




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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def __init__(self	:				Optional[int]    ,      a__	:				List[str]    ,      a__	:				List[Any]=7    ,      a__	:				List[Any]=3    ,      a__	:				List[Any]=18    ,      a__	:				Any=30    ,      a__	:				Tuple=400    ,      a__	:				Any=True    ,      a__	:				Optional[Any]=None    ,      a__	:				List[Any]=True    ,      ):





         """simple docstring"""



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


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         return {
             # here we create 2 clusters for the sake of simplicity
             "clusters": np.asarray(
                 [
                     [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                     [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
                 ]				),
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
         }






@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    Any							=   ImageGPTImageProcessor if is_vision_available() else None


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       ImageGPTImageProcessingTester(self				)


      @property
      def        a			(self	:				Tuple				):





         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       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        a			(self	:				Optional[int]				):





         """simple docstring"""



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

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


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.image_processing_class(**self.image_processor_dict				)
         __snake_case								=       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        a			(self	:				int				):





         """simple docstring"""



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

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

         __snake_case								=       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        a			(self	:				Optional[int]				):





         """simple docstring"""



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

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

         __snake_case								=       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        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         pass






def lowerCamelCase__   (							)				->    Optional[Any]:
   __snake_case								=       load_dataset('''hf-internal-testing/fixtures_image_utils'''	,				split='''test'''	)

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

   __snake_case								=       [imagea, imagea]

   return images







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


      @slow
      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''				)

         __snake_case								=       prepare_images()

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

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

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

         # test batched
         __snake_case								=       image_processing(a__    ,      return_tensors='''pt'''				)

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

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








import math






def lowerCamelCase__   (							snake_case_     :  int	)				->    list[int]:
   __snake_case								=       []
   __snake_case								=       2
   __snake_case								=       int(math.sqrt(snake_case_	)	)  # Size of every segment
   __snake_case								=       [True] * (end + 1)
   __snake_case								=       []

   while start <= end:
      if temp[start] is True:
         in_prime.append(snake_case_	)
         for i in range(start * start	,				end + 1	,				snake_case_	):
            __snake_case								=       False
      start += 1
   prime += in_prime

   __snake_case								=       end + 1
   __snake_case								=       min(2 * end	,				snake_case_	)

   while low <= n:
      __snake_case								=       [True] * (high - low + 1)
      for each in in_prime:
         __snake_case								=       math.floor(low / each	) * each
         if t < low:
            t += each

         for j in range(snake_case_	,				high + 1	,				snake_case_	):
            __snake_case								=       False

      for j in range(len(snake_case_	)	):
         if temp[j] is True:
            prime.append(j + low	)

      __snake_case								=       high + 1
      __snake_case								=       min(high + end	,				snake_case_	)

   return prime







print(sieve(10**6))

