



'''simple docstring'''






import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


UpperCamelCase__:     Tuple										=    logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm
						import torch_xla.debug.metrics as met

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  str	,    *__snake_case    :  Union[str, Any]	,    __snake_case    :  List[Any]=None	,    __snake_case    :  Union[str, Any]=None	,    __snake_case    :  Optional[Any]=None	,    **__snake_case    :  str	)    ->	str:
								super().__init__(*__snake_case	,    **__snake_case	)
								UpperCAmelCase				: Optional[int]											=	eval_examples
								UpperCAmelCase				: Dict											=	post_process_function
								UpperCAmelCase				: Union[str, Any]											=	quant_trainer_args
								UpperCAmelCase				: Union[str, Any]											=	128  # default number of calibration samples







	def   A			(		self    :  Tuple	,    __snake_case    :  List[Any]=None	)    ->	Tuple:
								if calib_dataset is None and self.calib_dataset is None:
															raise ValueError('''Trainer: calibration requires an calib_dataset.'''	)
								UpperCAmelCase				: Dict											=	calib_dataset if calib_dataset is not None else self.calib_dataset

								UpperCAmelCase				: Optional[Any]											=	self._remove_unused_columns(__snake_case	,    description='''Calibration'''	)

								return DataLoader(
								    __snake_case	,    batch_size=self.args.eval_batch_size	,    collate_fn=self.data_collator	,    drop_last=self.args.dataloader_drop_last	,    num_workers=self.args.dataloader_num_workers	,    pin_memory=self.args.dataloader_pin_memory	,    shuffle=__snake_case	,    )







	def   A			(		self    :  Optional[int]	,    __snake_case    :  Tuple=None	)    ->	Dict:
								UpperCAmelCase				: Optional[Any]											=	self.train_dataset if calib_dataset is None else calib_dataset
								UpperCAmelCase				: int											=	self.get_calib_dataloader(__snake_case	)

								UpperCAmelCase				: Any											=	self.model
								quant_trainer.configure_model(__snake_case	,    self.quant_trainer_args	,    calib=__snake_case	)
								model.eval()
								quant_trainer.enable_calibration(__snake_case	)

								logger.info('''***** Running calibration *****'''	)
								logger.info(F"""  Num examples = {self.calib_num}"""	)
								logger.info(F"""  Batch size = {calib_dataloader.batch_size}"""	)

								for step, inputs in enumerate(__snake_case	):
															# Prediction step
															UpperCAmelCase				: int											=	self.prediction_step(__snake_case	,    __snake_case	,    prediction_loss_only=__snake_case	)
															if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																						break

								quant_trainer.finish_calibration(__snake_case	,    self.quant_trainer_args	)
								UpperCAmelCase				: Optional[int]											=	model







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  str=None	,    __snake_case    :  str=None	,    __snake_case    :  str=None	,    __snake_case    :  str = "eval"	)    ->	int:
								UpperCAmelCase				: Any											=	self.eval_dataset if eval_dataset is None else eval_dataset
								UpperCAmelCase				: Optional[int]											=	self.get_eval_dataloader(__snake_case	)
								UpperCAmelCase				: Union[str, Any]											=	self.eval_examples if eval_examples is None else eval_examples

								# Temporarily disable metric computation, we will do it in the loop here.
								UpperCAmelCase				: int											=	self.compute_metrics
								UpperCAmelCase				: Any											=	None
								UpperCAmelCase				: Optional[int]											=	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
															UpperCAmelCase				: str											=	eval_loop(
															    __snake_case	,    description='''Evaluation'''	,    prediction_loss_only=True if compute_metrics is None else None	,    ignore_keys=__snake_case	,    )
								finally:
															UpperCAmelCase				: Dict											=	compute_metrics

								if self.post_process_function is not None and self.compute_metrics is not None:
															UpperCAmelCase				: List[Any]											=	self.post_process_function(__snake_case	,    __snake_case	,    output.predictions	)
															UpperCAmelCase				: int											=	self.compute_metrics(__snake_case	)

