


'''simple docstring'''


from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase=0		):


          '''simple docstring'''





          __A	:       Tuple      =							1.0 if scale is None else scale
          __A	:       Tuple      =							0.0 if loc is None else loc

          super().__init__(lowerCamelCase			,     [AffineTransform(loc=self.loc			,     scale=self.scale			,     event_dim=lowerCamelCase		)]		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return self.base_dist.mean * self.scale + self.loc


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return self.base_dist.variance * self.scale**2


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return self.variance.sqrt()







class 							__magic_name__	(       nn.Module ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase		):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)
          __A	:       str      =							args_dim
          __A	:       List[str]      =							nn.ModuleList([nn.Linear(lowerCamelCase			,     lowerCamelCase		) for dim in args_dim.values()]		)
          __A	:       str      =							domain_map


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Any      =							[proj(lowerCamelCase		) for proj in self.proj]

          return self.domain_map(*lowerCamelCase		)







class 							__magic_name__	(       nn.Module ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase		):


          '''simple docstring'''





          super().__init__()
          __A	:       Optional[int]      =							function


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     *lowerCamelCase		):


          '''simple docstring'''





          return self.function(lowerCamelCase			,     *lowerCamelCase		)







class 							__magic_name__	:

     """simple docstring"""






     lowerCamelCase__       						=				42
     lowerCamelCase__       						=				42
     lowerCamelCase__       						=				42


     def __init__(   self			,     lowerCamelCase = 1		):


          '''simple docstring'''





          __A	:       Dict      =							dim
          __A	:       Optional[Any]      =							{k: dim * self.args_dim[k] for k in self.args_dim}


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          if self.dim == 1:
               return self.distribution_class(*lowerCamelCase		)
          else:
               return Independent(self.distribution_class(*lowerCamelCase		)			,     1		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None			,     ):


          '''simple docstring'''





          __A	:       Optional[int]      =							self._base_distribution(lowerCamelCase		)
          if loc is None and scale is None:
               return distr
          else:
               return AffineTransformed(lowerCamelCase			,     loc=lowerCamelCase			,     scale=lowerCamelCase			,     event_dim=self.event_dim		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return () if self.dim == 1 else (self.dim,)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return len(self.event_shape		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return 0.0


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          return ParameterProjection(
              in_features=lowerCamelCase			,     args_dim=self.args_dim			,     domain_map=LambdaLayer(self.domain_map		)			,     )


     def lowerCAmelCase__  (   self			,     *lowerCamelCase		):


          '''simple docstring'''





          raise NotImplementedError()


     @staticmethod
     def lowerCAmelCase__  (   lowerCamelCase		):


          '''simple docstring'''





          return (x + torch.sqrt(torch.square(lowerCamelCase		) + 4.0		)) / 2.0







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				{"df": 1, "loc": 1, "scale": 1}
     lowerCamelCase__       						=				StudentT


     @classmethod
     def lowerCAmelCase__  (   cls			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[int]      =							cls.squareplus(lowerCamelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
          __A	:       Any      =							2.0 + cls.squareplus(lowerCamelCase		)
          return df.squeeze(-1		), loc.squeeze(-1		), scale.squeeze(-1		)







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				{"loc": 1, "scale": 1}
     lowerCamelCase__       						=				Normal


     @classmethod
     def lowerCAmelCase__  (   cls			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							cls.squareplus(lowerCamelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
          return loc.squeeze(-1		), scale.squeeze(-1		)







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				{"total_count": 1, "logits": 1}
     lowerCamelCase__       						=				NegativeBinomial


     @classmethod
     def lowerCAmelCase__  (   cls			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       List[str]      =							cls.squareplus(lowerCamelCase		)
          return total_count.squeeze(-1		), logits.squeeze(-1		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A  ,__A	:       Tuple      =							distr_args
          if self.dim == 1:
               return self.distribution_class(total_count=lowerCamelCase			,     logits=lowerCamelCase		)
          else:
               return Independent(self.distribution_class(total_count=lowerCamelCase			,     logits=lowerCamelCase		)			,     1		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None		):


          '''simple docstring'''





          __A  ,__A	:       Optional[int]      =							distr_args

          if scale is not None:
               # See scaling property of Gamma.
               logits += scale.log()

          return self._base_distribution((total_count, logits)		)





'''simple docstring'''


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
      _UpperCamelCase 							=  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""")
      _UpperCamelCase 							=  parser.parse_args()

      if args.model_type == "roberta":
            _UpperCamelCase 							=  RobertaForMaskedLM.from_pretrained(args.model_name)
            _UpperCamelCase 							=  """roberta"""
      elif args.model_type == "gpt2":
            _UpperCamelCase 							=  GPTaLMHeadModel.from_pretrained(args.model_name)
            _UpperCamelCase 							=  """transformer"""

      _UpperCamelCase 							=  model.state_dict()
      _UpperCamelCase 							=  {}

      # Embeddings #
      if args.model_type == "gpt2":
            for param_name in ["wte.weight", "wpe.weight"]:
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.{param_name}"""]
      else:
            for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                  _UpperCamelCase 							=  F"""{prefix}.embeddings.{w}.weight"""
                  _UpperCamelCase 							=  state_dict[param_name]
            for w in ["weight", "bias"]:
                  _UpperCamelCase 							=  F"""{prefix}.embeddings.LayerNorm.{w}"""
                  _UpperCamelCase 							=  state_dict[param_name]

    # Transformer Blocks #
      _UpperCamelCase 							=  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"]:
                              _UpperCamelCase 							=  state_dict[
                                  F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
                              ]
                  _UpperCamelCase 							=  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"]:
                              _UpperCamelCase 							=  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"]:
                  _UpperCamelCase 							=  state_dict[F"""{layer}"""]
            if args.vocab_transform:
                  for w in ["weight", "bias"]:
                        _UpperCamelCase 							=  state_dict[F"""lm_head.dense.{w}"""]
                        _UpperCamelCase 							=  state_dict[F"""lm_head.layer_norm.{w}"""]
      elif args.model_type == "gpt2":
            for w in ["weight", "bias"]:
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.ln_f.{w}"""]
            _UpperCamelCase 							=  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)

