




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     lowerCamelCase__(					__lowerCamelCase):
      def __init__(				self: Dict    ,       UpperCamelCase_: UNetaDModel    ,       UpperCamelCase_: UNetaDModel    ,       UpperCamelCase_: DDPMScheduler    ,       UpperCamelCase_: Optional[int]    ,       ):
             super().__init__()
             __lowerCamelCase   =						value_function
             __lowerCamelCase   =						unet
             __lowerCamelCase   =						scheduler
             __lowerCamelCase   =						env
             __lowerCamelCase   =						env.get_dataset()
             __lowerCamelCase   =						{}
             for key in self.data.keys():
                    try:
                           __lowerCamelCase   =						self.data[key].mean()
                    except:  # noqa: E722
                           pass
             __lowerCamelCase   =						{}
             for key in self.data.keys():
                    try:
                           __lowerCamelCase   =						self.data[key].std()
                    except:  # noqa: E722
                           pass
             __lowerCamelCase   =						env.observation_space.shape[0]
             __lowerCamelCase   =						env.action_space.shape[0]
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: List[str]   ):
             return (x_in - self.means[key]) / self.stds[key]
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: Optional[int]   ):
             return x_in * self.stds[key] + self.means[key]
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: int   ):
             if type(UpperCamelCase_   ) is dict:
                    return {k: self.to_torch(UpperCamelCase_   ) for k, v in x_in.items()}
             elif torch.is_tensor(UpperCamelCase_   ):
                    return x_in.to(self.unet.device   )
             return torch.tensor(UpperCamelCase_    ,       device=self.unet.device   )
      def 			lowerCAmelCase__						(				self: Optional[int]    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: Union[str, Any]   ):
             for key, val in cond.items():
                    __lowerCamelCase   =						val.clone()
             return x_in
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: int   ):
             __lowerCamelCase   =						x.shape[0]
             __lowerCamelCase   =						None
             for i in tqdm.tqdm(self.scheduler.timesteps   ):
                    # create batch of timesteps to pass into model
                    __lowerCamelCase   =						torch.full((batch_size,)    ,       UpperCamelCase_    ,       device=self.unet.device    ,       dtype=torch.long   )
                    for _ in range(UpperCamelCase_   ):
                           with torch.enable_grad():
                                  x.requires_grad_()

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

                                  __lowerCamelCase   =						self.scheduler._get_variance(UpperCamelCase_   )
                                  __lowerCamelCase   =						torch.exp(0.5 * posterior_variance   )
                                  __lowerCamelCase   =						model_std * grad

                           __lowerCamelCase   =						0
                           __lowerCamelCase   =						x.detach()
                           __lowerCamelCase   =						x + scale * grad
                           __lowerCamelCase   =						self.reset_xa(UpperCamelCase_    ,       UpperCamelCase_    ,       self.action_dim   )

                    __lowerCamelCase   =						self.unet(x.permute(0    ,       2    ,       1   )    ,       UpperCamelCase_   ).sample.permute(0    ,       2    ,       1   )

                    # TODO: verify deprecation of this kwarg
                    __lowerCamelCase   =						self.scheduler.step(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       predict_epsilon=UpperCamelCase_   )["""prev_sample"""]

                    # apply conditions to the trajectory (set the initial state)
                    __lowerCamelCase   =						self.reset_xa(UpperCamelCase_    ,       UpperCamelCase_    ,       self.action_dim   )
                    __lowerCamelCase   =						self.to_torch(UpperCamelCase_   )
             return x, y




      def __call__(				self: List[str]    ,       UpperCamelCase_: int    ,       UpperCamelCase_: List[Any]=64    ,       UpperCamelCase_: Optional[Any]=32    ,       UpperCamelCase_: Tuple=2    ,       UpperCamelCase_: List[str]=0.1   ):
             # normalize the observations and create  batch dimension
             __lowerCamelCase   =						self.normalize(UpperCamelCase_    ,       """observations"""   )
             __lowerCamelCase   =						obs[None].repeat(UpperCamelCase_    ,       axis=0   )

