





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       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			str					,	UpperCamelCase__     :			str					,	UpperCamelCase__     :			PreTrainedTokenizer					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			Optional[int] = None					,	)->List[str]:
       A__		   =		{}

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

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

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

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

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

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

       A__		   =		(
           tf.data.Dataset.from_generator(
               UpperCamelCase__					,	({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:
              A__		   =		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

       A__		   =		(
           tf.data.Dataset.from_generator(
               UpperCamelCase__					,	({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:
              A__		   =		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

       A__		   =		(
           tf.data.Dataset.from_generator(
               UpperCamelCase__					,	({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:
              A__		   =		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

       return train_ds, val_ds, test_ds, labelaid


a__: List[str]							   =						logging.getLogger(__name__)




@dataclass
class        SCREAMING_SNAKE_CASE__					:
 __SCREAMING_SNAKE_CASE							=      field(metadata={'''help''': '''Which column contains the label'''} )
 __SCREAMING_SNAKE_CASE							=      field(default=UpperCamelCase__							,   metadata={'''help''': '''The path of the training file'''} )
 __SCREAMING_SNAKE_CASE							=      field(default=UpperCamelCase__							,   metadata={'''help''': '''The path of the development file'''} )
 __SCREAMING_SNAKE_CASE							=      field(default=UpperCamelCase__							,   metadata={'''help''': '''The path of the test file'''} )
 __SCREAMING_SNAKE_CASE							=      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.'''
         )
     }							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )




@dataclass
class        SCREAMING_SNAKE_CASE__					:
 __SCREAMING_SNAKE_CASE							=      field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
 __SCREAMING_SNAKE_CASE							=      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.
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,   )


def       UpperCamelCase__(			)->Optional[int]:
       # 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.
       A__		   =		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
       A__   ,      A__   ,      A__		   =		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.

       A__		   =		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					,	)

       A__   ,      A__   ,      A__   ,      A__		   =		get_tfds(
           train_file=data_args.train_file					,	eval_file=data_args.dev_file					,	test_file=data_args.test_file					,	tokenizer=UpperCamelCase__					,	label_column_id=data_args.label_column_id					,	max_seq_length=data_args.max_seq_length					,	)

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

       with training_args.strategy.scope():
              A__		   =		TFAutoModelForSequenceClassification.from_pretrained(
                  model_args.model_name_or_path					,	from_pt=bool('''.bin''' in model_args.model_name_or_path    )					,	config=UpperCamelCase__					,	cache_dir=model_args.cache_dir					,	)

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

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

       # Initialize our Trainer
       A__		   =		TFTrainer(
           model=UpperCamelCase__					,	args=UpperCamelCase__					,	train_dataset=UpperCamelCase__					,	eval_dataset=UpperCamelCase__					,	compute_metrics=UpperCamelCase__					,	)

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

       # Evaluation
       A__		   =		{}
       if training_args.do_eval:
              logger.info('''*** Evaluate ***'''    )
              A__		   =		trainer.evaluate()
              A__		   =		os.path.join(training_args.output_dir					,	'''eval_results.txt'''    )

              with open(UpperCamelCase__					,	'''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(UpperCamelCase__    )

       return results


if __name__ == "__main__":
       main()












from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a__: Tuple							   =						TypeVar('T')


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (position - 1) // 2


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (2 * position) + 1


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (2 * position) + 2




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        A__		   =		[]
        A__		   =		{}
        A__		   =		0



 def __len__(				self					):
        return self.elements



 def __repr__(				self					):
        return str(self.heap					)



 def 							UpperCamelCase     (				self					):
        # Check if the priority queue is empty
        return self.elements == 0



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Add an element with given priority to the queue
        self.heap.append((elem, weight)					)
        A__		   =		self.elements
        self.elements += 1
        self._bubble_up(__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        # Remove and return the element with lowest weight (highest priority)
        if self.elements > 1:
               self._swap_nodes(0,self.elements - 1					)
        A__   ,      A__		   =		self.heap.pop()
        del self.position_map[elem]
        self.elements -= 1
        if self.elements > 0:
               A__   ,      A__		   =		self.heap[0]
               self._bubble_down(__lowerCamelCase					)
        return elem



