


'''simple docstring'''






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		__A :  List[Any]    							=				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 :  List[Any]    							=				parser.parse_args()

		if args.model_type == "roberta":
				__A :  List[str]    							=				RobertaForMaskedLM.from_pretrained(args.model_name)
				__A :  Tuple    							=				"roberta"
		elif args.model_type == "gpt2":
				__A :  Dict    							=				GPTaLMHeadModel.from_pretrained(args.model_name)
				__A :  Optional[int]    							=				"transformer"

		__A :  Any    							=				model.state_dict()
		__A :  List[Any]    							=				{}

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

				# Transformer Blocks #
		__A :  Optional[int]    							=				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 :  Any    							=				state_dict[
										    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
										]
						__A :  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"]:
										__A :  List[Any]    							=				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 :  Any    							=				state_dict[F'''{layer}''']
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								__A :  Any    							=				state_dict[F'''lm_head.dense.{w}''']
								__A :  str    							=				state_dict[F'''lm_head.layer_norm.{w}''']
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						__A :  Optional[Any]    							=				state_dict[F'''{prefix}.ln_f.{w}''']
				__A :  Dict    							=				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'''






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .notes_encoder import SpectrogramNotesEncoder
  from .continous_encoder import SpectrogramContEncoder
  from .pipeline_spectrogram_diffusion import (
      SpectrogramContEncoder,
      SpectrogramDiffusionPipeline,
      TaFilmDecoder,
  )

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .midi_utils import MidiProcessor



