import os
import string
import sys


_lowerCamelCase					:   Optional[Any]							=		1 << 8

_lowerCamelCase					:   List[Any]							=		{
    '''tab''': ord('''\t'''),
    '''newline''': ord('''\r'''),
    '''esc''': 27,
    '''up''': 65 + ARROW_KEY_FLAG,
    '''down''': 66 + ARROW_KEY_FLAG,
    '''right''': 67 + ARROW_KEY_FLAG,
    '''left''': 68 + ARROW_KEY_FLAG,
    '''mod_int''': 91,
    '''undefined''': sys.maxsize,
    '''interrupt''': 3,
    '''insert''': 50,
    '''delete''': 51,
    '''pg_up''': 53,
    '''pg_down''': 54,
}

_lowerCamelCase					:   Union[str, Any]							=		KEYMAP['''up''']
_lowerCamelCase					:   List[Any]							=		KEYMAP['''left''']

if sys.platform == "win32":
							_lowerCamelCase					:   Optional[int]							=		[]
							_lowerCamelCase					:   List[str]							=		{
							    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(10):
							_lowerCamelCase					:   Optional[int]							=		ord(str(i))





def 	__lowerCamelCase							():

					'''simple docstring'''





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

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

															# If it is a prefix char, get second part
															if ch in (b"\x00", b"\xe0"):
																				SCREAMING_SNAKE_CASE    			=       ch + msvcrt.getch()
																				# Translate actual Win chars to bullet char types
																				try:
																									SCREAMING_SNAKE_CASE    			=       chr(WIN_KEYMAP[cha]  )
																									WIN_CH_BUFFER.append(chr(KEYMAP["mod_int"]  )  )
																									WIN_CH_BUFFER.append(lowercase__  )
																									if ord(lowercase__  ) in (
																									    KEYMAP["insert"] - 1 << 9,
																									    KEYMAP["delete"] - 1 << 9,
																									    KEYMAP["pg_up"] - 1 << 9,
																									    KEYMAP["pg_down"] - 1 << 9,
																									):
																														WIN_CH_BUFFER.append(chr(1_2_6  )  )
																									SCREAMING_SNAKE_CASE    			=       chr(KEYMAP["esc"]  )
																				except KeyError:
																									SCREAMING_SNAKE_CASE    			=       cha[1]
															else:
																				SCREAMING_SNAKE_CASE    			=       ch.decode(lowercase__  )
										else:
															SCREAMING_SNAKE_CASE    			=       WIN_CH_BUFFER.pop(0  )
					elif os.name == "posix":
										import termios
										import tty

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





def 	__lowerCamelCase							():

					'''simple docstring'''





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

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

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





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor


class 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					List[Any]=7	,							_UpperCamelCase   :					Any=3	,							_UpperCamelCase   :					str=18	,							_UpperCamelCase   :					Tuple=30	,							_UpperCamelCase   :					Optional[int]=400	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Optional[int]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					List[str]=[0.5, 0.5, 0.5]	,							)					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"height": 18, "width": 18}
												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       min_resolution
												SCREAMING_SNAKE_CASE    			=       max_resolution
												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       do_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean
												SCREAMING_SNAKE_CASE    			=       image_std





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



												'''simple docstring'''

												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 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Any								=							DPTImageProcessor if is_vision_available() else None




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DPTImageProcessingTester(self      )




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



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_mean"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_std"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_normalize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_resize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

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

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




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



												'''simple docstring'''

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

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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 	__snake_case(						self   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							numpify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							np.ndarray      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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 	__snake_case(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							torchify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							torch.Tensor      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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"],
												    )	,							)




