




"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	tempfile.mkdtemp()

       # fmt: off
       UpperCamelCase              =	['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
       # fmt: on
       UpperCamelCase              =	os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['''vocab_file'''])
       with open(self.vocab_file       ,					'''w'''       ,					encoding='''utf-8''') as vocab_writer:
           vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))

       UpperCamelCase              =	{
           '''do_resize''': True,
           '''size''': {'''height''': 1_8, '''width''': 1_8},
           '''do_normalize''': True,
           '''image_mean''': [0.5, 0.5, 0.5],
           '''image_std''': [0.5, 0.5, 0.5],
       }
       UpperCamelCase              =	os.path.join(self.tmpdirname       ,					lowerCamelCase_)
       with open(self.image_processor_file       ,					'''w'''       ,					encoding='''utf-8''') as fp:
           json.dump(lowerCamelCase_       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					**lowerCamelCase_)				->   int:
       return BertTokenizer.from_pretrained(self.tmpdirname       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					**lowerCamelCase_)				->   int:
       return ViTImageProcessor.from_pretrained(self.tmpdirname       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict:
       shutil.rmtree(self.tmpdirname)







   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase              =	[np.random.randint(2_5_5       ,					size=(3, 3_0, 4_0_0)       ,					dtype=np.uinta)]

       UpperCamelCase              =	[Image.fromarray(np.moveaxis(lowerCamelCase_       ,					0       ,					-1)) for x in image_inputs]

       return image_inputs







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	self.get_tokenizer()
       UpperCamelCase              =	self.get_image_processor()

       UpperCamelCase              =	VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase_       ,					image_processor=lowerCamelCase_)

       processor.save_pretrained(self.tmpdirname)
       UpperCamelCase              =	VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname)

       self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer.get_vocab())
       self.assertIsInstance(processor.tokenizer       ,					(BertTokenizer, BertTokenizerFast))

       self.assertEqual(processor.image_processor.to_json_string()       ,					image_processor.to_json_string())
       self.assertIsInstance(processor.image_processor       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	VisionTextDualEncoderProcessor(
           tokenizer=self.get_tokenizer()       ,					image_processor=self.get_image_processor())
       processor.save_pretrained(self.tmpdirname)

       UpperCamelCase              =	self.get_tokenizer(bos_token='''(BOS)'''       ,					eos_token='''(EOS)''')
       UpperCamelCase              =	self.get_image_processor(do_normalize=lowerCamelCase_       ,					padding_value=1.0)

       UpperCamelCase              =	VisionTextDualEncoderProcessor.from_pretrained(
           self.tmpdirname       ,					bos_token='''(BOS)'''       ,					eos_token='''(EOS)'''       ,					do_normalize=lowerCamelCase_       ,					padding_value=1.0)

       self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer_add_kwargs.get_vocab())
       self.assertIsInstance(processor.tokenizer       ,					(BertTokenizer, BertTokenizerFast))

       self.assertEqual(processor.image_processor.to_json_string()       ,					image_processor_add_kwargs.to_json_string())
       self.assertIsInstance(processor.image_processor       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	self.get_image_processor()
       UpperCamelCase              =	self.get_tokenizer()

       UpperCamelCase              =	VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase_       ,					image_processor=lowerCamelCase_)

       UpperCamelCase              =	self.prepare_image_inputs()

       UpperCamelCase              =	image_processor(lowerCamelCase_       ,					return_tensors='''np''')
       UpperCamelCase              =	processor(images=lowerCamelCase_       ,					return_tensors='''np''')

       for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()       ,					input_processor[key].sum()       ,					delta=1e-2)







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	self.get_image_processor()
       UpperCamelCase              =	self.get_tokenizer()

       UpperCamelCase              =	VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase_       ,					image_processor=lowerCamelCase_)

       UpperCamelCase              =	'''lower newer'''

       UpperCamelCase              =	processor(text=lowerCamelCase_)

       UpperCamelCase              =	tokenizer(lowerCamelCase_)

       for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]       ,					encoded_processor[key])







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	self.get_image_processor()
       UpperCamelCase              =	self.get_tokenizer()

       UpperCamelCase              =	VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase_       ,					image_processor=lowerCamelCase_)

       UpperCamelCase              =	'''lower newer'''
       UpperCamelCase              =	self.prepare_image_inputs()

       UpperCamelCase              =	processor(text=lowerCamelCase_       ,					images=lowerCamelCase_)

       self.assertListEqual(list(inputs.keys())       ,					['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''])

       # test if it raises when no input is passed
       with self.assertRaises(lowerCamelCase_):
           processor()







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	self.get_image_processor()
       UpperCamelCase              =	self.get_tokenizer()

       UpperCamelCase              =	VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase_       ,					image_processor=lowerCamelCase_)

       UpperCamelCase              =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase              =	processor.batch_decode(lowerCamelCase_)
       UpperCamelCase              =	tokenizer.batch_decode(lowerCamelCase_)

       self.assertListEqual(lowerCamelCase_       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	self.get_image_processor()
       UpperCamelCase              =	self.get_tokenizer()

       UpperCamelCase              =	VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase_       ,					image_processor=lowerCamelCase_)

       UpperCamelCase              =	'''lower newer'''
       UpperCamelCase              =	self.prepare_image_inputs()

       UpperCamelCase              =	processor(text=lowerCamelCase_       ,					images=lowerCamelCase_)

       self.assertListEqual(list(inputs.keys())       ,					processor.model_input_names)

'''simple docstring'''

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


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Tuple	=			"""van"""


 def __init__(    self  : List[Any]			,							a__  : Dict=224			,							a__  : Dict=3			,							a__  : Union[str, Any]=[7, 3, 3, 3]			,							a__  : Optional[Any]=[4, 2, 2, 2]			,							a__  : Optional[Any]=[64, 128, 320, 512]			,							a__  : List[str]=[3, 3, 12, 3]			,							a__  : Any=[8, 8, 4, 4]			,							a__  : Optional[int]="gelu"			,							a__  : List[Any]=0.02			,							a__  : Tuple=1e-6			,							a__  : List[str]=1e-2			,							a__  : List[str]=0.0			,							a__  : List[Any]=0.0			,							**a__  : Tuple			,							):
  '''simple docstring'''

  super().__init__(**a__    )
  lowerCAmelCase__     :   Any     		=	image_size
  lowerCAmelCase__     :   Tuple     		=	num_channels
  lowerCAmelCase__     :   List[Any]     		=	patch_sizes
  lowerCAmelCase__     :   Dict     		=	strides
  lowerCAmelCase__     :   List[str]     		=	hidden_sizes
  lowerCAmelCase__     :   Union[str, Any]     		=	depths
  lowerCAmelCase__     :   Tuple     		=	mlp_ratios
  lowerCAmelCase__     :   Optional[Any]     		=	hidden_act
  lowerCAmelCase__     :   Dict     		=	initializer_range
  lowerCAmelCase__     :   int     		=	layer_norm_eps
  lowerCAmelCase__     :   Optional[Any]     		=	layer_scale_init_value
  lowerCAmelCase__     :   List[str]     		=	drop_path_rate
  lowerCAmelCase__     :   Any     		=	dropout_rate





