





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=tempfile.mkdtemp()

         UpperCAmelCase_											=BlipImageProcessor()
         UpperCAmelCase_											=GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"       )

         UpperCAmelCase_											=BlipaProcessor(_lowerCAmelCase		,  _lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )



      def 	lowerCAmelCase__    (	self:						Dict		,  **_lowerCAmelCase:						Dict       )						->    str:
         '''simple docstring'''

         return AutoProcessor.from_pretrained(self.tmpdirname		,  **_lowerCAmelCase       ).tokenizer



      def 	lowerCAmelCase__    (	self:						List[Any]		,  **_lowerCAmelCase:						str       )						->    Optional[Any]:
         '''simple docstring'''

         return AutoProcessor.from_pretrained(self.tmpdirname		,  **_lowerCAmelCase       ).image_processor



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Optional[int]:
         '''simple docstring'''

         shutil.rmtree(self.tmpdirname       )



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=[np.random.randint(255		,  size=(3, 30, 400)		,  dtype=np.uinta       )]

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

         return image_inputs



      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=BlipaProcessor(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_											=BlipaProcessor.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		,  _lowerCAmelCase       )

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



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.get_image_processor()
         UpperCAmelCase_											=self.get_tokenizer()

         UpperCAmelCase_											=BlipaProcessor(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 	lowerCAmelCase__    (	self:						Dict       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.get_image_processor()
         UpperCAmelCase_											=self.get_tokenizer()

         UpperCAmelCase_											=BlipaProcessor(tokenizer=_lowerCAmelCase		,  image_processor=_lowerCAmelCase       )

         UpperCAmelCase_											="lower newer"

         UpperCAmelCase_											=processor(text=_lowerCAmelCase       )

         UpperCAmelCase_											=tokenizer(_lowerCAmelCase		,  return_token_type_ids=_lowerCAmelCase       )

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



      def 	lowerCAmelCase__    (	self:						int       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.get_image_processor()
         UpperCAmelCase_											=self.get_tokenizer()

         UpperCAmelCase_											=BlipaProcessor(tokenizer=_lowerCAmelCase		,  image_processor=_lowerCAmelCase       )

         UpperCAmelCase_											="lower newer"
         UpperCAmelCase_											=self.prepare_image_inputs()

         UpperCAmelCase_											=processor(text=_lowerCAmelCase		,  images=_lowerCAmelCase       )

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

         # test if it raises when no input is passed
         with pytest.raises(_lowerCAmelCase       ):
            processor()



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.get_image_processor()
         UpperCAmelCase_											=self.get_tokenizer()

         UpperCAmelCase_											=BlipaProcessor(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 	lowerCAmelCase__    (	self:						Any       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=self.get_image_processor()
         UpperCAmelCase_											=self.get_tokenizer()

         UpperCAmelCase_											=BlipaProcessor(tokenizer=_lowerCAmelCase		,  image_processor=_lowerCAmelCase       )

         UpperCAmelCase_											="lower newer"
         UpperCAmelCase_											=self.prepare_image_inputs()

         UpperCAmelCase_											=processor(text=_lowerCAmelCase		,  images=_lowerCAmelCase       )

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()       )		,  ["pixel_values", "input_ids", "attention_mask"]       )











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


__lowercase  :   Optional[int]						      =logging.get_logger(__name__)

__lowercase  :   Union[str, Any]						      ={
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}

class      A       (	__lowercase						):
      _snake_case							     ='''van'''



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

         super().__init__(**_lowerCAmelCase       )
         UpperCAmelCase_											=image_size
         UpperCAmelCase_											=num_channels
         UpperCAmelCase_											=patch_sizes
         UpperCAmelCase_											=strides
         UpperCAmelCase_											=hidden_sizes
         UpperCAmelCase_											=depths
         UpperCAmelCase_											=mlp_ratios
         UpperCAmelCase_											=hidden_act
         UpperCAmelCase_											=initializer_range
         UpperCAmelCase_											=layer_norm_eps
         UpperCAmelCase_											=layer_scale_init_value
         UpperCAmelCase_											=drop_path_rate
         UpperCAmelCase_											=dropout_rate




