






import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

def      __lowercase					( snake_case ):







				"""simple docstring"""






				return 1.0 / (1.0 + np.exp(-_outputs ))

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      np.max(_outputs,  axis=-1,  keepdims=snake_case )
				__magic_name__		:Any        =      np.exp(_outputs - maxes )
				return shifted_exp / shifted_exp.sum(axis=-1,  keepdims=snake_case )
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  '''sigmoid'''
				a__					       =  '''softmax'''
				a__					       =  '''none'''







@add_end_docstrings(
    lowerCamelCase			,				r'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''			,				)
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  False
				a__					       =  ClassificationFunction.NONE







				def __init__(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__(**__lowerCAmelCase     )

								self.check_model_type(
								    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
								    if self.framework == '''tf'''
								    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING     )







				def 			A	(						self       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=""       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								# Using "" as default argument because we're going to use `top_k=None` in user code to declare
								# "No top_k"
								__magic_name__		:Dict        =      tokenizer_kwargs

								__magic_name__		:List[str]        =      {}
								if hasattr(self.model.config       ,					'''return_all_scores'''     ) and return_all_scores is None:
												__magic_name__		:int        =      self.model.config.return_all_scores

								if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) or top_k is None:
												__magic_name__		:str        =      top_k
												__magic_name__		:Any        =      False
								elif return_all_scores is not None:
												warnings.warn(
												    '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
												    ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''       ,					__lowerCAmelCase       ,					)
												if return_all_scores:
																__magic_name__		:int        =      None
												else:
																__magic_name__		:Tuple        =      1

								if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):
												__magic_name__		:str        =      ClassificationFunction[function_to_apply.upper()]

								if function_to_apply is not None:
												__magic_name__		:Union[str, Any]        =      function_to_apply
								return preprocess_params, {}, postprocess_params







				def __call__(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      super().__call__(*__lowerCAmelCase       ,					**__lowerCAmelCase     )
								# TODO try and retrieve it in a nicer way from _sanitize_parameters.
								__magic_name__		:Optional[Any]        =      '''top_k''' not in kwargs
								if isinstance(args[0]       ,					__lowerCAmelCase     ) and _legacy:
												# This pipeline is odd, and return a list when single item is run
												return [result]
								else:
												return result







				def 			A	(						self       ,					__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.framework
								if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):
												return self.tokenizer(**__lowerCAmelCase       ,					return_tensors=__lowerCAmelCase       ,					**__lowerCAmelCase     )
								elif isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) and len(__lowerCAmelCase     ) == 1 and isinstance(inputs[0]       ,					__lowerCAmelCase     ) and len(inputs[0]     ) == 2:
												# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
												return self.tokenizer(
												    text=inputs[0][0]       ,					text_pair=inputs[0][1]       ,					return_tensors=__lowerCAmelCase       ,					**__lowerCAmelCase     )
								elif isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):
												# This is likely an invalid usage of the pipeline attempting to pass text pairs.
												raise ValueError(
												    '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
												    ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''     )
								return self.tokenizer(__lowerCAmelCase       ,					return_tensors=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return self.model(**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=1       ,					__lowerCAmelCase=True     ):
								"""simple docstring"""
								# `_legacy` is used to determine if we're running the naked pipeline and in backward
								# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
								# the more natural result containing the list.
								# Default value before `set_parameters`
								if function_to_apply is None:
												if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																__magic_name__		:Union[str, Any]        =      ClassificationFunction.SIGMOID
												elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																__magic_name__		:Tuple        =      ClassificationFunction.SOFTMAX
												elif hasattr(self.model.config       ,					'''function_to_apply'''     ) and function_to_apply is None:
																__magic_name__		:Dict        =      self.model.config.function_to_apply
												else:
																__magic_name__		:Tuple        =      ClassificationFunction.NONE

								__magic_name__		:List[Any]        =      model_outputs['''logits'''][0]
								__magic_name__		:Union[str, Any]        =      outputs.numpy()

								if function_to_apply == ClassificationFunction.SIGMOID:
												__magic_name__		:int        =      sigmoid(__lowerCAmelCase     )
								elif function_to_apply == ClassificationFunction.SOFTMAX:
												__magic_name__		:Optional[int]        =      softmax(__lowerCAmelCase     )
								elif function_to_apply == ClassificationFunction.NONE:
												__magic_name__		:List[str]        =      outputs
								else:
												raise ValueError(F'''Unrecognized `function_to_apply` argument: {function_to_apply}'''     )

								if top_k == 1 and _legacy:
												return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

								__magic_name__		:Union[str, Any]        =      [
								    {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(__lowerCAmelCase     )
								]
								if not _legacy:
												dict_scores.sort(key=lambda __lowerCAmelCase     : x["score"]       ,					reverse=__lowerCAmelCase     )
												if top_k is not None:
																__magic_name__		:Optional[int]        =      dict_scores[:top_k]
								return dict_scores














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,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case = None,  ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      {}

				if train_file is not None:
								__magic_name__		:List[Any]        =      [train_file]
				if eval_file is not None:
								__magic_name__		:int        =      [eval_file]
				if test_file is not None:
								__magic_name__		:Any        =      [test_file]

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

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

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

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

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

				__magic_name__		:Optional[Any]        =      (
				    tf.data.Dataset.from_generator(
				        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:
								__magic_name__		:Optional[int]        =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

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

				__magic_name__		:Optional[int]        =      (
				    tf.data.Dataset.from_generator(
				        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:
								__magic_name__		:Dict        =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

				return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE__      :			List[Any]							=		logging.getLogger(__name__)
@dataclass
class 						lowerCamelCase_				:
				a__					       =  field(metadata={'''help''': '''Which column contains the label'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the training file'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the development file'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the test file'''} )
				a__					       =  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.'''
				        )
				    }			,				)
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







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







def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:str        =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
				__magic_name__     ,				__magic_name__     ,				__magic_name__		: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.

				__magic_name__		:Union[str, Any]        =      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,  )

				__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:int        =      get_tfds(
				    train_file=data_args.train_file,  eval_file=data_args.dev_file,  test_file=data_args.test_file,  tokenizer=snake_case,  label_column_id=data_args.label_column_id,  max_seq_length=data_args.max_seq_length,  )

				__magic_name__		:Optional[Any]        =      AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path,  num_labels=len(snake_case ),  labelaid=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():
								__magic_name__		:Dict        =      TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path,  from_pt=bool('''.bin''' in model_args.model_name_or_path ),  config=snake_case,  cache_dir=model_args.cache_dir,  )

				def compute_metrics(snake_case ) -> Dict:
								__magic_name__		:str        =      np.argmax(p.predictions,  axis=1 )

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

				# Initialize our Trainer
				__magic_name__		:str        =      TFTrainer(
				    model=snake_case,  args=snake_case,  train_dataset=snake_case,  eval_dataset=snake_case,  compute_metrics=snake_case,  )

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

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

								with open(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(snake_case )

				return results


if __name__ == "__main__":
					main()






