




import copy
import os
from typing import Union

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


lowercase__				:      List[str]	   = logging.get_logger(__name__)

lowercase__				:      List[str]	   = {
    "Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json",
    "Salesforce/blip-vqa-capfit-large": (
        "https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-base": (
        "https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-large": (
        "https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"
    ),
    "Salesforce/blip-itm-base-coco": "https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json",
    "Salesforce/blip-itm-large-coco": "https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json",
    "Salesforce/blip-itm-base-flikr": "https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json",
    "Salesforce/blip-itm-large-flikr": (
        "https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"
    ),
}




class       a__	( UpperCamelCase__				):
     a		:      str		 =     """blip_text_model"""







     def __init__(     self  ,		A=30524  ,		A=768  ,		A=768  ,		A=3072  ,		A=768  ,		A=12  ,		A=8  ,		A=512  ,		A="gelu"  ,		A=1e-12  ,		A=0.0  ,		A=0.0  ,		A=0.0_2  ,		A=30522  ,		A=2  ,		A=0  ,		A=102  ,		A=True  ,		A=True  ,		**A  ,		)  ->   Optional[int]:



          '''simple docstring'''



          super().__init__(
              pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A  ,		sep_token_id=A  ,		**A  ,		)

          a     =				vocab_size
          a     =				hidden_size
          a     =				encoder_hidden_size
          a     =				intermediate_size
          a     =				projection_dim
          a     =				hidden_dropout_prob
          a     =				num_hidden_layers
          a     =				num_attention_heads
          a     =				max_position_embeddings
          a     =				layer_norm_eps
          a     =				hidden_act
          a     =				initializer_range
          a     =				attention_probs_dropout_prob
          a     =				is_decoder
          a     =				use_cache







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A  ,		**A			)  ->   "PretrainedConfig":



          '''simple docstring'''



          cls._set_token_in_kwargs(A			)

          a  ,				a     =				cls.get_config_dict(A  ,		**A			)

          # get the text config dict if we are loading from BlipConfig
          if config_dict.get("model_type"			) == "blip":
               a     =				config_dict["text_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       a__	( UpperCamelCase__				):
     a		:      Optional[Any]		 =     """blip_vision_model"""







     def __init__(     self  ,		A=768  ,		A=3072  ,		A=512  ,		A=12  ,		A=12  ,		A=384  ,		A=16  ,		A="gelu"  ,		A=1e-5  ,		A=0.0  ,		A=1e-10  ,		**A  ,		)  ->   Dict:



          '''simple docstring'''



          super().__init__(**A			)

          a     =				hidden_size
          a     =				intermediate_size
          a     =				projection_dim
          a     =				num_hidden_layers
          a     =				num_attention_heads
          a     =				patch_size
          a     =				image_size
          a     =				initializer_range
          a     =				attention_dropout
          a     =				layer_norm_eps
          a     =				hidden_act







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A  ,		**A			)  ->   "PretrainedConfig":



          '''simple docstring'''



          cls._set_token_in_kwargs(A			)

          a  ,				a     =				cls.get_config_dict(A  ,		**A			)

          # get the vision config dict if we are loading from BlipConfig
          if config_dict.get("model_type"			) == "blip":
               a     =				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       a__	( UpperCamelCase__				):
     a		:      List[str]		 =     """blip"""
     a		:      List[Any]		 =     True







     def __init__(     self  ,		A=None  ,		A=None  ,		A=512  ,		A=2.6_5_9_2  ,		A=256  ,		**A  ,		)  ->   Union[str, Any]:



          '''simple docstring'''



          super().__init__(**A			)

          if text_config is None:
               a     =				{}
               logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values."			)

          if vision_config is None:
               a     =				{}
               logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."			)

          a     =				BlipTextConfig(**A			)
          a     =				BlipVisionConfig(**A			)

          a     =				self.vision_config.hidden_size

          a     =				projection_dim
          a     =				logit_scale_init_value
          a     =				1.0
          a     =				0.0_2
          a     =				image_text_hidden_size







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A  ,		A  ,		**A			)  ->   List[str]:



          '''simple docstring'''



          return cls(text_config=text_config.to_dict()  ,		vision_config=vision_config.to_dict()  ,		**A			)







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



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







import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class       a__	( pl.LightningModule				):







     def __init__(     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          super().__init__()
          a     =				model
          a     =				2
          a     =				nn.Linear(self.model.config.hidden_size  ,		self.num_labels			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          pass







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # load longformer model from model identifier
     a     =				LongformerModel.from_pretrained(__UpperCamelCase)
     a     =				LightningModel(__UpperCamelCase)

     a     =				torch.load(__UpperCamelCase		, map_location=torch.device("cpu"))
     lightning_model.load_state_dict(ckpt["state_dict"])

     # init longformer question answering model
     a     =				LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase)

     # transfer weights
     longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
     longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
     longformer_for_qa.eval()

     # save model
     longformer_for_qa.save_pretrained(__UpperCamelCase)

     print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''')


if __name__ == "__main__":
   lowercase__				:      Optional[Any]	   = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--longformer_model",
       default=None,
       type=str,
       required=True,
       help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
   )
   parser.add_argument(
       "--longformer_question_answering_ckpt_path",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch Lightning Checkpoint.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   lowercase__				:      Union[str, Any]	   = parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )

