


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     if "model" in orig_key:
          __UpperCamelCase    							=orig_key.replace('model.'			,     ''				)
     if "norm1" in orig_key:
          __UpperCamelCase    							=orig_key.replace('norm1'			,     'attention.output.LayerNorm'				)
     if "norm2" in orig_key:
          __UpperCamelCase    							=orig_key.replace('norm2'			,     'output.LayerNorm'				)
     if "norm" in orig_key:
          __UpperCamelCase    							=orig_key.replace('norm'			,     'LayerNorm'				)
     if "transformer" in orig_key:
          __UpperCamelCase    							=orig_key.split('.'				)[0].split('_'				)[-1]
          __UpperCamelCase    							=orig_key.replace(F'transformer_{layer_num}'			,     F'encoder.layer.{layer_num}'				)
     if "mha.attn" in orig_key:
          __UpperCamelCase    							=orig_key.replace('mha.attn'			,     'attention.self'				)
     if "mha" in orig_key:
          __UpperCamelCase    							=orig_key.replace('mha'			,     'attention'				)
     if "W_q" in orig_key:
          __UpperCamelCase    							=orig_key.replace('W_q'			,     'self.query'				)
     if "W_k" in orig_key:
          __UpperCamelCase    							=orig_key.replace('W_k'			,     'self.key'				)
     if "W_v" in orig_key:
          __UpperCamelCase    							=orig_key.replace('W_v'			,     'self.value'				)
     if "ff1" in orig_key:
          __UpperCamelCase    							=orig_key.replace('ff1'			,     'intermediate.dense'				)
     if "ff2" in orig_key:
          __UpperCamelCase    							=orig_key.replace('ff2'			,     'output.dense'				)
     if "ff" in orig_key:
          __UpperCamelCase    							=orig_key.replace('ff'			,     'output.dense'				)
     if "mlm_class" in orig_key:
          __UpperCamelCase    							=orig_key.replace('mlm.mlm_class'			,     'cls.predictions.decoder'				)
     if "mlm" in orig_key:
          __UpperCamelCase    							=orig_key.replace('mlm'			,     'cls.predictions.transform'				)
     if "cls" not in orig_key:
          __UpperCamelCase    							='yoso.' + orig_key

     return orig_key

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					List[str]				):
     for key in orig_state_dict.copy().keys():
          __UpperCamelCase    							=orig_state_dict.pop(SCREAMING_SNAKE_CASE__				)

          if ("pooler" in key) or ("sen_class" in key):
               continue
          else:
               __UpperCamelCase    							=val

     __UpperCamelCase    							=orig_state_dict['cls.predictions.decoder.bias']
     __UpperCamelCase    							=torch.arange(SCREAMING_SNAKE_CASE__				).expand((1, -1)				) + 2

     return orig_state_dict

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					List[str]				):
     __UpperCamelCase    							=torch.load(SCREAMING_SNAKE_CASE__			,     map_location='cpu'				)['model_state_dict']
     __UpperCamelCase    							=YosoConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=YosoForMaskedLM(SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=convert_checkpoint_helper(config.max_position_embeddings			,     SCREAMING_SNAKE_CASE__				)

     print(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__":
   _A		       =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The json file for YOSO model config.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A		       =						parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)













"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)  # pylint: disable=invalid-name







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: Optional[int]	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Dict=768						)						->			List[Any]:
				super().__init__(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					proj_size

				UpperCAmelCase_     : Optional[Any]			     =					CLIPVisionModel(lowerCAmelCase_						)
				UpperCAmelCase_     : Tuple			     =					PaintByExampleMapper(lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					nn.LayerNorm(config.hidden_size						)
				UpperCAmelCase_     : List[Any]			     =					nn.Linear(config.hidden_size	,	self.proj_size						)

				# uncondition for scaling
				UpperCAmelCase_     : Optional[int]			     =					nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Dict=False						)						->			Union[str, Any]:
				UpperCAmelCase_     : Optional[int]			     =					self.model(pixel_values=lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[Any]			     =					clip_output.pooler_output
				UpperCAmelCase_     : List[Any]			     =					self.mapper(latent_states[:, None]						)
				UpperCAmelCase_     : List[str]			     =					self.final_layer_norm(lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					self.proj_out(lowerCAmelCase_						)
				if return_uncond_vector:
							return latent_states, self.uncond_vector

				return latent_states







class 			UpperCamelCase_       (nn.Module     ):





	def __init__(      self					: Dict	,	lowerCAmelCase_					: Union[str, Any]						)						->			Tuple:
				super().__init__()
				UpperCAmelCase_     : List[Any]			     =					(config.num_hidden_layers + 1) // 5
				UpperCAmelCase_     : Optional[Any]			     =					config.hidden_size
				UpperCAmelCase_     : List[str]			     =					1
				UpperCAmelCase_     : Union[str, Any]			     =					nn.ModuleList(
				    [
				        BasicTransformerBlock(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	activation_fn="gelu"	,	attention_bias=lowerCAmelCase_						)
				        for _ in range(lowerCAmelCase_						)
				    ]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: List[str]						)						->			str:
				for block in self.blocks:
							UpperCAmelCase_     : int			     =					block(lowerCAmelCase_						)

				return hidden_states



