



"""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	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						PreTrainedTokenizer					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Optional[int] = None					,     ):






				'''simple docstring'''



				_UpperCAmelCase					=    {}

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

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

				if len(_SCREAMING_SNAKE_CASE				) == 1:
								for k in files.keys():
												_UpperCAmelCase					=    ds[k].map(
												    lambda _SCREAMING_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():
												_UpperCAmelCase					=    ds[k].map(
												    lambda _SCREAMING_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]:
												_UpperCAmelCase					=    {k: v for k, v in ex.items() if k in input_names}
												_UpperCAmelCase					=    labelaid[ex[label_name]]
												yield (d, label)

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

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

				_UpperCAmelCase					=    (
				    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:
								_UpperCAmelCase					=    train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

				_UpperCAmelCase					=    (
				    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:
								_UpperCAmelCase					=    val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

				_UpperCAmelCase					=    (
				    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:
								_UpperCAmelCase					=    test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

				return train_ds, val_ds, test_ds, labelaid


__A   :							Any										=   logging.getLogger(__name__)
@dataclass
class 			_a :
			"""simple docstring"""
			UpperCamelCase__			      =	field(metadata={"""help""": """Which column contains the label"""})
			UpperCamelCase__			      =	field(default=lowerCAmelCase			,							metadata={"""help""": """The path of the training file"""})
			UpperCamelCase__			      =	field(default=lowerCAmelCase			,							metadata={"""help""": """The path of the development file"""})
			UpperCamelCase__			      =	field(default=lowerCAmelCase			,							metadata={"""help""": """The path of the test file"""})
			UpperCamelCase__			      =	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."""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""})



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







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
				_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    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.

				_UpperCAmelCase					=    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					,     )

				_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    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					,     )

				_UpperCAmelCase					=    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():
								_UpperCAmelCase					=    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(_SCREAMING_SNAKE_CASE   :						EvalPrediction				) -> Dict:
								_UpperCAmelCase					=    np.argmax(p.predictions					,     axis=1				)

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

				# Initialize our Trainer
				_UpperCAmelCase					=    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
				_UpperCAmelCase					=    {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''				)
								_UpperCAmelCase					=    trainer.evaluate()
								_UpperCAmelCase					=    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()








"""simple docstring"""


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
					import transformers







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				random.seed(_SCREAMING_SNAKE_CASE				)
				np.random.seed(_SCREAMING_SNAKE_CASE				)
				torch.manual_seed(_SCREAMING_SNAKE_CASE				)
				torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE				)
				# ^^ safe to call this function even if cuda is not available
class 			_a :
			"""simple docstring"""



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]				,   __UpperCamelCase     :    float = 0.9_9_9_9				,   __UpperCamelCase     :    float = 0.0				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    Union[float, int] = 1.0				,   __UpperCamelCase     :    Union[float, int] = 2 / 3				,   __UpperCamelCase     :    Optional[Any] = None				,   __UpperCamelCase     :    Dict[str, Any] = None				,   **__UpperCamelCase     :    Optional[Any]				,   )->Tuple:

							if isinstance(__UpperCamelCase				,   torch.nn.Module   ):
											_UpperCAmelCase					=    (
											    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
											    '''Please pass the parameters of the module instead.'''
											)
											deprecate(
											    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''				,   '''1.0.0'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase				,   )
											_UpperCAmelCase					=    parameters.parameters()

											# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
											_UpperCAmelCase					=    True

							if kwargs.get('''max_value'''				,   __UpperCamelCase   ) is not None:
											_UpperCAmelCase					=    '''The `max_value` argument is deprecated. Please use `decay` instead.'''
											deprecate('''max_value'''				,   '''1.0.0'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase   )
											_UpperCAmelCase					=    kwargs['''max_value''']

							if kwargs.get('''min_value'''				,   __UpperCamelCase   ) is not None:
											_UpperCAmelCase					=    '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
											deprecate('''min_value'''				,   '''1.0.0'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase   )
											_UpperCAmelCase					=    kwargs['''min_value''']

							_UpperCAmelCase					=    list(__UpperCamelCase   )
							_UpperCAmelCase					=    [p.clone().detach() for p in parameters]

							if kwargs.get('''device'''				,   __UpperCamelCase   ) is not None:
											_UpperCAmelCase					=    '''The `device` argument is deprecated. Please use `to` instead.'''
											deprecate('''device'''				,   '''1.0.0'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase   )
											self.to(device=kwargs['''device''']   )

							_UpperCAmelCase					=    None

							_UpperCAmelCase					=    decay
							_UpperCAmelCase					=    min_decay
							_UpperCAmelCase					=    update_after_step
							_UpperCAmelCase					=    use_ema_warmup
							_UpperCAmelCase					=    inv_gamma
							_UpperCAmelCase					=    power
							_UpperCAmelCase					=    0
							_UpperCAmelCase					=    None  # set in `step()`

							_UpperCAmelCase					=    model_cls
							_UpperCAmelCase					=    model_config



			@classmethod
			def  lowercase__						(		cls     :    List[str]				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Any   )->"EMAModel":
							_UpperCAmelCase		,						_UpperCAmelCase					=    model_cls.load_config(__UpperCamelCase				,   return_unused_kwargs=__UpperCamelCase   )
							_UpperCAmelCase					=    model_cls.from_pretrained(__UpperCamelCase   )

							_UpperCAmelCase					=    cls(model.parameters()				,   model_cls=__UpperCamelCase				,   model_config=model.config   )

							ema_model.load_state_dict(__UpperCamelCase   )
							return ema_model



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    Any   )->Optional[Any]:
							if self.model_cls is None:
											raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''   )

							if self.model_config is None:
											raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''   )

