




'''simple docstring'''
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()


A							:      Optional[int] 	=       logging.getLogger(__name__)






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






@dataclass
class        SCREAMING_SNAKE_CASE:
       snake_case_ :     str    =					field(
           metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}     )
       snake_case_ :     Optional[str]    =					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_ :     bool    =					field(
           default=__A   ,      metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )

def 				snake_case_  (   ):



    """simple docstring"""




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

    __lowercase							=						import_module("""tasks"""			)

    try:
        __lowercase							=						getattr(a__    ,model_args.task_type			)
        __lowercase							=						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"""    ,a__			)

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

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

    __lowercase							=						AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,num_labels=a__    ,idalabel=a__    ,labelaid={label: i for i, label in enumerate(a__			)}    ,cache_dir=model_args.cache_dir    ,)
    __lowercase							=						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():
        __lowercase							=						TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path    ,from_pt=bool(""".bin""" in model_args.model_name_or_path			)    ,config=a__    ,cache_dir=model_args.cache_dir    ,)

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

        for i in range(a__			):
            for j in range(a__			):
                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(a__		:	EvalPrediction			) -> Dict:
        __lowercase				,__lowercase							=						align_predictions(p.predictions    ,p.label_ids			)

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

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

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

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

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

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

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

            results.update(a__			)

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

        __lowercase				,__lowercase				,__lowercase							=						trainer.predict(test_dataset.get_dataset()			)
        __lowercase				,__lowercase							=						align_predictions(a__    ,a__			)
        __lowercase							=						classification_report(a__    ,a__			)

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

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

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

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

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

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

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

                        writer.write(a__			)
                    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 HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Any    =					(HeunDiscreteScheduler,)
       snake_case_ :     Any    =					10

       def     snake_case__			(    self					,		**lowerCamelCase__     )    ->					int:

           """simple docstring"""



           __lowercase							=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**lowerCamelCase__     )
           return config

       def     snake_case__			(    self     )    ->					Optional[Any]:

           """simple docstring"""



           for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           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=lowerCamelCase__					,		beta_end=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCamelCase__					,		clip_sample=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



           for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config(prediction_type="""v_prediction"""     )
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_9_3_4E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 6.1_1_1_2E-1_0     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps					,		device=lowerCamelCase__     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if str(lowerCamelCase__     ).startswith("""cpu"""     ):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           elif str(lowerCamelCase__     ).startswith("""mps"""     ):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.00_02     ) < 1E-2
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3




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

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__					,		use_karras_sigmas=lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps					,		device=lowerCamelCase__     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           assert abs(result_sum.item() - 0.0_00_15     ) < 1E-2
           assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7     ) < 1E-2




