import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
	lowerCamelCase_    :		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""")
	lowerCamelCase_    :		Optional[int]							=	parser.parse_args()

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

	lowerCamelCase_    :		Optional[Any]							=	model.state_dict()
	lowerCamelCase_    :		Union[str, Any]							=	{}

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

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

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase_    :		Union[str, Any]							=	{"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	["""GPTNeoXTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		List[str]							=	[
	    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXForCausalLM""",
	    """GPTNeoXForQuestionAnswering""",
	    """GPTNeoXForSequenceClassification""",
	    """GPTNeoXForTokenClassification""",
	    """GPTNeoXLayer""",
	    """GPTNeoXModel""",
	    """GPTNeoXPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCamelCase_    :		Dict							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




