

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
_lowerCAmelCase  :      List[Any]			 =False
_lowerCAmelCase  :      Optional[Any]			 =False



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	return TrainCommand(SCREAMING_SNAKE_CASE      )

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					@staticmethod
					def    _UpperCAmelCase    (				lowerCamelCase__						):
						UpperCAmelCase__:	str									= parser.add_parser("train"				,      help="CLI tool to train a model on a task."						)

						train_parser.add_argument(
						    "--train_data"				,      type=lowerCamelCase__				,      required=lowerCamelCase__				,      help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."				,      )
						train_parser.add_argument(
						    "--column_label"				,      type=lowerCamelCase__				,      default=0				,      help="Column of the dataset csv file with example labels."						)
						train_parser.add_argument(
						    "--column_text"				,      type=lowerCamelCase__				,      default=1				,      help="Column of the dataset csv file with example texts."						)
						train_parser.add_argument(
						    "--column_id"				,      type=lowerCamelCase__				,      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=lowerCamelCase__				,      default=""				,      help="path to validation dataset."						)
						train_parser.add_argument(
						    "--validation_split"				,      type=lowerCamelCase__				,      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=lowerCamelCase__				,      default="./"				,      help="path to saved the trained model."						)

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





					def __init__(				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	List[str]									= logging.get_logger("transformers-cli/training"						)

						UpperCAmelCase__:	Union[str, Any]									= "tf" if is_tf_available() else "torch"

						os.makedirs(args.output				,      exist_ok=lowerCamelCase__						)
						UpperCAmelCase__:	List[Any]									= args.output

						UpperCAmelCase__:	Tuple									= args.column_label
						UpperCAmelCase__:	str									= args.column_text
						UpperCAmelCase__:	Any									= args.column_id

						self.logger.info(F"Loading {args.task} pipeline for {args.model}"						)
						if args.task == "text_classification":
							UpperCAmelCase__:	List[Any]									= 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}"						)
						UpperCAmelCase__:	str									= 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				,      )
						UpperCAmelCase__:	List[str]									= None
						if args.validation_data:
							self.logger.info(F"Loading validation dataset from {args.validation_data}"						)
							UpperCAmelCase__:	Tuple									= 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				,      )

						UpperCAmelCase__:	Tuple									= args.validation_split
						UpperCAmelCase__:	List[Any]									= args.train_batch_size
						UpperCAmelCase__:	str									= args.valid_batch_size
						UpperCAmelCase__:	Any									= args.learning_rate
						UpperCAmelCase__:	Any									= args.adam_epsilon





					def    _UpperCAmelCase    (				self						):
						if self.framework == "tf":
							return self.run_tf()
						return self.run_torch()





					def    _UpperCAmelCase    (				self						):
						raise NotImplementedError





					def    _UpperCAmelCase    (				self						):
						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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase  :      Tuple			 ={
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Tuple			 =["""MobileNetV2FeatureExtractor"""]
					_lowerCAmelCase  :      Optional[int]			 =["""MobileNetV2ImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[str]			 =[
					    """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileNetV2ForImageClassification""",
					    """MobileNetV2ForSemanticSegmentation""",
					    """MobileNetV2Model""",
					    """MobileNetV2PreTrainedModel""",
					    """load_tf_weights_in_mobilenet_v2""",
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileNetVaConfig,
					    MobileNetVaOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
										from .image_processing_mobilenet_va import MobileNetVaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilenet_va import (
										    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaForSemanticSegmentation,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

					_lowerCAmelCase  :      Any			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)