
"""simple docstring"""


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  __lowerCamelCase  ( unittest.TestCase			):




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=7							,						UpperCAmelCase=3							,						UpperCAmelCase=18							,						UpperCAmelCase=30							,						UpperCAmelCase=400							,						UpperCAmelCase=True							,						UpperCAmelCase=None							,						UpperCAmelCase=True							,						UpperCAmelCase=None							,						UpperCAmelCase=True							,						UpperCAmelCase=[0.5, 0.5, 0.5]							,						UpperCAmelCase=[0.5, 0.5, 0.5]							,						):
							lowerCamelCase_					=  size if size is not None else {'''shortest_edge''': 18}
							lowerCamelCase_					=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							lowerCamelCase_					=  parent
							lowerCamelCase_					=  batch_size
							lowerCamelCase_					=  num_channels
							lowerCamelCase_					=  image_size
							lowerCamelCase_					=  min_resolution
							lowerCamelCase_					=  max_resolution
							lowerCamelCase_					=  do_resize
							lowerCamelCase_					=  size
							lowerCamelCase_					=  do_center_crop
							lowerCamelCase_					=  crop_size
							lowerCamelCase_					=  do_normalize
							lowerCamelCase_					=  image_mean
							lowerCamelCase_					=  image_std





				def        UpperCAmelCase__						(				self   ):
							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  __lowerCamelCase  ( lowerCAmelCase						,	unittest.TestCase			):
				a__:      Tuple          =    LevitImageProcessor if is_vision_available() else None




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  LevitImageProcessingTester(self   )




				@property
				def        UpperCAmelCase__						(				self   ):
							return self.image_processor_tester.prepare_image_processor_dict()




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  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}   )

							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							# Initialize image_processing
							lowerCamelCase_					=  self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							lowerCamelCase_					=  prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCAmelCase   )
							for image in image_inputs:
										self.assertIsInstance(UpperCAmelCase							,						Image.Image   )

							# Test not batched input
							lowerCamelCase_					=  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
							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							# Initialize image_processing
							lowerCamelCase_					=  self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							lowerCamelCase_					=  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
							lowerCamelCase_					=  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
							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							# Initialize image_processing
							lowerCamelCase_					=  self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							lowerCamelCase_					=  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
							lowerCamelCase_					=  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
							lowerCamelCase_					=  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'''],
							    )							,						)






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase_							:					Optional[Any]		 =   [
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str=True       )					->				List[Any]:
	if with_config:
		return [
		    {
		        "testcase_name": d["dataset"] + "/" + d["config_name"],
		        "dataset": d["dataset"],
		        "config_name": d["config_name"],
		    }
		    for d in DATASETS_ON_HF_GCP
		]
	else:
		return [
		    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
		]






@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowercase	)	)
class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Optional[int]     					=				None
	_lowercase		:   str     					=				None




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Optional[int]					,      __A						:     Optional[Any]							):
		with TemporaryDirectory() as tmp_dir:
			__A :			List[Any]		    =			dataset_module_factory(__A					,      cache_dir=__A							)

			__A :			Tuple		    =			import_main_class(dataset_module.module_path					,      dataset=__A							)

			__A :			DatasetBuilder		    =			builder_cls(
			    cache_dir=__A					,      config_name=__A					,      hash=dataset_module.hash					,      )

			__A :			List[Any]		    =			"""/""".join(
			    [
			        HF_GCP_BASE_URL,
			        builder_instance._relative_data_dir(with_hash=__A							).replace(os.sep					,      """/"""							),
			        config.DATASET_INFO_FILENAME,
			    ]							)
			__A :			Union[str, Any]		    =			cached_path(__A					,      cache_dir=__A							)
			self.assertTrue(os.path.exists(__A							)							)






@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict       )					->				Optional[Any]:
	__A :			Optional[Any]		    =			tmp_path_factory.mktemp("""test_hf_gcp"""       ) / """test_wikipedia_simple"""
	__A :			Union[str, Any]		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			List[Any]		    =			import_main_class(dataset_module.module_path       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	__A :			Any		    =			None
	builder_instance.download_and_prepare()
	__A :			Union[str, Any]		    =			builder_instance.as_dataset()
	assert ds







@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				List[str]:
	__A :			Tuple		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			str		    =			import_main_class(dataset_module.module_path			,dataset=a__       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	__A :			Optional[int]		    =			builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(a__			,a__       )
	assert "train" in ds
	assert isinstance(ds["""train"""]			,a__       )
	assert next(iter(ds["""train"""]       )       )





