

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						:					Dict					,       __lowerCamelCase						:					Union[str, Any]					,       __lowerCamelCase						:					Tuple=7					,       __lowerCamelCase						:					Dict=3					,       __lowerCamelCase						:					List[Any]=1_8					,       __lowerCamelCase						:					Union[str, Any]=3_0					,       __lowerCamelCase						:					Optional[int]=4_0_0					,       __lowerCamelCase						:					Optional[int]=True					,       __lowerCamelCase						:					Union[str, Any]=None					,       __lowerCamelCase						:					Any=True					,       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     size if size is not None else {"""height""": 1_8, """width""": 1_8}
         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        _lowercase				(		self						:					Dict       )    ->   int:






         """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   __lowercase				(  __snake_case ,					unittest.TestCase  ):
      UpperCamelCase								=       ImageGPTImageProcessor if is_vision_available() else None





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






         """simple docstring"""







         UpperCAmelCase							=     ImageGPTImageProcessingTester(self       )





      @property
      def        _lowercase				(		self						:					Tuple       )    ->   Optional[Any]:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





      def        _lowercase				(		self						:					str       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     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						:					Optional[int]       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size					,       {"""height""": 1_8, """width""": 1_8}       )

         UpperCAmelCase							=     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						:					Dict       )    ->   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(__lowerCamelCase					,       obj[key]       )       )
            else:
               self.assertEqual(obj[key]					,       __lowerCamelCase       )





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






         """simple docstring"""







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

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

         UpperCAmelCase							=     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       )    ->   Any:






         """simple docstring"""







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

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

         UpperCAmelCase							=     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						:					List[Any]       )    ->   Tuple:






         """simple docstring"""







         pass

def   _UpperCamelCase				(						)						->Optional[Any]:
   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   __lowercase				(  unittest.TestCase  ):





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






         """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_0_2_4)       )

         UpperCAmelCase							=     [3_0_6, 1_9_1, 1_9_1]
         self.assertEqual(encoding.input_ids[0, :3].tolist()					,       __lowerCamelCase       )

         # test batched
         UpperCAmelCase							=     image_processing(__lowerCamelCase					,       return_tensors="""pt"""       )

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

         UpperCAmelCase							=     [3_0_3, 1_3, 1_3]
         self.assertEqual(encoding.input_ids[1, -3:].tolist()					,       __lowerCamelCase       )







import argparse


__a     =			"""docs/source/_static/js/custom.js"""
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:
   with open(lowerCAmelCase_  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      UpperCAmelCase							=     f.readlines()
   UpperCAmelCase							=     0

   # First let's put the right version
   while not lines[index].startswith("""const stableVersion ="""   ):
      index += 1
   UpperCAmelCase							=     F"""const stableVersion = \"v{version}\"\n"""

   # Then update the dictionary
   while not lines[index].startswith("""const versionMapping = {"""   ):
      index += 1

   # We go until the end
   while not lines[index].startswith("""}"""   ):
      index += 1
   # We add the new version at the end
   lines[index - 1] += F"""    \"v{version}\": \"v{version}\",\n"""

   with open(lowerCAmelCase_  ,						"""w"""  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      f.writelines(lowerCAmelCase_   )


if __name__ == "__main__":
 __a     =			argparse.ArgumentParser()
 parser.add_argument("""--version""", help="""Release version.""")
 __a     =			parser.parse_args()
 update_custom_js(args.version)



