
from numpy import exp, pi, sqrt







def     A__   (  lowerCamelCase       ,      lowerCamelCase = 0.0       ,      lowerCamelCase = 1.0  )					->		int:
				return 1 / sqrt(2 * pi * sigma**2  ) * exp(-((x - mu) ** 2) / (2 * sigma**2)  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()








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	:    Optional[int]  ,		snake_case_	:    Dict  ,		snake_case_	:    Tuple=7  ,		snake_case_	:    Optional[Any]=3  ,		snake_case_	:    Dict=18  ,		snake_case_	:    Dict=30  ,		snake_case_	:    Union[str, Any]=400  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    Any=None  ,		snake_case_	:    List[str]=True  ,		):
							UpperCamelCase_:							Dict		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Union[str, Any]		=  parent
							UpperCamelCase_:							Tuple		=  batch_size
							UpperCamelCase_:							List[str]		=  num_channels
							UpperCamelCase_:							Optional[int]		=  image_size
							UpperCamelCase_:							Dict		=  min_resolution
							UpperCamelCase_:							Optional[int]		=  max_resolution
							UpperCamelCase_:							str		=  do_resize
							UpperCamelCase_:							Tuple		=  size
							UpperCamelCase_:							Dict		=  do_normalize




			def  lowerCAmelCase__		(    self	:    str ):
							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'''

			__UpperCamelCase :  Optional[int]    =	ImageGPTImageProcessor if is_vision_available() else None

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Any		=  ImageGPTImageProcessingTester(self )

			@property
			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(snake_case_  ,		"""clusters""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""size""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize""" ) )

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18} )

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

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict )
							UpperCamelCase_:							Optional[int]		=  json.loads(image_processor.to_json_string() )
							for key, value in self.image_processor_dict.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		obj[key] ) )
											else:
															self.assertEqual(obj[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict )

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

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

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											image_processor_first.save_pretrained(snake_case_ )
											UpperCamelCase_:							Optional[int]		=  self.image_processing_class.from_pretrained(snake_case_ ).to_dict()

							UpperCamelCase_:							Union[str, Any]		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )




			@unittest.skip("""ImageGPT requires clusters at initialization""" )
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							pass







def     A__   (  )					->		Optional[int]:
				UpperCamelCase_:							Optional[int]		=  load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,      split="""test"""  )

				UpperCamelCase_:							Tuple		=  Image.open(dataset[4]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[5]["""file"""]  )

				UpperCamelCase_:							List[str]		=  [imagea, imagea]

				return images





@require_vision
@require_torch
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@slow
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							List[Any]		=  ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )

							UpperCamelCase_:							List[str]		=  prepare_images()

							# test non-batched
							UpperCamelCase_:							List[str]		=  image_processing(images[0]  ,		return_tensors="""pt""" )

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

							UpperCamelCase_:							Union[str, Any]		=  [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,		snake_case_ )

							# test batched
							UpperCamelCase_:							Optional[int]		=  image_processing(snake_case_  ,		return_tensors="""pt""" )

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

							UpperCamelCase_:							str		=  [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,		snake_case_ )