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Update the weight of the given key
        A__		   =		self.position_map[elem]
        A__		   =		(elem, weight)
        if position > 0:
               A__		   =		get_parent_position(__lowerCamelCase					)
               A__   ,      A__		   =		self.heap[parent_position]
               if parent_weight > weight:
                      self._bubble_up(__lowerCamelCase					)
               else:
                      self._bubble_down(__lowerCamelCase					)
        else:
               self._bubble_down(__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Place a node at the proper position (upward movement) [to be used internally
        # only]
        A__		   =		self.position_map[elem]
        if curr_pos == 0:
               return None
        A__		   =		get_parent_position(__lowerCamelCase					)
        A__   ,      A__		   =		self.heap[curr_pos]
        A__   ,      A__		   =		self.heap[parent_position]
        if parent_weight > weight:
               self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
               return self._bubble_up(__lowerCamelCase					)
        return None



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Place a node at the proper position (downward movement) [to be used
        # internally only]
        A__		   =		self.position_map[elem]
        A__   ,      A__		   =		self.heap[curr_pos]
        A__		   =		get_child_left_position(__lowerCamelCase					)
        A__		   =		get_child_right_position(__lowerCamelCase					)
        if child_left_position < self.elements and child_right_position < self.elements:
               A__   ,      A__		   =		self.heap[child_left_position]
               A__   ,      A__		   =		self.heap[child_right_position]
               if child_right_weight < child_left_weight and child_right_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        if child_left_position < self.elements:
               A__   ,      A__		   =		self.heap[child_left_position]
               if child_left_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        else:
               return None
        if child_right_position < self.elements:
               A__   ,      A__		   =		self.heap[child_right_position]
               if child_right_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        return None




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Swap the nodes at the given positions
        A__		   =		self.heap[nodea_pos][0]
        A__		   =		self.heap[nodea_pos][0]
        A__   ,      A__		   =		(
            self.heap[nodea_pos],
            self.heap[nodea_pos],
        )
        A__		   =		nodea_pos
        A__		   =		nodea_pos




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        A__		   =		{}
        A__		   =		0



 def __repr__(				self					):
        return str(self.connections					)



 def __len__(				self					):
        return self.nodes



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Add a node in the graph if it is not in the graph
        if node not in self.connections:
               A__		   =		{}
               self.nodes += 1




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        # Add an edge between 2 nodes in the graph
        self.add_node(__lowerCamelCase					)
        self.add_node(__lowerCamelCase					)
        A__		   =		weight
        A__		   =		weight


def       UpperCamelCase__(			UpperCamelCase__     :			GraphUndirectedWeighted[T]					,	)->tuple[dict[T, int], dict[T, T | None]]:
       A__		   =		{node: maxsize for node in graph.connections}
       A__		   =		{node: None for node in graph.connections}

       A__		   =		MinPriorityQueue()
       for node, weight in dist.items():
              priority_queue.push(UpperCamelCase__					,	UpperCamelCase__    )

       if priority_queue.is_empty():
              return dist, parent

       # initialization
       A__		   =		priority_queue.extract_min()
       A__		   =		0
       for neighbour in graph.connections[node]:
              if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                     A__		   =		dist[node] + graph.connections[node][neighbour]
                     priority_queue.update_key(UpperCamelCase__					,	dist[neighbour]    )
                     A__		   =		node

    # running prim's algorithm
       while not priority_queue.is_empty():
              A__		   =		priority_queue.extract_min()
              for neighbour in graph.connections[node]:
                     if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                            A__		   =		dist[node] + graph.connections[node][neighbour]
                            priority_queue.update_key(UpperCamelCase__					,	dist[neighbour]    )
                            A__		   =		node
       return dist, parent