             __lowerCamelCase   =						{0: self.to_torch(UpperCamelCase_   )}
             __lowerCamelCase   =						(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)
             __lowerCamelCase   =						randn_tensor(UpperCamelCase_    ,       device=self.unet.device   )
             __lowerCamelCase   =						self.reset_xa(UpperCamelCase_    ,       UpperCamelCase_    ,       self.action_dim   )
             __lowerCamelCase   =						self.to_torch(UpperCamelCase_   )

             # run the diffusion process
             __lowerCamelCase,   __lowerCamelCase   =						self.run_diffusion(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )

             # sort output trajectories by value
             __lowerCamelCase   =						y.argsort(0    ,       descending=UpperCamelCase_   ).squeeze()
             __lowerCamelCase   =						x[sorted_idx]
             __lowerCamelCase   =						sorted_values[:, :, : self.action_dim]
             __lowerCamelCase   =						actions.detach().cpu().numpy()
             __lowerCamelCase   =						self.de_normalize(UpperCamelCase_    ,       key="""actions"""   )

             # select the action with the highest value
             if y is not None:
                    __lowerCamelCase   =						0
             else:
                    # if we didn't run value guiding, select a random action
                    __lowerCamelCase   =						np.random.randint(0    ,       UpperCamelCase_   )

             __lowerCamelCase   =						denorm_actions[selected_index, 0]
             return denorm_actions






import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A__								=						logging.getLogger(__name__)


@dataclass
class       a							:
		__lowerCAmelCase	:    str											=					field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default="""NER"""  ,							metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
		__lowerCAmelCase	:    bool											=					field(default=__lowerCamelCase  ,							metadata={"""help""": """Set this flag to use fast tokenization."""}				)
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,							)


@dataclass
class       a							:
		__lowerCAmelCase	:    str											=					field(
		    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}  ,							)
		__lowerCAmelCase	:    int											=					field(
		    default=1_28  ,							metadata={
		        """help""": (
		            """The maximum total input sequence length after tokenization. Sequences longer """
		            """than this will be truncated, sequences shorter will be padded."""
		        )
		    }  ,							)
		__lowerCAmelCase	:    bool											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      Any     		=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
				if len(sys.argv      ) == 2 and sys.argv[1].endswith('''.json'''      ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								snake_case__		, snake_case__		, snake_case__	:      str     		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								snake_case__		, snake_case__		, snake_case__	:      List[Any]     		=	parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir      )
				    and os.listdir(training_args.output_dir      )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    ''' --overwrite_output_dir to overcome.'''      )

				snake_case__	:      int     		=	import_module('''tasks'''      )
				try:
								snake_case__	:      Optional[int]     		=	getattr(__lowerCAmelCase  ,  model_args.task_type      )
								snake_case__	:      TokenClassificationTask     		=	token_classification_task_clazz()
				except AttributeError:
								raise ValueError(
								    f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
								    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""      )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,  datefmt='''%m/%d/%Y %H:%M:%S'''  ,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,  )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''  ,  training_args.local_rank  ,  training_args.device  ,  training_args.n_gpu  ,  bool(training_args.local_rank != -1      )  ,  training_args.fpaa  ,  )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank      ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('''Training/evaluation parameters %s'''  ,  __lowerCAmelCase      )

				# Set seed
				set_seed(training_args.seed      )

				# Prepare CONLL-2003 task
				snake_case__	:      Optional[int]     		=	token_classification_task.get_labels(data_args.labels      )
				snake_case__	:      Dict[int, str]     		=	dict(enumerate(__lowerCAmelCase      )      )
				snake_case__	:      Optional[Any]     		=	len(__lowerCAmelCase      )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				snake_case__	:      List[str]     		=	AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,  num_labels=__lowerCAmelCase  ,  idalabel=__lowerCAmelCase  ,  labelaid={label: i for i, label in enumerate(__lowerCAmelCase      )}  ,  cache_dir=model_args.cache_dir  ,  )
				snake_case__	:      List[str]     		=	AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,  cache_dir=model_args.cache_dir  ,  use_fast=model_args.use_fast  ,  )
				snake_case__	:      Tuple     		=	AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path  ,  from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )  ,  config=__lowerCAmelCase  ,  cache_dir=model_args.cache_dir  ,  )

