






"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

	_lowerCAmelCase         =							model.state_dict()
	_lowerCAmelCase         =							{}

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

    # Transformer Blocks #
	_lowerCAmelCase         =							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         =							state_dict[
					    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
					]
			_lowerCAmelCase         =							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         =							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         =							state_dict[F'{layer}']
		if args.vocab_transform:
			for w in ["weight", "bias"]:
				_lowerCAmelCase         =							state_dict[F'lm_head.dense.{w}']
				_lowerCAmelCase         =							state_dict[F'lm_head.layer_norm.{w}']
	elif args.model_type == "gpt2":
		for w in ["weight", "bias"]:
			_lowerCAmelCase         =							state_dict[F'{prefix}.ln_f.{w}']
		_lowerCAmelCase         =							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"""
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(snake_case__					)					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):

	# Base Case
	if index == len(snake_case__					):
		return True

	# Recursive Step
	for i in range(snake_case__					):
		if valid_coloring(graph[index]   ,   snake_case__   ,   snake_case__					):
			# Color current vertex
			A_		:						Dict	    =   i
			# Validate coloring
			if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   index + 1					):
				return True
			# Backtrack
			A_		:						Union[str, Any]	    =   -1
	return False






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						int	    =   [-1] * len(snake_case__					)

	if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   0					):
		return colored_vertices

	return []

