



'''simple docstring'''

import warnings

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


UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   List[Any]									=		"mvp"
						UpperCAmelCase							:   str									=		["past_key_values"]
						UpperCAmelCase							:   Optional[int]									=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


						def __init__( self     ,	A_=5_0267     ,	A_=1024     ,	A_=12     ,	A_=4096     ,	A_=16     ,	A_=12     ,	A_=4096     ,	A_=16     ,	A_=0.0     ,	A_=0.0     ,	A_="gelu"     ,	A_=1024     ,	A_=0.1     ,	A_=0.0     ,	A_=0.0     ,	A_=0.0_2     ,	A_=0.0     ,	A_=False     ,	A_=True     ,	A_=1     ,	A_=0     ,	A_=2     ,	A_=True     ,	A_=2     ,	A_=2     ,	A_=False     ,	A_=100     ,	A_=800     ,	**A_     ,	)     ->     Tuple:
							lowerCAmelCase   =     vocab_size
							lowerCAmelCase   =     max_position_embeddings
							lowerCAmelCase   =     d_model
							lowerCAmelCase   =     encoder_ffn_dim
							lowerCAmelCase   =     encoder_layers
							lowerCAmelCase   =     encoder_attention_heads
							lowerCAmelCase   =     decoder_ffn_dim
							lowerCAmelCase   =     decoder_layers
							lowerCAmelCase   =     decoder_attention_heads
							lowerCAmelCase   =     dropout
							lowerCAmelCase   =     attention_dropout
							lowerCAmelCase   =     activation_dropout
							lowerCAmelCase   =     activation_function
							lowerCAmelCase   =     init_std
							lowerCAmelCase   =     encoder_layerdrop
							lowerCAmelCase   =     decoder_layerdrop
							lowerCAmelCase   =     classifier_dropout
							lowerCAmelCase   =     use_cache
							lowerCAmelCase   =     encoder_layers
							lowerCAmelCase   =     scale_embedding  # scale factor will be sqrt(d_model) if True
							lowerCAmelCase   =     use_prompt
							lowerCAmelCase   =     prompt_length
							lowerCAmelCase   =     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_				):
								lowerCAmelCase   =     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'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     BatchEncoding:
							if images is None and text is None:
								raise ValueError("""You have to specify either images or text."""				)

							# Get only text
							if images is None:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     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_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

							if text is not None:
								lowerCAmelCase   =     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_     ,	)
							else:
								lowerCAmelCase   =     None

							if text_encoding is not None:
								encoding_image_processor.update(A_				)

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)