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		,				lowerCAmelCase_		,				lowerCAmelCase_=7		,				lowerCAmelCase_=3		,				lowerCAmelCase_=18		,				lowerCAmelCase_=30		,				lowerCAmelCase_=4_00		,				lowerCAmelCase_=True		,				lowerCAmelCase_=None		,				lowerCAmelCase_=True		,				)  ->					int:
        _A        =  size if size is not None else {"""height""": 18, """width""": 18}
        _A        =  parent
        _A        =  batch_size
        _A        =  num_channels
        _A        =  image_size
        _A        =  min_resolution
        _A        =  max_resolution
        _A        =  do_resize
        _A        =  size
        _A        =  do_normalize



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        return {
            # here we create 2 clusters for the sake of simplicity
            "clusters": np.asarray(
                [
                    [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                    [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
                ]							),
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
        }



@require_torch
@require_vision
class 				a							(			_snake_case       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:int    			=		ImageGPTImageProcessor if is_vision_available() else None



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  ImageGPTImageProcessingTester(self							)



 @property
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        return self.image_processor_tester.prepare_image_processor_dict()



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(__snake_case		,				"""clusters"""							)							)
        self.assertTrue(hasattr(__snake_case		,				"""do_resize"""							)							)
        self.assertTrue(hasattr(__snake_case		,				"""size"""							)							)
        self.assertTrue(hasattr(__snake_case		,				"""do_normalize"""							)							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,				{"""height""": 18, """width""": 18}							)

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



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        _A        =  json.loads(image_processor.to_json_string()							)
        for key, value in self.image_processor_dict.items():
               if key == "clusters":
                      self.assertTrue(np.array_equal(__snake_case		,				obj[key]							)							)
               else:
                      self.assertEqual(obj[key]		,				__snake_case							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.image_processing_class(**self.image_processor_dict							)

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

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



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.image_processing_class(**self.image_processor_dict							)

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

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



 @unittest.skip("""ImageGPT requires clusters at initialization"""							)
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        pass






def  snake_case  (       )						->   Optional[int]:
       _A        =  load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,	split="""test""")

       _A        =  Image.open(dataset[4]["""file"""])
       _A        =  Image.open(dataset[5]["""file"""])

       _A        =  [imagea, imagea]

       return images







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







 """simple docstring"""






 @slow
 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""							)

        _A        =  prepare_images()

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

        self.assertIsInstance(encoding.input_ids		,				torch.LongTensor							)
        self.assertEqual(encoding.input_ids.shape		,				(1, 10_24)							)

        _A        =  [3_06, 1_91, 1_91]
        self.assertEqual(encoding.input_ids[0, :3].tolist()		,				__snake_case							)

        # test batched
        _A        =  image_processing(__snake_case		,				return_tensors="""pt"""							)

        self.assertIsInstance(encoding.input_ids		,				torch.LongTensor							)
        self.assertEqual(encoding.input_ids.shape		,				(2, 10_24)							)

        _A        =  [3_03, 13, 13]
        self.assertEqual(encoding.input_ids[1, -3:].tolist()		,				__snake_case							)








'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      0
				while len(SCREAMING_SNAKE_CASE__     ) > 1:
								_SCREAMING_SNAKE_CASE :       Any     		=      0
								# Consider two files with minimum cost to be merged
								for _ in range(2     ):
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      files.index(min(SCREAMING_SNAKE_CASE__     )     )
												temp += files[min_index]
												files.pop(SCREAMING_SNAKE_CASE__     )
								files.append(SCREAMING_SNAKE_CASE__     )
								optimal_merge_cost += temp
				return optimal_merge_cost


if __name__ == "__main__":
				import doctest

				doctest.testmod()




