




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 MobileNetVaImageProcessor





class 		A__				(   unittest.TestCase ):






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase=7				,     lowerCamelCase=3				,     lowerCamelCase=18				,     lowerCamelCase=30				,     lowerCamelCase=400				,     lowerCamelCase=True				,     lowerCamelCase=None				,     lowerCamelCase=True				,     lowerCamelCase=None				,     )      ->   Optional[int]:




						"""simple docstring"""






						__magic_name__					:							Tuple		   =							size if size is not None else {'''shortest_edge''': 20}
						__magic_name__					:							List[str]		   =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
						__magic_name__					:							List[str]		   =							parent
						__magic_name__					:							Union[str, Any]		   =							batch_size
						__magic_name__					:							str		   =							num_channels
						__magic_name__					:							Any		   =							image_size
						__magic_name__					:							List[str]		   =							min_resolution
						__magic_name__					:							str		   =							max_resolution
						__magic_name__					:							str		   =							do_resize
						__magic_name__					:							Optional[Any]		   =							size
						__magic_name__					:							Any		   =							do_center_crop
						__magic_name__					:							Tuple		   =							crop_size






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




						"""simple docstring"""






						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						}





@require_torch
@require_vision
class 		A__				(   __SCREAMING_SNAKE_CASE		,     unittest.TestCase ):
	lowerCamelCase__		: Optional[Any]				    =MobileNetVaImageProcessor if is_vision_available() else None






	def  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							MobileNetVaImageProcessingTester(self						)






	@property
	def  lowercase      (	self						)      ->   List[str]:




						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()






	def  lowercase      (	self						)      ->   Optional[int]:




						"""simple docstring"""






						__magic_name__					:							Tuple		   =							self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''do_resize'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''size'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''do_center_crop'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''center_crop'''						)						)






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




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size				,     {'''shortest_edge''': 20}						)
						self.assertEqual(image_processor.crop_size				,     {'''height''': 18, '''width''': 18}						)

						__magic_name__					:							str		   =							self.image_processing_class.from_dict(self.image_processor_dict				,     size=42				,     crop_size=84						)
						self.assertEqual(image_processor.size				,     {'''shortest_edge''': 42}						)
						self.assertEqual(image_processor.crop_size				,     {'''height''': 84, '''width''': 84}						)






	def  lowercase      (	self						)      ->   List[Any]:




						"""simple docstring"""






						pass






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






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

						# Test not batched input
						__magic_name__					:							Optional[int]		   =							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.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )

						# Test batched
						__magic_name__					:							Optional[int]		   =							image_processing(lowerCamelCase				,     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.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )






	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							Tuple		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						__magic_name__					:							Union[str, Any]		   =							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
						__magic_name__					:							List[str]		   =							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.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )

						# Test batched
						__magic_name__					:							Union[str, Any]		   =							image_processing(lowerCamelCase				,     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.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )






	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							Tuple		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						__magic_name__					:							Any		   =							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
						__magic_name__					:							List[Any]		   =							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.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )

						# Test batched
						__magic_name__					:							Union[str, Any]		   =							image_processing(lowerCamelCase				,     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.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )









from typing import Any





class 		A__				:






	def __init__(	self						)      ->   int:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							[]
						__magic_name__					:							str		   =							0






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




						"""simple docstring"""






						__magic_name__					:							str		   =							'''<''' + str(self.stack						)[1:-1] + '''>'''
						return printed






	def  lowercase      (	self				,     lowerCamelCase						)      ->   None:




						"""simple docstring"""






						self.stack.append(lowerCamelCase						)
						__magic_name__					:							Tuple		   =							self.length + 1






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						self.rotate(1						)
						__magic_name__					:							Union[str, Any]		   =							self.stack[self.length - 1]
						__magic_name__					:							Union[str, Any]		   =							self.stack[:-1]
						self.rotate(self.length - 1						)
						__magic_name__					:							List[Any]		   =							self.length - 1
						return dequeued






	def  lowercase      (	self				,     lowerCamelCase						)      ->   None:




						"""simple docstring"""






						for _ in range(lowerCamelCase						):
											__magic_name__					:							str		   =							self.stack[0]
											__magic_name__					:							Optional[Any]		   =							self.stack[1:]
											self.put(lowerCamelCase						)
											__magic_name__					:							List[Any]		   =							self.length - 1






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							self.get()
						self.put(lowerCamelCase						)
						self.rotate(self.length - 1						)
						return front






	def  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						return self.length



