



'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_A				:    Optional[int]     						=logging.get_logger(__name__)

_A				:    Optional[int]     						={
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""marian"""
					A_						    =						["""past_key_values"""]
					A_						    =						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




					def __init__(  self				:	Any     ,  UpperCamelCase_				:	Union[str, Any]=5_8101     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	str=1024     ,  UpperCamelCase_				:	Optional[int]=12     ,  UpperCamelCase_				:	str=4096     ,  UpperCamelCase_				:	Optional[int]=16     ,  UpperCamelCase_				:	Optional[Any]=12     ,  UpperCamelCase_				:	Union[str, Any]=4096     ,  UpperCamelCase_				:	Tuple=16     ,  UpperCamelCase_				:	Any=0.0     ,  UpperCamelCase_				:	int=0.0     ,  UpperCamelCase_				:	Tuple=True     ,  UpperCamelCase_				:	Union[str, Any]=True     ,  UpperCamelCase_				:	str="gelu"     ,  UpperCamelCase_				:	Optional[int]=1024     ,  UpperCamelCase_				:	Optional[Any]=0.1     ,  UpperCamelCase_				:	Union[str, Any]=0.0     ,  UpperCamelCase_				:	List[str]=0.0     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	str=5_8100     ,  UpperCamelCase_				:	List[str]=False     ,  UpperCamelCase_				:	Union[str, Any]=5_8100     ,  UpperCamelCase_				:	int=0     ,  UpperCamelCase_				:	str=0     ,  UpperCamelCase_				:	str=True     ,  **UpperCamelCase_				:	Any     ,  )  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		vocab_size
						_lowercase					:       Tuple       =		decoder_vocab_size or vocab_size
						_lowercase					:       Optional[int]       =		max_position_embeddings
						_lowercase					:       Tuple       =		d_model
						_lowercase					:       Optional[Any]       =		encoder_ffn_dim
						_lowercase					:       Optional[Any]       =		encoder_layers
						_lowercase					:       List[Any]       =		encoder_attention_heads
						_lowercase					:       int       =		decoder_ffn_dim
						_lowercase					:       List[Any]       =		decoder_layers
						_lowercase					:       List[Any]       =		decoder_attention_heads
						_lowercase					:       Tuple       =		dropout
						_lowercase					:       int       =		attention_dropout
						_lowercase					:       str       =		activation_dropout
						_lowercase					:       int       =		activation_function
						_lowercase					:       Any       =		init_std
						_lowercase					:       Dict       =		encoder_layerdrop
						_lowercase					:       Optional[Any]       =		decoder_layerdrop
						_lowercase					:       List[Any]       =		use_cache
						_lowercase					:       List[str]       =		encoder_layers
						_lowercase					:       int       =		scale_embedding  # scale factor will be sqrt(d_model) if True
						_lowercase					:       int       =		share_encoder_decoder_embeddings
						super().__init__(
						    pad_token_id=UpperCamelCase_     ,  eos_token_id=UpperCamelCase_     ,  is_encoder_decoder=UpperCamelCase_     ,  decoder_start_token_id=UpperCamelCase_     ,  forced_eos_token_id=UpperCamelCase_     ,  **UpperCamelCase_     ,  )






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''





						if self.task in ["default", "seq2seq-lm"]:
							_lowercase					:       List[str]       =		OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
							        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
							    ]					)

							if self.use_past:
								_lowercase					:       Tuple       =		{0: 'batch'}
								_lowercase					:       List[str]       =		{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
								_lowercase					:       Tuple       =		{0: 'batch', 1: 'decoder_sequence'}
								_lowercase					:       int       =		{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
								self.fill_with_past_key_values_(UpperCamelCase_     ,  direction='inputs'					)
						elif self.task == "causal-lm":
							# TODO: figure this case out.
							_lowercase					:       Union[str, Any]       =		OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
							        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
							    ]					)
							if self.use_past:
								_lowercase  ,    _lowercase					:       List[Any]       =		self.num_layers
								for i in range(UpperCamelCase_					):
									_lowercase					:       str       =		{0: 'batch', 2: 'past_sequence + sequence'}
									_lowercase					:       Dict       =		{0: 'batch', 2: 'past_sequence + sequence'}
						else:
							_lowercase					:       Optional[Any]       =		OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
							        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
							        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
							        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
							    ]					)

						return common_inputs




					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
					def 						__UpperCAmelCase     (  self				:	str					)  ->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''





						if self.task in ["default", "seq2seq-lm"]:
							_lowercase					:       List[str]       =		super().outputs
						else:
							_lowercase					:       int       =		super(UpperCamelCase_     ,  self					).outputs
							if self.use_past:
								_lowercase  ,    _lowercase					:       str       =		self.num_layers
								for i in range(UpperCamelCase_					):
									_lowercase					:       Any       =		{0: 'batch', 2: 'past_sequence + sequence'}
									_lowercase					:       Any       =		{0: 'batch', 2: 'past_sequence + sequence'}
						return common_outputs




