"""simple docstring"""



import warnings

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


__lowercase		:		int														=							logging.get_logger(__name__)

__lowercase		:		Any														=							{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Tuple							=					'''mvp'''
    UpperCamelCase_	:					Union[str, Any]							=					['''past_key_values''']
    UpperCamelCase_	:					List[str]							=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
    def __init__(		self							:     List[Any]	,      A_							:     Optional[Any]=50_267	,      A_							:     Union[str, Any]=1_024	,      A_							:     List[str]=12	,      A_							:     Dict=4_096	,      A_							:     int=16	,      A_							:     Optional[Any]=12	,      A_							:     Optional[int]=4_096	,      A_							:     Tuple=16	,      A_							:     Optional[Any]=0.0	,      A_							:     Any=0.0	,      A_							:     Optional[Any]="gelu"	,      A_							:     Tuple=1_024	,      A_							:     str=0.1	,      A_							:     List[str]=0.0	,      A_							:     Union[str, Any]=0.0	,      A_							:     Union[str, Any]=0.02	,      A_							:     str=0.0	,      A_							:     Optional[int]=False	,      A_							:     Any=True	,      A_							:     List[str]=1	,      A_							:     int=0	,      A_							:     Dict=2	,      A_							:     int=True	,      A_							:     Optional[int]=2	,      A_							:     Any=2	,      A_							:     Optional[int]=False	,      A_							:     Any=100	,      A_							:     str=800	,      **A_							:     Optional[Any]	,      )    ->       Any:
         __snake_case				=							vocab_size
         __snake_case				=							max_position_embeddings
         __snake_case				=							d_model
         __snake_case				=							encoder_ffn_dim
         __snake_case				=							encoder_layers
         __snake_case				=							encoder_attention_heads
         __snake_case				=							decoder_ffn_dim
         __snake_case				=							decoder_layers
         __snake_case				=							decoder_attention_heads
         __snake_case				=							dropout
         __snake_case				=							attention_dropout
         __snake_case				=							activation_dropout
         __snake_case				=							activation_function
         __snake_case				=							init_std
         __snake_case				=							encoder_layerdrop
         __snake_case				=							decoder_layerdrop
         __snake_case				=							classifier_dropout
         __snake_case				=							use_cache
         __snake_case				=							encoder_layers
         __snake_case				=							scale_embedding  # scale factor will be sqrt(d_model) if True
         __snake_case				=							use_prompt
         __snake_case				=							prompt_length
         __snake_case				=							prompt_mid_dim

         super().__init__(
             pad_token_id=A_	,      bos_token_id=A_	,      eos_token_id=A_	,      is_encoder_decoder=A_	,      decoder_start_token_id=A_	,      forced_eos_token_id=A_	,      **A_	,      )

         if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''	,      A_    ):
              __snake_case				=							self.bos_token_id
              warnings.warn(
                  f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                  '''The config can simply be saved and uploaded again to be fixed.'''    )
"""simple docstring"""



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,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     str    )    ->       str:
         __snake_case				=							tempfile.mkdtemp()

         __snake_case				=							BlipImageProcessor()
         __snake_case				=							GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''    )
         __snake_case				=							BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )

         __snake_case				=							InstructBlipProcessor(A_	,      A_	,      A_    )

         processor.save_pretrained(self.tmpdirname    )
    def  lowercase					(		self							:     Tuple	,      **A_							:     str    )    ->       List[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).tokenizer
    def  lowercase					(		self							:     Union[str, Any]	,      **A_							:     Tuple    )    ->       Union[str, Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).image_processor
    def  lowercase					(		self							:     Union[str, Any]	,      **A_							:     Tuple    )    ->       Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).qformer_tokenizer
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Tuple:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

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

         return image_inputs
    def  lowercase					(		self							:     int    )    ->       Dict:
         __snake_case				=							InstructBlipProcessor(
             tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()	,      qformer_tokenizer=self.get_qformer_tokenizer()	,      )
         processor.save_pretrained(self.tmpdirname    )

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

         __snake_case				=							InstructBlipProcessor.from_pretrained(
             self.tmpdirname	,      bos_token='''(BOS)'''	,      eos_token='''(EOS)'''	,      do_normalize=A_	,      padding_value=1.0    )

         self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer	,      A_    )

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
         self.assertIsInstance(processor.qformer_tokenizer	,      A_    )
    def  lowercase					(		self							:     int    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      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  lowercase					(		self							:     List[str]    )    ->       Optional[int]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_	,      return_token_type_ids=A_    )
         __snake_case				=							qformer_tokenizer(A_	,      return_token_type_ids=A_    )

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

         for key in encoded_tokens_qformer.keys():
              self.assertListEqual(encoded_tokens_qformer[key]	,      encoded_processor['''qformer_''' + key]    )
    def  lowercase					(		self							:     List[str]    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

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

         __snake_case				=							processor(text=A_	,      images=A_    )

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

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     str    )    ->       Union[str, Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

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

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )







    def  lowercase					(		self							:     int    )    ->       List[str]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

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

         __snake_case				=							processor(text=A_	,      images=A_    )

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