
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 			lowercase_		(      unittest.TestCase       ):


       def __init__(    self						,       __A						,       __A=7						,       __A=3						,       __A=18						,       __A=30						,       __A=400						,       __A=True						,       __A=None						,       __A=True						,       )						->    int:
              SCREAMING_SNAKE_CASE_							:				Dict		=size if size is not None else {'''height''': 18, '''width''': 18}
              SCREAMING_SNAKE_CASE_							:				List[Any]		=parent
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=batch_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=num_channels
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=image_size
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=min_resolution
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=max_resolution
              SCREAMING_SNAKE_CASE_							:				Tuple		=do_resize
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=size
              SCREAMING_SNAKE_CASE_							:				Dict		=do_normalize





       def 	_snake_case				(    self				)						->    Union[str, Any]:
              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 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						ImageGPTImageProcessor if is_vision_available() else None


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				int		=ImageGPTImageProcessingTester(self				)


       @property
       def 	_snake_case				(    self				)						->    Optional[Any]:
              return self.image_processor_tester.prepare_image_processor_dict()


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Any		=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 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.image_processing_class.from_dict(self.image_processor_dict				)
              self.assertEqual(image_processor.size						,       {'''height''': 18, '''width''': 18}				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=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				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.image_processing_class(**self.image_processor_dict				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=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 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processing_class(**self.image_processor_dict				)

              with tempfile.TemporaryDirectory() as tmpdirname:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=os.path.join(__A						,       '''image_processor.json'''				)
                     image_processor_first.to_json_file(__A				)
                     SCREAMING_SNAKE_CASE_							:				Any		=self.image_processing_class.from_json_file(__A				).to_dict()

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=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 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.image_processing_class(**self.image_processor_dict				)

              with tempfile.TemporaryDirectory() as tmpdirname:
                     image_processor_first.save_pretrained(__A				)
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.image_processing_class.from_pretrained(__A				).to_dict()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=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 	_snake_case				(    self				)						->    Dict:
              pass






def 							SCREAMING_SNAKE_CASE_	(					)			->						Dict:
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=load_dataset('''hf-internal-testing/fixtures_image_utils'''			,							split='''test'''				)

       SCREAMING_SNAKE_CASE_							:				Dict		=Image.open(dataset[4]['''file''']				)
       SCREAMING_SNAKE_CASE_							:				Any		=Image.open(dataset[5]['''file''']				)

       SCREAMING_SNAKE_CASE_							:				List[str]		=[imagea, imagea]

       return images


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


       @slow
       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Tuple		=ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=prepare_images()

              # test non-batched
              SCREAMING_SNAKE_CASE_							:				List[str]		=image_processing(images[0]						,       return_tensors='''pt'''				)

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

              SCREAMING_SNAKE_CASE_							:				List[str]		=[306, 191, 191]
              self.assertEqual(encoding.input_ids[0, :3].tolist()						,       __A				)

              # test batched
              SCREAMING_SNAKE_CASE_							:				str		=image_processing(__A						,       return_tensors='''pt'''				)

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

              SCREAMING_SNAKE_CASE_							:				Any		=[303, 13, 13]
              self.assertEqual(encoding.input_ids[1, -3:].tolist()						,       __A				)


def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list				)			->						float:
       SCREAMING_SNAKE_CASE_							:				Dict		=0
       while len(UpperCAmelCase_				) > 1:
              SCREAMING_SNAKE_CASE_							:				Tuple		=0
              # Consider two files with minimum cost to be merged
              for _ in range(2				):
                     SCREAMING_SNAKE_CASE_							:				int		=files.index(min(UpperCAmelCase_				)				)
                     temp += files[min_index]
                     files.pop(UpperCAmelCase_				)
              files.append(UpperCAmelCase_				)
              optimal_merge_cost += temp
       return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()