															# Prefix all keys with metric_key_prefix + '_'
															for key in list(metrics.keys()	):
																						if not key.startswith(F"""{metric_key_prefix}_"""	):
																													UpperCAmelCase				: List[Any]											=	metrics.pop(__snake_case	)

															self.log(__snake_case	)
								else:
															UpperCAmelCase				: int											=	{}

								if self.args.tpu_metrics_debug or self.args.debug:
															# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
															xm.master_print(met.metrics_report()	)

								UpperCAmelCase				: List[Any]											=	self.callback_handler.on_evaluate(self.args	,    self.state	,    self.control	,    __snake_case	)
								return metrics







	def   A			(		self    :  Dict	,    __snake_case    :  List[str]	,    __snake_case    :  Optional[int]	,    __snake_case    :  Tuple=None	,    __snake_case    :  str = "test"	)    ->	Tuple:
								UpperCAmelCase				: List[str]											=	self.get_test_dataloader(__snake_case	)

								# Temporarily disable metric computation, we will do it in the loop here.
								UpperCAmelCase				: str											=	self.compute_metrics
								UpperCAmelCase				: Any											=	None
								UpperCAmelCase				: Tuple											=	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
															UpperCAmelCase				: Dict											=	eval_loop(
															    __snake_case	,    description='''Prediction'''	,    prediction_loss_only=True if compute_metrics is None else None	,    ignore_keys=__snake_case	,    )
								finally:
															UpperCAmelCase				: List[str]											=	compute_metrics

								if self.post_process_function is None or self.compute_metrics is None:
															return output

								UpperCAmelCase				: int											=	self.post_process_function(__snake_case	,    __snake_case	,    output.predictions	,    '''predict'''	)
								UpperCAmelCase				: Dict											=	self.compute_metrics(__snake_case	)

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()	):
															if not key.startswith(F"""{metric_key_prefix}_"""	):
																						UpperCAmelCase				: int											=	metrics.pop(__snake_case	)

								return PredictionOutput(predictions=predictions.predictions	,    label_ids=predictions.label_ids	,    metrics=__snake_case	)







	def   A			(		self    :  str	,    __snake_case    :  Optional[int]="./"	)    ->	Optional[Any]:
								UpperCAmelCase				: Optional[Any]											=	self.eval_dataset
								UpperCAmelCase				: List[Any]											=	self.get_eval_dataloader(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	next(iter(__snake_case	)	)

								# saving device - to make it consistent
								UpperCAmelCase				: Union[str, Any]											=	torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''	)

								# convert to tuple
								UpperCAmelCase				: str											=	tuple(v.to(__snake_case	) for k, v in batch.items()	)

								logger.info('''Converting model to be onnx compatible'''	)
								from pytorch_quantization.nn import TensorQuantizer

								UpperCAmelCase				: Tuple											=	True

								UpperCAmelCase				: Dict											=	self.model.to(__snake_case	)

								model.eval()
								model.float()

								UpperCAmelCase				: List[Any]											=	model.module if hasattr(__snake_case	,    '''module'''	) else model
								quant_trainer.configure_model(__snake_case	,    self.quant_trainer_args	)

								UpperCAmelCase				: List[Any]											=	os.path.join(__snake_case	,    '''model.onnx'''	)
								logger.info(F"""exporting model to {output_model_file}"""	)

								UpperCAmelCase				: List[str]											=	{0: '''batch_size''', 1: '''seq_len'''}

								torch.onnx.export(
								    __snake_case	,    __snake_case	,    __snake_case	,    export_params=__snake_case	,    opset_version=13	,    do_constant_folding=__snake_case	,    input_names=['''input_ids''', '''attention_mask''', '''token_type_ids''']	,    output_names=['''output_start_logits''', '''output_end_logits''']	,    dynamic_axes={
								        '''input_ids''': axes,
								        '''attention_mask''': axes,
								        '''token_type_ids''': axes,
								        '''output_start_logits''': axes,
								        '''output_end_logits''': axes,
								    }	,    verbose=__snake_case	,    )
								logger.info('''onnx export finished'''	)










'''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





