






import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


UpperCamelCase												=      logging.getLogger(__name__)



@dataclass
class  __UpperCAmelCase						:
			__snake_case							: List[str]							=				field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       )
			__snake_case							: List[str]							=				field(
			    default=_UpperCAmelCase			,metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
			__snake_case							: List[str]							=				field(
			    default="NER"			,metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}       )
			__snake_case							: Union[str, Any]							=				field(
			    default=_UpperCAmelCase			,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}       )
			__snake_case							: Optional[int]							=				field(default=_UpperCAmelCase			,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.
			__snake_case							: Any							=				field(
			    default=_UpperCAmelCase			,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,)



@dataclass
class  __UpperCAmelCase						:
			__snake_case							: Tuple							=				field(
			    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}       )
			__snake_case							: Tuple							=				field(
			    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}       )
			__snake_case							: int							=				field(
			    default=128			,metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }			,)
			__snake_case							: Any							=				field(
			    default=_UpperCAmelCase			,metadata={"help": "Overwrite the cached training and evaluation sets"}       )

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






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
						_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    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."""						)

						_SCREAMING_SNAKE_CASE       =    import_module("""tasks"""						)

						try:
												_SCREAMING_SNAKE_CASE       =    getattr(snake_case__			,model_args.task_type						)
												_SCREAMING_SNAKE_CASE       =    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			,)
						logger.info(
						    """n_replicas: %s, distributed training: %s, 16-bits training: %s"""			,training_args.n_replicas			,bool(training_args.n_replicas > 1						)			,training_args.fpaa			,)
						logger.info("""Training/evaluation parameters %s"""			,snake_case__						)

						# Prepare Token Classification task
						_SCREAMING_SNAKE_CASE       =    token_classification_task.get_labels(data_args.labels						)
						_SCREAMING_SNAKE_CASE       =    dict(enumerate(snake_case__						)						)
						_SCREAMING_SNAKE_CASE       =    len(snake_case__						)

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

						_SCREAMING_SNAKE_CASE       =    AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,num_labels=snake_case__			,idalabel=snake_case__			,labelaid={label: i for i, label in enumerate(snake_case__						)}			,cache_dir=model_args.cache_dir			,)
						_SCREAMING_SNAKE_CASE       =    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			,)

						with training_args.strategy.scope():
												_SCREAMING_SNAKE_CASE       =    TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path			,from_pt=bool(""".bin""" in model_args.model_name_or_path						)			,config=snake_case__			,cache_dir=model_args.cache_dir			,)

						# Get datasets
						_SCREAMING_SNAKE_CASE       =    (
						    TFTokenClassificationDataset(
						        token_classification_task=snake_case__			,data_dir=data_args.data_dir			,tokenizer=snake_case__			,labels=snake_case__			,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
						)
						_SCREAMING_SNAKE_CASE       =    (
						    TFTokenClassificationDataset(
						        token_classification_task=snake_case__			,data_dir=data_args.data_dir			,tokenizer=snake_case__			,labels=snake_case__			,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(snake_case__			,snake_case__						) -> Tuple[List[int], List[int]]:
												_SCREAMING_SNAKE_CASE       =    np.argmax(snake_case__			,axis=2						)
												_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    preds.shape
												_SCREAMING_SNAKE_CASE       =    [[] for _ in range(snake_case__						)]
												_SCREAMING_SNAKE_CASE       =    [[] for _ in range(snake_case__						)]

												for i in range(snake_case__						):
																		for j in range(snake_case__						):
																								if label_ids[i, j] != -1_00:
																														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(snake_case__						) -> Dict:
												_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    align_predictions(p.predictions			,p.label_ids						)

												return {
												    "precision": precision_score(snake_case__			,snake_case__						),
												    "recall": recall_score(snake_case__			,snake_case__						),
												    "f1": fa_score(snake_case__			,snake_case__						),
												}

