



'''simple docstring'''






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
						UpperCamelCase__:     Dict										=    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__:     str										=    parser.parse_args()

						if args.model_type == "roberta":
												UpperCamelCase__:     List[str]										=    RobertaForMaskedLM.from_pretrained(args.model_name)
												UpperCamelCase__:     Dict										=    '''roberta'''
						elif args.model_type == "gpt2":
												UpperCamelCase__:     Dict										=    GPTaLMHeadModel.from_pretrained(args.model_name)
												UpperCamelCase__:     str										=    '''transformer'''

						UpperCamelCase__:     str										=    model.state_dict()
						UpperCamelCase__:     List[str]										=    {}

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

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






import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Union[str, Any]	,    __snake_case    :  UNetaDModel	,    __snake_case    :  UNetaDModel	,    __snake_case    :  DDPMScheduler	,    __snake_case    :  List[Any]	,    )    ->	List[str]:
								super().__init__()
								UpperCAmelCase				: Any											=	value_function
								UpperCAmelCase				: List[str]											=	unet
								UpperCAmelCase				: Dict											=	scheduler
								UpperCAmelCase				: Union[str, Any]											=	env
								UpperCAmelCase				: Dict											=	env.get_dataset()
								UpperCAmelCase				: Tuple											=	{}
								for key in self.data.keys():
															try:
																						UpperCAmelCase				: Optional[int]											=	self.data[key].mean()
															except:  # noqa: E722
																						pass
								UpperCAmelCase				: Any											=	{}
								for key in self.data.keys():
															try:
																						UpperCAmelCase				: str											=	self.data[key].std()
															except:  # noqa: E722
																						pass
								UpperCAmelCase				: int											=	env.observation_space.shape[0]
								UpperCAmelCase				: Optional[Any]											=	env.action_space.shape[0]







	def   A			(		self    :  Any	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Union[str, Any]	)    ->	Optional[int]:
								return (x_in - self.means[key]) / self.stds[key]







	def   A			(		self    :  Dict	,    __snake_case    :  Any	,    __snake_case    :  Dict	)    ->	Any:
								return x_in * self.stds[key] + self.means[key]







	def   A			(		self    :  Any	,    __snake_case    :  int	)    ->	List[Any]:
								if type(__snake_case	) is dict:
															return {k: self.to_torch(__snake_case	) for k, v in x_in.items()}
								elif torch.is_tensor(__snake_case	):
															return x_in.to(self.unet.device	)
								return torch.tensor(__snake_case	,    device=self.unet.device	)







	def   A			(		self    :  int	,    __snake_case    :  Optional[int]	,    __snake_case    :  str	,    __snake_case    :  Union[str, Any]	)    ->	Dict:
								for key, val in cond.items():
															UpperCAmelCase				: List[Any]											=	val.clone()
								return x_in







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  Dict	,    __snake_case    :  List[Any]	,    __snake_case    :  Any	,    __snake_case    :  Dict	)    ->	Any:
								UpperCAmelCase				: Any											=	x.shape[0]
								UpperCAmelCase				: Any											=	None
								for i in tqdm.tqdm(self.scheduler.timesteps	):
															# create batch of timesteps to pass into model
															UpperCAmelCase				: Tuple											=	torch.full((batch_size,)	,    __snake_case	,    device=self.unet.device	,    dtype=torch.long	)
															for _ in range(__snake_case	):
																						with torch.enable_grad():
																													x.requires_grad_()

																													# permute to match dimension for pre-trained models
																													UpperCAmelCase				: Any											=	self.value_function(x.permute(0	,    2	,    1	)	,    __snake_case	).sample
																													UpperCAmelCase				: Dict											=	torch.autograd.grad([y.sum()]	,    [x]	)[0]

																													UpperCAmelCase				: Tuple											=	self.scheduler._get_variance(__snake_case	)
																													UpperCAmelCase				: Optional[int]											=	torch.exp(0.5 * posterior_variance	)
																													UpperCAmelCase				: str											=	model_std * grad

																						UpperCAmelCase				: Dict											=	0
																						UpperCAmelCase				: Optional[int]											=	x.detach()
																						UpperCAmelCase				: List[str]											=	x + scale * grad
																						UpperCAmelCase				: List[str]											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)

															UpperCAmelCase				: str											=	self.unet(x.permute(0	,    2	,    1	)	,    __snake_case	).sample.permute(0	,    2	,    1	)

															# TODO: verify deprecation of this kwarg
															UpperCAmelCase				: Optional[int]											=	self.scheduler.step(__snake_case	,    __snake_case	,    __snake_case	,    predict_epsilon=__snake_case	)['''prev_sample''']

															# apply conditions to the trajectory (set the initial state)
															UpperCAmelCase				: Any											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
															UpperCAmelCase				: List[str]											=	self.to_torch(__snake_case	)
								return x, y







	def __call__(		self    :  Any	,    __snake_case    :  List[str]	,    __snake_case    :  Optional[Any]=64	,    __snake_case    :  Optional[int]=32	,    __snake_case    :  Union[str, Any]=2	,    __snake_case    :  Optional[int]=0.1	)    ->	Tuple:
								# normalize the observations and create  batch dimension
								UpperCAmelCase				: Optional[Any]											=	self.normalize(__snake_case	,    '''observations'''	)
								UpperCAmelCase				: Optional[int]											=	obs[None].repeat(__snake_case	,    axis=0	)

								UpperCAmelCase				: Tuple											=	{0: self.to_torch(__snake_case	)}
								UpperCAmelCase				: Tuple											=	(batch_size, planning_horizon, self.state_dim + self.action_dim)

								# generate initial noise and apply our conditions (to make the trajectories start at current state)
								UpperCAmelCase				: Union[str, Any]											=	randn_tensor(__snake_case	,    device=self.unet.device	)
								UpperCAmelCase				: int											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
								UpperCAmelCase				: List[Any]											=	self.to_torch(__snake_case	)

								# run the diffusion process
								UpperCAmelCase							,	UpperCAmelCase				: Optional[int]											=	self.run_diffusion(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)

								# sort output trajectories by value
								UpperCAmelCase				: str											=	y.argsort(0	,    descending=__snake_case	).squeeze()
								UpperCAmelCase				: Tuple											=	x[sorted_idx]
								UpperCAmelCase				: List[Any]											=	sorted_values[:, :, : self.action_dim]
								UpperCAmelCase				: str											=	actions.detach().cpu().numpy()
								UpperCAmelCase				: List[str]											=	self.de_normalize(__snake_case	,    key='''actions'''	)

								# select the action with the highest value
								if y is not None:
															UpperCAmelCase				: List[Any]											=	0
								else:
															# if we didn't run value guiding, select a random action
															UpperCAmelCase				: Optional[int]											=	np.random.randint(0	,    __snake_case	)

								UpperCAmelCase				: Optional[int]											=	denorm_actions[selected_index, 0]
								return denorm_actions





