


import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple				)	->		Optional[Any]:

       """simple docstring"""

       def wrapper(*__A :			str  ,    **__A :			Union[str, Any]				):
              a_					:		int							=  timeit.default_timer()
              a_					:		Optional[int]							=  func(*__A  ,    **__A				)
              a_					:		Union[str, Any]							=  timeit.default_timer() - starttime
              return delta

       a_					:		str							=  func.__name__

       return wrapper







def        SCREAMING_SNAKE_CASE_						(			__A :			dict  ,    __A :			Optional[Any]=1_00  ,    __A :			Dict=None				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		Dict							=  []
       a_					:		Dict							=  seq_shapes or {}
       for i in range(__A				):
              a_					:		int							=  {}
              for col_id, (k, v) in enumerate(features.items()				):
                     if isinstance(__A  ,    _ArrayXD				):
                            a_					:		Tuple							=  np.random.rand(*v.shape				).astype(v.dtype				)
                     elif isinstance(__A  ,    datasets.Value				):
                            if v.dtype == "string":
                                   a_					:		Union[str, Any]							=  'The small grey turtle was surprisingly fast when challenged.'
                            else:
                                   a_					:		List[str]							=  np.random.randint(10  ,    size=1				).astype(v.dtype				).item()
                     elif isinstance(__A  ,    datasets.Sequence				):
                            while isinstance(__A  ,    datasets.Sequence				):
                                   a_					:		Optional[Any]							=  v.feature
                            a_					:		str							=  seq_shapes[k]
                            a_					:		Optional[Any]							=  np.random.rand(*__A				).astype(v.dtype				)
                     a_					:		int							=  data

              dummy_data.append((i, example)				)

       return dummy_data







def        SCREAMING_SNAKE_CASE_						(			__A :			Dict  ,    __A :			List[str]  ,    __A :			str=1_00  ,    __A :			str=None				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		Optional[int]							=  generate_examples(__A  ,    num_examples=__A  ,    seq_shapes=__A				)

       with ArrowWriter(features=__A  ,    path=__A				) as writer:
              for key, record in dummy_data:
                     a_					:		List[str]							=  features.encode_example(__A				)
                     writer.write(__A				)

              a_					:		int							=  writer.finalize()

       if not num_final_examples == num_examples:
              raise ValueError(
                  F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""				)

       a_					:		Dict							=  datasets.Dataset.from_file(filename=__A  ,    info=datasets.DatasetInfo(features=__A				)				)

       return dataset



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)



