


import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowerCAmelCase       =							'\\n    Text data.\n    Second line of data.'

lowerCAmelCase       =							'file'
@pytest.fixture(scope='''session'''     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      tmp_path_factory.mktemp('''data'''     ) / (FILE_PATH + '''.zstd''')
		lowercase__            =      bytes(SCREAMING_SNAKE_CASE						,				'''utf-8'''     )
		with zstd.open(SCREAMING_SNAKE_CASE						,				'''wb'''     ) as f:
				f.write(SCREAMING_SNAKE_CASE     )
		return path
@pytest.fixture
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		with open(os.path.join(tmpfs.local_root_dir						,				SCREAMING_SNAKE_CASE     )						,				'''w'''     ) as f:
				f.write(SCREAMING_SNAKE_CASE     )
		return FILE_PATH
@pytest.mark.parametrize('''compression_format'''						,				['''gzip''', '''xz''', '''zstd''']     )
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
		lowercase__            =      input_paths[compression_format]
		lowercase__            =      tmp_path / '''cache'''
		lowercase__            =      DownloadConfig(cache_dir=SCREAMING_SNAKE_CASE						,				extract_compressed_file=SCREAMING_SNAKE_CASE     )
		lowercase__            =      cached_path(SCREAMING_SNAKE_CASE						,				download_config=SCREAMING_SNAKE_CASE     )
		with open(SCREAMING_SNAKE_CASE     ) as f:
				lowercase__            =      f.read()
		with open(SCREAMING_SNAKE_CASE     ) as f:
				lowercase__            =      f.read()
		assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted'''						,				[True, False]     )
@pytest.mark.parametrize('''default_cache_dir'''						,				[True, False]     )
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      '''custom_cache'''
		lowercase__            =      '''custom_extracted_dir'''
		lowercase__            =      tmp_path / '''custom_extracted_path'''
		if default_extracted:
				lowercase__            =      ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
		else:
				monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''						,				SCREAMING_SNAKE_CASE     )
				monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''						,				str(SCREAMING_SNAKE_CASE     )     )
				lowercase__            =      custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

		lowercase__            =      xz_file
		lowercase__            =      (
		    DownloadConfig(extract_compressed_file=SCREAMING_SNAKE_CASE     )
		    if default_cache_dir
		    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir						,				extract_compressed_file=SCREAMING_SNAKE_CASE     )
		)
		lowercase__            =      cached_path(SCREAMING_SNAKE_CASE						,				download_config=SCREAMING_SNAKE_CASE     )
		assert Path(SCREAMING_SNAKE_CASE     ).parent.parts[-2:] == expected
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      str(Path(SCREAMING_SNAKE_CASE     ).resolve()     )
		assert cached_path(SCREAMING_SNAKE_CASE     ) == text_file
		# relative path
		lowercase__            =      str(Path(SCREAMING_SNAKE_CASE     ).resolve().relative_to(Path(os.getcwd()     )     )     )
		assert cached_path(SCREAMING_SNAKE_CASE     ) == text_file
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      str(tmp_path.resolve() / '''__missing_file__.txt'''     )
		with pytest.raises(SCREAMING_SNAKE_CASE     ):
				cached_path(SCREAMING_SNAKE_CASE     )
		# relative path
		lowercase__            =      '''./__missing_file__.txt'''
		with pytest.raises(SCREAMING_SNAKE_CASE     ):
				cached_path(SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      get_from_cache(f'tmp://{tmpfs_file}'     )
		with open(SCREAMING_SNAKE_CASE     ) as f:
				lowercase__            =      f.read()
		assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE'''						,				SCREAMING_SNAKE_CASE     )
def 			_a (      ):


		"""simple docstring"""




