'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
       import PIL

__snake_case						  =		logging.get_logger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[List[ImageInput]]:




  if isinstance(_SCREAMING_SNAKE_CASE ,	(list, tuple)						) and isinstance(videos[0] ,	(list, tuple)						) and is_valid_image(videos[0][0]						):
    return videos

  elif isinstance(_SCREAMING_SNAKE_CASE ,	(list, tuple)						) and is_valid_image(videos[0]						):
    return [videos]

  elif is_valid_image(_SCREAMING_SNAKE_CASE						):
    return [[videos]]

  raise ValueError(f"""Could not make batched video from {videos}"""						)





class       _a    (		__snake_case      ):


  """simple docstring"""

  A_    							=						['''pixel_values''']




  def __init__(		self      :       List[str]   ,				lowercase_      :       List[Any] = True   ,				lowercase_      :       Dict = None   ,				lowercase_      :       Optional[Any] = PILImageResampling.BILINEAR   ,				lowercase_      :       Optional[int] = True   ,				lowercase_      :       List[Any] = None   ,				lowercase_      :       Union[str, Any] = True   ,				lowercase_      :       Tuple = 1 / 255   ,				lowercase_      :       Union[str, Any] = True   ,				lowercase_      :       List[Any] = True   ,				lowercase_      :       str = None   ,				lowercase_      :       Tuple = None   ,				**lowercase_      :       str   ,				):







    '''simple docstring'''


    super().__init__(**snake_case_      )
    lowercase_     =       size if size is not None else {"""shortest_edge""": 256}
    lowercase_     =       get_size_dict(snake_case_   ,				default_to_square=snake_case_      )
    lowercase_     =       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
    lowercase_     =       get_size_dict(snake_case_   ,				param_name="""crop_size"""      )

    lowercase_     =       do_resize
    lowercase_     =       size
    lowercase_     =       do_center_crop
    lowercase_     =       crop_size
    lowercase_     =       resample
    lowercase_     =       do_rescale
    lowercase_     =       rescale_factor
    lowercase_     =       offset
    lowercase_     =       do_normalize
    lowercase_     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    lowercase_     =       image_std if image_std is not None else IMAGENET_STANDARD_STD




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       List[str]   ,				lowercase_      :       Union[str, Any]   ,				lowercase_      :       List[str] = PILImageResampling.BILINEAR   ,				lowercase_      :       int = None   ,				**lowercase_      :       Union[str, Any]   ,				):







    '''simple docstring'''


    lowercase_     =       get_size_dict(snake_case_   ,				default_to_square=snake_case_      )
    if "shortest_edge" in size:
      lowercase_     =       get_resize_output_image_size(snake_case_   ,				size["""shortest_edge"""]   ,				default_to_square=snake_case_      )
    elif "height" in size and "width" in size:
      lowercase_     =       (size["""height"""], size["""width"""])
    else:
      raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
    return resize(snake_case_   ,				size=snake_case_   ,				resample=snake_case_   ,				data_format=snake_case_   ,				**snake_case_      )




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       List[Any]   ,				lowercase_      :       str   ,				lowercase_      :       int = None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


    lowercase_     =       get_size_dict(snake_case_      )
    if "height" not in size or "width" not in size:
      raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""      )
    return center_crop(snake_case_   ,				size=(size["""height"""], size["""width"""])   ,				data_format=snake_case_   ,				**snake_case_      )




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       Union[str, Any]   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       Optional[Any] = True   ,				lowercase_      :       List[str] = None   ,				**lowercase_      :       str   ,				):







    '''simple docstring'''


    lowercase_     =       image.astype(np.floataa      )
    if offset:
      lowercase_     =       image - (scale / 2)
    return rescale(snake_case_   ,				scale=snake_case_   ,				data_format=snake_case_   ,				**snake_case_      )




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       Dict   ,				lowercase_      :       Dict   ,				lowercase_      :       Any = None   ,				**lowercase_      :       Dict   ,				):







