



"""simple docstring"""







import os
import string
import sys


UpperCamelCase     =							1 << 8

UpperCamelCase     =							{
    """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,
}

UpperCamelCase     =							KEYMAP["""up"""]
UpperCamelCase     =							KEYMAP["""left"""]

if sys.platform == "win32":
			UpperCamelCase     =							[]
			UpperCamelCase     =							{
			    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):
			UpperCamelCase     =							ord(str(i))

def       _lowerCamelCase				(							)	-> int:






	"""simple docstring"""





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

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

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

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




def       _lowerCamelCase				(							)	-> List[str]:






	"""simple docstring"""





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

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

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








"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=7 ,     lowerCamelCase__=3 ,     lowerCamelCase__=18 ,     lowerCamelCase__=30 ,     lowerCamelCase__=400 ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     )      ->							int:
    lowercase__       :       int				=						size if size is not None else {"""height""": 18, """width""": 18}
    lowercase__       :       Optional[Any]				=						parent
    lowercase__       :       Dict				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Tuple				=						image_size
    lowercase__       :       str				=						min_resolution
    lowercase__       :       Optional[Any]				=						max_resolution
    lowercase__       :       Union[str, Any]				=						do_resize
    lowercase__       :       Dict				=						size
    lowercase__       :       Optional[Any]				=						do_normalize







   def 					UpperCAmelCase__(						self						)      ->							Any:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
            ]						),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }

@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    ImageGPTImageProcessor if is_vision_available() else None


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						ImageGPTImageProcessingTester(self						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """clusters"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_resize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """size"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       Optional[Any]				=						self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size ,     {"""height""": 18, """width""": 18}						)

    lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_dict(self.image_processor_dict ,     size=42						)
    self.assertEqual(image_processor.size ,     {"""height""": 42, """width""": 42}						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    lowercase__       :       Optional[Any]				=						json.loads(image_processor.to_json_string()						)
    for key, value in self.image_processor_dict.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     obj[key]						)						)
     else:
      self.assertEqual(obj[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     lowercase__       :       Optional[int]				=						os.path.join(lowerCamelCase__ ,     """image_processor.json"""						)
     image_processor_first.to_json_file(lowerCamelCase__						)
     lowercase__       :       str				=						self.image_processing_class.from_json_file(lowerCamelCase__						).to_dict()

    lowercase__       :       Optional[int]				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_pretrained(lowerCamelCase__						).to_dict()

    lowercase__       :       Dict				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)







   @unittest.skip("""ImageGPT requires clusters at initialization"""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


def    _lowerCamelCase   (     ):
 lowercase__       :       Tuple				=						load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,		split="""test"""  )

 lowercase__       :       Optional[int]				=						Image.open(dataset[4]["""file"""]  )
 lowercase__       :       Union[str, Any]				=						Image.open(dataset[5]["""file"""]  )

 lowercase__       :       Optional[int]				=						[imagea, imagea]

 return images






@require_vision
@require_torch
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Optional[int]				=						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""						)

    lowercase__       :       int				=						prepare_images()

    # test non-batched
    lowercase__       :       int				=						image_processing(images[0] ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (1, 1024)						)

    lowercase__       :       Any				=						[306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist() ,     lowerCamelCase__						)

    # test batched
    lowercase__       :       str				=						image_processing(lowerCamelCase__ ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (2, 1024)						)

    lowercase__       :       Optional[int]				=						[303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist() ,     lowerCamelCase__						)