






'''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_ (    __snake_case     :							str	,	__snake_case     :							str	,	__snake_case     :							str	,	__snake_case     :							PreTrainedTokenizer	,	__snake_case     :							int	,	__snake_case     :							Optional[int] = None	,	)     -> Union[str, Any]:





				'''simple docstring'''


				snake_case__      :str         =   {}

				if train_file is not None:
								snake_case__      :Dict         =   [train_file]
				if eval_file is not None:
								snake_case__      :Union[str, Any]         =   [eval_file]
				if test_file is not None:
								snake_case__      :str         =   [test_file]

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

				if len(_SCREAMING_SNAKE_CASE      ) == 1:
								for k in files.keys():
												snake_case__      :Optional[int]         =   ds[k].map(
												    lambda __snake_case      : tokenizer.batch_encode_plus(
												        example[features_name[0]]	,	truncation=_SCREAMING_SNAKE_CASE	,	max_length=_SCREAMING_SNAKE_CASE	,	padding="max_length"      )	,	batched=_SCREAMING_SNAKE_CASE	,	)
				elif len(_SCREAMING_SNAKE_CASE      ) == 2:
								for k in files.keys():
												snake_case__      :Any         =   ds[k].map(
												    lambda __snake_case      : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])	,	truncation=_SCREAMING_SNAKE_CASE	,	max_length=_SCREAMING_SNAKE_CASE	,	padding="max_length"	,	)	,	batched=_SCREAMING_SNAKE_CASE	,	)

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

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

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

				snake_case__      :int         =   (
				    tf.data.Dataset.from_generator(
				        _SCREAMING_SNAKE_CASE	,	({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:
								snake_case__      :List[Any]         =   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

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

				snake_case__      :List[str]         =   (
				    tf.data.Dataset.from_generator(
				        _SCREAMING_SNAKE_CASE	,	({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:
								snake_case__      :Dict         =   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

				return train_ds, val_ds, test_ds, labelaid


__UpperCAmelCase							:   Any							=  logging.getLogger(__name__)


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




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





def 					lowercase_ (    )     -> int:





				'''simple docstring'''


				snake_case__      :Union[str, Any]         =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
				snake_case__    ,						snake_case__    ,						snake_case__      :Any         =   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.

				snake_case__      :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	,	)

				snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :List[Any]         =   get_tfds(
				    train_file=data_args.train_file	,	eval_file=data_args.dev_file	,	test_file=data_args.test_file	,	tokenizer=_SCREAMING_SNAKE_CASE	,	label_column_id=data_args.label_column_id	,	max_seq_length=data_args.max_seq_length	,	)

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

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

				def compute_metrics(__snake_case     :							EvalPrediction      ) -> Dict:
								snake_case__      :Tuple         =   np.argmax(p.predictions	,	axis=1      )

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

				# Initialize our Trainer
				snake_case__      :Optional[int]         =   TFTrainer(
				    model=_SCREAMING_SNAKE_CASE	,	args=_SCREAMING_SNAKE_CASE	,	train_dataset=_SCREAMING_SNAKE_CASE	,	eval_dataset=_SCREAMING_SNAKE_CASE	,	compute_metrics=_SCREAMING_SNAKE_CASE	,	)

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

				# Evaluation
				snake_case__      :Optional[Any]         =   {}
				if training_args.do_eval:
								logger.info("*** Evaluate ***"      )
								snake_case__      :Optional[int]         =   trainer.evaluate()
								snake_case__      :Any         =   os.path.join(training_args.output_dir	,	"eval_results.txt"      )

								with open(_SCREAMING_SNAKE_CASE	,	"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(_SCREAMING_SNAKE_CASE      )

				return results


if __name__ == "__main__":
	main()







from typing import Any





def 					lowercase_ (    __snake_case     :							list	,	__snake_case     :							list	,	__snake_case     :							dict	,	__snake_case     :							dict	,	__snake_case     :							dict	,	)     -> list:





				'''simple docstring'''


				_validation(
				    __snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	)
				# Creates data structures and fill initial step
				snake_case__      :dict         =   {}
				snake_case__      :dict         =   {}
				for state in states_space:
								snake_case__      :List[Any]         =   observations_space[0]
								snake_case__      :str         =   (
								    initial_probabilities[state] * emission_probabilities[state][observation]
								)
								snake_case__      :str         =   None

