"""simple docstring"""







import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				Tuple						)    ->     List[str]:







     '''simple docstring'''


     __A					:					Any								=		pt_attn_layer.in_proj_weight.chunk(3  ,       dim=0						)
     __A					:					Any								=		pt_attn_layer.in_proj_bias.chunk(3  ,       dim=0						)

     __A					:					Tuple								=		pt_attn_layer.out_proj.weight
     __A					:					Optional[int]								=		pt_attn_layer.out_proj.bias

     __A					:					Optional[int]								=		q_proj
     __A					:					Union[str, Any]								=		q_proj_bias

     __A					:					Optional[int]								=		k_proj
     __A					:					List[str]								=		k_proj_bias

     __A					:					Any								=		v_proj
     __A					:					Optional[int]								=		v_proj_bias

     __A					:					Optional[int]								=		out_proj_weights
     __A					:					str								=		out_proj_bias




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Tuple						)    ->     Optional[Any]:







     '''simple docstring'''


     copy_linear(hf_mlp.fca  ,       pt_mlp.c_fc						)
     copy_linear(hf_mlp.fca  ,       pt_mlp.c_proj						)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				int  ,       _SCREAMING_SNAKE_CASE				:				int						)    ->     Optional[int]:







     '''simple docstring'''


     __A					:					str								=		pt_linear.weight
     __A					:					List[str]								=		pt_linear.bias




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[int]  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     int:







     '''simple docstring'''


     copy_linear(hf_layer.layer_norma  ,       pt_layer.ln_a						)
     copy_linear(hf_layer.layer_norma  ,       pt_layer.ln_a						)

     # copy MLP
     copy_mlp(hf_layer.mlp  ,       pt_layer.mlp						)

     # copy attn
     copy_attn_layer(hf_layer.self_attn  ,       pt_layer.attn						)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[int]  ,       _SCREAMING_SNAKE_CASE				:				int						)    ->     List[Any]:







     '''simple docstring'''


     for hf_layer, pt_layer in zip(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						):
          copy_layer(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[int]  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     List[Any]:







     '''simple docstring'''


     __A					:					Optional[int]								=		pt_model.token_embedding.weight
     __A					:					Optional[Any]								=		pt_model.positional_embedding

     # copy layer norm
     copy_linear(hf_encoder.final_layer_norm  ,       pt_model.ln_final						)

     # copy hidden layers
     copy_layers(hf_encoder.encoder.layers  ,       pt_model.transformer.resblocks						)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[Any]  ,       _SCREAMING_SNAKE_CASE				:				int						)    ->     Optional[Any]:







     '''simple docstring'''


     __A					:					Union[str, Any]								=		pt_model.text_projection.data.T

     # copy text encoder
     copy_encoder(hf_model.text_model  ,       _SCREAMING_SNAKE_CASE						)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[int]  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     int:







     '''simple docstring'''


     __A					:					int								=		pt_model.visual.proj.data.T

     # copy layer norms
     copy_linear(hf_model.vision_model.pre_layrnorm  ,       pt_model.visual.ln_pre						)
     copy_linear(hf_model.vision_model.post_layernorm  ,       pt_model.visual.ln_post						)

     # copy embeds
     __A					:					Union[str, Any]								=		pt_model.visual.conva.weight.data
     __A					:					Dict								=		pt_model.visual.class_embedding
     __A					:					Union[str, Any]								=		pt_model.visual.positional_embedding.data

     # copy encoder
     copy_layers(hf_model.vision_model.encoder.layers  ,       pt_model.visual.transformer.resblocks						)




@torch.no_grad()
def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Dict  ,       _SCREAMING_SNAKE_CASE				:				int  ,       _SCREAMING_SNAKE_CASE				:				List[str]=None						)    ->     str:







     '''simple docstring'''


     if config_path is not None:
          __A					:					Optional[int]								=		CLIPConfig.from_pretrained(_SCREAMING_SNAKE_CASE						)
     else:
          __A					:					List[Any]								=		CLIPConfig(projection_dim=512  ,       text_config={}  ,       vision_config={}						)

     __A					:					List[str]								=		CLIPModel(_SCREAMING_SNAKE_CASE						).eval()

     __A					:					str								=		load(_SCREAMING_SNAKE_CASE  ,       device='cpu'  ,       jit=_SCREAMING_SNAKE_CASE						)
     __A					:					Dict								=		pt_model.eval()

     copy_text_model_and_projection(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)
     copy_vison_model_and_projection(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)
     __A					:					Optional[int]								=		pt_model.logit_scale

     __A					:					Dict								=		torch.arange(0  ,       77						).unsqueeze(0						)
     __A					:					int								=		torch.randn(1  ,       3  ,       224  ,       224						)