    '''simple docstring'''


    return normalize(snake_case_   ,				mean=snake_case_   ,				std=snake_case_   ,				data_format=snake_case_   ,				**snake_case_      )




  def 		lowerCamelCase__  (		self      :       str   ,				lowercase_      :       Union[str, Any]   ,				lowercase_      :       Union[str, Any] = None   ,				lowercase_      :       List[str] = None   ,				lowercase_      :       Union[str, Any] = None   ,				lowercase_      :       Dict = None   ,				lowercase_      :       Tuple = None   ,				lowercase_      :       str = None   ,				lowercase_      :       int = None   ,				lowercase_      :       int = None   ,				lowercase_      :       Optional[Any] = None   ,				lowercase_      :       Tuple = None   ,				lowercase_      :       Any = None   ,				lowercase_      :       Optional[Any] = ChannelDimension.FIRST   ,				):







    '''simple docstring'''


    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_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."""      )

    if offset and not do_rescale:
      raise ValueError("""For offset, do_rescale must also be set to True."""      )

    # All transformations expect numpy arrays.
    lowercase_     =       to_numpy_array(snake_case_      )

    if do_resize:
      lowercase_     =       self.resize(image=snake_case_   ,				size=snake_case_   ,				resample=snake_case_      )

    if do_center_crop:
      lowercase_     =       self.center_crop(snake_case_   ,				size=snake_case_      )

    if do_rescale:
      lowercase_     =       self.rescale(image=snake_case_   ,				scale=snake_case_   ,				offset=snake_case_      )

    if do_normalize:
      lowercase_     =       self.normalize(image=snake_case_   ,				mean=snake_case_   ,				std=snake_case_      )

    lowercase_     =       to_channel_dimension_format(snake_case_   ,				snake_case_      )
    return image





  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       Dict   ,				lowercase_      :       Any = None   ,				lowercase_      :       List[str] = None   ,				lowercase_      :       str = None   ,				lowercase_      :       str = None   ,				lowercase_      :       Dict = None   ,				lowercase_      :       Dict = None   ,				lowercase_      :       Optional[Any] = None   ,				lowercase_      :       List[Any] = None   ,				lowercase_      :       Tuple = None   ,				lowercase_      :       str = None   ,				lowercase_      :       Any = None   ,				lowercase_      :       Optional[Any] = None   ,				lowercase_      :       int = ChannelDimension.FIRST   ,				**lowercase_      :       Union[str, Any]   ,				):







    '''simple docstring'''


    lowercase_     =       do_resize if do_resize is not None else self.do_resize
    lowercase_     =       resample if resample is not None else self.resample
    lowercase_     =       do_center_crop if do_center_crop is not None else self.do_center_crop
    lowercase_     =       do_rescale if do_rescale is not None else self.do_rescale
    lowercase_     =       rescale_factor if rescale_factor is not None else self.rescale_factor
    lowercase_     =       offset if offset is not None else self.offset
    lowercase_     =       do_normalize if do_normalize is not None else self.do_normalize
    lowercase_     =       image_mean if image_mean is not None else self.image_mean
    lowercase_     =       image_std if image_std is not None else self.image_std

    lowercase_     =       size if size is not None else self.size
    lowercase_     =       get_size_dict(snake_case_   ,				default_to_square=snake_case_      )
    lowercase_     =       crop_size if crop_size is not None else self.crop_size
    lowercase_     =       get_size_dict(snake_case_   ,				param_name="""crop_size"""      )

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

    lowercase_     =       make_batched(snake_case_      )

    lowercase_     =       [
        [
            self._preprocess_image(
                image=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_   ,				offset=snake_case_   ,				do_normalize=snake_case_   ,				image_mean=snake_case_   ,				image_std=snake_case_   ,				data_format=snake_case_   ,				)
            for img in video
        ]
        for video in videos
    ]

    lowercase_     =       {"""pixel_values""": videos}
    return BatchFeature(data=snake_case_   ,				tensor_type=snake_case_      )








import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__A							:			List[Any]         =			"http://www.mocksite.com/file1.txt"
__A							:			List[Any]         =			"\"text\": [\"foo\", \"foo\"]"
__A							:			Dict         =			"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"







class      lowerCamelCase:
     '''simple docstring'''


     __magic_name__   =			200
     __magic_name__   =			{'Content-Length': '100'}
     __magic_name__   =			{}




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         return [bytes(snake_case_				,     'utf-8'							)]





def 				__lowerCAmelCase(       *_SCREAMING_SNAKE_CASE							,     **_SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    return MockResponse()





@pytest.mark.parametrize('urls_type'							,     [str, list, dict]				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    import requests

    monkeypatch.setattr(_SCREAMING_SNAKE_CASE							,     'request'							,     _SCREAMING_SNAKE_CASE				)

