


'''simple docstring'''







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class 	UpperCamelCase__					( __SCREAMING_SNAKE_CASE	):



     """simple docstring"""






     __magic_name__    			=    ['image_processor', 'tokenizer']
     __magic_name__    			=    'BlipImageProcessor'
     __magic_name__    			=    'AutoTokenizer'







     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            super().__init__(_a      ,	_a   )

            # add QFormer tokenizer
            _lowerCAmelCase    :							Dict   			=       qformer_tokenizer







     def __call__( self      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = True      ,	snake_case__ = False      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = 0      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = True      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            if images is None and text is None:
                   raise ValueError('You have to specify at least images or text.'   )

            _lowerCAmelCase    :							str   			=       BatchFeature()

            if text is not None:
                   _lowerCAmelCase    :							Union[str, Any]   			=       self.tokenizer(
                       text=_a      ,	add_special_tokens=_a      ,	padding=_a      ,	truncation=_a      ,	max_length=_a      ,	stride=_a      ,	pad_to_multiple_of=_a      ,	return_attention_mask=_a      ,	return_overflowing_tokens=_a      ,	return_special_tokens_mask=_a      ,	return_offsets_mapping=_a      ,	return_token_type_ids=_a      ,	return_length=_a      ,	verbose=_a      ,	return_tensors=_a      ,	**_a      ,	)
                   encoding.update(_a   )
                   _lowerCAmelCase    :							List[Any]   			=       self.qformer_tokenizer(
                       text=_a      ,	add_special_tokens=_a      ,	padding=_a      ,	truncation=_a      ,	max_length=_a      ,	stride=_a      ,	pad_to_multiple_of=_a      ,	return_attention_mask=_a      ,	return_overflowing_tokens=_a      ,	return_special_tokens_mask=_a      ,	return_offsets_mapping=_a      ,	return_token_type_ids=_a      ,	return_length=_a      ,	verbose=_a      ,	return_tensors=_a      ,	**_a      ,	)
                   _lowerCAmelCase    :							Union[str, Any]   			=       qformer_text_encoding.pop('input_ids'   )
                   _lowerCAmelCase    :							Tuple   			=       qformer_text_encoding.pop('attention_mask'   )

            if images is not None:
                   _lowerCAmelCase    :							Optional[int]   			=       self.image_processor(_a      ,	return_tensors=_a   )
                   encoding.update(_a   )

            return encoding







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            return self.tokenizer.batch_decode(*_a      ,	**_a   )







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            return self.tokenizer.decode(*_a      ,	**_a   )







     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tokenizer.model_input_names
            _lowerCAmelCase    :							Tuple   			=       self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







     def a	( self      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            if os.path.isfile(_a   ):
                   raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
            os.makedirs(_a      ,	exist_ok=_a   )
            _lowerCAmelCase    :							Optional[int]   			=       os.path.join(_a      ,	'qformer_tokenizer'   )
            self.qformer_tokenizer.save_pretrained(_a   )
            return super().save_pretrained(_a      ,	**_a   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       AutoTokenizer.from_pretrained(_a      ,	subfolder='qformer_tokenizer'   )
            _lowerCAmelCase    :							Any   			=       cls._get_arguments_from_pretrained(_a      ,	**_a   )
            args.append(_a   )
            return cls(*_a   )





'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase    :     str					=						logging.get_logger(__name__)

lowerCAmelCase    :     int					=						{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "data2vec-text"







     def __init__( self      ,	snake_case__=3_0522      ,	snake_case__=768      ,	snake_case__=12      ,	snake_case__=12      ,	snake_case__=3072      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=1E-12      ,	snake_case__=1      ,	snake_case__=0      ,	snake_case__=2      ,	snake_case__="absolute"      ,	snake_case__=True      ,	snake_case__=None      ,	**snake_case__      ,	):

            '''simple docstring'''

            super().__init__(pad_token_id=snake_case__      ,	bos_token_id=snake_case__      ,	eos_token_id=snake_case__      ,	**snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       vocab_size
            _lowerCAmelCase    :							Tuple   			=       hidden_size
            _lowerCAmelCase    :							Dict   			=       num_hidden_layers
            _lowerCAmelCase    :							int   			=       num_attention_heads
            _lowerCAmelCase    :							str   			=       hidden_act
            _lowerCAmelCase    :							Any   			=       intermediate_size
            _lowerCAmelCase    :							Any   			=       hidden_dropout_prob
            _lowerCAmelCase    :							Optional[int]   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							str   			=       max_position_embeddings
            _lowerCAmelCase    :							Any   			=       type_vocab_size
            _lowerCAmelCase    :							int   			=       initializer_range
            _lowerCAmelCase    :							List[str]   			=       layer_norm_eps
            _lowerCAmelCase    :							List[Any]   			=       position_embedding_type
            _lowerCAmelCase    :							str   			=       use_cache
            _lowerCAmelCase    :							Union[str, Any]   			=       classifier_dropout
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     @property
     def a	( self   ):

            '''simple docstring'''

            if self.task == "multiple-choice":
                   _lowerCAmelCase    :							Union[str, Any]   			=       {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   _lowerCAmelCase    :							List[Any]   			=       {0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                ]   )

