




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)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a							     =					{}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)