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						):






 '''simple docstring'''






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







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        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						):






 '''simple docstring'''






 UpperCAmelCase_     			= ImageGPTImageProcessor if is_vision_available() else None




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = ImageGPTImageProcessingTester(self)




 @property
 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase__      ,   'clusters'))
        self.assertTrue(hasattr(lowercase__      ,   'do_resize'))
        self.assertTrue(hasattr(lowercase__      ,   'size'))
        self.assertTrue(hasattr(lowercase__      ,   'do_normalize'))




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



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

        lowerCAmelCase__					     = 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 :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processing_class(**self.image_processor_dict)
        lowerCAmelCase__					     = json.loads(image_processor.to_json_string())
        for key, value in self.image_processor_dict.items():
               if key == "clusters":
                      self.assertTrue(np.array_equal(lowercase__      ,   obj[key]))
               else:
                      self.assertEqual(obj[key]      ,   lowercase__)




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



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

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

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




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



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

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

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




 @unittest.skip('ImageGPT requires clusters at initialization')
 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        pass






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = load_dataset('hf-internal-testing/fixtures_image_utils'     ,  split='test'     )

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

       lowerCAmelCase__					     = [imagea, imagea]

       return images



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






 '''simple docstring'''






 @slow
 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



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

        lowerCAmelCase__					     = prepare_images()

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

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

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

        # test batched
        lowerCAmelCase__					     = image_processing(lowercase__      ,   return_tensors='pt')

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

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

import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= 1
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= None




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        return self.__class__(**{k: copy.deepcopy(lowercase__) for k, v in self.__dict__.items()})
