





"""simple docstring"""





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

       SCREAMING_SNAKE_CASE__					=   model.state_dict()
       SCREAMING_SNAKE_CASE__					=   {}

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

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




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   TapasConfig.from_json_file(__lowercase       )
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     							=   reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   True
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   0.66_4694
          _UpperCAmelCase     							=   0.20_7951
          _UpperCAmelCase     							=   0.12_1194
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          _UpperCAmelCase     							=   0.035_2513

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   False
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   36.4519
          _UpperCAmelCase     							=   0.90_3421
          _UpperCAmelCase     							=   222.088
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   0.76_3141

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "TABFACT":
          _UpperCAmelCase     							=   TapasForSequenceClassification(config=__lowercase       )
     elif task == "MLM":
          _UpperCAmelCase     							=   TapasForMaskedLM(config=__lowercase       )
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     							=   TapasModel(config=__lowercase       )
     else:
          raise ValueError(f'Task {task} not supported.'       )

     print(f'Building PyTorch model from configuration: {config}'       )
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(__lowercase			,     __lowercase			,     __lowercase       )

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'       )
     model.save_pretrained(__lowercase       )

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'       )
     _UpperCAmelCase     							=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"			,     model_max_length=512       )
     tokenizer.save_pretrained(__lowercase       )

     print("Used relative position embeddings:"			,     model.config.reset_position_index_per_cell       )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :List[str]       =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
  )
  parser.add_argument(
      '''--reset_position_index_per_cell''',
      default=False,
      action='''store_true''',
      help='''Whether to use relative position embeddings or not. Defaults to True.''',
  )
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--tapas_config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained TAPAS model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  __SCREAMING_SNAKE_CASE     :List[str]       =				parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )




