






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


lowerCamelCase	:							str				=logging.get_logger(__name__)





class 			__a  (    __lowerCamelCase       ):
 _lowerCAmelCase       :       Optional[int]        = ["""pixel_values"""]







 def __init__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PILImageResampling.BICUBIC							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Union[int, float] = 1 / 2_55							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD							,   **SCREAMING_SNAKE_CASE					:   List[Any]							,   ):


      '''simple docstring'''






      super().__init__(**__lowercase						)
      UpperCamelCase__			:   List[str]       				=  size if size is not None else {'''shortest_edge''': 2_24}
      UpperCamelCase__			:   int       				=  get_size_dict(__lowercase							,   default_to_square=__lowercase						)
      UpperCamelCase__			:   List[Any]       				=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
      UpperCamelCase__			:   Any       				=  get_size_dict(__lowercase							,   param_name="crop_size"						)

      UpperCamelCase__			:   Union[str, Any]       				=  do_resize
      UpperCamelCase__			:   Dict       				=  size
      UpperCamelCase__			:   Optional[int]       				=  resample
      UpperCamelCase__			:   Dict       				=  do_center_crop
      UpperCamelCase__			:   str       				=  crop_size
      UpperCamelCase__			:   str       				=  do_rescale
      UpperCamelCase__			:   int       				=  rescale_factor
      UpperCamelCase__			:   str       				=  do_normalize
      UpperCamelCase__			:   Any       				=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      UpperCamelCase__			:   Dict       				=  image_std if image_std is not None else IMAGENET_DEFAULT_STD







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PILImageResampling.BICUBIC							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   List[Any]							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  get_size_dict(__lowercase							,   default_to_square=__lowercase						)
      # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
      if "shortest_edge" in size:
           UpperCamelCase__			:   Any       				=  int((2_56 / 2_24) * size["shortest_edge"]						)
           UpperCamelCase__			:   int       				=  get_resize_output_image_size(__lowercase							,   size=__lowercase							,   default_to_square=__lowercase						)
           UpperCamelCase__			:   List[str]       				=  {'''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(
          __lowercase							,   size=(size_dict["height"], size_dict["width"])							,   resample=__lowercase							,   data_format=__lowercase							,   **__lowercase						)







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Optional[int]							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  get_size_dict(__lowercase						)
      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(__lowercase							,   size=(size["height"], size["width"])							,   data_format=__lowercase							,   **__lowercase						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Union[int, float]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   List[Any]							,   ):


      '''simple docstring'''






      return rescale(__lowercase							,   scale=__lowercase							,   data_format=__lowercase							,   **__lowercase						)







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Union[float, List[float]]							,   SCREAMING_SNAKE_CASE					:   Union[float, List[float]]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Optional[Any]							,   ):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   ImageInput							,   SCREAMING_SNAKE_CASE					:   Optional[bool] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Dict[str, int]] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = None							,   SCREAMING_SNAKE_CASE					:   Optional[bool] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Dict[str, int]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[bool] = None							,   SCREAMING_SNAKE_CASE					:   Optional[float] = None							,   SCREAMING_SNAKE_CASE					:   Optional[bool] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, Iterable[float]]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, Iterable[float]]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[TensorType] = None							,   SCREAMING_SNAKE_CASE					:   ChannelDimension = ChannelDimension.FIRST							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  do_resize if do_resize is not None else self.do_resize
      UpperCamelCase__			:   List[Any]       				=  resample if resample is not None else self.resample
      UpperCamelCase__			:   int       				=  do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase__			:   List[str]       				=  do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase__			:   List[str]       				=  rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase__			:   List[str]       				=  do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase__			:   Optional[Any]       				=  image_mean if image_mean is not None else self.image_mean
      UpperCamelCase__			:   Optional[int]       				=  image_std if image_std is not None else self.image_std

      UpperCamelCase__			:   Tuple       				=  size if size is not None else self.size
      UpperCamelCase__			:   List[Any]       				=  get_size_dict(__lowercase							,   default_to_square=__lowercase						)
      UpperCamelCase__			:   Union[str, Any]       				=  crop_size if crop_size is not None else self.crop_size
      UpperCamelCase__			:   Optional[int]       				=  get_size_dict(__lowercase							,   param_name="crop_size"						)

      UpperCamelCase__			:   str       				=  make_list_of_images(__lowercase						)

      if not valid_images(__lowercase						):
           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.
      UpperCamelCase__			:   Tuple       				=  [to_numpy_array(__lowercase						) for image in images]

      if do_resize:
           UpperCamelCase__			:   Dict       				=  [self.resize(__lowercase							,   __lowercase							,   __lowercase						) for image in images]

      if do_center_crop:
           UpperCamelCase__			:   Any       				=  [self.center_crop(__lowercase							,   __lowercase						) for image in images]

      if do_rescale:
           UpperCamelCase__			:   Tuple       				=  [self.rescale(__lowercase							,   __lowercase						) for image in images]

      if do_normalize:
           UpperCamelCase__			:   Optional[Any]       				=  [self.normalize(__lowercase							,   __lowercase							,   __lowercase						) for image in images]

      UpperCamelCase__			:   int       				=  [to_channel_dimension_format(__lowercase							,   __lowercase						) for image in images]

      UpperCamelCase__			:   Union[str, Any]       				=  {'''pixel_values''': images}
      return BatchFeature(data=__lowercase							,   tensor_type=__lowercase						)




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__								=						'''http://www.mocksite.com/file1.txt'''
A__								=						'''"text": ["foo", "foo"]'''
A__								=						'''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''


class       a							:
		__lowerCAmelCase	:    Optional[int]											=					2_00
		__lowerCAmelCase	:    List[str]											=					{"""Content-Length""": """100"""}
		__lowerCAmelCase	:    Dict											=					{}



		def 	__lowerCamelCase			(     self     :Dict   ,**__lowercase     :List[Any]  ):
						return [bytes(__lowercase   ,'''utf-8'''  )]



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


				"""simple docstring"""

				return MockResponse()



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


				"""simple docstring"""

				import requests

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

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



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


				"""simple docstring"""

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



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				assert path.endswith('''.jsonl'''      )
				for num_items, line in enumerate(__lowerCAmelCase  ,  start=1      ):
								snake_case__	:      Any     		=	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       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				snake_case__	:      Any     		=	request.getfixturevalue(__lowerCAmelCase      )
				snake_case__	:      Union[str, Any]     		=	DownloadManager()
				for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__lowerCAmelCase      )  ,  start=1      ):
								_test_jsonl(__lowerCAmelCase  ,  __lowerCAmelCase      )
				assert num_jsonl == 2



@pytest.mark.parametrize('''archive_nested_jsonl'''  ,  ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']      )
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					List[str]:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	request.getfixturevalue(__lowerCAmelCase      )
				snake_case__	:      Optional[int]     		=	DownloadManager()
				for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__lowerCAmelCase      )  ,  start=1      ):
								for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__lowerCAmelCase      )  ,  start=1      ):
												_test_jsonl(__lowerCAmelCase  ,  __lowerCAmelCase      )
				assert num_tar == 1
				assert num_jsonl == 2





def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

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