						# Initialize our Trainer
						_SCREAMING_SNAKE_CASE       =    TFTrainer(
						    model=snake_case__			,args=snake_case__			,train_dataset=train_dataset.get_dataset() if train_dataset else None			,eval_dataset=eval_dataset.get_dataset() if eval_dataset else None			,compute_metrics=snake_case__			,)

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir						)

						# Evaluation
						_SCREAMING_SNAKE_CASE       =    {}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""						)

												_SCREAMING_SNAKE_CASE       =    trainer.evaluate()
												_SCREAMING_SNAKE_CASE       =    os.path.join(training_args.output_dir			,"""eval_results.txt"""						)

												with open(snake_case__			,"""w"""						) as writer:
																		logger.info("""***** Eval results *****"""						)

																		for key, value in result.items():
																								logger.info("""  %s = %s"""			,snake_case__			,snake_case__						)
																								writer.write("""%s = %s\n""" % (key, value)						)

																		results.update(snake_case__						)

    # Predict
						if training_args.do_predict:
												_SCREAMING_SNAKE_CASE       =    TFTokenClassificationDataset(
												    token_classification_task=snake_case__			,data_dir=data_args.data_dir			,tokenizer=snake_case__			,labels=snake_case__			,model_type=config.model_type			,max_seq_length=data_args.max_seq_length			,overwrite_cache=data_args.overwrite_cache			,mode=Split.test			,)

												_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    trainer.predict(test_dataset.get_dataset()						)
												_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    align_predictions(snake_case__			,snake_case__						)
												_SCREAMING_SNAKE_CASE       =    classification_report(snake_case__			,snake_case__						)

												logger.info("""\n%s"""			,snake_case__						)

												_SCREAMING_SNAKE_CASE       =    os.path.join(training_args.output_dir			,"""test_results.txt"""						)

												with open(snake_case__			,"""w"""						) as writer:
																		writer.write("""%s\n""" % report						)

												# Save predictions
												_SCREAMING_SNAKE_CASE       =    os.path.join(training_args.output_dir			,"""test_predictions.txt"""						)

												with open(snake_case__			,"""w"""						) as writer:
																		with open(os.path.join(data_args.data_dir			,"""test.txt"""						)			,"""r"""						) as f:
																								_SCREAMING_SNAKE_CASE       =    0

																								for line in f:
																														if line.startswith("""-DOCSTART-"""						) or line == "" or line == "\n":
																																				writer.write(snake_case__						)

																																				if not preds_list[example_id]:
																																										example_id += 1
																														elif preds_list[example_id]:
																																				_SCREAMING_SNAKE_CASE       =    line.split()[0] + """ """ + preds_list[example_id].pop(0						) + """\n"""

																																				writer.write(snake_case__						)
																														else:
																																				logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""			,line.split()[0]						)

						return results


if __name__ == "__main__":
		main()









'''simple docstring'''







import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	(KDPMaAncestralDiscreteScheduler,)
     _snake_case							   =	10






     def      _UpperCamelCase (self     :						int					,					**lowercase__     :						Optional[int]       )	->							Dict:
           __A		 =   {
               "num_train_timesteps": 1100,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**lowercase__       )
           return config






     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,					[0.00_02, 0.0_02, 0.02]       ):
                 self.check_over_configs(beta_start=lowercase__					,					beta_end=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Tuple:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Any:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_77       ) < 1e-2
           assert abs(result_mean.item() - 18.03_31       ) < 5e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							str:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							List[Any]:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config(prediction_type="v_prediction"       )
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           __A		 =   torch.manual_seed(0       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 3_28.99_70       ) < 1e-2
           assert abs(result_mean.item() - 0.42_84       ) < 1e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							int:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps					,					device=lowercase__       )
           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter.to(lowercase__       ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_18       ) < 1e-1
           assert abs(result_mean.item() - 18.03_31       ) < 1e-3


