'''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 LevitImageProcessor






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=3       ,    __lowerCAmelCase=18       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  size if size is not None else {"""shortest_edge""": 18}
					lowerCAmelCase   					=  crop_size if crop_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_center_crop
					lowerCAmelCase   					=  crop_size
					lowerCAmelCase   					=  do_normalize
					lowerCAmelCase   					=  image_mean
					lowerCAmelCase   					=  image_std







			def 							a_       (   self):




					"""simple docstring"""


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







@require_torch
@require_vision
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       LevitImageProcessor if is_vision_available() else None



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  LevitImageProcessingTester(self)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  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       ,    """do_center_crop"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """size"""))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size       ,    {"""shortest_edge""": 18})
					self.assertEqual(image_processor.crop_size       ,    {"""height""": 18, """width""": 18})

					lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					pass



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					lowerCAmelCase   					=  prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase       ,    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.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					lowerCAmelCase   					=  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
					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.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					lowerCAmelCase   					=  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
					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.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  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"""],
					    )       ,    )





'''simple docstring'''




import os
import string
import sys


__lowercase          =  1 << 8

__lowercase          =  {
    '''tab''': ord('''\t'''),
    '''newline''': ord('''\r'''),
    '''esc''': 2_7,
    '''up''': 6_5 + ARROW_KEY_FLAG,
    '''down''': 6_6 + ARROW_KEY_FLAG,
    '''right''': 6_7 + ARROW_KEY_FLAG,
    '''left''': 6_8 + ARROW_KEY_FLAG,
    '''mod_int''': 9_1,
    '''undefined''': sys.maxsize,
    '''interrupt''': 3,
    '''insert''': 5_0,
    '''delete''': 5_1,
    '''pg_up''': 5_3,
    '''pg_down''': 5_4,
}

__lowercase          =  KEYMAP['''up''']
__lowercase          =  KEYMAP['''left''']

if sys.platform == "win32":
						__lowercase          =  []
						__lowercase          =  {
						    B'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
						    B'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
						    B'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
						    B'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
						    B'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
						    B'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
						    B'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
						    B'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
						}

for i in range(1_0):
						__lowercase          =  ord(str(i))






def 				snake_case__							(			)    ->	List[Any]:

		'''simple docstring'''


		if os.name == "nt":
				import msvcrt

				lowerCAmelCase   					=  """mbcs"""
				# Flush the keyboard buffer
				while msvcrt.kbhit():
						msvcrt.getch()
				if len(_A ) == 0:
						# Read the keystroke
						lowerCAmelCase   					=  msvcrt.getch()

						# If it is a prefix char, get second part
						if ch in (b"\x00", b"\xe0"):
								lowerCAmelCase   					=  ch + msvcrt.getch()
								# Translate actual Win chars to bullet char types
								try:
										lowerCAmelCase   					=  chr(WIN_KEYMAP[cha] )
										WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
										WIN_CH_BUFFER.append(_A )
										if ord(_A ) in (
										    KEYMAP["insert"] - 1 << 9,
										    KEYMAP["delete"] - 1 << 9,
										    KEYMAP["pg_up"] - 1 << 9,
										    KEYMAP["pg_down"] - 1 << 9,
										):
												WIN_CH_BUFFER.append(chr(126 ) )
										lowerCAmelCase   					=  chr(KEYMAP["""esc"""] )
								except KeyError:
										lowerCAmelCase   					=  cha[1]
						else:
								lowerCAmelCase   					=  ch.decode(_A )
				else:
						lowerCAmelCase   					=  WIN_CH_BUFFER.pop(0 )
		elif os.name == "posix":
				import termios
				import tty

				lowerCAmelCase   					=  sys.stdin.fileno()
				lowerCAmelCase   					=  termios.tcgetattr(_A )
				try:
						tty.setraw(_A )
						lowerCAmelCase   					=  sys.stdin.read(1 )
				finally:
						termios.tcsetattr(_A      ,   termios.TCSADRAIN      ,   _A )
		return ch






def 				snake_case__							(			)    ->	Tuple:

		'''simple docstring'''


		lowerCAmelCase   					=  get_raw_chars()
		if ord(_A ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
				return char

		elif ord(_A ) == KEYMAP["esc"]:
				lowerCAmelCase   					=  get_raw_chars()
				if ord(_A ) == KEYMAP["mod_int"]:
						lowerCAmelCase   					=  get_raw_chars()
						if ord(_A ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_A ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
								return chr(ord(_A ) + ARROW_KEY_FLAG )
						else:
								return KEYMAP["undefined"]
				else:
						return get_raw_chars()

		else:
				if char in string.printable:
						return char
				else:
						return KEYMAP["undefined"]




