




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				):







 '''simple docstring'''





 def __init__(       self:	Union[str, Any],   a_:	int,   a_:	Optional[int]=7,   a_:	Any=3,   a_:	Any=18,   a_:	Union[str, Any]=30,   a_:	Union[str, Any]=400,   a_:	Any=True,   a_:	Tuple=None,   a_:	Tuple=True,   ):




   '''simple docstring'''



   _snake_case							:	Tuple			=						size if size is not None else {"""height""": 18, """width""": 18}
   _snake_case							:	Dict			=						parent
   _snake_case							:	int			=						batch_size
   _snake_case							:	Tuple			=						num_channels
   _snake_case							:	Optional[int]			=						image_size
   _snake_case							:	Dict			=						min_resolution
   _snake_case							:	Optional[int]			=						max_resolution
   _snake_case							:	Any			=						do_resize
   _snake_case							:	Optional[int]			=						size
   _snake_case							:	Tuple			=						do_normalize


 def 				UpperCamelCase_     (       self:	Dict	):




   '''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(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      ImageGPTImageProcessor if is_vision_available() else None


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	str			=						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'''



   _snake_case							:	Dict			=						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 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						self.image_processing_class.from_dict(self.image_processor_dict	)
   self.assertEqual(image_processor.size,   {"""height""": 18, """width""": 18}	)

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


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processing_class(**self.image_processor_dict	)
   _snake_case							:	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 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						self.image_processing_class(**self.image_processor_dict	)

   with tempfile.TemporaryDirectory() as tmpdirname:
     _snake_case							:	List[str]			=						os.path.join(a_,   """image_processor.json"""	)
     image_processor_first.to_json_file(a_	)
     _snake_case							:	Tuple			=						self.image_processing_class.from_json_file(a_	).to_dict()

   _snake_case							:	List[str]			=						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 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.image_processing_class(**self.image_processor_dict	)

   with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(a_	)
     _snake_case							:	Any			=						self.image_processing_class.from_pretrained(a_	).to_dict()

   _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_	)


 @unittest.skip("""ImageGPT requires clusters at initialization"""	)
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   pass







def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Optional[int]			=						load_dataset("""hf-internal-testing/fixtures_image_utils"""	,     split="""test"""					)

  _snake_case							:	Dict			=						Image.open(dataset[4]["""file"""]					)
  _snake_case							:	Union[str, Any]			=						Image.open(dataset[5]["""file"""]					)

  _snake_case							:	Optional[Any]			=						[imagea, imagea]

  return images






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







 '''simple docstring'''





 @slow
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	str			=						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""	)

   _snake_case							:	List[str]			=						prepare_images()

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

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

   _snake_case							:	Any			=						[306, 191, 191]
   self.assertEqual(encoding.input_ids[0, :3].tolist(),   a_	)

   # test batched
   _snake_case							:	Union[str, Any]			=						image_processing(a_,   return_tensors="""pt"""	)

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

   _snake_case							:	Tuple			=						[303, 13, 13]
   self.assertEqual(encoding.input_ids[1, -3:].tolist(),   a_	)








"""simple docstring"""


from ...processing_utils import ProcessorMixin






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["image_processor", "feature_extractor"]
 lowercase__				      =      "TvltImageProcessor"
 lowercase__				      =      "TvltFeatureExtractor"


 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Union[str, Any]	):




   '''simple docstring'''



   super().__init__(image_processor=a_,   feature_extractor=a_	)

   _snake_case							:	Any			=						image_processor
   _snake_case							:	Dict			=						feature_extractor


 def __call__(       self:	int,   a_:	str=None,   a_:	Tuple=None,   a_:	Dict=None,   a_:	str=None,   a_:	Optional[int]=False,   a_:	Tuple=False,   *a_:	List[str],   **a_:	int,   ):




   '''simple docstring'''



   if images is None and audio is None:
     raise ValueError("""You need to specify either an `images` or `audio` input to process."""	)

   _snake_case							:	Optional[int]			=						None
   if images is not None:
     _snake_case							:	Tuple			=						self.image_processor(a_,   mask_pixel=a_,   *a_,   **a_	)
   if images_mixed is not None:
     _snake_case							:	Optional[int]			=						self.image_processor(a_,   is_mixed=a_,   *a_,   **a_	)
   if audio is not None:
     _snake_case							:	Any			=						self.feature_extractor(
         a_,   *a_,   sampling_rate=a_,   mask_audio=a_,   **a_	)

   _snake_case							:	List[str]			=						{}
   if audio is not None:
     output_dict.update(a_	)
   if images is not None:
     output_dict.update(a_	)
   if images_mixed_dict is not None:
     output_dict.update(a_	)
   return output_dict


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processor.model_input_names
   _snake_case							:	List[str]			=						self.feature_extractor.model_input_names
   return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names	)	)


