


'''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    __magic_name__				(						unittest.TestCase							):






       def __init__(     self    ,				snake_case    ,				snake_case=7    ,				snake_case=3    ,				snake_case=1_8    ,				snake_case=3_0    ,				snake_case=4_0_0    ,				snake_case=True    ,				snake_case=None    ,				snake_case=True    ,				)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =size if size is not None else {'height': 1_8, 'width': 1_8}
          _UpperCAmelCase  :  Any				       =parent
          _UpperCAmelCase  :  Optional[Any]				       =batch_size
          _UpperCAmelCase  :  Any				       =num_channels
          _UpperCAmelCase  :  Optional[Any]				       =image_size
          _UpperCAmelCase  :  Optional[int]				       =min_resolution
          _UpperCAmelCase  :  int				       =max_resolution
          _UpperCAmelCase  :  str				       =do_resize
          _UpperCAmelCase  :  List[str]				       =size
          _UpperCAmelCase  :  List[str]				       =do_normalize






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




          '''simple docstring'''
          return {
              # here we create 2 clusters for the sake of simplicity
              "clusters": np.asarray(
                  [
                      [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                      [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                  ]),
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
          }






@require_torch
@require_vision
class    __magic_name__				(						lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =ImageGPTImageProcessor if is_vision_available() else None






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =ImageGPTImageProcessingTester(self)






       @property
       def      lowerCAmelCase						(     self)		->    Any:




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






       def      lowerCAmelCase						(     self)		->    Tuple:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =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      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size    ,				{'height': 1_8, 'width': 1_8})

          _UpperCAmelCase  :  str				       =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      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =self.image_processing_class(**self.image_processor_dict)
          _UpperCAmelCase  :  Optional[Any]				       =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      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =self.image_processing_class(**self.image_processor_dict)

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

          _UpperCAmelCase  :  Dict				       =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      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =self.image_processing_class(**self.image_processor_dict)

          with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(snake_case)
             _UpperCAmelCase  :  Optional[int]				       =self.image_processing_class.from_pretrained(snake_case).to_dict()

          _UpperCAmelCase  :  Tuple				       =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      lowerCAmelCase						(     self)		->    Tuple:




          '''simple docstring'''
          pass






def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =load_dataset('hf-internal-testing/fixtures_image_utils'			,       split='test'				)

   _UpperCAmelCase  :  List[str]				       =Image.open(dataset[4]['file']				)
   _UpperCAmelCase  :  Optional[int]				       =Image.open(dataset[5]['file']				)

   _UpperCAmelCase  :  Tuple				       =[imagea, imagea]

   return images






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






       @slow
       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small')

          _UpperCAmelCase  :  Optional[int]				       =prepare_images()

          # test non-batched
          _UpperCAmelCase  :  List[Any]				       =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  :  Tuple				       =[3_0_6, 1_9_1, 1_9_1]
          self.assertEqual(encoding.input_ids[0, :3].tolist()    ,				snake_case)

          # test batched
          _UpperCAmelCase  :  List[str]				       =image_processing(snake_case    ,				return_tensors='pt')

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

          _UpperCAmelCase  :  Optional[Any]				       =[3_0_3, 1_3, 1_3]
          self.assertEqual(encoding.input_ids[1, -3:].tolist()    ,				snake_case)





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

 lowercase				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

