
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 							A__		(	unittest.TestCase):
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=7    ,  __magic_name__=3    ,  __magic_name__=1_8    ,  __magic_name__=3_0    ,  __magic_name__=4_0_0    ,  __magic_name__=True    ,  __magic_name__=None    ,  __magic_name__=True    ,  ):
       lowerCamelCase		:	List[str]							=      size if size is not None else {"""height""": 1_8, """width""": 1_8}
       lowerCamelCase		:	Any							=      parent
       lowerCamelCase		:	Union[str, Any]							=      batch_size
       lowerCamelCase		:	int							=      num_channels
       lowerCamelCase		:	Union[str, Any]							=      image_size
       lowerCamelCase		:	int							=      min_resolution
       lowerCamelCase		:	Union[str, Any]							=      max_resolution
       lowerCamelCase		:	Dict							=      do_resize
       lowerCamelCase		:	List[Any]							=      size
       lowerCamelCase		:	Any							=      do_normalize






      def 						UpperCamelCase__							(      self      ):
       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 							A__		(	__SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    Dict           =			ImageGPTImageProcessor if is_vision_available() else None
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      ImageGPTImageProcessingTester(self      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.image_processor_tester.prepare_image_processor_dict()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(__magic_name__    ,  """clusters"""      )      )
       self.assertTrue(hasattr(__magic_name__    ,  """do_resize"""      )      )
       self.assertTrue(hasattr(__magic_name__    ,  """size"""      )      )
       self.assertTrue(hasattr(__magic_name__    ,  """do_normalize"""      )      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size    ,  {"""height""": 1_8, """width""": 1_8}      )

       lowerCamelCase		:	Union[str, Any]							=      self.image_processing_class.from_dict(self.image_processor_dict    ,  size=4_2      )
       self.assertEqual(image_processor.size    ,  {"""height""": 4_2, """width""": 4_2}      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      self.image_processing_class(**self.image_processor_dict      )
       lowerCamelCase		:	List[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(__magic_name__    ,  obj[key]      )      )
        else:
         self.assertEqual(obj[key]    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      self.image_processing_class(**self.image_processor_dict      )

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

       lowerCamelCase		:	Optional[int]							=      image_processor_first.to_dict()
       for key, value in image_processor_first.items():
        if key == "clusters":
         self.assertTrue(np.array_equal(__magic_name__    ,  image_processor_second[key]      )      )
        else:
         self.assertEqual(image_processor_first[key]    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      self.image_processing_class(**self.image_processor_dict      )

       with tempfile.TemporaryDirectory() as tmpdirname:
        image_processor_first.save_pretrained(__magic_name__      )
        lowerCamelCase		:	int							=      self.image_processing_class.from_pretrained(__magic_name__      ).to_dict()

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






      @unittest.skip("""ImageGPT requires clusters at initialization"""      )
      def 						UpperCamelCase__							(      self      ):
       pass





def     _a					(						):
 lowerCamelCase		:	Union[str, Any]							=      load_dataset("""hf-internal-testing/fixtures_image_utils""",	split="""test"""							)

 lowerCamelCase		:	Tuple							=      Image.open(dataset[4]["""file"""]							)
 lowerCamelCase		:	Optional[int]							=      Image.open(dataset[5]["""file"""]							)

 lowerCamelCase		:	str							=      [imagea, imagea]

 return images





@require_vision
@require_torch
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""      )

       lowerCamelCase		:	Optional[int]							=      prepare_images()

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

       self.assertIsInstance(encoding.input_ids    ,  torch.LongTensor      )
       self.assertEqual(encoding.input_ids.shape    ,  (1, 1_0_2_4)      )

       lowerCamelCase		:	Any							=      [3_0_6, 1_9_1, 1_9_1]
       self.assertEqual(encoding.input_ids[0, :3].tolist()    ,  __magic_name__      )

       # test batched
       lowerCamelCase		:	List[str]							=      image_processing(__magic_name__    ,  return_tensors="""pt"""      )

       self.assertIsInstance(encoding.input_ids    ,  torch.LongTensor      )
       self.assertEqual(encoding.input_ids.shape    ,  (2, 1_0_2_4)      )

       lowerCamelCase		:	int							=      [3_0_3, 1_3, 1_3]
       self.assertEqual(encoding.input_ids[1, -3:].tolist()    ,  __magic_name__      )






def     _a					(						lowerCamelCase							):

 if p < 2:
  raise ValueError("""p should not be less than 2!"""							)
 elif p == 2:
  return True

 lowerCamelCase		:	Any							=      4
 lowerCamelCase		:	List[str]							=      (1 << p) - 1
 for _ in range(p - 2							):
  lowerCamelCase		:	List[Any]							=      ((s * s) - 2) % m
 return s == 0


if __name__ == "__main__":
      print(lucas_lehmer_test(7))
      print(lucas_lehmer_test(1_1))




