






"""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    __lowerCamelCase    (  unittest.TestCase  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=7 ,				snake_case_=3 ,				snake_case_=18 ,				snake_case_=30 ,				snake_case_=400 ,				snake_case_=True ,				snake_case_=None ,				snake_case_=True ,				)		->       Any:
       UpperCamelCase__         =    size if size is not None else {'height': 18, 'width': 18}
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    image_size
       UpperCamelCase__         =    min_resolution
       UpperCamelCase__         =    max_resolution
       UpperCamelCase__         =    do_resize
       UpperCamelCase__         =    size
       UpperCamelCase__         =    do_normalize







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       return {
           # here we create 2 clusters for the sake of simplicity
           "clusters": np.asarray(
               [
                   [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                   [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
               ]      ),
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
       }







@require_torch
@require_vision
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Any 						=ImageGPTImageProcessor if is_vision_available() else None

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    ImageGPTImageProcessingTester(self      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return self.image_processor_tester.prepare_image_processor_dict()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size ,				{'height': 18, 'width': 18}      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )

       with tempfile.TemporaryDirectory() as tmpdirname:
           UpperCamelCase__         =    os.path.join(snake_case_ ,				'image_processor.json'      )
           image_processor_first.to_json_file(snake_case_      )
           UpperCamelCase__         =    self.image_processing_class.from_json_file(snake_case_      ).to_dict()

       UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )

       with tempfile.TemporaryDirectory() as tmpdirname:
           image_processor_first.save_pretrained(snake_case_      )
           UpperCamelCase__         =    self.image_processing_class.from_pretrained(snake_case_      ).to_dict()

       UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       pass







def   lowerCAmelCase_( )					->	List[Any]:
    """simple docstring"""

    UpperCamelCase__         =    load_dataset('hf-internal-testing/fixtures_image_utils' ,	split='test'     )

    UpperCamelCase__         =    Image.open(dataset[4]['file']     )
    UpperCamelCase__         =    Image.open(dataset[5]['file']     )

    UpperCamelCase__         =    [imagea, imagea]

    return images





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

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

       UpperCamelCase__         =    prepare_images()

       # test non-batched
       UpperCamelCase__         =    image_processing(images[0] ,				return_tensors='pt'      )

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

       UpperCamelCase__         =    [306, 191, 191]
       self.assertEqual(encoding.input_ids[0, :3].tolist() ,				snake_case_      )

       # test batched
       UpperCamelCase__         =    image_processing(snake_case_ ,				return_tensors='pt'      )

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

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












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	bool:
    """simple docstring"""

    UpperCamelCase__         =    (1 + 24 * n) ** 0.5
    return ((1 + root) / 6) % 1 == 0







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 50_00					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    [(i * (3 * i - 1)) // 2 for i in range(1 ,	SCREAMING_SNAKE_CASE					)]
    for i, pentagonal_i in enumerate(SCREAMING_SNAKE_CASE					):
        for j in range(SCREAMING_SNAKE_CASE ,	len(SCREAMING_SNAKE_CASE					)					):
            UpperCamelCase__         =    pentagonal_nums[j]
            UpperCamelCase__         =    pentagonal_i + pentagonal_j
            UpperCamelCase__         =    pentagonal_j - pentagonal_i
            if is_pentagonal(SCREAMING_SNAKE_CASE					) and is_pentagonal(SCREAMING_SNAKE_CASE					):
                return b

    return -1


if __name__ == "__main__":
  print(F"""{solution() = }""")




