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					:	int       =  False
__lowerCamelCase					:	Any       =  False




def       _snake_case	(   lowerCAmelCase							:   Namespace	):
		"""simple docstring"""







		return TrainCommand(lowerCAmelCase	)







class 		a__						(					A__					):

					@staticmethod
					def 				__UpperCamelCase   (    _A      :       ArgumentParser    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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=32,help="Batch size for training."    )
							train_parser.add_argument("--valid_batch_size",type=_A,default=64,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      :       Optional[int],_A      :       Namespace    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    logging.get_logger("transformers-cli/training"    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    "tf" if is_tf_available() else "torch"

							os.makedirs(args.output,exist_ok=_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    args.output

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    args.column_label
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    args.column_text
							SCREAMING_SNAKE_CASE_  :		int				      =    args.column_id

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

							SCREAMING_SNAKE_CASE_  :		str				      =    args.validation_split
							SCREAMING_SNAKE_CASE_  :		int				      =    args.train_batch_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    args.valid_batch_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    args.learning_rate
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    args.adam_epsilon

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							raise NotImplementedError






					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							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    )




"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="data2vec-vision"

       def __init__(						self		,     snake_case__=768		,     snake_case__=12		,     snake_case__=12		,     snake_case__=3_072		,     snake_case__="gelu"		,     snake_case__=0.0		,     snake_case__=0.0		,     snake_case__=0.02		,     snake_case__=1e-12		,     snake_case__=224		,     snake_case__=16		,     snake_case__=3		,     snake_case__=False		,     snake_case__=False		,     snake_case__=False		,     snake_case__=False		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=True		,     snake_case__=[3, 5, 7, 11]		,     snake_case__=[1, 2, 3, 6]		,     snake_case__=True		,     snake_case__=0.4		,     snake_case__=256		,     snake_case__=1		,     snake_case__=False		,     snake_case__=255		,     **snake_case__		,     ):






         """simple docstring"""





         super().__init__(**snake_case__						)

         lowerCAmelCase     :     Tuple								=							hidden_size
         lowerCAmelCase     :     List[Any]								=							num_hidden_layers
         lowerCAmelCase     :     Tuple								=							num_attention_heads
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Optional[int]								=							hidden_act
         lowerCAmelCase     :     Dict								=							hidden_dropout_prob
         lowerCAmelCase     :     Optional[Any]								=							attention_probs_dropout_prob
         lowerCAmelCase     :     int								=							initializer_range
         lowerCAmelCase     :     Dict								=							layer_norm_eps

         lowerCAmelCase     :     Optional[int]								=							image_size
         lowerCAmelCase     :     Optional[Any]								=							patch_size
         lowerCAmelCase     :     Optional[Any]								=							num_channels
         lowerCAmelCase     :     Union[str, Any]								=							use_mask_token
         lowerCAmelCase     :     str								=							use_absolute_position_embeddings
         lowerCAmelCase     :     Any								=							use_relative_position_bias
         lowerCAmelCase     :     List[str]								=							use_shared_relative_position_bias
         lowerCAmelCase     :     str								=							layer_scale_init_value
         lowerCAmelCase     :     Union[str, Any]								=							drop_path_rate
         lowerCAmelCase     :     Any								=							use_mean_pooling
         # decode head attributes (semantic segmentation)
         lowerCAmelCase     :     Optional[int]								=							out_indices
         lowerCAmelCase     :     Union[str, Any]								=							pool_scales
         # auxiliary head attributes (semantic segmentation)
         lowerCAmelCase     :     str								=							use_auxiliary_head
         lowerCAmelCase     :     int								=							auxiliary_loss_weight
         lowerCAmelCase     :     Tuple								=							auxiliary_channels
         lowerCAmelCase     :     List[str]								=							auxiliary_num_convs
         lowerCAmelCase     :     Tuple								=							auxiliary_concat_input
         lowerCAmelCase     :     List[str]								=							semantic_loss_ignore_index


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   =version.parse("1.11"				)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]						)







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 1e-4





