


'''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 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					str,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					str = None,				):
 __A  :		str       =			{}

 if train_file is not None:
  __A  :		Dict       =			[train_file]
 if eval_file is not None:
  __A  :		Any       =			[eval_file]
 if test_file is not None:
  __A  :		Dict       =			[test_file]

 __A  :		Optional[int]       =			datasets.load_dataset('csv',				data_files=__lowerCAmelCase			)
 __A  :		Any       =			list(ds[list(files.keys()			)[0]].features.keys()			)
 __A  :		Any       =			features_name.pop(__lowerCAmelCase			)
 __A  :		List[Any]       =			list(set(ds[list(files.keys()			)[0]][label_name]			)			)
 __A  :		int       =			{label: i for i, label in enumerate(__lowerCAmelCase			)}
 __A  :		Union[str, Any]       =			tokenizer.model_input_names
 __A  :		Union[str, Any]       =			{}

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

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

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

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

 __A  :		Union[str, Any]       =			(
     tf.data.Dataset.from_generator(
         __lowerCAmelCase,				({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  :		str       =			train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]			)			)			)

 __A  :		str       =			(
     tf.data.Dataset.from_generator(
         __lowerCAmelCase,				({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  :		Optional[Any]       =			val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]			)			)			)

 __A  :		Optional[Any]       =			(
     tf.data.Dataset.from_generator(
         __lowerCAmelCase,				({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  :		Any       =			test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]			)			)			)

 return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_ :     Tuple								=      logging.getLogger(__name__)

@dataclass
class 						_lowerCamelCase					:




 '''simple docstring'''
 __lowercase				:		Tuple    							=						field(metadata={'''help''': '''Which column contains the label'''}				)
 __lowercase				:		Tuple    							=						field(default=__UpperCAmelCase    ,		metadata={'''help''': '''The path of the training file'''}				)
 __lowercase				:		Any    							=						field(default=__UpperCAmelCase    ,		metadata={'''help''': '''The path of the development file'''}				)
 __lowercase				:		str    							=						field(default=__UpperCAmelCase    ,		metadata={'''help''': '''The path of the test file'''}				)
 __lowercase				:		str    							=						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.'''
         )
     }    ,		)
 __lowercase				:		Optional[Any]    							=						field(
     default=__UpperCAmelCase    ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}				)


@dataclass
class 						_lowerCamelCase					:




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


def 			_lowercase       (				):
 __A  :		Tuple       =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
 __A     ,__A     ,__A  :		str       =			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  :		int       =			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  :		Optional[Any]       =			get_tfds(
     train_file=data_args.train_file,				eval_file=data_args.dev_file,				test_file=data_args.test_file,				tokenizer=__lowerCAmelCase,				label_column_id=data_args.label_column_id,				max_seq_length=data_args.max_seq_length,				)

 __A  :		List[Any]       =			AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path,				num_labels=len(__lowerCAmelCase			),				labelaid=__lowerCAmelCase,				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  :		str       =			TFAutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path,				from_pt=bool('.bin' in model_args.model_name_or_path			),				config=__lowerCAmelCase,				cache_dir=model_args.cache_dir,				)

 def compute_metrics(UpperCamelCase__       :					List[str]			) -> Dict:
  __A  :		Tuple       =			np.argmax(p.predictions,				axis=1			)

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

 # Initialize our Trainer
 __A  :		Union[str, Any]       =			TFTrainer(
     model=__lowerCAmelCase,				args=__lowerCAmelCase,				train_dataset=__lowerCAmelCase,				eval_dataset=__lowerCAmelCase,				compute_metrics=__lowerCAmelCase,				)

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

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

  with open(__lowerCAmelCase,				'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(__lowerCAmelCase			)

 return results


if __name__ == "__main__":
 main()




'''simple docstring'''
from heapq import heappop, heappush

import numpy as np


def 			_lowercase       (				UpperCamelCase__       :					np.ndarray,				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					bool,				):
 __A     ,__A  :		Optional[Any]       =			grid.shape
 __A  :		List[Any]       =			[-1, 1, 0, 0]
 __A  :		Optional[int]       =			[0, 0, -1, 1]
 if allow_diagonal:
  dx += [-1, -1, 1, 1]
  dy += [-1, 1, -1, 1]

 __A     ,__A  :		int       =			[(0, source)], set()
 __A  :		Any       =			np.full((rows, cols),				np.inf			)
 __A  :		int       =			0
 __A  :		Any       =			np.empty((rows, cols),				dtype=UpperCamelCase__			)
 __A  :		List[Any]       =			None

 while queue:
  ((__A)     ,(__A))  :		List[Any]       =			heappop(UpperCamelCase__			)
  if (x, y) in visited:
   continue
  visited.add((x, y)			)

  if (x, y) == destination:
   __A  :		Tuple       =			[]
   while (x, y) != source:
    path.append((x, y)			)
    __A     ,__A  :		int       =			predecessors[x, y]
   path.append(UpperCamelCase__			)  # add the source manually
   path.reverse()
   return matrix[destination], path

  for i in range(len(UpperCamelCase__			)			):
   __A     ,__A  :		List[Any]       =			x + dx[i], y + dy[i]
   if 0 <= nx < rows and 0 <= ny < cols:
    __A  :		str       =			grid[nx][ny]
    if next_node == 1 and matrix[nx, ny] > dist + 1:
     heappush(UpperCamelCase__,				(dist + 1, (nx, ny))			)
     __A  :		int       =			dist + 1
     __A  :		List[str]       =			(x, y)

 return np.inf, []


if __name__ == "__main__":
 import doctest

 doctest.testmod()






