
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					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : PreTrainedTokenizer				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Optional[int] = None				,					):
       snake_case       :   int							=      {}

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

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

       if len(__lowerCamelCase    ) == 1:
              for k in files.keys():
                     snake_case       :   Tuple							=      ds[k].map(
                         lambda __lowerCamelCase    : 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():
                     snake_case       :   List[Any]							=      ds[k].map(
                         lambda __lowerCamelCase    : 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]:
                     snake_case       :   Dict							=      {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)

       def gen_val():
              for ex in transformed_ds[datasets.Split.VALIDATION]:
                     snake_case       :   str							=      {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_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       :   List[str]							=      labelaid[ex[label_name]]
                     yield (d, label)

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

       snake_case       :   Tuple							=      (
           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:
              snake_case       :   List[str]							=      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

       snake_case       :   Optional[int]							=      (
           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:
              snake_case       :   str							=      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

       return train_ds, val_ds, test_ds, labelaid


__lowerCamelCase							=       logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase		:
   A__					:		int						     =     field(metadata={"help": "Which column contains the label"} )
   A__					:		str						     =     field(default=A_       ,metadata={"help": "The path of the training file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,metadata={"help": "The path of the development file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,metadata={"help": "The path of the test file"} )
   A__					:		int						     =     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__					:		bool						     =     field(
       default=A_       ,metadata={"help": "Overwrite the cached training and evaluation sets"} )






@dataclass
class   UpperCAmelCase		:
   A__					:		str						     =     field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
   A__					:		bool						     =     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__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,)



def 			UpperCamelCase					(    ):
       # 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.
       snake_case       :   List[Any]							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
       snake_case				,					snake_case				,					snake_case       :   int							=      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       :   Tuple							=      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       :   Tuple							=      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				,					)

       snake_case       :   Optional[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():
              snake_case       :   int							=      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(__lowerCamelCase     : EvalPrediction    ) -> Dict:
              snake_case       :   Optional[int]							=      np.argmax(p.predictions				,					axis=1    )

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

       # Initialize our Trainer
       snake_case       :   int							=      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
       snake_case       :   int							=      {}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"    )
              snake_case       :   Any							=      trainer.evaluate()
              snake_case       :   List[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()







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 			UpperCamelCase					(    __lowerCamelCase     : List[Any]    ):
       return 1.0 / (1.0 + np.exp(-_outputs    ))


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       snake_case       :   Tuple							=      np.max(_outputs				,					axis=-1				,					keepdims=__lowerCamelCase    )
       snake_case       :   int							=      np.exp(_outputs - maxes    )
       return shifted_exp / shifted_exp.sum(axis=-1				,					keepdims=__lowerCamelCase    )






class   UpperCAmelCase		(						A_ ):
   A__					:		Any						     =     "sigmoid"
   A__					:		str						     =     "softmax"
   A__					:		int						     =     "none"






@add_end_docstrings(
    A_       ,r"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "       ,)
class   UpperCAmelCase		(						A_ ):
   A__					:		int						     =     False
   A__					:		Union[str, Any]						     =     ClassificationFunction.NONE



   def __init__(self		:					List[str]    ,						**snake_case__		:					int )		->	str:


          '''simple docstring'''


          super().__init__(**snake_case__ )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					List[str]=None    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Union[str, Any]=""    ,						**snake_case__		:					List[str] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      tokenizer_kwargs

          snake_case       :   List[Any]							=      {}
          if hasattr(self.model.config    ,						"return_all_scores" ) and return_all_scores is None:
                 snake_case       :   Optional[int]							=      self.model.config.return_all_scores

          if isinstance(snake_case__    ,						snake_case__ ) or top_k is None:
                 snake_case       :   List[Any]							=      top_k
                 snake_case       :   str							=      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`."    ,						snake_case__    ,						)
                 if return_all_scores:
                        snake_case       :   List[str]							=      None
                 else:
                        snake_case       :   Optional[int]							=      1

          if isinstance(snake_case__    ,						snake_case__ ):
                 snake_case       :   Dict							=      ClassificationFunction[function_to_apply.upper()]

          if function_to_apply is not None:
                 snake_case       :   Optional[int]							=      function_to_apply
          return preprocess_params, {}, postprocess_params



   def __call__(self		:					Dict    ,						*snake_case__		:					List[str]    ,						**snake_case__		:					int )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      super().__call__(*snake_case__    ,						**snake_case__ )
          # TODO try and retrieve it in a nicer way from _sanitize_parameters.
          snake_case       :   Tuple							=      "top_k" not in kwargs
          if isinstance(args[0]    ,						snake_case__ ) and _legacy:
                 # This pipeline is odd, and return a list when single item is run
                 return [result]
          else:
                 return result



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					Tuple    ,						**snake_case__		:					Union[str, Any] )		->	Dict[str, GenericTensor]:


          '''simple docstring'''


          snake_case       :   int							=      self.framework
          if isinstance(snake_case__    ,						snake_case__ ):
                 return self.tokenizer(**snake_case__    ,						return_tensors=snake_case__    ,						**snake_case__ )
          elif isinstance(snake_case__    ,						snake_case__ ) and len(snake_case__ ) == 1 and isinstance(inputs[0]    ,						snake_case__ ) 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=snake_case__    ,						**snake_case__ )
          elif isinstance(snake_case__    ,						snake_case__ ):
                 # 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(snake_case__    ,						return_tensors=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					Union[str, Any] )		->	int:


          '''simple docstring'''


          return self.model(**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					List[str]=None    ,						snake_case__		:					Dict=1    ,						snake_case__		:					Tuple=True )		->	str:


          '''simple docstring'''


          if function_to_apply is None:
                 if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                        snake_case       :   Tuple							=      ClassificationFunction.SIGMOID
                 elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                        snake_case       :   Tuple							=      ClassificationFunction.SOFTMAX
                 elif hasattr(self.model.config    ,						"function_to_apply" ) and function_to_apply is None:
                        snake_case       :   Tuple							=      self.model.config.function_to_apply
                 else:
                        snake_case       :   int							=      ClassificationFunction.NONE

          snake_case       :   Any							=      model_outputs["logits"][0]
          snake_case       :   List[str]							=      outputs.numpy()

          if function_to_apply == ClassificationFunction.SIGMOID:
                 snake_case       :   Optional[Any]							=      sigmoid(snake_case__ )
          elif function_to_apply == ClassificationFunction.SOFTMAX:
                 snake_case       :   Union[str, Any]							=      softmax(snake_case__ )
          elif function_to_apply == ClassificationFunction.NONE:
                 snake_case       :   Optional[Any]							=      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()}

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





