


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      :					Union[str, Any]		,						_lowercase      :					Tuple		,						_lowercase      :					str=7		,						_lowercase      :					int=3		,						_lowercase      :					Union[str, Any]=18		,						_lowercase      :					Optional[int]=30		,						_lowercase      :					Union[str, Any]=4_00		,						_lowercase      :					Optional[Any]=True		,						_lowercase      :					List[str]=None		,						_lowercase      :					Optional[int]=True		,						):




         """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 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return {
             # here we create 2 clusters for the sake of simplicity
             "clusters": np.asarray(
                 [
                     [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                     [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
                 ] ),
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
         }




@require_torch
@require_vision
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  ImageGPTImageProcessor if is_vision_available() else None






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     ImageGPTImageProcessingTester(self )






  @property
  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(_lowercase		,						"clusters" ) )
         self.assertTrue(hasattr(_lowercase		,						"do_resize" ) )
         self.assertTrue(hasattr(_lowercase		,						"size" ) )
         self.assertTrue(hasattr(_lowercase		,						"do_normalize" ) )






  def 			_UpperCAmelCase  (					self      :					List[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 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """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(_lowercase		,						obj[key] ) )
                else:
                       self.assertEqual(obj[key]		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







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

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

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






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







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

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

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






  @unittest.skip("ImageGPT requires clusters at initialization" )
  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         pass





def __UpperCAmelCase       (		)						->					int:



       '''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     lowercase__  (					unittest.TestCase		):






  @slow
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """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, 10_24) )

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

         # test batched
         UpperCAmelCase__										=     image_processing(_lowercase		,						return_tensors="pt" )

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

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









import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


A      = logging.get_logger(__name__)


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					Union[str, Any]		,						*_lowercase      :					Any		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         warnings.warn(
             "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use DPTImageProcessor instead."		,						_lowercase		,						)
         super().__init__(*_lowercase		,						**_lowercase )





