





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



class    _a     :






							"""simple docstring"""

							snake_case_							 =						2_00
							snake_case_							 =						{"Content-Length": "100"}
							snake_case_							 =						{}

							def      A_  (      self				:     str							,	**a				:     Union[str, Any]    )	->str:
													return [bytes(a							,	"utf-8"    )]







def       UpperCAmelCase							(     *_lowerCamelCase      :  Dict			,   **_lowerCamelCase      :  int     ):





						'''simple docstring'''


						return MockResponse()







@pytest.mark.parametrize("urls_type"			,   [str, list, dict]     )
def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Any			,   _lowerCamelCase      :  List[str]     ):





						'''simple docstring'''


						import requests

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

						SCREAMING_SNAKE_CASE__     :				str   =					URL
						if issubclass(_lowerCamelCase			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					url
						elif issubclass(_lowerCamelCase			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Any   =					[url]
						elif issubclass(_lowerCamelCase			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					{"train": url}
						SCREAMING_SNAKE_CASE__     :				Tuple   =					"dummy"
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"downloads"
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					tmp_path
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					DownloadConfig(
						    cache_dir=os.path.join(_lowerCamelCase			,   _lowerCamelCase     )			,   use_etag=_lowerCamelCase			,   )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					DownloadManager(dataset_name=_lowerCamelCase			,   download_config=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					dl_manager.download(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					urls
						for downloaded_paths in [downloaded_paths]:
												if isinstance(_lowerCamelCase			,   _lowerCamelCase     ):
																		SCREAMING_SNAKE_CASE__     :				str   =					[downloaded_paths]
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					[urls]
												elif isinstance(_lowerCamelCase			,   _lowerCamelCase     ):
																		assert "train" in downloaded_paths.keys()
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					downloaded_paths.values()
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					urls.values()
												assert downloaded_paths
												for downloaded_path, input_url in zip(_lowerCamelCase			,   _lowerCamelCase     ):
																		assert downloaded_path == dl_manager.downloaded_paths[input_url]
																		SCREAMING_SNAKE_CASE__     :				Dict   =					Path(_lowerCamelCase     )
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					downloaded_path.parts
																		assert parts[-1] == HASH
																		assert parts[-2] == cache_subdir
																		assert downloaded_path.exists()
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					downloaded_path.read_text()
																		assert content == CONTENT
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					downloaded_path.with_suffix(".json"     )
																		assert metadata_downloaded_path.exists()
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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      :  Any			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  Any     ):





						'''simple docstring'''


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







def       UpperCAmelCase							(     _lowerCamelCase      :  Any			,   _lowerCamelCase      :  List[Any]     ):





						'''simple docstring'''


						assert path.endswith(".jsonl"     )
						for num_items, line in enumerate(_lowerCamelCase			,   start=1     ):
												SCREAMING_SNAKE_CASE__     :				List[str]   =					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      :  Tuple			,   _lowerCamelCase      :  str     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[Any]   =					request.getfixturevalue(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[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       UpperCAmelCase							(     _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					request.getfixturevalue(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					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      :  int     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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






import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import BridgeTowerImageProcessor



class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     Any							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     int = 32							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,	a				:     Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,	a				:     bool = True							,	a				:     Any=7							,	a				:     str=30							,	a				:     Dict=4_00							,	a				:     Optional[int]=3							,	)	->int:
													SCREAMING_SNAKE_CASE__     :				int   =					parent
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else {"shortest_edge": 2_88}
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					size_divisor
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Tuple   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_std
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_pad
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					batch_size
													SCREAMING_SNAKE_CASE__     :				int   =					num_channels
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					min_resolution
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max_resolution

							def      A_  (      self				:     List[str]    )	->Tuple:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}






							def      A_  (      self				:     int							,	a				:     Optional[int]							,	a				:     Union[str, Any]=False    )	->Optional[Any]:
													if not batched:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.size["shortest_edge"]
																			SCREAMING_SNAKE_CASE__     :				Dict   =					image_inputs[0]
																			if isinstance(a							,	Image.Image    ):
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					image.size
																			else:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					image.shape[1], image.shape[2]
																			SCREAMING_SNAKE_CASE__     :				Any   =					size / min(a							,	a    )
																			if h < w:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					size, scale * w
																			else:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					scale * h, size

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					int((13_33 / 8_00) * size    )
																			if max(a							,	a    ) > max_size:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					max_size / max(a							,	a    )
																									SCREAMING_SNAKE_CASE__     :				int   =					newh * scale
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					neww * scale

																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					int(newh + 0.5    ), int(neww + 0.5    )
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					(
																			    newh // self.size_divisor * self.size_divisor,
																			    neww // self.size_divisor * self.size_divisor,
																			)

													else:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
																			for image in image_inputs:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.get_expected_values([image]    )
																									expected_values.append((expected_height, expected_width)    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[0]    )[0]
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[1]    )[1]

													return expected_height, expected_width







@require_torch
@require_vision
class    _a     (				lowercase__					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						BridgeTowerImageProcessor if is_vision_available() else None

							def      A_  (      self				:     List[Any]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Any   =					BridgeTowerImageProcessingTester(self    )

							@property
							def      A_  (      self				:     Optional[int]    )	->Optional[Any]:
													return self.image_processor_tester.prepare_image_processor_dict()

							def      A_  (      self				:     Tuple    )	->str:
													SCREAMING_SNAKE_CASE__     :				int   =					self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(a							,	"image_mean"    )    )
													self.assertTrue(hasattr(a							,	"image_std"    )    )
													self.assertTrue(hasattr(a							,	"do_normalize"    )    )
													self.assertTrue(hasattr(a							,	"do_resize"    )    )
													self.assertTrue(hasattr(a							,	"size"    )    )
													self.assertTrue(hasattr(a							,	"size_divisor"    )    )

							def      A_  (      self				:     List[Any]    )	->List[Any]:
													pass

							def      A_  (      self				:     Tuple    )	->Optional[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	Image.Image    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				int   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)

							def      A_  (      self				:     Optional[int]    )	->Any:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				str   =					self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :				str   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a							,	numpify=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	np.ndarray    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)






							def      A_  (      self				:     str    )	->Optional[int]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a							,	torchify=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	torch.Tensor    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				Any   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)