





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

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

   return orig_key

def   a_   (			lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Any     ):
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase							     =     orig_state_dict.pop(lowerCAmelCase_     )

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

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

   return orig_state_dict

def   a_   (			lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Union[str, Any],     lowerCAmelCase_				:      Any     ):
   __lowerCAmelCase							     =     torch.load(lowerCAmelCase_,     map_location='cpu'     )['model_state_dict']
   __lowerCAmelCase							     =     YosoConfig.from_json_file(lowerCAmelCase_     )
   __lowerCAmelCase							     =     YosoForMaskedLM(lowerCAmelCase_     )

   __lowerCAmelCase							     =     convert_checkpoint_helper(config.max_position_embeddings,     lowerCAmelCase_     )

   print(model.load_state_dict(lowerCAmelCase_     )     )
   model.eval()
   model.save_pretrained(lowerCAmelCase_     )

   print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )


if __name__ == "__main__":
     _snake_case  :   Union[str, Any]						   =  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.'
     )
     _snake_case  :   Optional[Any]						   =  parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  @register_to_config
  def __init__(    self			:		List[Any] ,  lowerCAmelCase_			:		int = 6_5_5_3_6 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 2 ,  lowerCAmelCase_			:		int = 2 ,  lowerCAmelCase_			:		int = 0 ,  lowerCAmelCase_			:		str = "fourier" ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,  lowerCAmelCase_			:		Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,  lowerCAmelCase_			:		Tuple[str] = "UNetMidBlock1D" ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		Tuple[int] = (3_2, 3_2, 6_4) ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		int = 8 ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		bool = False ,  )							->						Optional[int]:
     super().__init__()
     __lowerCAmelCase							     =     sample_size

     # time
     if time_embedding_type == "fourier":
        __lowerCAmelCase							     =     GaussianFourierProjection(
            embedding_size=8 ,  set_W_to_weight=lowerCAmelCase_ ,  log=lowerCAmelCase_ ,  flip_sin_to_cos=lowerCAmelCase_					)
        __lowerCAmelCase							     =     2 * block_out_channels[0]
     elif time_embedding_type == "positional":
        __lowerCAmelCase							     =     Timesteps(
            block_out_channels[0] ,  flip_sin_to_cos=lowerCAmelCase_ ,  downscale_freq_shift=lowerCAmelCase_					)
        __lowerCAmelCase							     =     block_out_channels[0]

     if use_timestep_embedding:
        __lowerCAmelCase							     =     block_out_channels[0] * 4
        __lowerCAmelCase							     =     TimestepEmbedding(
            in_channels=lowerCAmelCase_ ,  time_embed_dim=lowerCAmelCase_ ,  act_fn=lowerCAmelCase_ ,  out_dim=block_out_channels[0] ,  )

     __lowerCAmelCase							     =     nn.ModuleList([]					)
     __lowerCAmelCase							     =     None
     __lowerCAmelCase							     =     nn.ModuleList([]					)
     __lowerCAmelCase							     =     None

     # down
     __lowerCAmelCase							     =     in_channels
     for i, down_block_type in enumerate(lowerCAmelCase_					):
        __lowerCAmelCase							     =     output_channel
        __lowerCAmelCase							     =     block_out_channels[i]

        if i == 0:
           input_channel += extra_in_channels

        __lowerCAmelCase							     =     i == len(lowerCAmelCase_					) - 1

        __lowerCAmelCase							     =     get_down_block(
            lowerCAmelCase_ ,  num_layers=lowerCAmelCase_ ,  in_channels=lowerCAmelCase_ ,  out_channels=lowerCAmelCase_ ,  temb_channels=block_out_channels[0] ,  add_downsample=not is_final_block or downsample_each_block ,  )
        self.down_blocks.append(lowerCAmelCase_					)

     # mid
     __lowerCAmelCase							     =     get_mid_block(
         lowerCAmelCase_ ,  in_channels=block_out_channels[-1] ,  mid_channels=block_out_channels[-1] ,  out_channels=block_out_channels[-1] ,  embed_dim=block_out_channels[0] ,  num_layers=lowerCAmelCase_ ,  add_downsample=lowerCAmelCase_ ,  )

     # up
     __lowerCAmelCase							     =     list(reversed(lowerCAmelCase_					)					)
     __lowerCAmelCase							     =     reversed_block_out_channels[0]
     if out_block_type is None:
        __lowerCAmelCase							     =     out_channels
     else:
        __lowerCAmelCase							     =     block_out_channels[0]

     for i, up_block_type in enumerate(lowerCAmelCase_					):
        __lowerCAmelCase							     =     output_channel
        __lowerCAmelCase							     =     (
            reversed_block_out_channels[i + 1] if i < len(lowerCAmelCase_					) - 1 else final_upsample_channels
        )

        __lowerCAmelCase							     =     i == len(lowerCAmelCase_					) - 1

        __lowerCAmelCase							     =     get_up_block(
            lowerCAmelCase_ ,  num_layers=lowerCAmelCase_ ,  in_channels=lowerCAmelCase_ ,  out_channels=lowerCAmelCase_ ,  temb_channels=block_out_channels[0] ,  add_upsample=not is_final_block ,  )
        self.up_blocks.append(lowerCAmelCase_					)
        __lowerCAmelCase							     =     output_channel

     # out
     __lowerCAmelCase							     =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,  3_2					)
     __lowerCAmelCase							     =     get_out_block(
         out_block_type=lowerCAmelCase_ ,  num_groups_out=lowerCAmelCase_ ,  embed_dim=block_out_channels[0] ,  out_channels=lowerCAmelCase_ ,  act_fn=lowerCAmelCase_ ,  fc_dim=block_out_channels[-1] // 4 ,  )






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		torch.FloatTensor ,  lowerCAmelCase_			:		Union[torch.Tensor, float, int] ,  lowerCAmelCase_			:		bool = True ,  )							->						Union[UNetaDOutput, Tuple]:
     __lowerCAmelCase							     =     timestep
     if not torch.is_tensor(lowerCAmelCase_					):
        __lowerCAmelCase							     =     torch.tensor([timesteps] ,  dtype=torch.long ,  device=sample.device					)
     elif torch.is_tensor(lowerCAmelCase_					) and len(timesteps.shape					) == 0:
        __lowerCAmelCase							     =     timesteps[None].to(sample.device					)

     __lowerCAmelCase							     =     self.time_proj(lowerCAmelCase_					)
     if self.config.use_timestep_embedding:
        __lowerCAmelCase							     =     self.time_mlp(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     timestep_embed[..., None]
        __lowerCAmelCase							     =     timestep_embed.repeat([1, 1, sample.shape[2]]					).to(sample.dtype					)
        __lowerCAmelCase							     =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])					)

     # 2. down
     __lowerCAmelCase							     =     ()
     for downsample_block in self.down_blocks:
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     downsample_block(hidden_states=lowerCAmelCase_ ,  temb=lowerCAmelCase_					)
        down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
        __lowerCAmelCase							     =     self.mid_block(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks					):
        __lowerCAmelCase							     =     down_block_res_samples[-1:]
        __lowerCAmelCase							     =     down_block_res_samples[:-1]
        __lowerCAmelCase							     =     upsample_block(lowerCAmelCase_ ,  res_hidden_states_tuple=lowerCAmelCase_ ,  temb=lowerCAmelCase_					)

     # 5. post-process
     if self.out_block:
        __lowerCAmelCase							     =     self.out_block(lowerCAmelCase_ ,  lowerCAmelCase_					)

     if not return_dict:
        return (sample,)

     return UNetaDOutput(sample=lowerCAmelCase_					)

