



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_       :Any				  =				logging.getLogger(__name__)




@dataclass
class  snake_case__						:






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     field(
         metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}					)
     _SCREAMING_SNAKE_CASE            =     field(
         default=lowerCAmelCase_    ,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}					)
     _SCREAMING_SNAKE_CASE            =     field(
         default="""NER"""    ,					metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}					)
     _SCREAMING_SNAKE_CASE            =     field(
         default=lowerCAmelCase_    ,					metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}					)
     _SCREAMING_SNAKE_CASE            =     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.
     _SCREAMING_SNAKE_CASE            =     field(
         default=lowerCAmelCase_    ,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,					)




@dataclass
class  snake_case__						:






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     field(
         metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}					)
     _SCREAMING_SNAKE_CASE            =     field(
         metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}					)
     _SCREAMING_SNAKE_CASE            =     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."""
             )
         }    ,					)
     _SCREAMING_SNAKE_CASE            =     field(
         default=lowerCAmelCase_    ,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)


def 	lowercase_							():
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
       snake_case__							:     Tuple            =     HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
       snake_case__							:     Optional[int]            =     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__							:     Optional[Any]            =     import_module('tasks'				)

       try:
              snake_case__							:     Union[str, Any]            =     getattr(A		, 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		, )
       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
       snake_case__							:     List[str]            =     token_classification_task.get_labels(data_args.labels				)
       snake_case__							:     Dict[int, str]            =     dict(enumerate(A				)				)
       snake_case__							:     str            =     len(A				)

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

       snake_case__							:     Dict            =     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		, )
       snake_case__							:     List[Any]            =     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():
              snake_case__							:     Any            =     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
       snake_case__							:     Optional[Any]            =     (
           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
       )
       snake_case__							:     Optional[Any]            =     (
           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]]:
              snake_case__							:     Dict            =     np.argmax(A		, axis=2				)
              snake_case__							:     str            =     preds.shape
              snake_case__							:     str            =     [[] for _ in range(A				)]
              snake_case__							:     Optional[Any]            =     [[] for _ in range(A				)]

              for i in range(A				):
                     for j in range(A				):
                            if label_ids[i, j] != -1_0_0:
                                   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:
              snake_case__							:     Dict            =     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
       snake_case__							:     List[Any]            =     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
       snake_case__							:     int            =     {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'				)

              snake_case__							:     int            =     trainer.evaluate()
              snake_case__							:     List[Any]            =     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:
              snake_case__							:     Optional[Any]            =     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		, )

              snake_case__							:     List[str]            =     trainer.predict(test_dataset.get_dataset()				)
              snake_case__							:     Tuple            =     align_predictions(A		, A				)
              snake_case__							:     List[str]            =     classification_report(A		, A				)

              logger.info('\n%s'		, A				)

              snake_case__							:     Dict            =     os.path.join(training_args.output_dir		, 'test_results.txt'				)

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

              # Save predictions
              snake_case__							:     Union[str, Any]            =     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:
                            snake_case__							:     str            =     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]:
                                          snake_case__							:     List[Any]            =     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()











from manim import *




class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     def        lowercase_       (	self  :  List[Any] )       ->Dict:
            snake_case__							:     str            =     Rectangle(height=0.5,			width=0.5 )
            snake_case__							:     str            =     Rectangle(height=0.4_6,			width=0.4_6 ).set_stroke(width=0 )
            snake_case__							:     List[Any]            =     Rectangle(height=0.2_5,			width=0.2_5 )

            snake_case__							:     Any            =     [mem.copy() for i in range(6 )]
            snake_case__							:     Optional[Any]            =     [mem.copy() for i in range(6 )]
            snake_case__							:     int            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Optional[int]            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Any            =     VGroup(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Optional[Any]            =     Text('CPU',			font_size=2_4 )
            snake_case__							:     str            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            cpu.move_to([-2.5, -0.5, 0] )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     [mem.copy() for i in range(4 )]
            snake_case__							:     int            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     str            =     Text('GPU',			font_size=2_4 )
            snake_case__							:     List[Any]            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            gpu.move_to([-1, -1, 0] )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     [mem.copy() for i in range(6 )]
            snake_case__							:     Union[str, Any]            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )

