

"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    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()




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      UpperCamelCase_					:	PreTrainedTokenizer,      UpperCamelCase_					:	int,      UpperCamelCase_					:	Optional[int] = None,      )     ->						Dict:
   '''simple docstring'''


   __lowercase								=						{}

   if train_file is not None:
      __lowercase								=						[train_file]
   if eval_file is not None:
      __lowercase								=						[eval_file]
   if test_file is not None:
      __lowercase								=						[test_file]

   __lowercase								=						datasets.load_dataset("csv",      data_files=_lowercase)
   __lowercase								=						list(ds[list(files.keys())[0]].features.keys())
   __lowercase								=						features_name.pop(_lowercase)
   __lowercase								=						list(set(ds[list(files.keys())[0]][label_name]))
   __lowercase								=						{label: i for i, label in enumerate(_lowercase)}
   __lowercase								=						tokenizer.model_input_names
   __lowercase								=						{}

   if len(_lowercase) == 1:
      for k in files.keys():
         __lowercase								=						ds[k].map(
             lambda UpperCamelCase_: tokenizer.batch_encode_plus(
                 example[features_name[0]],      truncation=_lowercase,      max_length=_lowercase,      padding="max_length"),      batched=_lowercase,      )
   elif len(_lowercase) == 2:
      for k in files.keys():
         __lowercase								=						ds[k].map(
             lambda UpperCamelCase_: tokenizer.batch_encode_plus(
                 (example[features_name[0]], example[features_name[1]]),      truncation=_lowercase,      max_length=_lowercase,      padding="max_length",      ),      batched=_lowercase,      )

   def gen_train():
      for ex in transformed_ds[datasets.Split.TRAIN]:
         __lowercase								=						{k: v for k, v in ex.items() if k in input_names}
         __lowercase								=						labelaid[ex[label_name]]
         yield (d, label)

   def gen_val():
      for ex in transformed_ds[datasets.Split.VALIDATION]:
         __lowercase								=						{k: v for k, v in ex.items() if k in input_names}
         __lowercase								=						labelaid[ex[label_name]]
         yield (d, label)

   def gen_test():
      for ex in transformed_ds[datasets.Split.TEST]:
         __lowercase								=						{k: v for k, v in ex.items() if k in input_names}
         __lowercase								=						labelaid[ex[label_name]]
         yield (d, label)

   __lowercase								=						(
       tf.data.Dataset.from_generator(
           _lowercase,      ({k: tf.intaa for k in input_names}, tf.intaa),      ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),      )
       if datasets.Split.TRAIN in transformed_ds
       else None
   )

   if train_ds is not None:
      __lowercase								=						train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

   __lowercase								=						(
       tf.data.Dataset.from_generator(
           _lowercase,      ({k: tf.intaa for k in input_names}, tf.intaa),      ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),      )
       if datasets.Split.VALIDATION in transformed_ds
       else None
   )

   if val_ds is not None:
      __lowercase								=						val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

   __lowercase								=						(
       tf.data.Dataset.from_generator(
           _lowercase,      ({k: tf.intaa for k in input_names}, tf.intaa),      ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),      )
       if datasets.Split.TEST in transformed_ds
       else None
   )

   if test_ds is not None:
      __lowercase								=						test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

   return train_ds, val_ds, test_ds, labelaid


_a :    Tuple							=							logging.getLogger(__name__)




@dataclass
class  _lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase     : int       		=     field(metadata={"help": "Which column contains the label"}       )
 __UpperCAmelCase     : str       		=     field(default=a__					,metadata={"help": "The path of the training file"}       )
 __UpperCAmelCase     : Optional[str]       		=     field(default=a__					,metadata={"help": "The path of the development file"}       )
 __UpperCAmelCase     : Optional[str]       		=     field(default=a__					,metadata={"help": "The path of the test file"}       )
 __UpperCAmelCase     : int       		=     field(
     default=1_2_8					,metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }					,)
 __UpperCAmelCase     : bool       		=     field(
     default=a__					,metadata={"help": "Overwrite the cached training and evaluation sets"}       )







@dataclass
class  _lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase     : str       		=     field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       )
 __UpperCAmelCase     : Optional[str]       		=     field(
     default=a__					,metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
 __UpperCAmelCase     : Optional[str]       		=     field(
     default=a__					,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}       )
 __UpperCAmelCase     : 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.
 __UpperCAmelCase     : Optional[str]       		=     field(
     default=a__					,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,)







def   _A  (  )     ->						Optional[Any]:
   '''simple docstring'''


