
"""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 EfficientNetImageProcessor
class      lowerCAmelCase_						(							unittest.TestCase				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_=13  ,							snake_case_=3  ,							snake_case_=18  ,							snake_case_=30  ,							snake_case_=400  ,							snake_case_=True  ,							snake_case_=None  ,							snake_case_=True  ,							snake_case_=[0.5, 0.5, 0.5]  ,							snake_case_=[0.5, 0.5, 0.5]  ,							)   ->       List[Any]:
													__lowerCAmelCase        =       size if size is not None else {"""height""": 18, """width""": 18}
													__lowerCAmelCase        =       parent
													__lowerCAmelCase        =       batch_size
													__lowerCAmelCase        =       num_channels
													__lowerCAmelCase        =       image_size
													__lowerCAmelCase        =       min_resolution
													__lowerCAmelCase        =       max_resolution
													__lowerCAmelCase        =       do_resize
													__lowerCAmelCase        =       size
													__lowerCAmelCase        =       do_normalize
													__lowerCAmelCase        =       image_mean
													__lowerCAmelCase        =       image_std




							def  A__		(				self							)   ->       Union[str, Any]:
													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      lowerCAmelCase_						(							A__							,							unittest.TestCase				):



							'''simple docstring'''





							_snake_case		     =						EfficientNetImageProcessor if is_vision_available() else None




							def  A__		(				self							)   ->       str:
													__lowerCAmelCase        =       EfficientNetImageProcessorTester(self							)




							@property
							def  A__		(				self							)   ->       int:
													return self.image_processor_tester.prepare_image_processor_dict()




							def  A__		(				self							)   ->       Dict:
													__lowerCAmelCase        =       self.image_processing_class(**self.image_processor_dict							)
													self.assertTrue(hasattr(snake_case_  ,							"""image_mean"""							)							)
													self.assertTrue(hasattr(snake_case_  ,							"""image_std"""							)							)
													self.assertTrue(hasattr(snake_case_  ,							"""do_normalize"""							)							)
													self.assertTrue(hasattr(snake_case_  ,							"""do_resize"""							)							)
													self.assertTrue(hasattr(snake_case_  ,							"""size"""							)							)




							def  A__		(				self							)   ->       Union[str, Any]:
													__lowerCAmelCase        =       self.image_processing_class.from_dict(self.image_processor_dict							)
													self.assertEqual(image_processor.size  ,							{"""height""": 18, """width""": 18}							)

													__lowerCAmelCase        =       self.image_processing_class.from_dict(self.image_processor_dict  ,							size=42							)
													self.assertEqual(image_processor.size  ,							{"""height""": 42, """width""": 42}							)




							def  A__		(				self							)   ->       Union[str, Any]:
													# Initialize image_processing
													__lowerCAmelCase        =       self.image_processing_class(**self.image_processor_dict							)
													# create random PIL images
													__lowerCAmelCase        =       prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=snake_case_							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_  ,							Image.Image							)

													# Test not batched input
													__lowerCAmelCase        =       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
													__lowerCAmelCase        =       image_processing(snake_case_  ,							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  A__		(				self							)   ->       int:
													# Initialize image_processing
													__lowerCAmelCase        =       self.image_processing_class(**self.image_processor_dict							)
													# create random numpy tensors
													__lowerCAmelCase        =       prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=snake_case_  ,							numpify=snake_case_							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_  ,							np.ndarray							)

													# Test not batched input
													__lowerCAmelCase        =       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
													__lowerCAmelCase        =       image_processing(snake_case_  ,							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  A__		(				self							)   ->       Union[str, Any]:
													# Initialize image_processing
													__lowerCAmelCase        =       self.image_processing_class(**self.image_processor_dict							)
													# create random PyTorch tensors
													__lowerCAmelCase        =       prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=snake_case_  ,							torchify=snake_case_							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_  ,							torch.Tensor							)

													# Test not batched input
													__lowerCAmelCase        =       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
													__lowerCAmelCase        =       image_processing(snake_case_  ,							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"""],
													    )  ,							)


"""simple docstring"""
from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_			      =					TypeVar('''T''')
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							Generic[T]				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_ = 10							)   ->       Tuple:
													__lowerCAmelCase        =       []
													__lowerCAmelCase        =       limit




							def __bool__(				self							)   ->       bool:
													return bool(self.stack							)




							def __str__(				self							)   ->       str:
													return str(self.stack							)




							def  A__		(				self  ,							snake_case_							)   ->       None:
													if len(self.stack							) >= self.limit:
																			raise StackOverflowError
													self.stack.append(snake_case_							)




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack.pop()




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack[-1]




							def  A__		(				self							)   ->       bool:
													return not bool(self.stack							)




							def  A__		(				self							)   ->       bool:
													return self.size() == self.limit




							def  A__		(				self							)   ->       int:
													return len(self.stack							)




							def __contains__(				self  ,							snake_case_							)   ->       bool:
													return item in self.stack

def 				lowercase      ():
						__lowerCAmelCase        =       Stack(10		)
						assert bool(_lowerCAmelCase		) is False
						assert stack.is_empty() is True
						assert stack.is_full() is False
						assert str(_lowerCAmelCase		) == "[]"

						try:
												__lowerCAmelCase        =       stack.pop()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						try:
												__lowerCAmelCase        =       stack.peek()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						for i in range(10		):
												assert stack.size() == i
												stack.push(_lowerCAmelCase		)

						assert bool(_lowerCAmelCase		)
						assert not stack.is_empty()
						assert stack.is_full()
						assert str(_lowerCAmelCase		) == str(list(range(10		)		)		)
						assert stack.pop() == 9
						assert stack.peek() == 8

						stack.push(100		)
						assert str(_lowerCAmelCase		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

						try:
												stack.push(200		)
												raise AssertionError  # This should not happen
						except StackOverflowError:
												assert True  # This should happen

						assert not stack.is_empty()
						assert stack.size() == 10

						assert 5 in stack
						assert 55 not in stack


if __name__ == "__main__":
			test_stack()
