





import json
import os
import tempfile

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


if is_torch_available():
		import numpy as np
		import torch

if is_vision_available():
		from PIL import Image





def     UpperCAmelCase						(   lowercase							,      lowercase=False							,      lowercase=False							,      lowercase=False							):







				"""simple docstring"""



				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				__lowercase			      =							[]
				for i in range(image_processor_tester.batch_size							):
								if equal_resolution:
												__lowercase			      =							__lowercase			      =							image_processor_tester.max_resolution
								else:
												# To avoid getting image width/height 0
												__lowercase			      =							image_processor_tester.min_resolution
												if getattr(_lowerCamelCase							,      '''size_divisor'''							,      _lowerCamelCase							):
																# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																__lowercase			      =							max(image_processor_tester.size_divisor							,      _lowerCamelCase							)
												__lowercase      ,					__lowercase			      =							np.random.choice(np.arange(_lowerCamelCase							,      image_processor_tester.max_resolution							)							,      2							)
								image_inputs.append(
								    np.random.randint(255							,      size=(image_processor_tester.num_channels, width, height)							,      dtype=np.uinta							)							)

				if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								__lowercase			      =							[Image.fromarray(np.moveaxis(_lowerCamelCase							,      0							,      -1							)							) for image in image_inputs]

				if torchify:
								__lowercase			      =							[torch.from_numpy(_lowerCamelCase							) for image in image_inputs]

				return image_inputs





def     UpperCAmelCase						(   lowercase							,      lowercase=10							,      lowercase=10							,      lowercase=False							,      lowercase=False							):







				"""simple docstring"""



				__lowercase			      =							[]
				for i in range(image_processor_tester.num_frames							):
								video.append(np.random.randint(255							,      size=(image_processor_tester.num_channels, width, height)							,      dtype=np.uinta							)							)

				if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								__lowercase			      =							[Image.fromarray(np.moveaxis(_lowerCamelCase							,      0							,      -1							)							) for frame in video]

				if torchify:
								__lowercase			      =							[torch.from_numpy(_lowerCamelCase							) for frame in video]

				return video





def     UpperCAmelCase						(   lowercase							,      lowercase=False							,      lowercase=False							,      lowercase=False							):







				"""simple docstring"""



				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				__lowercase			      =							[]
				for i in range(image_processor_tester.batch_size							):
								if equal_resolution:
												__lowercase			      =							__lowercase			      =							image_processor_tester.max_resolution
								else:
												__lowercase      ,					__lowercase			      =							np.random.choice(
												    np.arange(image_processor_tester.min_resolution							,      image_processor_tester.max_resolution							)							,      2							)
												__lowercase			      =							prepare_video(
												    image_processor_tester=_lowerCamelCase							,      width=_lowerCamelCase							,      height=_lowerCamelCase							,      numpify=_lowerCamelCase							,      torchify=_lowerCamelCase							,      )
								video_inputs.append(_lowerCamelCase							)

				return video_inputs




class        _UpperCamelCase				:


			"""simple docstring"""






			__a  :			Tuple      			=						None





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)
							__lowercase			      =							json.loads(image_processor.to_json_string()						)
							for key, value in self.image_processor_dict.items():
											self.assertEqual(obj[key]				,       __SCREAMING_SNAKE_CASE						)





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

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)

							with tempfile.TemporaryDirectory() as tmpdirname:
											__lowercase			      =							os.path.join(__SCREAMING_SNAKE_CASE				,       '''image_processor.json'''						)
											image_processor_first.to_json_file(__SCREAMING_SNAKE_CASE						)
											__lowercase			      =							self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE						)

							self.assertEqual(image_processor_second.to_dict()				,       image_processor_first.to_dict()						)





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

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)

							with tempfile.TemporaryDirectory() as tmpdirname:
											__lowercase			      =							image_processor_first.save_pretrained(__SCREAMING_SNAKE_CASE						)[0]
											check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE						)
											__lowercase			      =							self.image_processing_class.from_pretrained(__SCREAMING_SNAKE_CASE						)

							self.assertEqual(image_processor_second.to_dict()				,       image_processor_first.to_dict()						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class()
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)





			@require_torch
			@require_vision
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							if self.test_cast_dtype is not None:
											# Initialize image_processor
											__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)

											# create random PyTorch tensors
											__lowercase			      =							prepare_image_inputs(self.image_processor_tester				,       equal_resolution=__SCREAMING_SNAKE_CASE				,       torchify=__SCREAMING_SNAKE_CASE						)

											__lowercase			      =							image_processor(__SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''						)
											# for layoutLM compatiblity
											self.assertEqual(encoding.pixel_values.device				,       torch.device('''cpu'''						)						)
											self.assertEqual(encoding.pixel_values.dtype				,       torch.floataa						)

											__lowercase			      =							image_processor(__SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''						).to(torch.floataa						)
											self.assertEqual(encoding.pixel_values.device				,       torch.device('''cpu'''						)						)
											self.assertEqual(encoding.pixel_values.dtype				,       torch.floataa						)

											__lowercase			      =							image_processor(__SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''						).to('''cpu'''				,       torch.bfloataa						)
											self.assertEqual(encoding.pixel_values.device				,       torch.device('''cpu'''						)						)
											self.assertEqual(encoding.pixel_values.dtype				,       torch.bfloataa						)

											with self.assertRaises(__SCREAMING_SNAKE_CASE						):
															__lowercase			      =							image_processor(__SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''						).to(torch.bfloataa				,       '''cpu'''						)

											# Try with text + image feature
											__lowercase			      =							image_processor(__SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''						)
											encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]						)}						)
											__lowercase			      =							encoding.to(torch.floataa						)

											self.assertEqual(encoding.pixel_values.device				,       torch.device('''cpu'''						)						)
											self.assertEqual(encoding.pixel_values.dtype				,       torch.floataa						)
											self.assertEqual(encoding.input_ids.dtype				,       torch.long						)
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__a  :					int          =  5_0_0_0_0_0

__a   ,						__a  :					Any          =  os.path.split(__file__)
__a  :					Dict          =  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.select(range(0							,      len(lowercase							)							,      2							)							)





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.sort('''numbers'''							)





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.shuffle()





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.train_test_split(0.1							)





@get_duration
def     UpperCAmelCase						(   lowercase							,      lowercase=10							):







				"""simple docstring"""



				for shard_id in range(lowercase							):
								__lowercase			      =							dataset.shard(lowercase							,      lowercase							)





def     UpperCAmelCase						(   ):







				"""simple docstring"""



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

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


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