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

   # 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(
       F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, """
       F"""16-bits training: {training_args.fpaa}""")
   logger.info(F"""Training/evaluation parameters {training_args}""")

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

   __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,      )

   __lowercase								=						get_tfds(
       train_file=data_args.train_file,      eval_file=data_args.dev_file,      test_file=data_args.test_file,      tokenizer=_lowercase,      label_column_id=data_args.label_column_id,      max_seq_length=data_args.max_seq_length,      )

   __lowercase								=						AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path,      num_labels=len(_lowercase),      labelaid=_lowercase,      idalabel={id: label for label, id in labelaid.items()},      finetuning_task="text-classification",      cache_dir=model_args.cache_dir,      )

   with training_args.strategy.scope():
      __lowercase								=						TFAutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path,      from_pt=bool(".bin" in model_args.model_name_or_path),      config=_lowercase,      cache_dir=model_args.cache_dir,      )

   def compute_metrics(UpperCamelCase_					:	EvalPrediction) -> Dict:
      __lowercase								=						np.argmax(p.predictions,      axis=1)

      return {"acc": (preds == p.label_ids).mean()}

   # Initialize our Trainer
   __lowercase								=						TFTrainer(
       model=_lowercase,      args=_lowercase,      train_dataset=_lowercase,      eval_dataset=_lowercase,      compute_metrics=_lowercase,      )

   # 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(_lowercase,      "w") as writer:
         logger.info("***** Eval results *****")

         for key, value in result.items():
            logger.info(F"""  {key} = {value}""")
            writer.write(F"""{key} = {value}\n""")

         results.update(_lowercase)

   return results


if __name__ == "__main__":
   main()







"""simple docstring"""





from __future__ import annotations

from typing import Any




class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      Union[str, Any], UpperCAmelCase__							:      int     ):
    __lowercase								=						num_of_nodes
    __lowercase								=						[]
    __lowercase								=						{}


 def  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      int, UpperCAmelCase__							:      int, UpperCAmelCase__							:      int     ):
    self.m_edges.append([u_node, v_node, weight]     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):

    if self.m_component[u_node] == u_node:
       return u_node
    return self.find_component(self.m_component[u_node]     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):

    if self.m_component[u_node] != u_node:
       for k in self.m_component:
          __lowercase								=						self.find_component(UpperCAmelCase__     )


 def  _lowercase			(      self							:      str, UpperCAmelCase__							:      list[int], UpperCAmelCase__							:      int, UpperCAmelCase__							:      int     ):

    if component_size[u_node] <= component_size[v_node]:
       __lowercase								=						v_node
       component_size[v_node] += component_size[u_node]
       self.set_component(UpperCAmelCase__     )

    elif component_size[u_node] >= component_size[v_node]:
       __lowercase								=						self.find_component(UpperCAmelCase__     )
       component_size[u_node] += component_size[v_node]
       self.set_component(UpperCAmelCase__     )



 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						[]
    __lowercase								=						0

    __lowercase								=						[-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes     ):
       self.m_component.update({node: node}     )
       component_size.append(1     )

    __lowercase								=						self.m_num_of_nodes

    while num_of_components > 1:
       for edge in self.m_edges:
          __lowercase		,__lowercase		,__lowercase								=						edge

          __lowercase								=						self.m_component[u]
          __lowercase								=						self.m_component[v]

          if u_component != v_component:

             for component in (u_component, v_component):
                if (
                    minimum_weight_edge[component] == -1
                    or minimum_weight_edge[component][2] > w
                ):
                   __lowercase								=						[u, v, w]

       for edge in minimum_weight_edge:
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             __lowercase		,__lowercase		,__lowercase								=						edge

             __lowercase								=						self.m_component[u]
             __lowercase								=						self.m_component[v]

             if u_component != v_component:
                mst_weight += w
                self.union(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )
                print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""     )
                num_of_components -= 1

       __lowercase								=						[-1] * self.m_num_of_nodes
    print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""     )







def   _A  (  )     ->						None:
   '''simple docstring'''




if __name__ == "__main__":
   import doctest

   doctest.testmod()




