






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


lowerCamelCase_				: Tuple    	=							'''http://www.mocksite.com/file1.txt'''
lowerCamelCase_				: Any    	=							'''"text": ["foo", "foo"]'''
lowerCamelCase_				: Tuple    	=							'''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			__a							:							List[str]					     =					200
			__a							:							List[str]					     =					{"Content-Length": "100"}
			__a							:							List[Any]					     =					{}






			def 				A      (   self							:						Tuple ,      **lowercase							:						str			)							->    Dict:

									'''simple docstring'''







									return [bytes(lowercase ,      """utf-8"""			)]







def 	__magic_name__( *_A						,      **_A					):
						'''simple docstring'''





						return MockResponse()







@pytest.mark.parametrize("""urls_type"""						,      [str, list, dict]					)
def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						import requests

						monkeypatch.setattr(_A						,      """request"""						,      _A					)

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







@pytest.mark.parametrize("""paths_type"""						,      [str, list, dict]					)
def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						assert path.endswith(""".jsonl"""					)
						for num_items, line in enumerate(_A						,      start=1					):
												UpperCamelCase__         = 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 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





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







@pytest.mark.parametrize("""archive_nested_jsonl"""						,      ["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""]					)
def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A					):
						'''simple docstring'''





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








'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_				: Optional[int]    	=							logging.get_logger(__name__)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Optional[Any]					     =					["pixel_values"]






			def __init__(   self							:						str ,      lowercase							:						bool = True ,      lowercase							:						int = 3_2 ,      lowercase							:						List[Any]=PILImageResampling.BILINEAR ,      lowercase							:						bool = True ,      **lowercase							:						str ,      )							->    None:

									'''simple docstring'''







									UpperCamelCase__         = do_resize
									UpperCamelCase__         = do_rescale
									UpperCamelCase__         = size_divisor
									UpperCamelCase__         = resample
									super().__init__(**lowercase			)






			def 				A      (   self							:						Optional[int] ,      lowercase							:						np.ndarray ,      lowercase							:						int ,      lowercase							:						List[Any] ,      lowercase							:						Optional[ChannelDimension] = None ,      **lowercase							:						Any			)							->    np.ndarray:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = get_image_size(lowercase			)
									# Rounds the height and width down to the closest multiple of size_divisor
									UpperCamelCase__         = height // size_divisor * size_divisor
									UpperCamelCase__         = width // size_divisor * size_divisor
									UpperCamelCase__         = resize(lowercase ,      (new_h, new_w) ,      resample=lowercase ,      data_format=lowercase ,      **lowercase			)
									return image






			def 				A      (   self							:						int ,      lowercase							:						np.ndarray ,      lowercase							:						float ,      lowercase							:						Optional[ChannelDimension] = None ,      **lowercase							:						List[str]			)							->    np.ndarray:

									'''simple docstring'''







									return rescale(image=lowercase ,      scale=lowercase ,      data_format=lowercase ,      **lowercase			)







			def 				A      (   self							:						int ,      lowercase							:						Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[int] = None ,      lowercase							:						Optional[Any]=None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[Union[TensorType, str]] = None ,      lowercase							:						ChannelDimension = ChannelDimension.FIRST ,      **lowercase							:						str ,      )							->    BatchFeature:

									'''simple docstring'''







									UpperCamelCase__         = do_resize if do_resize is not None else self.do_resize
									UpperCamelCase__         = do_rescale if do_rescale is not None else self.do_rescale
									UpperCamelCase__         = size_divisor if size_divisor is not None else self.size_divisor
									UpperCamelCase__         = resample if resample is not None else self.resample

									if do_resize and size_divisor is None:
															raise ValueError("""size_divisor is required for resizing"""			)

									UpperCamelCase__         = make_list_of_images(lowercase			)

									if not valid_images(lowercase			):
															raise ValueError("""Invalid image(s)"""			)

									# All transformations expect numpy arrays.
									UpperCamelCase__         = [to_numpy_array(lowercase			) for img in images]

									if do_resize:
															UpperCamelCase__         = [self.resize(lowercase ,      size_divisor=lowercase ,      resample=lowercase			) for image in images]

									if do_rescale:
															UpperCamelCase__         = [self.rescale(lowercase ,      scale=1 / 2_5_5			) for image in images]

									UpperCamelCase__         = [to_channel_dimension_format(lowercase ,      lowercase			) for image in images]

									UpperCamelCase__         = {"""pixel_values""": images}
									return BatchFeature(data=lowercase ,      tensor_type=lowercase			)
