






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


snake_case__      :	int       	=      logging.get_logger(__name__)

snake_case__      :	str       	=      {
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}


class 						_a       (  UpperCamelCase_    ):






  """simple docstring"""





  A_						=   """mgp-str"""




  def __init__(     self		,		_UpperCAmelCase=[32, 128]		,		_UpperCAmelCase=4		,		_UpperCAmelCase=3		,		_UpperCAmelCase=27		,		_UpperCAmelCase=38		,		_UpperCAmelCase=50257		,		_UpperCAmelCase=30522		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=4.0		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=1e-5		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=False		,		_UpperCAmelCase=0.0_2		,		**_UpperCAmelCase		,		)			-> List[Any]:
     super().__init__(**_UpperCAmelCase )

     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     max_token_length
     UpperCamelCase_									=     num_character_labels
     UpperCamelCase_									=     num_bpe_labels
     UpperCamelCase_									=     num_wordpiece_labels
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     mlp_ratio
     UpperCamelCase_									=     distilled
     UpperCamelCase_									=     layer_norm_eps
     UpperCamelCase_									=     drop_rate
     UpperCamelCase_									=     qkv_bias
     UpperCamelCase_									=     attn_drop_rate
     UpperCamelCase_									=     drop_path_rate
     UpperCamelCase_									=     output_aa_attentions
     UpperCamelCase_									=     initializer_range














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 						_a       (  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': 18, 'width': 18},
         '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		,		_UpperCAmelCase )
     with open(self.image_processor_file		,		'w'		,		encoding='utf-8' ) as fp:
        json.dump(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[Any]:
     return BertTokenizer.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[int]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     shutil.rmtree(self.tmpdirname )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     [np.random.randint(255		,		size=(3, 30, 400)		,		dtype=np.uinta )]

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

     return image_inputs




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.get_tokenizer()
     UpperCamelCase_									=     self.get_image_processor()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     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		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     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=_UpperCAmelCase		,		padding_value=1.0 )

     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname		,		bos_token='(BOS)'		,		eos_token='(EOS)'		,		do_normalize=_UpperCAmelCase		,		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		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     image_processor(_UpperCAmelCase		,		return_tensors='np' )
     UpperCamelCase_									=     processor(images=_UpperCAmelCase		,		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 )			-> Optional[int]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'

     UpperCamelCase_									=     processor(text=_UpperCAmelCase )

     UpperCamelCase_									=     tokenizer(_UpperCAmelCase )

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




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

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

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     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(_UpperCAmelCase ):
        processor()




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

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

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

     UpperCamelCase_									=     processor.batch_decode(_UpperCAmelCase )
     UpperCamelCase_									=     tokenizer.batch_decode(_UpperCAmelCase )

     self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

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

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

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





