"""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       ,  a__       ,  a__=7       ,  a__=3       ,  a__=18       ,  a__=30       ,  a__=400       ,  a__=True       ,  a__=None       ,  a__=True       ,  ):
         _lowerCAmelCase		: List[Any]						    =  size if size is not None else {"""height""": 18, """width""": 18}
         _lowerCAmelCase		: Optional[int]						    =  parent
         _lowerCAmelCase		: Optional[int]						    =  batch_size
         _lowerCAmelCase		: Union[str, Any]						    =  num_channels
         _lowerCAmelCase		: Optional[int]						    =  image_size
         _lowerCAmelCase		: Union[str, Any]						    =  min_resolution
         _lowerCAmelCase		: str						    =  max_resolution
         _lowerCAmelCase		: Optional[Any]						    =  do_resize
         _lowerCAmelCase		: Tuple						    =  size
         _lowerCAmelCase		: Optional[Any]						    =  do_normalize






       def 					__A						(	self						):
         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 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
       _UpperCamelCase							:    Dict	      =	ImageGPTImageProcessor if is_vision_available() else None






       def 					__A						(	self						):
         _lowerCAmelCase		: Any						    =  ImageGPTImageProcessingTester(self						)






       @property
       def 					__A						(	self						):
         return self.image_processor_tester.prepare_image_processor_dict()






       def 					__A						(	self						):
         _lowerCAmelCase		: int						    =  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 					__A						(	self						):
         _lowerCAmelCase		: Any						    =  self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size       ,  {"""height""": 18, """width""": 18}						)

         _lowerCAmelCase		: Union[str, Any]						    =  self.image_processing_class.from_dict(self.image_processor_dict       ,  size=42						)
         self.assertEqual(image_processor.size       ,  {"""height""": 42, """width""": 42}						)






       def 					__A						(	self						):
         _lowerCAmelCase		: int						    =  self.image_processing_class(**self.image_processor_dict						)
         _lowerCAmelCase		: List[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 					__A						(	self						):
         _lowerCAmelCase		: Union[str, Any]						    =  self.image_processing_class(**self.image_processor_dict						)

         with tempfile.TemporaryDirectory() as tmpdirname:
           _lowerCAmelCase		: Union[str, Any]						    =  os.path.join(a__       ,  """image_processor.json"""						)
           image_processor_first.to_json_file(a__						)
           _lowerCAmelCase		: str						    =  self.image_processing_class.from_json_file(a__						).to_dict()

         _lowerCAmelCase		: Tuple						    =  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 					__A						(	self						):
         _lowerCAmelCase		: int						    =  self.image_processing_class(**self.image_processor_dict						)

         with tempfile.TemporaryDirectory() as tmpdirname:
           image_processor_first.save_pretrained(a__						)
           _lowerCAmelCase		: str						    =  self.image_processing_class.from_pretrained(a__						).to_dict()

         _lowerCAmelCase		: 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__						)






       @unittest.skip("""ImageGPT requires clusters at initialization"""						)
       def 					__A						(	self						):
         pass







def 							SCREAMING_SNAKE_CASE  (    ) ->   Dict:
  _lowerCAmelCase		: str						    =  load_dataset("""hf-internal-testing/fixtures_image_utils"""    ,split="""test"""			)

  _lowerCAmelCase		: List[Any]						    =  Image.open(dataset[4]["""file"""]			)
  _lowerCAmelCase		: Tuple						    =  Image.open(dataset[5]["""file"""]			)

  _lowerCAmelCase		: List[str]						    =  [imagea, imagea]

  return images






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






       @slow
       def 					__A						(	self						):
         _lowerCAmelCase		: Union[str, Any]						    =  ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""						)

         _lowerCAmelCase		: Dict						    =  prepare_images()

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

         self.assertIsInstance(encoding.input_ids       ,  torch.LongTensor						)
         self.assertEqual(encoding.input_ids.shape       ,  (1, 1024)						)

         _lowerCAmelCase		: Optional[int]						    =  [306, 191, 191]
         self.assertEqual(encoding.input_ids[0, :3].tolist()       ,  a__						)

         # test batched
         _lowerCAmelCase		: List[Any]						    =  image_processing(a__       ,  return_tensors="""pt"""						)

         self.assertIsInstance(encoding.input_ids       ,  torch.LongTensor						)
         self.assertEqual(encoding.input_ids.shape       ,  (2, 1024)						)

         _lowerCAmelCase		: List[Any]						    =  [303, 13, 13]
         self.assertEqual(encoding.input_ids[1, -3:].tolist()       ,  a__						)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_a   :	int 					=     {
    'configuration_altclip': [
        'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AltCLIPConfig',
        'AltCLIPTextConfig',
        'AltCLIPVisionConfig',
    ],
    'processing_altclip': ['AltCLIPProcessor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Dict 					=     [
         'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
         'AltCLIPPreTrainedModel',
         'AltCLIPModel',
         'AltCLIPTextModel',
         'AltCLIPVisionModel',
     ]


if TYPE_CHECKING:
     from .configuration_altclip import (
         ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AltCLIPConfig,
         AltCLIPTextConfig,
         AltCLIPVisionConfig,
     )
     from .processing_altclip import AltCLIPProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_altclip import (
              ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              AltCLIPModel,
              AltCLIPPreTrainedModel,
              AltCLIPTextModel,
              AltCLIPVisionModel,
          )


else:
     import sys

     _a   :	List[Any] 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




