

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 _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				size if size is not None else {"height": 18, "width": 18}
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				parent
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				batch_size
				SCREAMING_SNAKE_CASE      :					Tuple    =				num_channels
				SCREAMING_SNAKE_CASE      :					Any    =				image_size
				SCREAMING_SNAKE_CASE      :					List[Any]    =				min_resolution
				SCREAMING_SNAKE_CASE      :					str    =				max_resolution
				SCREAMING_SNAKE_CASE      :					List[Any]    =				do_resize
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				size
				SCREAMING_SNAKE_CASE      :					Dict    =				do_normalize






			def 							__UpperCamelCase       (	self							:       Tuple		)							->				Optional[Any]:

				"""simple docstring"""

				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 _UpperCamelCase							(							__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=ImageGPTImageProcessor if is_vision_available() else None
			def 							__UpperCamelCase       (	self							:       Dict		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				ImageGPTImageProcessingTester(self		)
			@property
			def 							__UpperCamelCase       (	self							:       str		)							->				Any:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(a					,					"clusters"		)		)
				self.assertTrue(hasattr(a					,					"do_resize"		)		)
				self.assertTrue(hasattr(a					,					"size"		)		)
				self.assertTrue(hasattr(a					,					"do_normalize"		)		)
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Optional[Any]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE      :					List[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]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.image_processing_class(**self.image_processor_dict		)
				SCREAMING_SNAKE_CASE      :					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(a					,					obj[key]		)		)
					else:
						self.assertEqual(obj[key]					,					a		)
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				self.image_processing_class(**self.image_processor_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE      :					int    =				os.path.join(a					,					"image_processor.json"		)
					image_processor_first.to_json_file(a		)
					SCREAMING_SNAKE_CASE      :					Any    =				self.image_processing_class.from_json_file(a		).to_dict()

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				image_processor_first.to_dict()
				for key, value in image_processor_first.items():
					if key == "clusters":
						self.assertTrue(np.array_equal(a					,					image_processor_second[key]		)		)
					else:
						self.assertEqual(image_processor_first[key]					,					a		)
			def 							__UpperCamelCase       (	self							:       str		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.image_processing_class(**self.image_processor_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
					image_processor_first.save_pretrained(a		)
					SCREAMING_SNAKE_CASE      :					Tuple    =				self.image_processing_class.from_pretrained(a		).to_dict()

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






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

				"""simple docstring"""

				pass

def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					List[Any]    =				load_dataset("hf-internal-testing/fixtures_image_utils"							,	split="test")

	SCREAMING_SNAKE_CASE      :					Dict    =				Image.open(dataset[4]["file"])
	SCREAMING_SNAKE_CASE      :					List[Any]    =				Image.open(dataset[5]["file"])

	SCREAMING_SNAKE_CASE      :					List[Any]    =				[imagea, imagea]

	return images






@require_vision
@require_torch
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			@slow
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"		)

				SCREAMING_SNAKE_CASE      :					Dict    =				prepare_images()

				# test non-batched
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				image_processing(images[0]					,					return_tensors="pt"		)

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

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[306, 191, 191]
				self.assertEqual(encoding.input_ids[0, :3].tolist()					,					a		)

				# test batched
				SCREAMING_SNAKE_CASE      :					Dict    =				image_processing(a					,					return_tensors="pt"		)

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

				SCREAMING_SNAKE_CASE      :					Any    =				[303, 13, 13]
				self.assertEqual(encoding.input_ids[1, -3:].tolist()					,					a		)



"""simple docstring"""






import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
							import ctypes
							import msvcrt  # noqa





							class      lowerCamelCase__   (     ctypes.Structure				):







												"""simple docstring"""


												__a	       =							[("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def      lowerCamelCase       (     )		->       Optional[int]:

							'''simple docstring'''



							if os.name == "nt":
														__UpperCAmelCase   :						Dict      							=					CursorInfo()
														__UpperCAmelCase   :						Any      							=					ctypes.windll.kernelaa.GetStdHandle(-1_1							)
														ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
														__UpperCAmelCase   :						Tuple      							=					False
														ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
							elif os.name == "posix":
														sys.stdout.write("""\033[?25l"""							)
														sys.stdout.flush()
def      lowerCamelCase       (     )		->       Optional[int]:

							'''simple docstring'''



							if os.name == "nt":
														__UpperCAmelCase   :						str      							=					CursorInfo()
														__UpperCAmelCase   :						int      							=					ctypes.windll.kernelaa.GetStdHandle(-1_1							)
														ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
														__UpperCAmelCase   :						Union[str, Any]      							=					True
														ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
							elif os.name == "posix":
														sys.stdout.write("""\033[?25h"""							)
														sys.stdout.flush()
@contextmanager
def      lowerCamelCase       (     )		->       str:

							'''simple docstring'''



							try:
														hide_cursor()
														yield
							finally:
														show_cursor()