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	PreTrainedTokenizer     ,  UpperCamelCase_				:	int = -1     ,  UpperCamelCase_				:	int = -1     ,  UpperCamelCase_				:	bool = False     ,  UpperCamelCase_				:	Optional[TensorType] = None     ,  )  ->       Mapping[str, Any]:



						'''simple docstring'''





						_lowercase					:       str       =		self._generate_dummy_inputs_for_encoder_and_decoder(
						    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)

						# Generate decoder inputs
						_lowercase					:       Any       =		seq_length if not self.use_past else 1
						_lowercase					:       List[str]       =		self._generate_dummy_inputs_for_encoder_and_decoder(
						    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
						_lowercase					:       Optional[int]       =		{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						_lowercase					:       str       =		dict(**UpperCamelCase_     ,  **UpperCamelCase_					)

						if self.use_past:
							if not is_torch_available():
								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
							else:
								import torch
							_lowercase  ,    _lowercase					:       Optional[Any]       =		common_inputs['input_ids'].shape
							_lowercase					:       List[str]       =		common_inputs['decoder_input_ids'].shape[1]
							_lowercase  ,    _lowercase					:       Tuple       =		self.num_attention_heads
							_lowercase					:       List[str]       =		(
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							_lowercase					:       List[Any]       =		decoder_seq_length + 3
							_lowercase					:       Optional[Any]       =		(
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							_lowercase					:       Any       =		torch.cat(
							    [common_inputs['decoder_attention_mask'], torch.ones(UpperCamelCase_     ,  UpperCamelCase_					)]     ,  dim=1					)

							_lowercase					:       Optional[Any]       =		[]
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							_lowercase  ,    _lowercase					:       Dict       =		self.num_layers
							_lowercase					:       Dict       =		min(UpperCamelCase_     ,  UpperCamelCase_					)
							_lowercase					:       int       =		max(UpperCamelCase_     ,  UpperCamelCase_					) - min_num_layers
							_lowercase					:       Union[str, Any]       =		'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

							for _ in range(UpperCamelCase_					):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(UpperCamelCase_					),
								        torch.zeros(UpperCamelCase_					),
								        torch.zeros(UpperCamelCase_					),
								        torch.zeros(UpperCamelCase_					),
								    )					)
							# TODO: test this.
							_lowercase					:       Dict       =		encoder_shape if remaining_side_name == 'encoder' else decoder_shape
							for _ in range(UpperCamelCase_     ,  UpperCamelCase_					):
								common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase_					), torch.zeros(UpperCamelCase_					))					)
						return common_inputs




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	PreTrainedTokenizer     ,  UpperCamelCase_				:	int = -1     ,  UpperCamelCase_				:	int = -1     ,  UpperCamelCase_				:	bool = False     ,  UpperCamelCase_				:	Optional[TensorType] = None     ,  )  ->       Mapping[str, Any]:



						'''simple docstring'''





						_lowercase					:       int       =		self._generate_dummy_inputs_for_encoder_and_decoder(
						    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)

						if self.use_past:
							if not is_torch_available():
								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
							else:
								import torch
							_lowercase  ,    _lowercase					:       Any       =		common_inputs['input_ids'].shape
							# Not using the same length for past_key_values
							_lowercase					:       Any       =		seqlen + 2
							_lowercase  ,    _lowercase					:       Union[str, Any]       =		self.num_layers
							_lowercase  ,    _lowercase					:       List[Any]       =		self.num_attention_heads
							_lowercase					:       Dict       =		(
							    batch,
							    num_encoder_attention_heads,
							    past_key_values_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)

