





"""simple docstring"""





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
      lowercase__				:Optional[int]				   =       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')
      lowercase__				:Optional[Any]				   =       parser.parse_args()

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

      lowercase__				:Any				   =       model.state_dict()
      lowercase__				:str				   =       {}

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

            # Transformer Blocks #
      lowercase__				:List[str]				   =       0
      for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
            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"]:
                              lowercase__				:Any				   =       state_dict[
                                  f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
                              ]
                  lowercase__				:Optional[Any]				   =       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"]:
                              lowercase__				: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"]:
                  lowercase__				:Dict				   =       state_dict[f"""{layer}"""]
            if args.vocab_transform:
                  for w in ["weight", "bias"]:
                        lowercase__				:Tuple				   =       state_dict[f"""lm_head.dense.{w}"""]
                        lowercase__				:List[Any]				   =       state_dict[f"""lm_head.layer_norm.{w}"""]
      elif args.model_type == "gpt2":
            for w in ["weight", "bias"]:
                  lowercase__				:str				   =       state_dict[f"""{prefix}.ln_f.{w}"""]
            lowercase__				:Union[str, 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"""





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


lowercase__				:int				   =       '3'

print('Python version:', sys.version)
print('transformers version:', transformers.__version__)

try:
  import torch

  print('Torch version:', torch.__version__)
  print('Cuda available:', torch.cuda.is_available())
  print('Cuda version:', torch.version.cuda)
  print('CuDNN version:', torch.backends.cudnn.version())
  print('Number of GPUs available:', torch.cuda.device_count())
  print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
  print('Torch version:', None)

try:
  import deepspeed

  print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
  print('DeepSpeed version:', None)

try:
  import tensorflow as tf

  print('TensorFlow version:', tf.__version__)
  print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
  print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
  print('TensorFlow version:', None)