





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 EfficientNetImageProcessor

class 			A_ (unittest.TestCase    ):
	def __init__(					self							,      _A							,      _A=1_3							,      _A=3							,      _A=1_8							,      _A=3_0							,      _A=4_0_0							,      _A=True							,      _A=None							,      _A=True							,      _A=[0.5, 0.5, 0.5]							,      _A=[0.5, 0.5, 0.5]							,      ):






				'''simple docstring'''







				UpperCAmelCase     	=       size if size is not None else {'''height''': 1_8, '''width''': 1_8}
				UpperCAmelCase     	=       parent
				UpperCAmelCase     	=       batch_size
				UpperCAmelCase     	=       num_channels
				UpperCAmelCase     	=       image_size
				UpperCAmelCase     	=       min_resolution
				UpperCAmelCase     	=       max_resolution
				UpperCAmelCase     	=       do_resize
				UpperCAmelCase     	=       size
				UpperCAmelCase     	=       do_normalize
				UpperCAmelCase     	=       image_mean
				UpperCAmelCase     	=       image_std







	def   _lowercase       (					self	):






				'''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,
				}

@require_torch
@require_vision
class 			A_ (a_  ,							unittest.TestCase    ):
	UpperCAmelCase__      =    EfficientNetImageProcessor if is_vision_available() else None
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       EfficientNetImageProcessorTester(self	)
	@property
	def   _lowercase       (					self	):






				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(_A							,      '''image_mean'''	)	)
				self.assertTrue(hasattr(_A							,      '''image_std'''	)	)
				self.assertTrue(hasattr(_A							,      '''do_normalize'''	)	)
				self.assertTrue(hasattr(_A							,      '''do_resize'''	)	)
				self.assertTrue(hasattr(_A							,      '''size'''	)	)
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       self.image_processing_class.from_dict(self.image_processor_dict	)
				self.assertEqual(image_processor.size							,      {'''height''': 1_8, '''width''': 1_8}	)

				UpperCAmelCase     	=       self.image_processing_class.from_dict(self.image_processor_dict							,      size=4_2	)
				self.assertEqual(image_processor.size							,      {'''height''': 4_2, '''width''': 4_2}	)
	def   _lowercase       (					self	):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase     	=       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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )							,      )

				# Test batched
				UpperCAmelCase     	=       image_processing(_A							,      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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )							,      )
	def   _lowercase       (					self	):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase     	=       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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )							,      )

				# Test batched
				UpperCAmelCase     	=       image_processing(_A							,      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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )							,      )







	def   _lowercase       (					self	):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase     	=       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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )							,      )

				# Test batched
				UpperCAmelCase     	=       image_processing(_A							,      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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )							,      )










import numpy as np





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       int:







			'''simple docstring'''

			UpperCAmelCase     	=       n_draws * class_counts / class_counts.sum()
			# floored means we don't overshoot n_samples, but probably undershoot
			UpperCAmelCase     	=       np.floor(UpperCamelCase__				)
			# we add samples according to how much "left over" probability
			# they had, until we arrive at n_samples
			UpperCAmelCase     	=       int(n_draws - floored.sum()				)
			if need_to_add > 0:
						UpperCAmelCase     	=       continuous - floored
						UpperCAmelCase     	=       np.sort(np.unique(UpperCamelCase__				)				)[::-1]
						# add according to remainder, but break ties
						# randomly to avoid biases
						for value in values:
									((UpperCAmelCase)	,    )     	=       np.where(remainder == value				)
									# if we need_to_add less than what's in inds
									# we draw randomly from them.
									# if we need to add more, we add them all and
									# go to the next value
									UpperCAmelCase     	=       min(len(UpperCamelCase__				)    ,    UpperCamelCase__				)
									UpperCAmelCase     	=       rng.choice(UpperCamelCase__    ,    size=UpperCamelCase__    ,    replace=UpperCamelCase__				)
									floored[inds] += 1
									need_to_add -= add_now
									if need_to_add == 0:
												break
			return floored.astype(np.intaa				)






def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__=10				)						->       Any:







			'''simple docstring'''

			UpperCAmelCase	,    UpperCAmelCase     	=       np.unique(UpperCamelCase__    ,    return_inverse=UpperCamelCase__				)
			UpperCAmelCase     	=       classes.shape[0]
			UpperCAmelCase     	=       np.bincount(UpperCamelCase__				)
			if np.min(UpperCamelCase__				) < 2:
						raise ValueError('''Minimum class count error'''				)
			if n_train < n_classes:
						raise ValueError(
						    '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)				)
			if n_test < n_classes:
						raise ValueError(
						    '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)				)
			UpperCAmelCase     	=       np.split(np.argsort(UpperCamelCase__    ,    kind='''mergesort'''				)    ,    np.cumsum(UpperCamelCase__				)[:-1]				)
			for _ in range(UpperCamelCase__				):
						UpperCAmelCase     	=       approximate_mode(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)
						UpperCAmelCase     	=       class_counts - n_i
						UpperCAmelCase     	=       approximate_mode(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)

						UpperCAmelCase     	=       []
						UpperCAmelCase     	=       []

						for i in range(UpperCamelCase__				):
									UpperCAmelCase     	=       rng.permutation(class_counts[i]				)
									UpperCAmelCase     	=       class_indices[i].take(UpperCamelCase__    ,    mode='''clip'''				)
									train.extend(perm_indices_class_i[: n_i[i]]				)
									test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]				)
						UpperCAmelCase     	=       rng.permutation(UpperCamelCase__				)
						UpperCAmelCase     	=       rng.permutation(UpperCamelCase__				)

						yield train, test