							_lowercase					:       Union[str, Any]       =		common_inputs['attention_mask'].dtype
							_lowercase					:       Union[str, Any]       =		torch.cat(
							    [common_inputs['attention_mask'], torch.ones(UpperCamelCase_     ,  UpperCamelCase_     ,  dtype=UpperCamelCase_					)]     ,  dim=1					)
							_lowercase					:       Optional[Any]       =		[
							    (torch.zeros(UpperCamelCase_					), torch.zeros(UpperCamelCase_					)) for _ in range(UpperCamelCase_					)
							]
						return common_inputs




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	PreTrainedTokenizer     ,  UpperCamelCase_				:	int = -1     ,  UpperCamelCase_				:	int = -1     ,  UpperCamelCase_				:	bool = False     ,  UpperCamelCase_				:	Optional[TensorType] = None     ,  )  ->       Mapping[str, Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		compute_effective_axis_dimension(
						    UpperCamelCase_     ,  fixed_dimension=OnnxConfig.default_fixed_batch     ,  num_token_to_add=0					)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						_lowercase					:       int       =		tokenizer.num_special_tokens_to_add(UpperCamelCase_					)
						_lowercase					:       int       =		compute_effective_axis_dimension(
						    UpperCamelCase_     ,  fixed_dimension=OnnxConfig.default_fixed_sequence     ,  num_token_to_add=UpperCamelCase_					)

						# Generate dummy inputs according to compute batch and sequence
						_lowercase					:       Optional[Any]       =		[' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
						_lowercase					:       List[str]       =		dict(tokenizer(UpperCamelCase_     ,  return_tensors=UpperCamelCase_					)					)
						return common_inputs




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	PreTrainedTokenizer     ,  UpperCamelCase_				:	int = -1     ,  UpperCamelCase_				:	int = -1     ,  UpperCamelCase_				:	bool = False     ,  UpperCamelCase_				:	Optional[TensorType] = None     ,  )  ->       Mapping[str, Any]:



						'''simple docstring'''





						if self.task in ["default", "seq2seq-lm"]:
							_lowercase					:       Optional[Any]       =		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
							    UpperCamelCase_     ,  batch_size=UpperCamelCase_     ,  seq_length=UpperCamelCase_     ,  is_pair=UpperCamelCase_     ,  framework=UpperCamelCase_					)

						else:
							_lowercase					:       Dict       =		self._generate_dummy_inputs_for_causal_lm(
							    UpperCamelCase_     ,  batch_size=UpperCamelCase_     ,  seq_length=UpperCamelCase_     ,  is_pair=UpperCamelCase_     ,  framework=UpperCamelCase_					)

						return common_inputs




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	str					)  ->       Tuple:



						'''simple docstring'''





						if self.task in ["default", "seq2seq-lm"]:
							_lowercase					:       Tuple       =		super()._flatten_past_key_values_(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
						else:
							_lowercase					:       int       =		super(UpperCamelCase_     ,  self					)._flatten_past_key_values_(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)




					@property
					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       float:



						'''simple docstring'''





						return 1E-4





'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A				:    int     						=logging.get_logger(__name__)

_A				:    Union[str, Any]     						={
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip_vision_model"""




					def __init__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	str=1408     ,  UpperCamelCase_				:	Tuple=6144     ,  UpperCamelCase_				:	Union[str, Any]=39     ,  UpperCamelCase_				:	Optional[Any]=16     ,  UpperCamelCase_				:	str=224     ,  UpperCamelCase_				:	Dict=14     ,  UpperCamelCase_				:	Dict="gelu"     ,  UpperCamelCase_				:	int=1E-6     ,  UpperCamelCase_				:	int=0.0     ,  UpperCamelCase_				:	List[str]=1E-10     ,  UpperCamelCase_				:	str=True     ,  **UpperCamelCase_				:	Dict     ,  )  ->       Any:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		hidden_size
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       Optional[int]       =		num_hidden_layers
						_lowercase					:       str       =		num_attention_heads
						_lowercase					:       Tuple       =		patch_size
						_lowercase					:       Dict       =		image_size
						_lowercase					:       Optional[int]       =		initializer_range
						_lowercase					:       List[Any]       =		attention_dropout
						_lowercase					:       int       =		layer_norm_eps
						_lowercase					:       Optional[int]       =		hidden_act
						_lowercase					:       str       =		qkv_bias




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  **UpperCamelCase_				:	List[str]					)  ->       "PretrainedConfig":



