



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 							_a							(      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_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      )					->      List[Any]:
						UpperCAmelCase_:		List[Any]       			=    size if size is not None else {"""shortest_edge""": 30}
						UpperCAmelCase_:		Union[str, Any]       			=    parent
						UpperCAmelCase_:		Dict       			=    batch_size
						UpperCAmelCase_:		Any       			=    num_channels
						UpperCAmelCase_:		Optional[Any]       			=    image_size
						UpperCAmelCase_:		Optional[int]       			=    min_resolution
						UpperCAmelCase_:		Optional[Any]       			=    max_resolution
						UpperCAmelCase_:		Optional[Any]       			=    do_resize
						UpperCAmelCase_:		Optional[int]       			=    size
						UpperCAmelCase_:		int       			=    size_divisor
						UpperCAmelCase_:		Optional[Any]       			=    do_normalize
						UpperCAmelCase_:		Any       			=    image_mean
						UpperCAmelCase_:		Dict       			=    image_std

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

									UpperCAmelCase_:		Optional[int]       			=    int((1333 / 800) * size				)
									if max(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				) > max_size:
												UpperCAmelCase_:		int       			=    max_size / max(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
												UpperCAmelCase_:		List[Any]       			=    newh * scale
												UpperCAmelCase_:		Optional[int]       			=    neww * scale

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

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

						return expected_height, expected_width







@require_torch
@require_vision
class 							_a							(      _lowerCAmelCase   ,		unittest.TestCase				):
			A					=  ViltImageProcessor if is_vision_available() else None

			def     __snake_case					(self				)					->      List[Any]:
						UpperCAmelCase_:		Tuple       			=    ViltImageProcessingTester(self				)

			@property
			def     __snake_case					(self				)					->      Optional[int]:
						return self.image_processor_tester.prepare_image_processor_dict()

			def     __snake_case					(self				)					->      Dict:
						UpperCAmelCase_:		List[Any]       			=    self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """image_mean"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """image_std"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """do_normalize"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """do_resize"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """size"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """size_divisor"""				)				)

			def     __snake_case					(self				)					->      List[str]:
						UpperCAmelCase_:		Dict       			=    self.image_processing_class.from_dict(self.image_processor_dict				)
						self.assertEqual(image_processor.size,      {"""shortest_edge""": 30}				)

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

			def     __snake_case					(self				)					->      List[Any]:
						pass

			def     __snake_case					(self				)					->      Union[str, Any]:
						# Initialize image_processing
						UpperCAmelCase_:		str       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						UpperCAmelCase_:		Union[str, Any]       			=    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
						UpperCAmelCase_:		str       			=    image_processing(image_inputs[0],      return_tensors="""pt"""				).pixel_values

						UpperCAmelCase_			,       UpperCAmelCase_:		str       			=    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_				)
						self.assertEqual(
						    encoded_images.shape,      (1, self.image_processor_tester.num_channels, expected_height, expected_width),      )

						# Test batched
						UpperCAmelCase_:		int       			=    image_processing(SCREAMING_SNAKE_CASE_,      return_tensors="""pt"""				).pixel_values

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

			def     __snake_case					(self				)					->      Any:
						# Initialize image_processing
						UpperCAmelCase_:		List[Any]       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random numpy tensors
						UpperCAmelCase_:		Any       			=    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
						UpperCAmelCase_:		List[Any]       			=    image_processing(image_inputs[0],      return_tensors="""pt"""				).pixel_values

						UpperCAmelCase_			,       UpperCAmelCase_:		List[str]       			=    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_				)
						self.assertEqual(
						    encoded_images.shape,      (1, self.image_processor_tester.num_channels, expected_height, expected_width),      )

						# Test batched
						UpperCAmelCase_:		Tuple       			=    image_processing(SCREAMING_SNAKE_CASE_,      return_tensors="""pt"""				).pixel_values

						UpperCAmelCase_			,       UpperCAmelCase_:		Dict       			=    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,      batched=SCREAMING_SNAKE_CASE_				)
						self.assertEqual(
						    encoded_images.shape,      (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    ),      )

			def     __snake_case					(self				)					->      int:
						# Initialize image_processing
						UpperCAmelCase_:		Dict       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random PyTorch tensors
						UpperCAmelCase_:		List[str]       			=    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
						UpperCAmelCase_:		Optional[Any]       			=    image_processing(image_inputs[0],      return_tensors="""pt"""				).pixel_values

						UpperCAmelCase_			,       UpperCAmelCase_:		Any       			=    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_				)
						self.assertEqual(
						    encoded_images.shape,      (1, self.image_processor_tester.num_channels, expected_height, expected_width),      )

						# Test batched
						UpperCAmelCase_:		Optional[Any]       			=    image_processing(SCREAMING_SNAKE_CASE_,      return_tensors="""pt"""				).pixel_values

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











import numpy as np







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[Any]     ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]       			=    np.exp(lowerCAmelCase__     )

			# Add up the all the exponentials
			UpperCAmelCase_:		Tuple       			=    np.sum(lowerCAmelCase__     )

			# Divide every exponent by the sum of all exponents
			UpperCAmelCase_:		str       			=    exponent_vector / sum_of_exponents

			return softmax_vector


if __name__ == "__main__":
	print(softmax((0,)))






