





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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       "instructblip_vision_model"






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







							"""simple docstring"""


							super().__init__(**_A	)

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






						@classmethod
						def UpperCAmelCase_				(      cls      :      Tuple		,   _A      :      Union[str, os.PathLike]		,   **_A      :      Optional[int]	)							->    "PretrainedConfig":







							"""simple docstring"""


							cls._set_token_in_kwargs(_A	)

							snake_case_						,snake_case_		:			Dict											=	cls.get_config_dict(_A		,   **_A	)

							# get the vision config dict if we are loading from InstructBlipConfig
							if config_dict.get('model_type'	) == "instructblip":
								snake_case_		:			Optional[int]											=	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(_A		,   **_A	)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Tuple       =       "instructblip_qformer"






						def __init__(      self      :      Any		,   _A      :      Any=30522		,   _A      :      Optional[int]=768		,   _A      :      Tuple=12		,   _A      :      List[Any]=12		,   _A      :      List[str]=3072		,   _A      :      int="gelu"		,   _A      :      List[str]=0.1		,   _A      :      str=0.1		,   _A      :      List[Any]=512		,   _A      :      Optional[int]=0.0_2		,   _A      :      List[str]=1E-12		,   _A      :      List[str]=0		,   _A      :      Union[str, Any]="absolute"		,   _A      :      Union[str, Any]=2		,   _A      :      int=1408		,   **_A      :      Tuple		,   )							->    List[str]:







							"""simple docstring"""


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

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






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







							"""simple docstring"""


							cls._set_token_in_kwargs(_A	)

							snake_case_						,snake_case_		:			Tuple											=	cls.get_config_dict(_A		,   **_A	)

							# get the qformer config dict if we are loading from InstructBlipConfig
							if config_dict.get('model_type'	) == "instructblip":
								snake_case_		:			Union[str, Any]											=	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(_A		,   **_A	)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			List[str]       =       "instructblip"
						__magic_name__:			str       =       True






						def __init__(      self      :      List[Any]		,   _A      :      Optional[int]=None		,   _A      :      Optional[Any]=None		,   _A      :      Optional[int]=None		,   _A      :      List[str]=32		,   **_A      :      List[Any]	)							->    List[str]:







							"""simple docstring"""


							super().__init__(**_A	)

							if vision_config is None:
								snake_case_		:			List[str]											=	{}
								logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'	)

							if qformer_config is None:
								snake_case_		:			int											=	{}
								logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'	)

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

							snake_case_		:			Optional[Any]											=	InstructBlipVisionConfig(**_A	)
							snake_case_		:			Tuple											=	InstructBlipQFormerConfig(**_A	)
							snake_case_		:			Optional[Any]											=	text_config['model_type'] if 'model_type' in text_config else 'opt'
							snake_case_		:			List[str]											=	CONFIG_MAPPING[text_model_type](**_A	)

							snake_case_		:			Optional[int]											=	self.text_config.tie_word_embeddings
							snake_case_		:			Optional[int]											=	self.text_config.is_encoder_decoder

							snake_case_		:			str											=	num_query_tokens
							snake_case_		:			Optional[int]											=	self.vision_config.hidden_size
							snake_case_		:			Any											=	self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
							snake_case_		:			Union[str, Any]											=	1.0
							snake_case_		:			str											=	0.0_2






						@classmethod
						def UpperCAmelCase_				(      cls      :      str		,   _A      :      InstructBlipVisionConfig		,   _A      :      InstructBlipQFormerConfig		,   _A      :      PretrainedConfig		,   **_A      :      List[Any]		,   )							->    Dict:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Tuple:







							"""simple docstring"""


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








import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	# Load checkpoint
	snake_case_		:			Union[str, Any]											=	torch.load(__a   , map_location='cpu'   )

	snake_case_		:			Union[str, Any]											=	chkpt['model']

	# We have the base model one level deeper than the original XLM repository
	snake_case_		:			str											=	{}
	for k, v in state_dict.items():
		if "pred_layer" in k:
			snake_case_		:			Tuple											=	v
		else:
			snake_case_		:			Dict											=	v

	snake_case_		:			Tuple											=	chkpt['params']
	snake_case_		:			List[Any]											=	{n: v for n, v in config.items() if not isinstance(__a   , (torch.FloatTensor, numpy.ndarray)   )}

	snake_case_		:			Optional[int]											=	chkpt['dico_word2id']
	snake_case_		:			List[str]											=	{s + '</w>' if s.find('@@'   ) == -1 and i > 13 else s.replace('@@'   , ''   ): i for s, i in vocab.items()}

	# Save pytorch-model
	snake_case_		:			List[str]											=	pytorch_dump_folder_path + '/' + WEIGHTS_NAME
	snake_case_		:			Dict											=	pytorch_dump_folder_path + '/' + CONFIG_NAME
	snake_case_		:			Any											=	pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""   )
	torch.save(__a   , __a   )

	print(f"""Save configuration file to {pytorch_config_dump_path}"""   )
	with open(__a   , 'w'   , encoding='utf-8'   ) as f:
		f.write(json.dumps(__a   , indent=2   ) + '\n'   )

	print(f"""Save vocab file to {pytorch_config_dump_path}"""   )
	with open(__a   , 'w'   , encoding='utf-8'   ) as f:
		f.write(json.dumps(__a   , indent=2   ) + '\n'   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)

