



'''simple docstring'''

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
UpperCAmelCase     		=   False
UpperCAmelCase     		=   False






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Namespace							)      ->						Tuple:







 """simple docstring"""
 return TrainCommand(_SCREAMING_SNAKE_CASE							)





class __snake_case(				__lowercase ):


      '''simple docstring'''



      @staticmethod
      def     __snake_case   ( A_				)     ->     List[Any]:
       lowerCAmelCase   =     parser.add_parser("""train"""     ,	help="""CLI tool to train a model on a task."""				)

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

       train_parser.add_argument(
           """--task"""     ,	type=UpperCAmelCase__     ,	default="""text_classification"""     ,	help="""Task to train the model on."""				)
       train_parser.add_argument(
           """--model"""     ,	type=UpperCAmelCase__     ,	default="""bert-base-uncased"""     ,	help="""Model\'s name or path to stored model."""				)
       train_parser.add_argument("""--train_batch_size"""     ,	type=UpperCAmelCase__     ,	default=32     ,	help="""Batch size for training."""				)
       train_parser.add_argument("""--valid_batch_size"""     ,	type=UpperCAmelCase__     ,	default=64     ,	help="""Batch size for validation."""				)
       train_parser.add_argument("""--learning_rate"""     ,	type=UpperCAmelCase__     ,	default=3e-5     ,	help="""Learning rate."""				)
       train_parser.add_argument("""--adam_epsilon"""     ,	type=UpperCAmelCase__     ,	default=1e-08     ,	help="""Epsilon for Adam optimizer."""				)
       train_parser.set_defaults(func=UpperCAmelCase__				)


      def __init__( self     ,	A_				)     ->     Union[str, Any]:
       lowerCAmelCase   =     logging.get_logger("""transformers-cli/training"""				)

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

       os.makedirs(args.output     ,	exist_ok=UpperCAmelCase__				)
       lowerCAmelCase   =     args.output

       lowerCAmelCase   =     args.column_label
       lowerCAmelCase   =     args.column_text
       lowerCAmelCase   =     args.column_id

       self.logger.info(f'Loading {args.task} pipeline for {args.model}'				)
       if args.task == "text_classification":
        lowerCAmelCase   =     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}'				)
       lowerCAmelCase   =     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     ,	)
       lowerCAmelCase   =     None
       if args.validation_data:
        self.logger.info(f'Loading validation dataset from {args.validation_data}'				)
        lowerCAmelCase   =     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     ,	)

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


      def     __snake_case   ( self				)     ->     Any:
       if self.framework == "tf":
        return self.run_tf()
       return self.run_torch()


      def     __snake_case   ( self				)     ->     Any:
       raise NotImplementedError




      def     __snake_case   ( self				)     ->     int:
       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'''

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)