						'''simple docstring'''





						cls._set_token_in_kwargs(UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       Tuple       =		cls.get_config_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
							_lowercase					:       Any       =		config_dict['vision_config']

						if "model_type" in config_dict and hasattr(cls     ,  'model_type'					) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
							    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''					)

						return cls.from_dict(UpperCamelCase_     ,  **UpperCamelCase_					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip_qformer"""




					def __init__(  self				:	Tuple     ,  UpperCamelCase_				:	Union[str, Any]=3_0522     ,  UpperCamelCase_				:	Union[str, Any]=768     ,  UpperCamelCase_				:	Tuple=12     ,  UpperCamelCase_				:	Optional[Any]=12     ,  UpperCamelCase_				:	List[str]=3072     ,  UpperCamelCase_				:	List[str]="gelu"     ,  UpperCamelCase_				:	Union[str, Any]=0.1     ,  UpperCamelCase_				:	List[str]=0.1     ,  UpperCamelCase_				:	Any=512     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	List[Any]=1E-12     ,  UpperCamelCase_				:	Optional[Any]=0     ,  UpperCamelCase_				:	str="absolute"     ,  UpperCamelCase_				:	List[Any]=2     ,  UpperCamelCase_				:	Any=1408     ,  **UpperCamelCase_				:	Dict     ,  )  ->       Any:



						'''simple docstring'''





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

						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       Optional[Any]       =		hidden_size
						_lowercase					:       Any       =		num_hidden_layers
						_lowercase					:       List[Any]       =		num_attention_heads
						_lowercase					:       Optional[int]       =		hidden_act
						_lowercase					:       Union[str, Any]       =		intermediate_size
						_lowercase					:       List[Any]       =		hidden_dropout_prob
						_lowercase					:       Dict       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		max_position_embeddings
						_lowercase					:       Optional[int]       =		initializer_range
						_lowercase					:       Tuple       =		layer_norm_eps
						_lowercase					:       List[str]       =		position_embedding_type
						_lowercase					:       str       =		cross_attention_frequency
						_lowercase					:       int       =		encoder_hidden_size




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  **UpperCamelCase_				:	List[str]					)  ->       "PretrainedConfig":



						'''simple docstring'''





						cls._set_token_in_kwargs(UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       List[str]       =		cls.get_config_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
							_lowercase					:       Optional[int]       =		config_dict['qformer_config']

						if "model_type" in config_dict and hasattr(cls     ,  'model_type'					) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
							    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''					)

						return cls.from_dict(UpperCamelCase_     ,  **UpperCamelCase_					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip"""
					A_						    =						True




					def __init__(  self				:	Any     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	Tuple=None     ,  UpperCamelCase_				:	Dict=None     ,  UpperCamelCase_				:	Union[str, Any]=32     ,  **UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						if vision_config is None:
							_lowercase					:       Any       =		{}
							logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'					)

						if qformer_config is None:
							_lowercase					:       List[Any]       =		{}
							logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'					)

						if text_config is None:
							_lowercase					:       List[Any]       =		{}
							logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'					)

						_lowercase					:       List[Any]       =		InstructBlipVisionConfig(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		InstructBlipQFormerConfig(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		text_config['model_type'] if 'model_type' in text_config else 'opt'
						_lowercase					:       int       =		CONFIG_MAPPING[text_model_type](**UpperCamelCase_					)

						_lowercase					:       str       =		self.text_config.tie_word_embeddings
						_lowercase					:       int       =		self.text_config.is_encoder_decoder

						_lowercase					:       Tuple       =		num_query_tokens
						_lowercase					:       str       =		self.vision_config.hidden_size
						_lowercase					:       Union[str, Any]       =		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						_lowercase					:       List[Any]       =		1.0
						_lowercase					:       int       =		0.02




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	Tuple     ,  UpperCamelCase_				:	InstructBlipVisionConfig     ,  UpperCamelCase_				:	InstructBlipQFormerConfig     ,  UpperCamelCase_				:	PretrainedConfig     ,  **UpperCamelCase_				:	Dict     ,  )  ->       List[str]:



						'''simple docstring'''





						return cls(
						    vision_config=vision_config.to_dict()     ,  qformer_config=qformer_config.to_dict()     ,  text_config=text_config.to_dict()     ,  **UpperCamelCase_     ,  )




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		copy.deepcopy(self.__dict__					)
						_lowercase					:       Optional[int]       =		self.vision_config.to_dict()
						_lowercase					:       Optional[Any]       =		self.qformer_config.to_dict()
						_lowercase					:       Tuple       =		self.text_config.to_dict()
						_lowercase					:       Dict       =		self.__class__.model_type
						return output
