


"""simple docstring"""


import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a_       							=    logging.get_logger(__name__)
class snake_case      (  __SCREAMING_SNAKE_CASE):
		__UpperCamelCase    =			'linear'
		__UpperCamelCase    =			'cosine'
		__UpperCamelCase    =			'cosine_with_restarts'
		__UpperCamelCase    =			'polynomial'
		__UpperCamelCase    =			'constant'
		__UpperCamelCase    =			'constant_with_warmup'
		__UpperCamelCase    =			'piecewise_constant'






def      a__ (				__lowercase     ,			__lowercase = -1   )	->	str:
				return LambdaLR(lowerCAmelCase__     ,			lambda __lowercase   : 1     ,			last_epoch=lowerCAmelCase__   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase = -1   )	->	Optional[int]:

				def lr_lambda(__lowercase   ):
								if current_step < num_warmup_steps:
												return float(lowerCAmelCase__   ) / float(max(1.0     ,			lowerCAmelCase__   )   )
								return 1.0

				return LambdaLR(lowerCAmelCase__     ,			lowerCAmelCase__     ,			last_epoch=lowerCAmelCase__   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase = -1   )	->	Optional[int]:
				_A					  =			{}
				_A					  =			step_rules.split(","   )
				for rule_str in rule_list[:-1]:
								_A  ,		_A					  =			rule_str.split(":"   )
								_A					  =			int(lowerCAmelCase__   )
								_A					  =			float(lowerCAmelCase__   )
								_A					  =			value
				_A					  =			float(rule_list[-1]   )

				def create_rules_function(__lowercase     ,			__lowercase   ):
								def rule_func(__lowercase   ) -> float:
												_A					  =			sorted(rules_dict.keys()   )
												for i, sorted_step in enumerate(lowerCAmelCase__   ):
																if steps < sorted_step:
																				return rules_dict[sorted_steps[i]]
												return last_lr_multiple

								return rule_func

				_A					  =			create_rules_function(lowerCAmelCase__     ,			lowerCAmelCase__   )

				return LambdaLR(lowerCAmelCase__     ,			lowerCAmelCase__     ,			last_epoch=lowerCAmelCase__   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase=-1   )	->	int:

				def lr_lambda(__lowercase   ):
								if current_step < num_warmup_steps:
												return float(lowerCAmelCase__   ) / float(max(1     ,			lowerCAmelCase__   )   )
								return max(
								    0.0     ,			float(num_training_steps - current_step   ) / float(max(1     ,			num_training_steps - num_warmup_steps   )   )   )

				return LambdaLR(lowerCAmelCase__     ,			lowerCAmelCase__     ,			lowerCAmelCase__   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase = 0.5     ,			__lowercase = -1   )	->	Tuple:

				def lr_lambda(__lowercase   ):
								if current_step < num_warmup_steps:
												return float(lowerCAmelCase__   ) / float(max(1     ,			lowerCAmelCase__   )   )
								_A					  =			float(current_step - num_warmup_steps   ) / float(max(1     ,			num_training_steps - num_warmup_steps   )   )
								return max(0.0     ,			0.5 * (1.0 + math.cos(math.pi * float(lowerCAmelCase__   ) * 2.0 * progress   ))   )

				return LambdaLR(lowerCAmelCase__     ,			lowerCAmelCase__     ,			lowerCAmelCase__   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase = 1     ,			__lowercase = -1   )	->	int:

				def lr_lambda(__lowercase   ):
								if current_step < num_warmup_steps:
												return float(lowerCAmelCase__   ) / float(max(1     ,			lowerCAmelCase__   )   )
								_A					  =			float(current_step - num_warmup_steps   ) / float(max(1     ,			num_training_steps - num_warmup_steps   )   )
								if progress >= 1.0:
												return 0.0
								return max(0.0     ,			0.5 * (1.0 + math.cos(math.pi * ((float(lowerCAmelCase__   ) * progress) % 1.0)   ))   )

				return LambdaLR(lowerCAmelCase__     ,			lowerCAmelCase__     ,			lowerCAmelCase__   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase=1E-7     ,			__lowercase=1.0     ,			__lowercase=-1   )	->	Optional[Any]:
				_A					  =			optimizer.defaults["lr"]
				if not (lr_init > lr_end):
								raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""   )

				def lr_lambda(__lowercase   ):
								if current_step < num_warmup_steps:
												return float(lowerCAmelCase__   ) / float(max(1     ,			lowerCAmelCase__   )   )
								elif current_step > num_training_steps:
												return lr_end / lr_init  # as LambdaLR multiplies by lr_init
								else:
												_A					  =			lr_init - lr_end
												_A					  =			num_training_steps - num_warmup_steps
												_A					  =			1 - (current_step - num_warmup_steps) / decay_steps
												_A					  =			lr_range * pct_remaining**power + lr_end
												return decay / lr_init  # as LambdaLR multiplies by lr_init

				return LambdaLR(lowerCAmelCase__     ,			lowerCAmelCase__     ,			lowerCAmelCase__   )


a_       							=    {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase = None     ,			__lowercase = None     ,			__lowercase = None     ,			__lowercase = 1     ,			__lowercase = 1.0     ,			__lowercase = -1     ,			)	->	Union[str, Any]:
				_A					  =			SchedulerType(lowerCAmelCase__   )
				_A					  =			TYPE_TO_SCHEDULER_FUNCTION[name]
				if name == SchedulerType.CONSTANT:
								return schedule_func(lowerCAmelCase__     ,			last_epoch=lowerCAmelCase__   )

				if name == SchedulerType.PIECEWISE_CONSTANT:
								return schedule_func(lowerCAmelCase__     ,			step_rules=lowerCAmelCase__     ,			last_epoch=lowerCAmelCase__   )

				# All other schedulers require `num_warmup_steps`
				if num_warmup_steps is None:
								raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""   )

				if name == SchedulerType.CONSTANT_WITH_WARMUP:
								return schedule_func(lowerCAmelCase__     ,			num_warmup_steps=lowerCAmelCase__     ,			last_epoch=lowerCAmelCase__   )

				# All other schedulers require `num_training_steps`
				if num_training_steps is None:
								raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""   )

				if name == SchedulerType.COSINE_WITH_RESTARTS:
								return schedule_func(
								    lowerCAmelCase__     ,			num_warmup_steps=lowerCAmelCase__     ,			num_training_steps=lowerCAmelCase__     ,			num_cycles=lowerCAmelCase__     ,			last_epoch=lowerCAmelCase__     ,			)

				if name == SchedulerType.POLYNOMIAL:
								return schedule_func(
								    lowerCAmelCase__     ,			num_warmup_steps=lowerCAmelCase__     ,			num_training_steps=lowerCAmelCase__     ,			power=lowerCAmelCase__     ,			last_epoch=lowerCAmelCase__     ,			)

				return schedule_func(
				    lowerCAmelCase__     ,			num_warmup_steps=lowerCAmelCase__     ,			num_training_steps=lowerCAmelCase__     ,			last_epoch=lowerCAmelCase__   )



"""simple docstring"""


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)