"""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						:	Optional[Any]        =					size if size is not None else {"""height""": 18, """width""": 18}
       _lowerCAmelCase						:	Optional[int]        =					parent
       _lowerCAmelCase						:	List[str]        =					batch_size
       _lowerCAmelCase						:	Optional[int]        =					num_channels
       _lowerCAmelCase						:	Optional[int]        =					image_size
       _lowerCAmelCase						:	int        =					min_resolution
       _lowerCAmelCase						:	int        =					max_resolution
       _lowerCAmelCase						:	Tuple        =					do_resize
       _lowerCAmelCase						:	Optional[int]        =					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							:      Optional[Any]		 =		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						:	Any        =					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						:	List[Any]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""height""": 18, """width""": 18}  )

       _lowerCAmelCase						:	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						:	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(a__    ,					obj[key]  )  )
         else:
           self.assertEqual(obj[key]    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )

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

       _lowerCAmelCase						:	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__  )






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

       with tempfile.TemporaryDirectory() as tmpdirname:
         image_processor_first.save_pretrained(a__  )
         _lowerCAmelCase						:	Optional[Any]        =					self.image_processing_class.from_pretrained(a__  ).to_dict()

       _lowerCAmelCase						:	Union[str, 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    __A  (		self  ):
       pass







def 							SCREAMING_SNAKE_CASE					(      )	->				Optional[Any]:
  _lowerCAmelCase						:	int        =					load_dataset("""hf-internal-testing/fixtures_image_utils"""      ,split="""test"""     )

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

  _lowerCAmelCase						:	str        =					[imagea, imagea]

  return images






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






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

       _lowerCAmelCase						:	List[Any]        =					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						:	Optional[int]        =					image_processing(a__    ,					return_tensors="""pt"""  )

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

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





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a				:		Any	   =       logging.get_logger(__name__)

_a				:		Any	   =       {
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		"visual_bert"






     def __init__(		self    ,					a__=30522    ,					a__=768    ,					a__=512    ,					a__=12    ,					a__=12    ,					a__=3072    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=2    ,					a__=0.0_2    ,					a__=1e-12    ,					a__=False    ,					a__=True    ,					a__=1    ,					a__=0    ,					a__=2    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )

       _lowerCAmelCase						:	Optional[Any]        =					vocab_size
       _lowerCAmelCase						:	int        =					max_position_embeddings
       _lowerCAmelCase						:	Optional[Any]        =					hidden_size
       _lowerCAmelCase						:	Optional[int]        =					visual_embedding_dim
       _lowerCAmelCase						:	Optional[int]        =					num_hidden_layers
       _lowerCAmelCase						:	List[Any]        =					num_attention_heads
       _lowerCAmelCase						:	Any        =					intermediate_size
       _lowerCAmelCase						:	List[Any]        =					hidden_act
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Optional[Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					initializer_range
       _lowerCAmelCase						:	List[Any]        =					type_vocab_size
       _lowerCAmelCase						:	Union[str, Any]        =					layer_norm_eps
       _lowerCAmelCase						:	Optional[Any]        =					bypass_transformer
       _lowerCAmelCase						:	List[Any]        =					special_visual_initialize