				# Fills the data structure with the probabilities of
				# different transitions and pointers to previous states
				for o in range(1	,	len(__snake_case      )      ):
								snake_case__      :Any         =   observations_space[o]
								snake_case__      :Tuple         =   observations_space[o - 1]
								for state in states_space:
												# Calculates the argmax for probability function
												snake_case__      :Tuple         =   ""
												snake_case__      :Union[str, Any]         =   -1
												for k_state in states_space:
																snake_case__      :int         =   (
																    probabilities[(k_state, prior_observation)]
																    * transition_probabilities[k_state][state]
																    * emission_probabilities[state][observation]
																)
																if probability > max_probability:
																				snake_case__      :str         =   probability
																				snake_case__      :Tuple         =   k_state

            # Update probabilities and pointers dicts
												snake_case__      :List[str]         =   (
												    probabilities[(arg_max, prior_observation)]
												    * transition_probabilities[arg_max][state]
												    * emission_probabilities[state][observation]
												)

												snake_case__      :List[str]         =   arg_max

    # The final observation
				snake_case__      :str         =   observations_space[len(__snake_case      ) - 1]

				# argmax for given final observation
				snake_case__      :Optional[int]         =   ""
				snake_case__      :List[str]         =   -1
				for k_state in states_space:
								snake_case__      :List[str]         =   probabilities[(k_state, final_observation)]
								if probability > max_probability:
												snake_case__      :List[str]         =   probability
												snake_case__      :int         =   k_state
				snake_case__      :Any         =   arg_max

				# Process pointers backwards
				snake_case__      :int         =   last_state
				snake_case__      :List[str]         =   []
				for o in range(len(__snake_case      ) - 1	,	-1	,	-1      ):
								result.append(__snake_case      )
								snake_case__      :List[str]         =   pointers[previous, observations_space[o]]
				result.reverse()

				return result





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	)     -> None:





				'''simple docstring'''


				_validate_not_empty(
				    __snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	)
				_validate_lists(__snake_case	,	__snake_case      )
				_validate_dicts(
				    __snake_case	,	__snake_case	,	__snake_case      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	)     -> None:





				'''simple docstring'''


				if not all(
				    [
				        observations_space,
				        states_space,
				        initial_probabilities,
				        transition_probabilities,
				        emission_probabilities,
				    ]      ):
								raise ValueError("There's an empty parameter"      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any      )     -> None:





				'''simple docstring'''


				_validate_list(__snake_case	,	"observations_space"      )
				_validate_list(__snake_case	,	"states_space"      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							str      )     -> None:





				'''simple docstring'''


				if not isinstance(_object	,	__snake_case      ):
								snake_case__      :Optional[int]         =   F'{var_name} must be a list'
								raise ValueError(__snake_case      )
				else:
								for x in _object:
												if not isinstance(__snake_case	,	__snake_case      ):
																snake_case__      :Any         =   F'{var_name} must be a list of strings'
																raise ValueError(__snake_case      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	)     -> None:





				'''simple docstring'''


				_validate_dict(__snake_case	,	"initial_probabilities"	,	__snake_case      )
				_validate_nested_dict(__snake_case	,	"transition_probabilities"      )
				_validate_nested_dict(__snake_case	,	"emission_probabilities"      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							str      )     -> None:





				'''simple docstring'''


				_validate_dict(_object	,	__snake_case	,	__snake_case      )
				for x in _object.values():
								_validate_dict(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							str	,	__snake_case     :							type	,	__snake_case     :							bool = False      )     -> None:





				'''simple docstring'''


				if not isinstance(_object	,	__snake_case      ):
								snake_case__      :str         =   F'{var_name} must be a dict'
								raise ValueError(__snake_case      )
				if not all(isinstance(__snake_case	,	__snake_case      ) for x in _object      ):
								snake_case__      :List[Any]         =   F'{var_name} all keys must be strings'
								raise ValueError(__snake_case      )
				if not all(isinstance(__snake_case	,	__snake_case      ) for x in _object.values()      ):
								snake_case__      :Optional[int]         =   "nested dictionary " if nested else ""
								snake_case__      :int         =   F'{var_name} {nested_text}all values must be {value_type.__name__}'
								raise ValueError(__snake_case      )


if __name__ == "__main__":
	from doctest import testmod

	testmod()