





"""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


lowerCAmelCase__   	=	logging.get_logger(__name__)

lowerCAmelCase__   	=	{
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}





class 				_lowerCamelCase					(				UpperCAmelCase_       ):
						UpperCAmelCase_       =	'instructblip_vision_model'


						def __init__(self      ,							__a=14_08      ,							__a=61_44      ,							__a=39      ,							__a=16      ,							__a=2_24      ,							__a=14      ,							__a="gelu"      ,							__a=1e-6      ,							__a=0.0      ,							__a=1e-1_0      ,							__a=True      ,							**__a      ,							)       ->      Dict:
								super().__init__(**__UpperCAmelCase						)

								UpperCamelCase      =		hidden_size
								UpperCamelCase      =		intermediate_size
								UpperCamelCase      =		num_hidden_layers
								UpperCamelCase      =		num_attention_heads
								UpperCamelCase      =		patch_size
								UpperCamelCase      =		image_size
								UpperCamelCase      =		initializer_range
								UpperCamelCase      =		attention_dropout
								UpperCamelCase      =		layer_norm_eps
								UpperCamelCase      =		hidden_act
								UpperCamelCase      =		qkv_bias




						@classmethod
						def 			snake_case_      (cls      ,							__a      ,							**__a						)       ->      "PretrainedConfig":
								cls._set_token_in_kwargs(__UpperCAmelCase						)

								UpperCamelCase      , UpperCamelCase      =		cls.get_config_dict(__UpperCAmelCase      ,							**__UpperCAmelCase						)

								# get the vision config dict if we are loading from InstructBlipConfig
								if config_dict.get("model_type"						) == "instructblip":
										UpperCamelCase      =		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					(				UpperCAmelCase_       ):
						UpperCAmelCase_       =	'instructblip_qformer'


						def __init__(self      ,							__a=3_05_22      ,							__a=7_68      ,							__a=12      ,							__a=12      ,							__a=30_72      ,							__a="gelu"      ,							__a=0.1      ,							__a=0.1      ,							__a=5_12      ,							__a=0.02      ,							__a=1e-1_2      ,							__a=0      ,							__a="absolute"      ,							__a=2      ,							__a=14_08      ,							**__a      ,							)       ->      Dict:
								super().__init__(pad_token_id=__UpperCAmelCase      ,							**__UpperCAmelCase						)

								UpperCamelCase      =		vocab_size
								UpperCamelCase      =		hidden_size
								UpperCamelCase      =		num_hidden_layers
								UpperCamelCase      =		num_attention_heads
								UpperCamelCase      =		hidden_act
								UpperCamelCase      =		intermediate_size
								UpperCamelCase      =		hidden_dropout_prob
								UpperCamelCase      =		attention_probs_dropout_prob
								UpperCamelCase      =		max_position_embeddings
								UpperCamelCase      =		initializer_range
								UpperCamelCase      =		layer_norm_eps
								UpperCamelCase      =		position_embedding_type
								UpperCamelCase      =		cross_attention_frequency
								UpperCamelCase      =		encoder_hidden_size




						@classmethod
						def 			snake_case_      (cls      ,							__a      ,							**__a						)       ->      "PretrainedConfig":
								cls._set_token_in_kwargs(__UpperCAmelCase						)

								UpperCamelCase      , UpperCamelCase      =		cls.get_config_dict(__UpperCAmelCase      ,							**__UpperCAmelCase						)

								# get the qformer config dict if we are loading from InstructBlipConfig
								if config_dict.get("model_type"						) == "instructblip":
										UpperCamelCase      =		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					(				UpperCAmelCase_       ):
						UpperCAmelCase_       =	'instructblip'
						UpperCAmelCase_       =	True


						def __init__(self      ,							__a=None      ,							__a=None      ,							__a=None      ,							__a=32      ,							**__a						)       ->      Optional[int]:
								super().__init__(**__UpperCAmelCase						)

								if vision_config is None:
										UpperCamelCase      =		{}
										logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."						)

								if qformer_config is None:
										UpperCamelCase      =		{}
										logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."						)

								if text_config is None:
										UpperCamelCase      =		{}
										logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."						)

								UpperCamelCase      =		InstructBlipVisionConfig(**__UpperCAmelCase						)
								UpperCamelCase      =		InstructBlipQFormerConfig(**__UpperCAmelCase						)
								UpperCamelCase      =		text_config["model_type"] if "model_type" in text_config else "opt"
								UpperCamelCase      =		CONFIG_MAPPING[text_model_type](**__UpperCAmelCase						)

								UpperCamelCase      =		self.text_config.tie_word_embeddings
								UpperCamelCase      =		self.text_config.is_encoder_decoder

								UpperCamelCase      =		num_query_tokens
								UpperCamelCase      =		self.vision_config.hidden_size
								UpperCamelCase      =		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
								UpperCamelCase      =		1.0
								UpperCamelCase      =		0.02


						@classmethod
						def 			snake_case_      (cls      ,							__a      ,							__a      ,							__a      ,							**__a      ,							)       ->      Tuple:
								return cls(
								    vision_config=vision_config.to_dict()      ,							qformer_config=qformer_config.to_dict()      ,							text_config=text_config.to_dict()      ,							**__UpperCAmelCase      ,							)




						def 			snake_case_      (self						)       ->      Union[str, Any]:
								UpperCamelCase      =		copy.deepcopy(self.__dict__						)
								UpperCamelCase      =		self.vision_config.to_dict()
								UpperCamelCase      =		self.qformer_config.to_dict()
								UpperCamelCase      =		self.text_config.to_dict()
								UpperCamelCase      =		self.__class__.model_type
								return output







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def    lowerCAmelCase_ (     __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  [
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(__A,  __A							)




def    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__		,      UpperCAmelCase__								=  emb.weight.shape
      UpperCAmelCase__								=  nn.Linear(__A,  __A,  bias=__A							)
      UpperCAmelCase__								=  emb.weight.data
      return lin_layer




def    lowerCAmelCase_ (     __A,  __A="facebook/mbart-large-en-ro",  __A=False,  __A=False							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  torch.load(__A,  map_location="cpu"							)["model"]
      remove_ignore_keys_(__A							)
      UpperCAmelCase__								=  state_dict["encoder.embed_tokens.weight"].shape[0]

      UpperCAmelCase__								=  MBartConfig.from_pretrained(__A,  vocab_size=__A							)
      if mbart_aa and finetuned:
            UpperCAmelCase__								=  "relu"

      UpperCAmelCase__								=  state_dict["decoder.embed_tokens.weight"]
      UpperCAmelCase__								=  MBartForConditionalGeneration(__A							)
      model.model.load_state_dict(__A							)

      if finetuned:
            UpperCAmelCase__								=  make_linear_from_emb(model.model.shared							)

      return model


if __name__ == "__main__":
      UpperCamelCase__											=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
      )
      parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      parser.add_argument(
          '--hf_config',
          default='facebook/mbart-large-cc25',
          type=str,
          help='Which huggingface architecture to use: mbart-large',
      )
      parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
      parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
      UpperCamelCase__											=					parser.parse_args()
      UpperCamelCase__											=					convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)