				# Get datasets
				snake_case__	:      Dict     		=	(
				    TokenClassificationDataset(
				        token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  model_type=config.model_type  ,  max_seq_length=data_args.max_seq_length  ,  overwrite_cache=data_args.overwrite_cache  ,  mode=Split.train  ,  )
				    if training_args.do_train
				    else None
				)
				snake_case__	:      Optional[Any]     		=	(
				    TokenClassificationDataset(
				        token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  model_type=config.model_type  ,  max_seq_length=data_args.max_seq_length  ,  overwrite_cache=data_args.overwrite_cache  ,  mode=Split.dev  ,  )
				    if training_args.do_eval
				    else None
				)

				def align_predictions(__lowerCAmelCase  ,  __lowerCAmelCase      ) -> Tuple[List[int], List[int]]:
								snake_case__	:      Any     		=	np.argmax(__lowerCAmelCase  ,  axis=2      )

								snake_case__		, snake_case__	:      List[Any]     		=	preds.shape

								snake_case__	:      List[Any]     		=	[[] for _ in range(__lowerCAmelCase      )]
								snake_case__	:      int     		=	[[] for _ in range(__lowerCAmelCase      )]

								for i in range(__lowerCAmelCase      ):
												for j in range(__lowerCAmelCase      ):
																if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																				out_label_list[i].append(label_map[label_ids[i][j]]      )
																				preds_list[i].append(label_map[preds[i][j]]      )

								return preds_list, out_label_list

				def compute_metrics(__lowerCAmelCase      ) -> Dict:
								snake_case__		, snake_case__	:      List[str]     		=	align_predictions(p.predictions  ,  p.label_ids      )
								return {
								    "accuracy_score": accuracy_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "precision": precision_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "recall": recall_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "f1": fa_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								}

				# Data collator
				snake_case__	:      Any     		=	DataCollatorWithPadding(__lowerCAmelCase  ,  pad_to_multiple_of=8      ) if training_args.fpaa else None

				# Initialize our Trainer
				snake_case__	:      Tuple     		=	Trainer(
				    model=__lowerCAmelCase  ,  args=__lowerCAmelCase  ,  train_dataset=__lowerCAmelCase  ,  eval_dataset=__lowerCAmelCase  ,  compute_metrics=__lowerCAmelCase  ,  data_collator=__lowerCAmelCase  ,  )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_process_zero():
												tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
				snake_case__	:      Any     		=	{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''      )

								snake_case__	:      Tuple     		=	trainer.evaluate()

								snake_case__	:      Optional[Any]     		=	os.path.join(training_args.output_dir  ,  '''eval_results.txt'''      )
								if trainer.is_world_process_zero():
												with open(__lowerCAmelCase  ,  '''w'''      ) as writer:
																logger.info('''***** Eval results *****'''      )
																for key, value in result.items():
																				logger.info('''  %s = %s'''  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
																				writer.write('''%s = %s\n''' % (key, value)      )

												results.update(__lowerCAmelCase      )

    # Predict
				if training_args.do_predict:
								snake_case__	:      Optional[Any]     		=	TokenClassificationDataset(
								    token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  model_type=config.model_type  ,  max_seq_length=data_args.max_seq_length  ,  overwrite_cache=data_args.overwrite_cache  ,  mode=Split.test  ,  )

								snake_case__		, snake_case__		, snake_case__	:      List[str]     		=	trainer.predict(__lowerCAmelCase      )
								snake_case__		, snake_case__	:      int     		=	align_predictions(__lowerCAmelCase  ,  __lowerCAmelCase      )

								snake_case__	:      Optional[int]     		=	os.path.join(training_args.output_dir  ,  '''test_results.txt'''      )
								if trainer.is_world_process_zero():
												with open(__lowerCAmelCase  ,  '''w'''      ) as writer:
																for key, value in metrics.items():
																				logger.info('''  %s = %s'''  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
																				writer.write('''%s = %s\n''' % (key, value)      )

        # Save predictions
								snake_case__	:      Any     		=	os.path.join(training_args.output_dir  ,  '''test_predictions.txt'''      )
								if trainer.is_world_process_zero():
												with open(__lowerCAmelCase  ,  '''w'''      ) as writer:
																with open(os.path.join(data_args.data_dir  ,  '''test.txt'''      )  ,  '''r'''      ) as f:
																				token_classification_task.write_predictions_to_file(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )

				return results



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				main()


if __name__ == "__main__":
				main()