            snake_case__							:     int            =     Text('Model',			font_size=2_4 )
            snake_case__							:     List[Any]            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            model.move_to([3, -1.0, 0] )
            self.add(_snake_case )

            snake_case__							:     Any            =     []
            snake_case__							:     Optional[int]            =     []

            for i, rect in enumerate(_snake_case ):
                   rect.set_stroke(_snake_case )

                   snake_case__							:     Union[str, Any]            =     Rectangle(height=0.4_6 / 4,			width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(_snake_case,			opacity=0.7 )

                   if i == 0:
                          cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ),			buff=0.0_2,			direction=_snake_case )
                          cpu_target.set_x(cpu_target.get_x() + 0.1 )
                   elif i == 3:
                          cpu_target.next_to(model_cpu_arr[0],			direction=_snake_case,			buff=0.0 )
                   else:
                          cpu_target.next_to(model_cpu_arr[i - 1],			direction=_snake_case,			buff=0.0 )
                   self.add(_snake_case )
                   model_cpu_arr.append(_snake_case )

            self.add(*_snake_case,			*_snake_case )

            snake_case__							:     Optional[Any]            =     [meta_mem.copy() for i in range(6 )]
            snake_case__							:     List[Any]            =     [meta_mem.copy() for i in range(6 )]
            snake_case__							:     Dict            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Tuple            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     int            =     VGroup(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Union[str, Any]            =     Text('Disk',			font_size=2_4 )
            snake_case__							:     int            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            disk.move_to([-4.0, -1.2_5, 0] )
            self.add(_snake_case,			_snake_case )

            snake_case__							:     str            =     []

            for i in range(6 ):
                   snake_case__							:     str            =     fill.copy().set_fill(_snake_case,			opacity=0.8 )
                   target.move_to(disk_left_col_base[i] ).scale(0.5 )
                   cpu_disk_arr.append(_snake_case )

            self.add(*_snake_case )

            snake_case__							:     int            =     Square(side_length=2.2 )
            key.move_to([-5, 2, 0] )

            snake_case__							:     int            =     MarkupText(
                F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',			font_size=1_8,			)

            key_text.move_to([-5, 2.4, 0] )

            self.add(_snake_case,			_snake_case )

            snake_case__							:     Optional[int]            =     MarkupText(
                F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''',			font_size=1_8,			)

            blue_text.next_to(_snake_case,			DOWN * 2.4,			aligned_edge=key_text.get_left() )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     MarkupText(F'''The offloaded weights are all sent to the CPU.''',			font_size=2_4 )
            step_a.move_to([2, 2, 0] )

            self.play(Write(_snake_case,			run_time=3 ) )

            for i in range(6 ):
                   snake_case__							:     int            =     cpu_disk_arr[i]
                   snake_case__							:     Optional[Any]            =     rect.copy().set_fill(_snake_case,			opacity=0.8 ).scale(2.0 )
                   cpa.generate_target()
                   cpa.target.move_to(model_base[i] )

                   if i == 0:
                          rect.set_fill(_snake_case,			opacity=0.8 )
                          rect.generate_target()
                          rect.target.move_to(cpu_left_col_base[0] ).scale(2.0 )

                          self.remove(
                              *_snake_case,			*_snake_case,			)

                   else:
                          rect.generate_target()
                          rect.target.move_to(cpu_left_col_base[i] ).scale(2.0 )
                   self.play(MoveToTarget(_snake_case ),			MoveToTarget(_snake_case ),			model_base[i].animate.set_stroke(_snake_case ) )
            self.play(FadeOut(_snake_case ) )

            snake_case__							:     Optional[Any]            =     MarkupText(
                F'''Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed.''',			font_size=2_4,			)
            step_a.move_to([2, 2, 0] )

            self.play(Write(_snake_case,			run_time=3 ) )

            snake_case__							:     str            =     []
            snake_case__							:     Optional[Any]            =     []
            for i in range(6 ):
                   snake_case__							:     List[Any]            =     Arrow(start=_snake_case,			end=_snake_case,			color=_snake_case,			buff=0.5 )
                   a.next_to(model_base[i].get_left(),			_snake_case,			buff=0.2 )
                   arrows.append(_snake_case )
                   animations.append(Write(_snake_case ) )
            self.play(*_snake_case )
            self.wait()






