
"""simple docstring"""


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	):






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







              """simple docstring"""
              UpperCamelCase					 =		size if size is not None else {"height": 18, "width": 18}
              UpperCamelCase					 =		parent
              UpperCamelCase					 =		batch_size
              UpperCamelCase					 =		num_channels
              UpperCamelCase					 =		image_size
              UpperCamelCase					 =		min_resolution
              UpperCamelCase					 =		max_resolution
              UpperCamelCase					 =		do_resize
              UpperCamelCase					 =		size
              UpperCamelCase					 =		do_normalize






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  str:







              """simple docstring"""
              return {
                  # here we create 2 clusters for the sake of simplicity
                  "clusters": np.asarray(
                      [
                          [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
                          [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
                      ]			),
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_normalize": self.do_normalize,
              }





@require_torch
@require_vision
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Union[str, Any]   =			ImageGPTImageProcessor if is_vision_available() else None






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		ImageGPTImageProcessingTester(self			)






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[int]:







              """simple docstring"""
              return self.image_processor_tester.prepare_image_processor_dict()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		self.image_processing_class(**self.image_processor_dict			)
              self.assertTrue(hasattr(UpperCAmelCase_       ,      "clusters"			)			)
              self.assertTrue(hasattr(UpperCAmelCase_       ,      "do_resize"			)			)
              self.assertTrue(hasattr(UpperCAmelCase_       ,      "size"			)			)
              self.assertTrue(hasattr(UpperCAmelCase_       ,      "do_normalize"			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		self.image_processing_class.from_dict(self.image_processor_dict			)
              self.assertEqual(image_processor.size       ,      {"height": 18, "width": 18}			)

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Union[str, Any]:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		self.image_processing_class(**self.image_processor_dict			)

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

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		self.image_processing_class(**self.image_processor_dict			)

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

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






       @unittest.skip("ImageGPT requires clusters at initialization"			)
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Any:







              """simple docstring"""
              pass





def 	lowerCamelCase__			(							)->							Dict:







       """simple docstring"""
       UpperCamelCase					 =		load_dataset("hf-internal-testing/fixtures_image_utils"		,      split="test"				)

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

       UpperCamelCase					 =		[imagea, imagea]

       return images



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






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"			)

              UpperCamelCase					 =		prepare_images()

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

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

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

              # test batched
              UpperCamelCase					 =		image_processing(UpperCAmelCase_       ,      return_tensors="pt"			)

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

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







"""simple docstring"""


def 	lowerCamelCase__			(							UpperCAmelCase_				)->							list:







       """simple docstring"""

       if len(UpperCAmelCase_				) <= 1:
              return [tuple(UpperCAmelCase_				)]

       UpperCamelCase					 =		[]

       def generate(UpperCAmelCase_		,      UpperCAmelCase_				):
              if k == 1:
                     res.append(tuple(arr[:]				)				)
                     return

              generate(k - 1		,      UpperCAmelCase_				)

              for i in range(k - 1				):
                     if k % 2 == 0:  # k is even
                            UpperCamelCase ,       UpperCamelCase					 =		arr[k - 1], arr[i]
                     else:  # k is odd
                            UpperCamelCase ,       UpperCamelCase					 =		arr[k - 1], arr[0]
                     generate(k - 1		,      UpperCAmelCase_				)

       generate(len(UpperCAmelCase_				)		,      UpperCAmelCase_				)
       return res


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE      =     input("""Enter numbers separated by a comma:\n""").strip()
      SCREAMING_SNAKE_CASE      =     [int(item) for item in user_input.split(""",""")]
      print(heaps(arr))





