



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			):






   """simple docstring"""





   def __init__(				self	:       Union[str, Any]			,     lowercase	:       Optional[int]			,     lowercase	:       Union[str, Any]=7			,     lowercase	:       Tuple=3			,     lowercase	:       str=18			,     lowercase	:       List[str]=30			,     lowercase	:       Dict=400			,     lowercase	:       List[Any]=True			,     lowercase	:       int=None			,     lowercase	:       List[str]=True			,     )						->					Dict:
         """simple docstring"""







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







   def 		snake_case__			(				self	:       Optional[Any]   )						->					Dict:
         """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 			_lowerCAmelCase   ( lowerCAmelCase__    ,					unittest.TestCase			):






   """simple docstring"""





   lowercase__      :  Any      =			ImageGPTImageProcessor if is_vision_available() else None





   def 		snake_case__			(				self	:       Optional[int]   )						->					str:
         """simple docstring"""







         __lowercase					=				ImageGPTImageProcessingTester(self   )





   @property
   def 		snake_case__			(				self	:       List[str]   )						->					List[str]:
         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





   def 		snake_case__			(				self	:       List[str]   )						->					int:
         """simple docstring"""







         __lowercase					=				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 		snake_case__			(				self	:       Tuple   )						->					Optional[int]:
         """simple docstring"""







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

         __lowercase					=				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	:       Tuple   )						->					int:
         """simple docstring"""







         __lowercase					=				self.image_processing_class(**self.image_processor_dict   )
         __lowercase					=				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 		snake_case__			(				self	:       List[str]   )						->					List[str]:
         """simple docstring"""







         __lowercase					=				self.image_processing_class(**self.image_processor_dict   )

         with tempfile.TemporaryDirectory() as tmpdirname:
               __lowercase					=				os.path.join(_lowerCamelCase			,     """image_processor.json"""   )
               image_processor_first.to_json_file(_lowerCamelCase   )
               __lowercase					=				self.image_processing_class.from_json_file(_lowerCamelCase   ).to_dict()

         __lowercase					=				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 		snake_case__			(				self	:       List[str]   )						->					Optional[Any]:
         """simple docstring"""







         __lowercase					=				self.image_processing_class(**self.image_processor_dict   )

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

         __lowercase					=				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 		snake_case__			(				self	:       int   )						->					List[str]:
         """simple docstring"""







         pass






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

      __lowercase					=				Image.open(dataset[4]["""file"""]   )
      __lowercase					=				Image.open(dataset[5]["""file"""]   )

      __lowercase					=				[imagea, imagea]

      return images







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






   """simple docstring"""





   @slow
   def 		snake_case__			(				self	:       Optional[Any]   )						->					List[Any]:
         """simple docstring"""







         __lowercase					=				ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""   )

         __lowercase					=				prepare_images()

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

         self.assertIsInstance(encoding.input_ids			,     torch.LongTensor   )
         self.assertEqual(encoding.input_ids.shape			,     (1, 1_024)   )

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

         # test batched
         __lowercase					=				image_processing(_lowerCamelCase			,     return_tensors="""pt"""   )

         self.assertIsInstance(encoding.input_ids			,     torch.LongTensor   )
         self.assertEqual(encoding.input_ids.shape			,     (2, 1_024)   )

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










def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						__lowercase					=				(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
						# formula for sum of series
						return total






def  UpperCAmelCase__    (    ) ->      List[str]:
						print(sum_of_series(1						,       1						,       10   )   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





