"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
    {"""dataset""": """snli""", """config_name""": """plain_text"""},
    {"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
    {"""dataset""": """wiki40b""", """config_name""": """en"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
    {"""dataset""": """natural_questions""", """config_name""": """default"""},
]





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict=True  )     ->	Dict:



					'''simple docstring'''




					if with_config:
										return [
										    {
										        "testcase_name": d["dataset"] + "/" + d["config_name"],
										        "dataset": d["dataset"],
										        "config_name": d["config_name"],
										    }
										    for d in DATASETS_ON_HF_GCP
										]
					else:
										return [
										    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
										]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowerCamelCase   )   )
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		None
					lowerCAmelCase_							=		None




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										with TemporaryDirectory() as tmp_dir:
															_snake_case													=			dataset_module_factory(lowerCAmelCase_			,  cache_dir=lowerCAmelCase_					)

															_snake_case													=			import_main_class(dataset_module.module_path			,  dataset=lowerCAmelCase_					)

															_snake_case													=			builder_cls(
															    cache_dir=lowerCAmelCase_			,  config_name=lowerCAmelCase_			,  hash=dataset_module.hash			,  )

															_snake_case													=			'/'.join(
															    [
															        HF_GCP_BASE_URL,
															        builder_instance._relative_data_dir(with_hash=lowerCAmelCase_					).replace(os.sep			,  '/'					),
															        config.DATASET_INFO_FILENAME,
															    ]					)
															_snake_case													=			cached_path(lowerCAmelCase_			,  cache_dir=lowerCAmelCase_					)
															self.assertTrue(os.path.exists(lowerCAmelCase_					)					)





@pytest.mark.integration
def   lowerCamelCase__       (       UpperCamelCase__     :    Any  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			tmp_path_factory.mktemp('test_hf_gcp'  ) / 'test_wikipedia_simple'
					_snake_case													=			dataset_module_factory('wikipedia'    ,      cache_dir=UpperCamelCase__  )
					_snake_case													=			import_main_class(dataset_module.module_path  )
					_snake_case													=			builder_cls(
					    cache_dir=UpperCamelCase__    ,      config_name='20220301.frr'    ,      hash=dataset_module.hash    ,      )
					# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
					_snake_case													=			None
					builder_instance.download_and_prepare()
					_snake_case													=			builder_instance.as_dataset()
					assert ds





@pytest.mark.integration
def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]  )     ->	Union[str, Any]:



					'''simple docstring'''




					_snake_case													=			dataset_module_factory('wikipedia'    ,      cache_dir=UpperCamelCase__  )
					_snake_case													=			import_main_class(dataset_module.module_path    ,      dataset=UpperCamelCase__  )
					_snake_case													=			builder_cls(
					    cache_dir=UpperCamelCase__    ,      config_name='20220301.frr'    ,      hash=dataset_module.hash    ,      )
					_snake_case													=			builder_instance.as_streaming_dataset()
					assert ds
					assert isinstance(UpperCamelCase__    ,      UpperCamelCase__  )
					assert "train" in ds
					assert isinstance(ds['train']    ,      UpperCamelCase__  )
					assert next(iter(ds['train']  )  )