		with pytest.raises(SCREAMING_SNAKE_CASE     ):
				cached_path('''https://huggingface.co'''     )
@patch('''datasets.config.HF_DATASETS_OFFLINE'''						,				SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      tmp_path_factory.mktemp('''data'''     ) / '''file.html'''
		with pytest.raises(SCREAMING_SNAKE_CASE     ):
				http_get('''https://huggingface.co'''						,				temp_file=SCREAMING_SNAKE_CASE     )
		with pytest.raises(SCREAMING_SNAKE_CASE     ):
				http_head('''https://huggingface.co'''     )
@patch('''datasets.config.HF_DATASETS_OFFLINE'''						,				SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      tmp_path_factory.mktemp('''data'''     ) / '''file.html'''
		with pytest.raises(SCREAMING_SNAKE_CASE     ):
				ftp_get('''ftp://huggingface.co'''						,				temp_file=SCREAMING_SNAKE_CASE     )
		with pytest.raises(SCREAMING_SNAKE_CASE     ):
				ftp_head('''ftp://huggingface.co'''     )
@patch('''datasets.config.HF_DATASETS_OFFLINE'''						,				SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      tmp_path_factory.mktemp('''data'''     ) / '''file.html'''
		with pytest.raises(SCREAMING_SNAKE_CASE     ):
				fsspec_get('''s3://huggingface.co'''						,				temp_file=SCREAMING_SNAKE_CASE     )
		with pytest.raises(SCREAMING_SNAKE_CASE     ):
				fsspec_head('''s3://huggingface.co'''     )






import unittest

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 LevitImageProcessor


class        _a							(			unittest.TestCase			):

							def __init__(							self:	Dict    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	Dict=7    ,						UpperCamelCase_:	Optional[int]=3    ,						UpperCamelCase_:	List[Any]=18    ,						UpperCamelCase_:	Dict=30    ,						UpperCamelCase_:	Optional[int]=400    ,						UpperCamelCase_:	Optional[int]=True    ,						UpperCamelCase_:	Union[str, Any]=None    ,						UpperCamelCase_:	List[str]=True    ,						UpperCamelCase_:	Dict=None    ,						UpperCamelCase_:	Optional[int]=True    ,						UpperCamelCase_:	Tuple=[0.5, 0.5, 0.5]    ,						UpperCamelCase_:	List[str]=[0.5, 0.5, 0.5]    ,						)			->   int:





									"""simple docstring"""







									lowercase__            =      size if size is not None else {'''shortest_edge''': 18}
									lowercase__            =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									lowercase__            =      parent
									lowercase__            =      batch_size
									lowercase__            =      num_channels
									lowercase__            =      image_size
									lowercase__            =      min_resolution
									lowercase__            =      max_resolution
									lowercase__            =      do_resize
									lowercase__            =      size
									lowercase__            =      do_center_crop
									lowercase__            =      crop_size
									lowercase__            =      do_normalize
									lowercase__            =      image_mean
									lowercase__            =      image_std


							def       lowerCamelCase_ (							self:	List[str]							)			->   Optional[Any]:





									"""simple docstring"""







									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "do_center_crop": self.do_center_crop,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}


@require_torch
@require_vision
class        _a							(			UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			List[str]        = LevitImageProcessor if is_vision_available() else None

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      LevitImageProcessingTester(self							)

							@property
							def       lowerCamelCase_ (							self:	List[Any]							)			->   Any:





									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   str:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''image_mean'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''image_std'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_normalize'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_resize'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_center_crop'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''size'''							)							)

							def       lowerCamelCase_ (							self:	str							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class.from_dict(self.image_processor_dict							)
									self.assertEqual(image_processor.size    ,						{'''shortest_edge''': 18}							)
									self.assertEqual(image_processor.crop_size    ,						{'''height''': 18, '''width''': 18}							)

									lowercase__            =      self.image_processing_class.from_dict(self.image_processor_dict    ,						size=42    ,						crop_size=84							)
									self.assertEqual(image_processor.size    ,						{'''shortest_edge''': 42}							)
									self.assertEqual(image_processor.crop_size    ,						{'''height''': 84, '''width''': 84}							)

							def       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""simple docstring"""







									pass

							def       lowerCamelCase_ (							self:	List[str]							)			->   int:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PIL images
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						Image.Image							)

									# Test not batched input
									lowercase__            =      image_processing(image_inputs[0]    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

									# Test batched
									lowercase__            =      image_processing(UpperCamelCase_    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random numpy tensors
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_    ,						numpify=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						np.ndarray							)

									# Test not batched input
									lowercase__            =      image_processing(image_inputs[0]    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

									# Test batched
									lowercase__            =      image_processing(UpperCamelCase_    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)


							def       lowerCamelCase_ (							self:	Any							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PyTorch tensors
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_    ,						torchify=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						torch.Tensor							)

									# Test not batched input
									lowercase__            =      image_processing(image_inputs[0]    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

									# Test batched
									lowercase__            =      image_processing(UpperCamelCase_    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)


