


"""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 ViltImageProcessor


class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=7	,					lowerCamelCase__=3	,					lowerCamelCase__=18	,					lowerCamelCase__=30	,					lowerCamelCase__=400	,					lowerCamelCase__=True	,					lowerCamelCase__=None	,					lowerCamelCase__=2	,					lowerCamelCase__=True	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					)   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__  :						str		=      size if size is not None else {"shortest_edge": 30}
								snake_case__  :						Optional[Any]		=      parent
								snake_case__  :						List[Any]		=      batch_size
								snake_case__  :						Tuple		=      num_channels
								snake_case__  :						str		=      image_size
								snake_case__  :						Dict		=      min_resolution
								snake_case__  :						Optional[int]		=      max_resolution
								snake_case__  :						List[str]		=      do_resize
								snake_case__  :						Optional[int]		=      size
								snake_case__  :						Optional[int]		=      size_divisor
								snake_case__  :						str		=      do_normalize
								snake_case__  :						List[str]		=      image_mean
								snake_case__  :						Tuple		=      image_std




							def        UpperCAmelCase (						self)   ->	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,
								    "size": self.size,
								    "size_divisor": self.size_divisor,
								}







							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__=False)   ->	str:







								'''simple docstring'''




								if not batched:
									snake_case__  :						List[Any]		=      self.size["shortest_edge"]
									snake_case__  :						Optional[int]		=      image_inputs[0]
									if isinstance(lowerCamelCase__	,					Image.Image):
										snake_case__,					snake_case__  :						Optional[Any]		=      image.size
									else:
										snake_case__,					snake_case__  :						Union[str, Any]		=      image.shape[1], image.shape[2]
									snake_case__  :						Union[str, Any]		=      size / min(lowerCamelCase__	,					lowerCamelCase__)
									if h < w:
										snake_case__,					snake_case__  :						Any		=      size, scale * w
									else:
										snake_case__,					snake_case__  :						Tuple		=      scale * h, size

									snake_case__  :						int		=      int((1_333 / 800) * size)
									if max(lowerCamelCase__	,					lowerCamelCase__) > max_size:
										snake_case__  :						Union[str, Any]		=      max_size / max(lowerCamelCase__	,					lowerCamelCase__)
										snake_case__  :						Tuple		=      newh * scale
										snake_case__  :						int		=      neww * scale

									snake_case__,					snake_case__  :						List[str]		=      int(newh + 0.5), int(neww + 0.5)
									snake_case__,					snake_case__  :						Optional[Any]		=      (
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

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

								return expected_height, expected_width


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Optional[int]				  =  ViltImageProcessor if is_vision_available() else None




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						str		=      ViltImageProcessingTester(self)




							@property
							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




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







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(lowerCamelCase__	,					"image_mean"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"image_std"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"do_normalize"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"do_resize"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"size"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"size_divisor"))




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size	,					{"shortest_edge": 30})

								snake_case__  :						Union[str, Any]		=      self.image_processing_class.from_dict(self.image_processor_dict	,					size=42)
								self.assertEqual(image_processor.size	,					{"shortest_edge": 42})




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								pass




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						List[str]		=      self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								snake_case__  :						int		=      prepare_image_inputs(self.image_processor_tester	,					equal_resolution=lowerCamelCase__)
								for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__	,					Image.Image)

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

								snake_case__,					snake_case__  :						int		=      self.image_processor_tester.get_expected_values(lowerCamelCase__)
								self.assertEqual(
								    encoded_images.shape	,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,					)

								# Test batched
								snake_case__  :						Union[str, Any]		=      image_processing(lowerCamelCase__	,					return_tensors="pt").pixel_values

								snake_case__,					snake_case__  :						Optional[int]		=      self.image_processor_tester.get_expected_values(lowerCamelCase__	,					batched=lowerCamelCase__)
								self.assertEqual(
								    encoded_images.shape	,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )	,					)




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







								'''simple docstring'''




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

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

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

								# Test batched
								snake_case__  :						Dict		=      image_processing(lowerCamelCase__	,					return_tensors="pt").pixel_values

								snake_case__,					snake_case__  :						int		=      self.image_processor_tester.get_expected_values(lowerCamelCase__	,					batched=lowerCamelCase__)
								self.assertEqual(
								    encoded_images.shape	,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )	,					)







							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




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

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

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

								# Test batched
								snake_case__  :						int		=      image_processing(lowerCamelCase__	,					return_tensors="pt").pixel_values

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











"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       Dict				      =       inspect.getfile(accelerate.test_utils   )
										snake_case__							:       Tuple				      =       os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_sync.py''']   )

				@require_cpu
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										debug_launcher(test_sync.main   ,num_processes=1   )

				@require_cpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										debug_launcher(test_sync.main   )

				@require_single_gpu
				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										test_sync.main()





				@require_multi_gpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										print(F'''Found {torch.cuda.device_count()} devices.'''   )
										snake_case__							:       Dict				      =       ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
										with patch_environment(omp_num_threads=1   ):
																execute_subprocess_async(SCREAMING_SNAKE_CASE_   ,env=os.environ.copy()   )


