


"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

 UpperCAmelCase				=						model.state_dict()
 UpperCAmelCase				=						{}

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

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


_lowercase							:   List[Any]						=						logging.get_logger(__name__)

_lowercase							:   List[str]						=						{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'mctct'

				def __init__(    self					, a__=8065					, a__=1536					, a__=36					, a__=6144					, a__=4					, a__=384					, a__=920					, a__=1e-5					, a__=0.3					, a__="relu"					, a__=0.02					, a__=0.3					, a__=0.3					, a__=1					, a__=0					, a__=2					, a__=1					, a__=0.3					, a__=1					, a__=(7,)					, a__=(3,)					, a__=80					, a__=1					, a__=None					, a__="sum"					, a__=False					, **a__					, )						->					Optional[Any]:
					super().__init__(**a__					, pad_token_id=a__					, bos_token_id=a__					, eos_token_id=a__ )
					A             =					vocab_size
					A             =					hidden_size
					A             =					num_hidden_layers
					A             =					intermediate_size
					A             =					num_attention_heads
					A             =					attention_head_dim
					A             =					max_position_embeddings
					A             =					layer_norm_eps
					A             =					layerdrop
					A             =					hidden_act
					A             =					initializer_range
					A             =					hidden_dropout_prob
					A             =					attention_probs_dropout_prob
					A             =					pad_token_id
					A             =					bos_token_id
					A             =					eos_token_id
					A             =					conv_glu_dim
					A             =					conv_dropout
					A             =					num_conv_layers
					A             =					input_feat_per_channel
					A             =					input_channels
					A             =					conv_channels
					A             =					ctc_loss_reduction
					A             =					ctc_zero_infinity

					# prevents config testing fail with exporting to json
					A             =					list(a__ )
					A             =					list(a__ )

					if len(self.conv_kernel ) != self.num_conv_layers:
						raise ValueError(
						    """Configuration for convolutional module is incorrect. """
						    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
						    f'but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, '
						    f'`config.num_conv_layers = {self.num_conv_layers}`.' )



