



'''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      UpperCamelCase_					(				unittest.TestCase     ):





	def __init__(    self						,						A						,						A=7						,						A=3						,						A=18						,						A=30						,						A=400						,						A=True						,						A=None						,						A=2						,						A=True						,						A=[0.5, 0.5, 0.5]						,						A=[0.5, 0.5, 0.5]						,						)     ->     Dict:
								UpperCAmelCase	:						Optional[int]					=						size if size is not None else {"""shortest_edge""": 30}
								UpperCAmelCase	:						Optional[Any]					=						parent
								UpperCAmelCase	:						Tuple					=						batch_size
								UpperCAmelCase	:						Any					=						num_channels
								UpperCAmelCase	:						Any					=						image_size
								UpperCAmelCase	:						Optional[Any]					=						min_resolution
								UpperCAmelCase	:						List[Any]					=						max_resolution
								UpperCAmelCase	:						List[Any]					=						do_resize
								UpperCAmelCase	:						Any					=						size
								UpperCAmelCase	:						Tuple					=						size_divisor
								UpperCAmelCase	:						int					=						do_normalize
								UpperCAmelCase	:						Dict					=						image_mean
								UpperCAmelCase	:						Union[str, Any]					=						image_std





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

															UpperCAmelCase	:						Optional[int]					=						int((1333 / 800) * size       )
															if max(lowerCAmelCase__						,						lowerCAmelCase__       ) > max_size:
																						UpperCAmelCase	:						Dict					=						max_size / max(lowerCAmelCase__						,						lowerCAmelCase__       )
																						UpperCAmelCase	:						int					=						newh * scale
																						UpperCAmelCase	:						List[str]					=						neww * scale

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

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

								return expected_height, expected_width




@require_torch
@require_vision
class      UpperCamelCase_					(				lowercase__ ,	unittest.TestCase     ):
	lowercase		=					ViltImageProcessor if is_vision_available() else None





	def 					_lowercase(    self       )     ->     Tuple:
								UpperCAmelCase	:						Union[str, Any]					=						ViltImageProcessingTester(self       )





	@property
	def 					_lowercase(    self       )     ->     Any:
								return self.image_processor_tester.prepare_image_processor_dict()





	def 					_lowercase(    self       )     ->     str:
								UpperCAmelCase	:						Dict					=						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 					_lowercase(    self       )     ->     str:
								UpperCAmelCase	:						Dict					=						self.image_processing_class.from_dict(self.image_processor_dict       )
								self.assertEqual(image_processor.size						,						{"""shortest_edge""": 30}       )

								UpperCAmelCase	:						Tuple					=						self.image_processing_class.from_dict(self.image_processor_dict						,						size=42       )
								self.assertEqual(image_processor.size						,						{"""shortest_edge""": 42}       )





	def 					_lowercase(    self       )     ->     str:
								pass





	def 					_lowercase(    self       )     ->     Dict:
								UpperCAmelCase	:						Union[str, Any]					=						self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								UpperCAmelCase	:						List[str]					=						prepare_image_inputs(self.image_processor_tester						,						equal_resolution=lowerCAmelCase__       )
								for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__						,						Image.Image       )

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

								UpperCAmelCase	,	UpperCAmelCase	:						str					=						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
								UpperCAmelCase	:						Dict					=						image_processing(lowerCAmelCase__						,						return_tensors="""pt"""       ).pixel_values

								UpperCAmelCase	,	UpperCAmelCase	:						Tuple					=						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 					_lowercase(    self       )     ->     Optional[int]:
								UpperCAmelCase	:						Optional[int]					=						self.image_processing_class(**self.image_processor_dict       )
								# create random numpy tensors
								UpperCAmelCase	:						Dict					=						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
								UpperCAmelCase	:						Tuple					=						image_processing(image_inputs[0]						,						return_tensors="""pt"""       ).pixel_values

								UpperCAmelCase	,	UpperCAmelCase	:						Union[str, 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
								UpperCAmelCase	:						Optional[int]					=						image_processing(lowerCAmelCase__						,						return_tensors="""pt"""       ).pixel_values

								UpperCAmelCase	,	UpperCAmelCase	:						Dict					=						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 					_lowercase(    self       )     ->     Dict:
								UpperCAmelCase	:						Optional[Any]					=						self.image_processing_class(**self.image_processor_dict       )
								# create random PyTorch tensors
								UpperCAmelCase	:						Optional[int]					=						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
								UpperCAmelCase	:						Optional[int]					=						image_processing(image_inputs[0]						,						return_tensors="""pt"""       ).pixel_values

								UpperCAmelCase	,	UpperCAmelCase	:						Union[str, 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
								UpperCAmelCase	:						str					=						image_processing(lowerCAmelCase__						,						return_tensors="""pt"""       ).pixel_values

								UpperCAmelCase	,	UpperCAmelCase	:						str					=						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,
								    )						,						)









from collections import namedtuple

__UpperCAmelCase      =				namedtuple("from_to", "from_ to")

__UpperCAmelCase      =				{
    "millimeter": "mm",
    "centimeter": "cm",
    "meter": "m",
    "kilometer": "km",
    "inch": "in",
    "inche": "in",  # Trailing 's' has been stripped off
    "feet": "ft",
    "foot": "ft",
    "yard": "yd",
    "mile": "mi",
}

__UpperCAmelCase      =				{
    "mm": from_to(0.001, 1_0_0_0),
    "cm": from_to(0.01, 1_0_0),
    "m": from_to(1, 1),
    "km": from_to(1_0_0_0, 0.001),
    "in": from_to(0.0_254, 39.3_701),
    "ft": from_to(0.3_048, 3.28_084),
    "yd": from_to(0.9_144, 1.09_361),
    "mi": from_to(1_609.34, 0.000_621_371),
}





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->float:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		from_type.lower().rstrip('s'			)
 SCREAMING_SNAKE_CASE				    =		TYPE_CONVERSION.get(lowercase_	,  lowercase_			)
 SCREAMING_SNAKE_CASE				    =		to_type.lower().rstrip('s'			)
 SCREAMING_SNAKE_CASE				    =		TYPE_CONVERSION.get(lowercase_	,  lowercase_			)
 if new_from not in METRIC_CONVERSION:
  SCREAMING_SNAKE_CASE				    =		(
      f'''Invalid \'from_type\' value: {from_type!r}.\n'''
      f'''Conversion abbreviations are: {', '.join(lowercase_			)}'''
  )
  raise ValueError(lowercase_			)
 if new_to not in METRIC_CONVERSION:
  SCREAMING_SNAKE_CASE				    =		(
      f'''Invalid \'to_type\' value: {to_type!r}.\n'''
      f'''Conversion abbreviations are: {', '.join(lowercase_			)}'''
  )
  raise ValueError(lowercase_			)
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
      import doctest

      doctest.testmod()




