




"""simple docstring"""



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


__lowercase		:		Optional[Any]														=							"http://www.mocksite.com/file1.txt"
__lowercase		:		Tuple														=							"\"text\": [\"foo\", \"foo\"]"
__lowercase		:		Tuple														=							"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"



class      _A	:




    """simple docstring"""




    UpperCamelCase_	:					Any							=					2_0_0
    UpperCamelCase_	:					int							=					{'''Content-Length''': '''100'''}
    UpperCamelCase_	:					int							=					{}
    def  lowercase					(		self							:     Dict	,      **A_							:     Tuple    )    ->       Dict:
         return [bytes(A_	,      '''utf-8'''    )]






def    SCREAMING_SNAKE_CASE       (     *snake_case,      **snake_case):
     return MockResponse()





@pytest.mark.parametrize('''urls_type''',      [str, list, dict])
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     import requests

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

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





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





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     assert path.endswith('''.jsonl''')
     for num_items, line in enumerate(snake_case,      start=1):
          __snake_case				=							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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							request.getfixturevalue(snake_case)
     __snake_case				=							DownloadManager()
     for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(snake_case),      start=1):
          _test_jsonl(snake_case,      snake_case)
     assert num_jsonl == 2





@pytest.mark.parametrize('''archive_nested_jsonl''',      ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path'''])
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							request.getfixturevalue(snake_case)
     __snake_case				=							DownloadManager()
     for num_tar, (path, file) in enumerate(dl_manager.iter_archive(snake_case),      start=1):
          for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(snake_case),      start=1):
               _test_jsonl(snake_case,      snake_case)
     assert num_tar == 1
     assert num_jsonl == 2





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							DownloadManager()
     for num_file, file in enumerate(dl_manager.iter_files(snake_case),      start=1):
          assert os.path.basename(snake_case) == ("test.txt" if num_file == 1 else "train.txt")
     assert num_file == 2
"""simple docstring"""



from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					['''pixel_values''']
    def __init__(		self							:     Optional[int]	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     bool = True	,      A_							:     Union[int, float] = 1 / 255	,      A_							:     bool = True	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD	,      **A_							:     Dict	,      )    ->       None:
         super().__init__(**A_    )
         __snake_case				=							size if size is not None else {'''shortest_edge''': 224}
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __snake_case				=							get_size_dict(A_	,      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 IMAGENET_DEFAULT_MEAN
         __snake_case				=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     int	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
              __snake_case				=							int((256 / 224) * size['''shortest_edge''']    )
              __snake_case				=							get_resize_output_image_size(A_	,      size=A_	,      default_to_square=A_    )
              __snake_case				=							{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
              raise ValueError(
                  f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"    )
         return resize(
             A_	,      size=(size_dict['''height'''], size_dict['''width'''])	,      resample=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     List[Any]	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_    )
         if "height" not in size or "width" not in size:
              raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"    )
         return center_crop(A_	,      size=(size['''height'''], size['''width'''])	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     np.ndarray	,      A_							:     Union[int, float]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[Any]	,      )    ->       np.ndarray:
         return rescale(A_	,      scale=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     np.ndarray	,      A_							:     Union[float, List[float]]	,      A_							:     Union[float, List[float]]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         return normalize(A_	,      mean=A_	,      std=A_	,      data_format=A_	,      **A_    )







    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     ImageInput	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     PILImageResampling = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[float] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[TensorType] = None	,      A_							:     ChannelDimension = ChannelDimension.FIRST	,      **A_							:     Any	,      )    ->       BatchFeature:
         __snake_case				=							do_resize if do_resize is not None else self.do_resize
         __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				=							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				=							size if size is not None else self.size
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else self.crop_size
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							make_list_of_images(A_    )

         if not valid_images(A_    ):
              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.'''    )

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

         if do_resize:
              __snake_case				=							[self.resize(A_	,      A_	,      A_    ) for image in images]

         if do_center_crop:
              __snake_case				=							[self.center_crop(A_	,      A_    ) for image in images]

         if do_rescale:
              __snake_case				=							[self.rescale(A_	,      A_    ) for image in images]

         if do_normalize:
              __snake_case				=							[self.normalize(A_	,      A_	,      A_    ) for image in images]

         __snake_case				=							[to_channel_dimension_format(A_	,      A_    ) for image in images]

         __snake_case				=							{'''pixel_values''': images}
         return BatchFeature(data=A_	,      tensor_type=A_    )