

import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


lowercase_       		=  logging.get_logger(__name__)


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def __init__(      self :       Any,*lowercase_ :       str,**lowercase_ :       int		)->    None:





        '''simple docstring'''







        warnings.warn(
            'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use CLIPImageProcessor instead.',lowercase_,)
        super().__init__(*lowercase_,**lowercase_		)


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   a_    					=       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')
   a_    					=       parser.parse_args()

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

   a_    					=       model.state_dict()
   a_    					=       {}

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

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