     __A					:					Tuple								=		hf_model(input_ids=_SCREAMING_SNAKE_CASE  ,       pixel_values=_SCREAMING_SNAKE_CASE  ,       return_dict=_SCREAMING_SNAKE_CASE						)
     __A					:					Dict								=		hf_outputs.logits_per_image
     __A					:					Dict								=		hf_outputs.logits_per_text
     __A					:					str								=		pt_model(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)

     assert torch.allclose(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       atol=1E-3						)
     assert torch.allclose(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       atol=1E-3						)

     hf_model.save_pretrained(_SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
       lowerCamelCase      : Union[str, Any]       =argparse.ArgumentParser()
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       lowerCamelCase      : Dict       =parser.parse_args()

       convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     if "model" in orig_key:
          _lowerCAmelCase :       Tuple	   =     orig_key.replace("model."		,				""					)
     if "norm1" in orig_key:
          _lowerCAmelCase :       Optional[Any]	   =     orig_key.replace("norm1"		,				"attention.output.LayerNorm"					)
     if "norm2" in orig_key:
          _lowerCAmelCase :       Optional[int]	   =     orig_key.replace("norm2"		,				"output.LayerNorm"					)
     if "norm" in orig_key:
          _lowerCAmelCase :       Any	   =     orig_key.replace("norm"		,				"LayerNorm"					)
     if "transformer" in orig_key:
          _lowerCAmelCase :       str	   =     orig_key.split("."					)[0].split("_"					)[-1]
          _lowerCAmelCase :       Any	   =     orig_key.replace(f'''transformer_{layer_num}'''		,				f'''encoder.layer.{layer_num}'''					)
     if "mha.attn" in orig_key:
          _lowerCAmelCase :       int	   =     orig_key.replace("mha.attn"		,				"attention.self"					)
     if "mha" in orig_key:
          _lowerCAmelCase :       Dict	   =     orig_key.replace("mha"		,				"attention"					)
     if "W_q" in orig_key:
          _lowerCAmelCase :       Union[str, Any]	   =     orig_key.replace("W_q"		,				"self.query"					)
     if "W_k" in orig_key:
          _lowerCAmelCase :       Tuple	   =     orig_key.replace("W_k"		,				"self.key"					)
     if "W_v" in orig_key:
          _lowerCAmelCase :       Optional[int]	   =     orig_key.replace("W_v"		,				"self.value"					)
     if "ff1" in orig_key:
          _lowerCAmelCase :       Tuple	   =     orig_key.replace("ff1"		,				"intermediate.dense"					)
     if "ff2" in orig_key:
          _lowerCAmelCase :       List[Any]	   =     orig_key.replace("ff2"		,				"output.dense"					)
     if "ff" in orig_key:
          _lowerCAmelCase :       str	   =     orig_key.replace("ff"		,				"output.dense"					)
     if "mlm_class" in orig_key:
          _lowerCAmelCase :       int	   =     orig_key.replace("mlm.mlm_class"		,				"cls.predictions.decoder"					)
     if "mlm" in orig_key:
          _lowerCAmelCase :       Union[str, Any]	   =     orig_key.replace("mlm"		,				"cls.predictions.transform"					)
     if "cls" not in orig_key:
          _lowerCAmelCase :       Optional[int]	   =     "nystromformer." + orig_key

     return orig_key







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Any:

     """simple docstring"""







     for key in orig_state_dict.copy().keys():
          _lowerCAmelCase :       List[str]	   =     orig_state_dict.pop(SCREAMING_SNAKE_CASE					)

          if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
               continue
          else:
               _lowerCAmelCase :       int	   =     val

     _lowerCAmelCase :       Tuple	   =     orig_state_dict["cls.predictions.decoder.bias"]
     _lowerCAmelCase :       Any	   =     (
         torch.arange(config.max_position_embeddings					).expand((1, -1)					) + 2
     )

     return orig_state_dict







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Optional[Any]	   =     torch.load(SCREAMING_SNAKE_CASE		,				map_location="cpu"					)["model_state_dict"]
     _lowerCAmelCase :       Optional[Any]	   =     NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       List[str]	   =     NystromformerForMaskedLM(SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       Union[str, Any]	   =     convert_checkpoint_helper(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)

     model.load_state_dict(SCREAMING_SNAKE_CASE					)
     model.eval()
     model.save_pretrained(SCREAMING_SNAKE_CASE					)

     print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''					)


if __name__ == "__main__":
  __UpperCAmelCase     					=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--pytorch_model_path', default=None, type=str, required=True, help='Path to Nystromformer pytorch checkpoint.'
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The json file for Nystromformer model config.',
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  __UpperCAmelCase     					=  parser.parse_args()
  convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






