






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


__UpperCamelCase      :      Any     						=						logging.get_logger(__name__)

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


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:str   =       'instructblip_vision_model'







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







		super().__init__(**_lowerCAmelCase			)

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







	@classmethod
	def  _a   (  cls      :							Tuple       ,				_lowerCAmelCase      :							Union[str, os.PathLike]       ,				**_lowerCAmelCase      :							Optional[int]			)		-> "PretrainedConfig":
		"""simple docstring"""







		cls._set_token_in_kwargs(_lowerCAmelCase			)

		__lowercase       ,						__lowercase             =  cls.get_config_dict(_lowerCAmelCase       ,				**_lowerCAmelCase			)

		# get the vision config dict if we are loading from InstructBlipConfig
		if config_dict.get("""model_type"""			) == "instructblip":
			__lowercase             =  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(_lowerCAmelCase       ,				**_lowerCAmelCase			)


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:int   =       'instructblip_qformer'







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







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

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







	@classmethod
	def  _a   (  cls      :							Optional[Any]       ,				_lowerCAmelCase      :							Union[str, os.PathLike]       ,				**_lowerCAmelCase      :							List[Any]			)		-> "PretrainedConfig":
		"""simple docstring"""







		cls._set_token_in_kwargs(_lowerCAmelCase			)

		__lowercase       ,						__lowercase             =  cls.get_config_dict(_lowerCAmelCase       ,				**_lowerCAmelCase			)

		# get the qformer config dict if we are loading from InstructBlipConfig
		if config_dict.get("""model_type"""			) == "instructblip":
			__lowercase             =  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(_lowerCAmelCase       ,				**_lowerCAmelCase			)


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:str   =       'instructblip'
	__snake_case	:Optional[Any]   =       True







	def __init__(  self      :							Optional[Any]       ,				_lowerCAmelCase      :							str=None       ,				_lowerCAmelCase      :							Union[str, Any]=None       ,				_lowerCAmelCase      :							Optional[int]=None       ,				_lowerCAmelCase      :							Dict=32       ,				**_lowerCAmelCase      :							List[str]			)		-> str:
		"""simple docstring"""







		super().__init__(**_lowerCAmelCase			)

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

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

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

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

		__lowercase             =  self.text_config.tie_word_embeddings
		__lowercase             =  self.text_config.is_encoder_decoder

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







	@classmethod
	def  _a   (  cls      :							Optional[Any]       ,				_lowerCAmelCase      :							InstructBlipVisionConfig       ,				_lowerCAmelCase      :							InstructBlipQFormerConfig       ,				_lowerCAmelCase      :							PretrainedConfig       ,				**_lowerCAmelCase      :							Any       ,				)		-> List[str]:
		"""simple docstring"""







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







	def  _a   (  self      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  copy.deepcopy(self.__dict__			)
		__lowercase             =  self.vision_config.to_dict()
		__lowercase             =  self.qformer_config.to_dict()
		__lowercase             =  self.text_config.to_dict()
		__lowercase             =  self.__class__.model_type
		return output









import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase      :      str     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  RobertaPreLayerNormConfig.from_pretrained(
	    lowerCamelCase				,  architectures=["""RobertaPreLayerNormForMaskedLM"""]					)

	# convert state_dict
	__lowercase             =  torch.load(hf_hub_download(repo_id=lowerCamelCase				,  filename="""pytorch_model.bin"""					)					)
	__lowercase             =  {}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""					):
			__lowercase             =  """roberta_prelayernorm.""" + tensor_key[len("""roberta."""					) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""					) or tensor_key.endswith(""".self.LayerNorm.bias"""					):
			continue

		__lowercase             =  tensor_value

	__lowercase             =  RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=lowerCamelCase				,  config=lowerCamelCase				,  state_dict=lowerCamelCase					)
	model.save_pretrained(lowerCamelCase					)

	# convert tokenizer
	__lowercase             =  AutoTokenizer.from_pretrained(lowerCamelCase					)
	tokenizer.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
						__UpperCamelCase      :      List[Any]     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--checkpoint-repo""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						__UpperCamelCase      :      Dict     						=						parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

