
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]     ,						_A							:							Union[str, Any]     ,						_A							:							Union[str, Any]=7     ,						_A							:							Any=3     ,						_A							:							Union[str, Any]=18     ,						_A							:							List[Any]=30     ,						_A							:							Tuple=400     ,						_A							:							Union[str, Any]=True     ,						_A							:							List[Any]=None     ,						_A							:							List[str]=True     ,						):







       """simple docstring"""




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




     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       return {
           # here we create 2 clusters for the sake of simplicity
           "clusters": np.asarray(
               [
                   [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                   [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
               ] ),
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
       }






@require_torch
@require_vision
class     __UpperCamelCase (							lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			ImageGPTImageProcessor if is_vision_available() else None



     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		ImageGPTImageProcessingTester(self )



     @property
     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       return self.image_processor_tester.prepare_image_processor_dict()



     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		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] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size     ,						{'''height''': 18, '''width''': 18} )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		self.image_processing_class.from_dict(self.image_processor_dict     ,						size=42 )
       self.assertEqual(image_processor.size     ,						{'''height''': 42, '''width''': 42} )



     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.image_processing_class(**self.image_processor_dict )
       __SCREAMING_SNAKE_CASE      :	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(_A     ,						obj[key] ) )
         else:
           self.assertEqual(obj[key]     ,						_A )



     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




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

       with tempfile.TemporaryDirectory() as tmpdirname:
         __SCREAMING_SNAKE_CASE      :	List[str]					=		os.path.join(_A     ,						'''image_processor.json''' )
         image_processor_first.to_json_file(_A )
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.image_processing_class.from_json_file(_A ).to_dict()

       __SCREAMING_SNAKE_CASE      :	List[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							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.image_processing_class(**self.image_processor_dict )

       with tempfile.TemporaryDirectory() as tmpdirname:
         image_processor_first.save_pretrained(_A )
         __SCREAMING_SNAKE_CASE      :	List[str]					=		self.image_processing_class.from_pretrained(_A ).to_dict()

       __SCREAMING_SNAKE_CASE      :	Tuple					=		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							:							List[Any] ):







       """simple docstring"""




       pass






def 							a__					(							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Dict					=		load_dataset('''hf-internal-testing/fixtures_image_utils'''       ,			split='''test'''							)

  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		Image.open(dataset[4]['''file''']							)
  __SCREAMING_SNAKE_CASE      :	str					=		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							:							Any ):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		prepare_images()

       # test non-batched
       __SCREAMING_SNAKE_CASE      :	Tuple					=		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[int]					=		[306, 191, 191]
       self.assertEqual(encoding.input_ids[0, :3].tolist()     ,						_A )

       # test batched
       __SCREAMING_SNAKE_CASE      :	str					=		image_processing(_A     ,						return_tensors='''pt''' )

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

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






'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						if number < 0:
												raise ValueError('number must not be negative'							)
						return number & (number - 1) == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()

