
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 convert_to_rgb, 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


if is_vision_available():
					import PIL


SCREAMING_SNAKE_CASE							:List[Any]							 =				logging.get_logger(__name__)





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     ["pixel_values"]






	def __init__(       self					: Optional[int]							,A					: bool = True							,A					: Dict[str, int] = None							,A					: PILImageResampling = PILImageResampling.BICUBIC							,A					: bool = True							,A					: Union[int, float] = 1 / 2_55							,A					: bool = True							,A					: Optional[Union[float, List[float]]] = None							,A					: Optional[Union[float, List[float]]] = None							,A					: bool = True							,**A					: str							,):
					super().__init__(**A )
					__A   			= size if size is not None else {"height": 3_84, "width": 3_84}
					__A   			= get_size_dict(A							,default_to_square=A )

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






	def 				UpperCamelCase_  (       self					: Any							,A					: np.ndarray							,A					: Dict[str, int]							,A					: PILImageResampling = PILImageResampling.BICUBIC							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: Optional[Any]							,):
					__A   			= get_size_dict(A							,default_to_square=A )
					if "height" not in size or "width" not in size:
									raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
					__A   			= (size["height"], size["width"])
					return resize(A							,size=A							,resample=A							,data_format=A							,**A )






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






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







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

					__A   			= size if size is not None else self.size
					__A   			= get_size_dict(A							,default_to_square=A )

					__A   			= 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 or resample is None:
									raise ValueError("Size and resample must be specified if do_resize 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:
									__A   			= [convert_to_rgb(A ) for image in images]

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

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

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

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

					__A   			= [to_channel_dimension_format(A							,A ) for image in images]

					__A   			= BatchFeature(data={"pixel_values": images}							,tensor_type=A )

					return encoded_outputs


"""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


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

class       lowerCAmelCase__				:
     __a						=					200
     __a						=					{"""Content-Length""": """100"""}
     __a						=					{}







     def      lowercase	(							self			:			List[str]    ,      **_lowerCamelCase			:			List[str]			):
       return [bytes(_lowerCamelCase    ,      '''utf-8'''			)]






def 					_UpperCAmelCase      ( *__lowerCamelCase   :       List[str] ,					**__lowerCamelCase   :       Dict		)     ->					Dict:
  return MockResponse()
@pytest.mark.parametrize('''urls_type''' ,					[str, list, dict]		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str		)     ->					int:
  import requests

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

  _snake_case					     =       URL
  if issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       url
  elif issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       [url]
  elif issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       {'''train''': url}
  _snake_case					     =       '''dummy'''
  _snake_case					     =       '''downloads'''
  _snake_case					     =       tmp_path
  _snake_case					     =       DownloadConfig(
      cache_dir=os.path.join(__lowerCamelCase ,					__lowerCamelCase		) ,					use_etag=__lowerCamelCase ,					)
  _snake_case					     =       DownloadManager(dataset_name=__lowerCamelCase ,					download_config=__lowerCamelCase		)
  _snake_case					     =       dl_manager.download(__lowerCamelCase		)
  _snake_case					     =       urls
  for downloaded_paths in [downloaded_paths]:
    if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
      _snake_case					     =       [downloaded_paths]
      _snake_case					     =       [urls]
    elif isinstance(__lowerCamelCase ,					__lowerCamelCase		):
      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(__lowerCamelCase ,					__lowerCamelCase		):
      assert downloaded_path == dl_manager.downloaded_paths[input_url]
      _snake_case					     =       Path(__lowerCamelCase		)
      _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 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       str ,					__lowerCamelCase   :       Optional[int]		)     ->					int:
  _snake_case					     =       str(__lowerCamelCase		)
  if issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       filename
  elif issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       [filename]
  elif issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       {'''train''': filename}
  _snake_case					     =       '''dummy'''
  _snake_case					     =       xz_file.parent
  _snake_case					     =       '''extracted'''
  _snake_case					     =       DownloadConfig(
      cache_dir=__lowerCamelCase ,					use_etag=__lowerCamelCase ,					)
  _snake_case					     =       DownloadManager(dataset_name=__lowerCamelCase ,					download_config=__lowerCamelCase		)
  _snake_case					     =       dl_manager.extract(__lowerCamelCase		)
  _snake_case					     =       paths
  for extracted_paths in [extracted_paths]:
    if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
      _snake_case					     =       [extracted_paths]
      _snake_case					     =       [paths]
    elif isinstance(__lowerCamelCase ,					__lowerCamelCase		):
      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(__lowerCamelCase ,					__lowerCamelCase		):
      assert extracted_path == dl_manager.extracted_paths[input_path]
      _snake_case					     =       Path(__lowerCamelCase		)
      _snake_case					     =       extracted_path.parts
      assert parts[-1] == hash_url_to_filename(__lowerCamelCase ,					etag=__lowerCamelCase		)
      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 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[Any]		)     ->					Dict:
  assert path.endswith('''.jsonl'''		)
  for num_items, line in enumerate(__lowerCamelCase ,					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 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       str		)     ->					Dict:
  _snake_case					     =       request.getfixturevalue(__lowerCamelCase		)
  _snake_case					     =       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 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       List[Any]		)     ->					Tuple:
  _snake_case					     =       request.getfixturevalue(__lowerCamelCase		)
  _snake_case					     =       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 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					List[Any]:
  _snake_case					     =       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



