
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
   raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
_A						      =				False
_A						      =				False



def 		lowercase_    (     A__	)							->							Union[str, Any]:
    """simple docstring"""

    return TrainCommand(A__	)







class   lowerCamelCase	(						A_						):





     @staticmethod
     def 	UpperCAmelCase(_A   :					ArgumentParser     )		->		int:
         snake_case				  =      parser.add_parser("train"       ,					help="CLI tool to train a model on a task."     )

         train_parser.add_argument(
             "--train_data"       ,					type=_A       ,					required=_A       ,					help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."       ,					)
         train_parser.add_argument(
             "--column_label"       ,					type=_A       ,					default=0       ,					help="Column of the dataset csv file with example labels."     )
         train_parser.add_argument(
             "--column_text"       ,					type=_A       ,					default=1       ,					help="Column of the dataset csv file with example texts."     )
         train_parser.add_argument(
             "--column_id"       ,					type=_A       ,					default=2       ,					help="Column of the dataset csv file with example ids."     )
         train_parser.add_argument(
             "--skip_first_row"       ,					action="store_true"       ,					help="Skip the first row of the csv file (headers)."     )

         train_parser.add_argument("--validation_data"       ,					type=_A       ,					default=""       ,					help="path to validation dataset."     )
         train_parser.add_argument(
             "--validation_split"       ,					type=_A       ,					default=0.1       ,					help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."       ,					)

         train_parser.add_argument("--output"       ,					type=_A       ,					default="./"       ,					help="path to saved the trained model."     )

         train_parser.add_argument(
             "--task"       ,					type=_A       ,					default="text_classification"       ,					help="Task to train the model on."     )
         train_parser.add_argument(
             "--model"       ,					type=_A       ,					default="bert-base-uncased"       ,					help="Model's name or path to stored model."     )
         train_parser.add_argument("--train_batch_size"       ,					type=_A       ,					default=3_2       ,					help="Batch size for training."     )
         train_parser.add_argument("--valid_batch_size"       ,					type=_A       ,					default=6_4       ,					help="Batch size for validation."     )
         train_parser.add_argument("--learning_rate"       ,					type=_A       ,					default=3E-5       ,					help="Learning rate."     )
         train_parser.add_argument("--adam_epsilon"       ,					type=_A       ,					default=1E-08       ,					help="Epsilon for Adam optimizer."     )
         train_parser.set_defaults(func=_A     )





     def __init__(self   :					Tuple       ,					_A   :					Namespace     )		->		int:
         snake_case				  =      logging.get_logger("transformers-cli/training"     )

         snake_case				  =      "tf" if is_tf_available() else "torch"

         os.makedirs(args.output       ,					exist_ok=_A     )
         snake_case				  =      args.output

         snake_case				  =      args.column_label
         snake_case				  =      args.column_text
         snake_case				  =      args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'     )
         if args.task == "text_classification":
             snake_case				  =      TextClassificationPipeline.from_pretrained(args.model     )
         elif args.task == "token_classification":
             raise NotImplementedError
         elif args.task == "question_answering":
             raise NotImplementedError

         self.logger.info(f'Loading dataset from {args.train_data}'     )
         snake_case				  =      Processor.create_from_csv(
             args.train_data       ,					column_label=args.column_label       ,					column_text=args.column_text       ,					column_id=args.column_id       ,					skip_first_row=args.skip_first_row       ,					)
         snake_case				  =      None
         if args.validation_data:
             self.logger.info(f'Loading validation dataset from {args.validation_data}'     )
             snake_case				  =      Processor.create_from_csv(
                 args.validation_data       ,					column_label=args.column_label       ,					column_text=args.column_text       ,					column_id=args.column_id       ,					skip_first_row=args.skip_first_row       ,					)

         snake_case				  =      args.validation_split
         snake_case				  =      args.train_batch_size
         snake_case				  =      args.valid_batch_size
         snake_case				  =      args.learning_rate
         snake_case				  =      args.adam_epsilon





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Any:
         if self.framework == "tf":
             return self.run_tf()
         return self.run_torch()





     def 	UpperCAmelCase(self   :					Any     )		->		Any:
         raise NotImplementedError





     def 	UpperCAmelCase(self   :					List[str]     )		->		Union[str, Any]:
         self.pipeline.fit(
             self.train_dataset       ,					validation_data=self.valid_dataset       ,					validation_split=self.validation_split       ,					learning_rate=self.learning_rate       ,					adam_epsilon=self.adam_epsilon       ,					train_batch_size=self.train_batch_size       ,					valid_batch_size=self.valid_batch_size       ,					)

         # Save trained pipeline
         self.pipeline.save_pretrained(self.output     )



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Dict     =	"canine"





     def __init__(self   :					List[Any]       ,					_A   :					Union[str, Any]=7_6_8       ,					_A   :					Any=1_2       ,					_A   :					List[Any]=1_2       ,					_A   :					List[Any]=3_0_7_2       ,					_A   :					Dict="gelu"       ,					_A   :					Optional[Any]=0.1       ,					_A   :					Tuple=0.1       ,					_A   :					str=1_6_3_8_4       ,					_A   :					Union[str, Any]=1_6       ,					_A   :					Any=0.02       ,					_A   :					List[str]=1E-12       ,					_A   :					Union[str, Any]=0       ,					_A   :					Dict=0Xe0_00       ,					_A   :					List[Any]=0Xe0_01       ,					_A   :					int=4       ,					_A   :					str=4       ,					_A   :					Optional[int]=8       ,					_A   :					Optional[Any]=1_6_3_8_4       ,					_A   :					Optional[Any]=1_2_8       ,					**_A   :					Union[str, Any]       ,					)		->		Union[str, Any]:
         super().__init__(pad_token_id=_A       ,					bos_token_id=_A       ,					eos_token_id=_A       ,					**_A     )

         snake_case				  =      max_position_embeddings
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_act
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      initializer_range
         snake_case				  =      type_vocab_size
         snake_case				  =      layer_norm_eps

         # Character config:
         snake_case				  =      downsampling_rate
         snake_case				  =      upsampling_kernel_size
         snake_case				  =      num_hash_functions
         snake_case				  =      num_hash_buckets
         snake_case				  =      local_transformer_stride