    _A     =							URL
    if issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							url
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							[url]
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							{'train': url}
    _A     =							'dummy'
    _A     =							'downloads'
    _A     =							tmp_path
    _A     =							DownloadConfig(
        cache_dir=os.path.join(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							,     use_etag=_SCREAMING_SNAKE_CASE							,     )
    _A     =							DownloadManager(dataset_name=_SCREAMING_SNAKE_CASE							,     download_config=_SCREAMING_SNAKE_CASE				)
    _A     =							dl_manager.download(_SCREAMING_SNAKE_CASE				)
    _A     =							urls
    for downloaded_paths in [downloaded_paths]:
        if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            _A     =							[downloaded_paths]
            _A     =							[urls]
        elif isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert "train" in downloaded_paths.keys()
            _A     =							downloaded_paths.values()
            _A     =							urls.values()
        assert downloaded_paths
        for downloaded_path, input_url in zip(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert downloaded_path == dl_manager.downloaded_paths[input_url]
            _A     =							Path(_SCREAMING_SNAKE_CASE				)
            _A     =							downloaded_path.parts
            assert parts[-1] == HASH
            assert parts[-2] == cache_subdir
            assert downloaded_path.exists()
            _A     =							downloaded_path.read_text()
            assert content == CONTENT
            _A     =							downloaded_path.with_suffix('.json'				)
            assert metadata_downloaded_path.exists()
            _A     =							json.loads(metadata_downloaded_path.read_text()				)
            assert metadata_content == {"url": URL, "etag": None}





@pytest.mark.parametrize('paths_type'							,     [str, list, dict]				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    _A     =							str(_SCREAMING_SNAKE_CASE				)
    if issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							filename
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							[filename]
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							{'train': filename}
    _A     =							'dummy'
    _A     =							xz_file.parent
    _A     =							'extracted'
    _A     =							DownloadConfig(
        cache_dir=_SCREAMING_SNAKE_CASE							,     use_etag=_SCREAMING_SNAKE_CASE							,     )
    _A     =							DownloadManager(dataset_name=_SCREAMING_SNAKE_CASE							,     download_config=_SCREAMING_SNAKE_CASE				)
    _A     =							dl_manager.extract(_SCREAMING_SNAKE_CASE				)
    _A     =							paths
    for extracted_paths in [extracted_paths]:
        if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            _A     =							[extracted_paths]
            _A     =							[paths]
        elif isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert "train" in extracted_paths.keys()
            _A     =							extracted_paths.values()
            _A     =							paths.values()
        assert extracted_paths
        for extracted_path, input_path in zip(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert extracted_path == dl_manager.extracted_paths[input_path]
            _A     =							Path(_SCREAMING_SNAKE_CASE				)
            _A     =							extracted_path.parts
            assert parts[-1] == hash_url_to_filename(_SCREAMING_SNAKE_CASE							,     etag=_SCREAMING_SNAKE_CASE				)
            assert parts[-2] == extracted_subdir
            assert extracted_path.exists()
            _A     =							extracted_path.read_text()
            _A     =							text_file.read_text()
            assert extracted_file_content == expected_file_content





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




    assert path.endswith('.jsonl'				)
    for num_items, line in enumerate(_SCREAMING_SNAKE_CASE							,     start=1				):
        _A     =							json.loads(line.decode('utf-8'				)				)
        assert item.keys() == {"col_1", "col_2", "col_3"}
    assert num_items == 4





@pytest.mark.parametrize('archive_jsonl'							,     ['tar_jsonl_path', 'zip_jsonl_path']				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							request.getfixturevalue(_SCREAMING_SNAKE_CASE				)
    _A     =							DownloadManager()
    for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        _test_jsonl(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    assert num_jsonl == 2





@pytest.mark.parametrize('archive_nested_jsonl'							,     ['tar_nested_jsonl_path', 'zip_nested_jsonl_path']				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							request.getfixturevalue(_SCREAMING_SNAKE_CASE				)
    _A     =							DownloadManager()
    for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
            _test_jsonl(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    assert num_tar == 1
    assert num_jsonl == 2





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							DownloadManager()
    for num_file, file in enumerate(dl_manager.iter_files(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        assert os.path.basename(_SCREAMING_SNAKE_CASE				) == ("test.txt" if num_file == 1 else "train.txt")
    assert num_file == 2