							_UpperCAmelCase					=    self.model_cls.from_config(self.model_config   )
							_UpperCAmelCase					=    self.state_dict()
							state_dict.pop('''shadow_params'''				,   __UpperCamelCase   )

							model.register_to_config(**__UpperCamelCase   )
							self.copy_to(model.parameters()   )
							model.save_pretrained(__UpperCamelCase   )



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    int   )->float:
							_UpperCAmelCase					=    max(0				,   optimization_step - self.update_after_step - 1   )

							if step <= 0:
											return 0.0

							if self.use_ema_warmup:
											_UpperCAmelCase					=    1 - (1 + step / self.inv_gamma) ** -self.power
							else:
											_UpperCAmelCase					=    (1 + step) / (1_0 + step)

							_UpperCAmelCase					=    min(__UpperCamelCase				,   self.decay   )
							# make sure decay is not smaller than min_decay
							_UpperCAmelCase					=    max(__UpperCamelCase				,   self.min_decay   )
							return cur_decay_value



			@torch.no_grad()
			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->Optional[Any]:
							if isinstance(__UpperCamelCase				,   torch.nn.Module   ):
											_UpperCAmelCase					=    (
											    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
											    '''Please pass the parameters of the module instead.'''
											)
											deprecate(
											    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''				,   '''1.0.0'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase				,   )
											_UpperCAmelCase					=    parameters.parameters()

							_UpperCAmelCase					=    list(__UpperCamelCase   )

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							_UpperCAmelCase					=    self.get_decay(self.optimization_step   )
							_UpperCAmelCase					=    decay
							_UpperCAmelCase					=    1 - decay

							_UpperCAmelCase					=    contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
											import deepspeed

							for s_param, param in zip(self.shadow_params				,   __UpperCamelCase   ):
											if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
															_UpperCAmelCase					=    deepspeed.zero.GatheredParameters(__UpperCamelCase				,   modifier_rank=__UpperCamelCase   )

											with context_manager():
															if param.requires_grad:
																			s_param.sub_(one_minus_decay * (s_param - param)   )
															else:
																			s_param.copy_(__UpperCamelCase   )



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							_UpperCAmelCase					=    list(__UpperCamelCase   )
							for s_param, param in zip(self.shadow_params				,   __UpperCamelCase   ):
											param.data.copy_(s_param.to(param.device   ).data   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Union[str, Any]=None   )->None:
							_UpperCAmelCase					=    [
							    p.to(device=__UpperCamelCase				,   dtype=__UpperCamelCase   ) if p.is_floating_point() else p.to(device=__UpperCamelCase   )
							    for p in self.shadow_params
							]



			def  lowercase__						(		self     :    Optional[Any]   )->dict:
							return {
							    "decay": self.decay,
							    "min_decay": self.min_decay,
							    "optimization_step": self.optimization_step,
							    "update_after_step": self.update_after_step,
							    "use_ema_warmup": self.use_ema_warmup,
							    "inv_gamma": self.inv_gamma,
							    "power": self.power,
							    "shadow_params": self.shadow_params,
							}



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							_UpperCAmelCase					=    [param.detach().cpu().clone() for param in parameters]



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							if self.temp_stored_params is None:
											raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''   )
							for c_param, param in zip(self.temp_stored_params				,   __UpperCamelCase   ):
											param.data.copy_(c_param.data   )

							# Better memory-wise.
							_UpperCAmelCase					=    None



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    dict   )->None:
							_UpperCAmelCase					=    copy.deepcopy(__UpperCamelCase   )

							_UpperCAmelCase					=    state_dict.get('''decay'''				,   self.decay   )
							if self.decay < 0.0 or self.decay > 1.0:
											raise ValueError('''Decay must be between 0 and 1'''   )

							_UpperCAmelCase					=    state_dict.get('''min_decay'''				,   self.min_decay   )
							if not isinstance(self.min_decay				,   __UpperCamelCase   ):
											raise ValueError('''Invalid min_decay'''   )

							_UpperCAmelCase					=    state_dict.get('''optimization_step'''				,   self.optimization_step   )
							if not isinstance(self.optimization_step				,   __UpperCamelCase   ):
											raise ValueError('''Invalid optimization_step'''   )

							_UpperCAmelCase					=    state_dict.get('''update_after_step'''				,   self.update_after_step   )
							if not isinstance(self.update_after_step				,   __UpperCamelCase   ):
											raise ValueError('''Invalid update_after_step'''   )

							_UpperCAmelCase					=    state_dict.get('''use_ema_warmup'''				,   self.use_ema_warmup   )
							if not isinstance(self.use_ema_warmup				,   __UpperCamelCase   ):
											raise ValueError('''Invalid use_ema_warmup'''   )

							_UpperCAmelCase					=    state_dict.get('''inv_gamma'''				,   self.inv_gamma   )
							if not isinstance(self.inv_gamma				,   (float, int)   ):
											raise ValueError('''Invalid inv_gamma'''   )

							_UpperCAmelCase					=    state_dict.get('''power'''				,   self.power   )
							if not isinstance(self.power				,   (float, int)   ):
											raise ValueError('''Invalid power'''   )

							_UpperCAmelCase					=    state_dict.get('''shadow_params'''				,   __UpperCamelCase   )
							if shadow_params is not None:
											_UpperCAmelCase					=    shadow_params
											if not isinstance(self.shadow_params				,   __UpperCamelCase   ):
															raise ValueError('''shadow_params must be a list'''   )
											if not all(isinstance(__UpperCamelCase				,   torch.Tensor   ) for p in self.shadow_params   ):
															raise ValueError('''shadow_params must all be Tensors'''   )



