



'''simple docstring'''


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   _lowerCamelCase	:       Tuple      	=	argparse.ArgumentParser(
       description=(
           "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
           " Distillation"
       )
   )
   parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
   parser.add_argument("--model_name", default="roberta-large", type=str)
   parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
   parser.add_argument("--vocab_transform", action="store_true")
   _lowerCamelCase	:       Any      	=	parser.parse_args()

   if args.model_type == "roberta":
      _lowerCamelCase	:       str      	=	RobertaForMaskedLM.from_pretrained(args.model_name)
      _lowerCamelCase	:       List[Any]      	=	"roberta"
   elif args.model_type == "gpt2":
      _lowerCamelCase	:       List[Any]      	=	GPTaLMHeadModel.from_pretrained(args.model_name)
      _lowerCamelCase	:       List[Any]      	=	"transformer"

   _lowerCamelCase	:       Union[str, Any]      	=	model.state_dict()
   _lowerCamelCase	:       Optional[int]      	=	{}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         _lowerCamelCase	:       List[str]      	=	state_dict[f'''{prefix}.{param_name}''']
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         _lowerCamelCase	:       Any      	=	f'''{prefix}.embeddings.{w}.weight'''
         _lowerCamelCase	:       int      	=	state_dict[param_name]
      for w in ["weight", "bias"]:
         _lowerCamelCase	:       Tuple      	=	f'''{prefix}.embeddings.LayerNorm.{w}'''
         _lowerCamelCase	:       Any      	=	state_dict[param_name]

    # Transformer Blocks #
   _lowerCamelCase	:       Union[str, Any]      	=	0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               _lowerCamelCase	:       int      	=	state_dict[
                   f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
               ]
         _lowerCamelCase	:       Tuple      	=	state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               _lowerCamelCase	:       Tuple      	=	state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         _lowerCamelCase	:       Any      	=	state_dict[f'''{layer}''']
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            _lowerCamelCase	:       Optional[Any]      	=	state_dict[f'''lm_head.dense.{w}''']
            _lowerCamelCase	:       Tuple      	=	state_dict[f'''lm_head.layer_norm.{w}''']
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         _lowerCamelCase	:       str      	=	state_dict[f'''{prefix}.ln_f.{w}''']
      _lowerCamelCase	:       Any      	=	state_dict["lm_head.weight"]

   print(f'''N layers selected for distillation: {std_idx}''')
   print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

   print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
   torch.save(compressed_sd, args.dump_checkpoint)






'''simple docstring'''


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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  2






    @register_to_config
    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					float = 0.0_2  ,						UpperCamelCase__      :					float = 1_0_0  ,						UpperCamelCase__      :					float = 1.0_0_7  ,						UpperCamelCase__      :					float = 8_0  ,						UpperCamelCase__      :					float = 0.0_5  ,						UpperCamelCase__      :					float = 5_0  ,						):


       """simple docstring"""







       UpperCamelCase					=  sigma_max

       # setable values
       UpperCamelCase					=  None
       UpperCamelCase					=  None
       UpperCamelCase					=  None  # sigma(t_i)






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  np.arange(0  ,						self.num_inference_steps )[::-1].copy()
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )
       UpperCamelCase					=  [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       UpperCamelCase					=  torch.tensor(UpperCamelCase__  ,						dtype=torch.floataa  ,						device=UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					Optional[torch.Generator] = None ):


       """simple docstring"""







       if self.config.s_min <= sigma <= self.config.s_max:
          UpperCamelCase					=  min(self.config.s_churn / self.num_inference_steps  ,						2**0.5 - 1 )
       else:
          UpperCamelCase					=  0

       # sample eps ~ N(0, S_noise^2 * I)
       UpperCamelCase					=  self.config.s_noise * randn_tensor(sample.shape  ,						generator=UpperCamelCase__ ).to(sample.device )
       UpperCamelCase					=  sigma + gamma * sigma
       UpperCamelCase					=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  sample_hat + sigma_hat * model_output
       UpperCamelCase					=  (sample_hat - pred_original_sample) / sigma_hat
       UpperCamelCase					=  sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
          return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  sample_prev + sigma_prev * model_output
       UpperCamelCase					=  (sample_prev - pred_original_sample) / sigma_prev
       UpperCamelCase					=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
          return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       raise NotImplementedError()

