
'''simple docstring'''







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase					:    List[Any]       				=			5_0_0_0_0_0

_lowercase				,    _lowercase					:    int       				=			os.path.split(__file__)
_lowercase					:    str       				=			os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					datasets.Dataset				):



						"""simple docstring"""





						lowercase_      :	List[str]						=			dataset.select(range(0 , len(__SCREAMING_SNAKE_CASE				) , 2				)				)
@get_duration
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					datasets.Dataset				):



						"""simple docstring"""





						lowercase_      :	Dict						=			dataset.sort('''numbers'''				)
@get_duration
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					datasets.Dataset				):



						"""simple docstring"""





						lowercase_      :	Tuple						=			dataset.shuffle()
@get_duration
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					datasets.Dataset				):



						"""simple docstring"""





						lowercase_      :	Tuple						=			dataset.train_test_split(0.1				)
@get_duration
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					datasets.Dataset , __SCREAMING_SNAKE_CASE   :					Union[str, Any]=10				):



						"""simple docstring"""





						for shard_id in range(__SCREAMING_SNAKE_CASE				):
												lowercase_      :	Union[str, Any]						=			dataset.shard(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)
def    snake_case_						(							):



						"""simple docstring"""





						lowercase_      :	Dict						=			{'''num examples''': SPEED_TEST_N_EXAMPLES}
						lowercase_      :	Any						=			(select, sort, shuffle, train_test_split, shard)
						with tempfile.TemporaryDirectory() as tmp_dir:
												print('''generating dataset'''				)
												lowercase_      :	Dict						=			datasets.Features({'''text''': datasets.Value('''string'''				), '''numbers''': datasets.Value('''float32'''				)}				)
												lowercase_      :	Optional[Any]						=			generate_example_dataset(
												    os.path.join(__SCREAMING_SNAKE_CASE , '''dataset.arrow'''				) , __SCREAMING_SNAKE_CASE , num_examples=__SCREAMING_SNAKE_CASE				)
												print('''Functions'''				)
												for func in functions:
																		print(func.__name__				)
																		lowercase_      :	Dict						=			func(__SCREAMING_SNAKE_CASE				)

						with open(__SCREAMING_SNAKE_CASE , '''wb'''				) as f:
												f.write(json.dumps(__SCREAMING_SNAKE_CASE				).encode('''utf-8'''				)				)


if __name__ == "__main__":  # useful to run the profiler
							benchmark_indices_mapping()





'''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 DeiTImageProcessor






class 					lowerCAmelCase__  (    unittest.TestCase	):






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=7							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=18							,				__SCREAMING_SNAKE_CASE=30							,				__SCREAMING_SNAKE_CASE=4_00							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			size if size is not None else {'''height''': 20, '''width''': 20}
											lowercase_      :	List[Any]						=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

											lowercase_      :	List[str]						=			parent
											lowercase_      :	Optional[Any]						=			batch_size
											lowercase_      :	int						=			num_channels
											lowercase_      :	List[str]						=			image_size
											lowercase_      :	Optional[Any]						=			min_resolution
											lowercase_      :	Tuple						=			max_resolution
											lowercase_      :	Union[str, Any]						=			do_resize
											lowercase_      :	List[str]						=			size
											lowercase_      :	Tuple						=			do_center_crop
											lowercase_      :	Dict						=			crop_size
											lowercase_      :	int						=			do_normalize
											lowercase_      :	Optional[int]						=			image_mean
											lowercase_      :	List[str]						=			image_std






					def       _snake_case       (  self						):


											"""simple docstring"""





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







@require_torch
@require_vision
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	DeiTImageProcessor if is_vision_available() else None
					lowerCAmelCase_	  =	True






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			DeiTImageProcessingTester(self						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return self.image_processor_tester.prepare_image_processor_dict()






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	str						=			self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_resize'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''size'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_center_crop'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''center_crop'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_normalize'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''image_mean'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''image_std'''						)						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size							,				{'''height''': 20, '''width''': 20}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 18, '''width''': 18}						)

											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict							,				size=42							,				crop_size=84						)
											self.assertEqual(image_processor.size							,				{'''height''': 42, '''width''': 42}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 84, '''width''': 84}						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											lowercase_      :	Optional[int]						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				Image.Image						)

											# Test not batched input
											lowercase_      :	List[Any]						=			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_      :	List[str]						=			image_processing(__SCREAMING_SNAKE_CASE							,				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       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											lowercase_      :	Tuple						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE							,				numpify=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				np.ndarray						)

											# Test not batched input
											lowercase_      :	Dict						=			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_      :	Union[str, Any]						=			image_processing(__SCREAMING_SNAKE_CASE							,				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       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											lowercase_      :	List[Any]						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE							,				torchify=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				torch.Tensor						)

											# Test not batched input
											lowercase_      :	List[str]						=			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_      :	Optional[int]						=			image_processing(__SCREAMING_SNAKE_CASE							,				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'''],
											    )							,				)



