





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		,      lowerCAmelCase__		,      lowerCAmelCase__=7		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      )   ->						List[Any]:



        '''simple docstring'''




        a__	:					int										=size if size is not None else {"height": 1_8, "width": 1_8}
        a__	:					Dict										=parent
        a__	:					Union[str, Any]										=batch_size
        a__	:					List[Any]										=num_channels
        a__	:					str										=image_size
        a__	:					Any										=min_resolution
        a__	:					Dict										=max_resolution
        a__	:					Optional[int]										=do_resize
        a__	:					List[str]										=size
        a__	:					Union[str, Any]										=do_normalize







      def    _lowercase  (    self							)   ->						Tuple:



        '''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 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					Dict						=			ImageGPTImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Tuple										=ImageGPTImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										=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    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[int]										=self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,      {"height": 1_8, "width": 1_8}							)

        a__	:					Optional[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    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Dict										=self.image_processing_class(**self.image_processor_dict							)
        a__	:					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(lowerCAmelCase__		,      obj[key]							)							)
          else:
            self.assertEqual(obj[key]		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.image_processing_class(**self.image_processor_dict							)

        with tempfile.TemporaryDirectory() as tmpdirname:
          a__	:					Tuple										=os.path.join(lowerCAmelCase__		,      "image_processor.json"							)
          image_processor_first.to_json_file(lowerCAmelCase__							)
          a__	:					List[Any]										=self.image_processing_class.from_json_file(lowerCAmelCase__							).to_dict()

        a__	:					Tuple										=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    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




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

        with tempfile.TemporaryDirectory() as tmpdirname:
          image_processor_first.save_pretrained(lowerCAmelCase__							)
          a__	:					List[Any]										=self.image_processing_class.from_pretrained(lowerCAmelCase__							).to_dict()

        a__	:					List[Any]										=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    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        pass
def      _A       (		):





  """simple docstring"""



  a__	:					Optional[int]										=load_dataset("hf-internal-testing/fixtures_image_utils"       ,       split="test"						)

  a__	:					Union[str, Any]										=Image.open(dataset[4]["file"]						)
  a__	:					Any										=Image.open(dataset[5]["file"]						)

  a__	:					str										=[imagea, imagea]

  return images







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







      @slow
      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					Tuple										=ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"							)

        a__	:					List[Any]										=prepare_images()

        # test non-batched
        a__	:					List[str]										=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)							)

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

        # test batched
        a__	:					Optional[Any]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							)

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

        a__	:					Tuple										=[3_0_3, 1_3, 1_3]
        self.assertEqual(encoding.input_ids[1, -3:].tolist()		,      lowerCAmelCase__							)













def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    raise ValueError("Input series is not valid, valid series - [2, 4, 6]"						)
  if len(SCREAMING_SNAKE_CASE						) == 0:
    raise ValueError("Input list must be a non empty list"						)
  if len(SCREAMING_SNAKE_CASE						) == 1:
    return True
  a__	:					Union[str, Any]										=series[1] - series[0]
  for index in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True
def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    raise ValueError("Input series is not valid, valid series - [2, 4, 6]"						)
  if len(SCREAMING_SNAKE_CASE						) == 0:
    raise ValueError("Input list must be a non empty list"						)
  a__	:					Any										=0
  for val in series:
    answer += val
  return answer / len(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






