





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 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Union[str, Any]       			=      ['''image_processor''', '''tokenizer''']
			lowerCamelCase_     :     Optional[Any]       			=      '''BlipImageProcessor'''
			lowerCamelCase_     :     Dict       			=      '''AutoTokenizer'''






			def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							super().__init__(__magic_name__					,							__magic_name__    )

							# add QFormer tokenizer
							snake_case_						:    List[Any]				=  qformer_tokenizer






			def __call__(self					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = True					,							__magic_name__ = False					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = 0					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = False					,							__magic_name__ = False					,							__magic_name__ = False					,							__magic_name__ = False					,							__magic_name__ = False					,							__magic_name__ = True					,							__magic_name__ = None					,							**__magic_name__					,							)     ->   BatchFeature:


							'''simple docstring'''



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

							snake_case_						:    int				=  BatchFeature()

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

							if images is not None:
											snake_case_						:    int				=  self.image_processor(__magic_name__					,							return_tensors=__magic_name__    )
											encoding.update(__magic_name__    )

							return encoding






			def 			lowerCamelCase  (self					,							*__magic_name__					,							**__magic_name__    )     ->   Any:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							*__magic_name__					,							**__magic_name__    )     ->   str:


							'''simple docstring'''



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






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    List[str]				=  self.tokenizer.model_input_names
							snake_case_						:    str				=  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							**__magic_name__    )     ->   int:


							'''simple docstring'''



							if os.path.isfile(__magic_name__    ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''    )
							os.makedirs(__magic_name__					,							exist_ok=__magic_name__    )
							snake_case_						:    Tuple				=  os.path.join(__magic_name__					,							'''qformer_tokenizer'''    )
							self.qformer_tokenizer.save_pretrained(__magic_name__    )
							return super().save_pretrained(__magic_name__					,							**__magic_name__    )






			@classmethod
			def 			lowerCamelCase  (cls					,							__magic_name__					,							**__magic_name__    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  AutoTokenizer.from_pretrained(__magic_name__					,							subfolder='''qformer_tokenizer'''    )
							snake_case_						:    List[Any]				=  cls._get_arguments_from_pretrained(__magic_name__					,							**__magic_name__    )
							args.append(__magic_name__    )
							return cls(*__magic_name__    )







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


lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''megatron-bert'''






			def __init__(self					,							__magic_name__=2_9056					,							__magic_name__=1024					,							__magic_name__=24					,							__magic_name__=16					,							__magic_name__=4096					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=2					,							__magic_name__=0.02					,							__magic_name__=1e-12					,							__magic_name__=0					,							__magic_name__="absolute"					,							__magic_name__=True					,							**__magic_name__					,							)     ->   Any:


							'''simple docstring'''



							super().__init__(pad_token_id=__magic_name__					,							**__magic_name__    )

							snake_case_						:    Union[str, Any]				=  vocab_size
							snake_case_						:    Dict				=  hidden_size
							snake_case_						:    Dict				=  num_hidden_layers
							snake_case_						:    Optional[int]				=  num_attention_heads
							snake_case_						:    int				=  hidden_act
							snake_case_						:    List[str]				=  intermediate_size
							snake_case_						:    Dict				=  hidden_dropout_prob
							snake_case_						:    str				=  attention_probs_dropout_prob
							snake_case_						:    str				=  max_position_embeddings
							snake_case_						:    Any				=  type_vocab_size
							snake_case_						:    int				=  initializer_range
							snake_case_						:    int				=  layer_norm_eps
							snake_case_						:    List[str]				=  position_embedding_type
							snake_case_						:    Dict				=  use_cache
