





from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__UpperCAmelCase       :   List[Any]				       =							logging.get_logger(__name__)  # pylint: disable=invalid-name







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    List[str]:
  if not path:
    return "pipe"

  for ext in PipelineDataFormat.SUPPORTED_FORMATS:
    if path.endswith(__SCREAMING_SNAKE_CASE):
      return ext

  raise Exception(
      F'''Unable to determine file format from file extension {path}. '''
      F'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}''')







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  __snake_case:						Union[str, Any]   =						pipeline(
      task=args.task					,					model=args.model if args.model else None					,					config=args.config					,					tokenizer=args.tokenizer					,					device=args.device					,					)
  __snake_case:						str   =						try_infer_format_from_ext(args.input) if args.format == """infer""" else args.format
  __snake_case:						Dict   =						PipelineDataFormat.from_str(
      format=__SCREAMING_SNAKE_CASE					,					output_path=args.output					,					input_path=args.input					,					column=args.column if args.column else nlp.default_input_names					,					overwrite=args.overwrite					,					)
  return RunCommand(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Dict							,				A :    Union[str, Any]							,				A :    List[str]		):
         __snake_case:						Optional[int]   =						nlp
         __snake_case:						Optional[Any]   =						reader




       @staticmethod
       def 							UpperCAmelCase__ (  A :    Union[str, Any]		):
         __snake_case:						Any   =						parser.add_parser("""run"""							,				help="""Run a pipeline through the CLI"""		)
         run_parser.add_argument("""--task"""							,				choices=get_supported_tasks()							,				help="""Task to run"""		)
         run_parser.add_argument("""--input"""							,				type=A							,				help="""Path to the file to use for inference"""		)
         run_parser.add_argument("""--output"""							,				type=A							,				help="""Path to the file that will be used post to write results."""		)
         run_parser.add_argument("""--model"""							,				type=A							,				help="""Name or path to the model to instantiate."""		)
         run_parser.add_argument("""--config"""							,				type=A							,				help="""Name or path to the model\'s config to instantiate."""		)
         run_parser.add_argument(
             """--tokenizer"""							,				type=A							,				help="""Name of the tokenizer to use. (default: same as the model name)"""		)
         run_parser.add_argument(
             """--column"""							,				type=A							,				help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""							,				)
         run_parser.add_argument(
             """--format"""							,				type=A							,				default="""infer"""							,				choices=PipelineDataFormat.SUPPORTED_FORMATS							,				help="""Input format to read from"""							,				)
         run_parser.add_argument(
             """--device"""							,				type=A							,				default=-1							,				help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""							,				)
         run_parser.add_argument("""--overwrite"""							,				action="""store_true"""							,				help="""Allow overwriting the output file."""		)
         run_parser.set_defaults(func=A		)






       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case  ,   __snake_case:						str   =						self._nlp, []

         for entry in self._reader:
           __snake_case:						int   =						nlp(**A		) if self._reader.is_multi_columns else nlp(A		)
           if isinstance(A							,				A		):
             outputs.append(A		)
           else:
             outputs += output

        # Saving data
         if self._nlp.binary_output:
           __snake_case:						List[str]   =						self._reader.save_binary(A		)
           logger.warning(f'''Current pipeline requires output to be in binary format, saving at {binary_path}'''		)
         else:
           self._reader.save(A		)








import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


UpperCAmelCase	      = logging.getLogger(__name__)







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Optional[int]							 =       """sequence-classification"""





	def __init__(       self		,    snake_case       ):
		if type(snake_case       ) == dict:
			lowercase       =    Namespace(**snake_case       )
		lowercase       =    glue_output_modes[hparams.task]
		lowercase       =    glue_tasks_num_labels[hparams.task]

		super().__init__(snake_case		,    snake_case		,    self.mode       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return self.model(**snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

		if self.config.model_type not in ["distilbert", "bart"]:
			lowercase       =    batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

		lowercase       =    self(**snake_case       )
		lowercase       =    outputs[0]

		lowercase       =    self.trainer.lr_schedulers[0]['scheduler']
		lowercase       =    {'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
		return {"loss": loss, "log": tensorboard_logs}





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.hparams
		lowercase       =    processors[args.task]()
		lowercase       =    processor.get_labels()

		for mode in ["train", "dev"]:
			lowercase       =    self._feature_file(snake_case       )
			if os.path.exists(snake_case       ) and not args.overwrite_cache:
				logger.info('Loading features from cached file %s'		,    snake_case       )
			else:
				logger.info('Creating features from dataset file at %s'		,    args.data_dir       )
				lowercase       =    (
				    processor.get_dev_examples(args.data_dir       )
				    if mode == 'dev'
				    else processor.get_train_examples(args.data_dir       )
				)
				lowercase       =    convert_examples_to_features(
				    snake_case		,    self.tokenizer		,    max_length=args.max_seq_length		,    label_list=self.labels		,    output_mode=args.glue_output_mode		,    )
				logger.info('Saving features into cached file %s'		,    snake_case       )
				torch.save(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = False       ):
		lowercase       =    'dev' if mode == 'test' else mode

		lowercase       =    self._feature_file(snake_case       )
		logger.info('Loading features from cached file %s'		,    snake_case       )
		lowercase       =    torch.load(snake_case       )
		lowercase       =    torch.tensor([f.input_ids for f in features]		,    dtype=torch.long       )
		lowercase       =    torch.tensor([f.attention_mask for f in features]		,    dtype=torch.long       )
		lowercase       =    torch.tensor([f.token_type_ids for f in features]		,    dtype=torch.long       )
		if self.hparams.glue_output_mode == "classification":
			lowercase       =    torch.tensor([f.label for f in features]		,    dtype=torch.long       )
		elif self.hparams.glue_output_mode == "regression":
			lowercase       =    torch.tensor([f.label for f in features]		,    dtype=torch.float       )

		return DataLoader(
		    TensorDataset(snake_case		,    snake_case		,    snake_case		,    snake_case       )		,    batch_size=snake_case		,    shuffle=snake_case		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

		if self.config.model_type not in ["distilbert", "bart"]:
			lowercase       =    batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

		lowercase       =    self(**snake_case       )
		lowercase  ,					lowercase       =    outputs[:2]
		lowercase       =    logits.detach().cpu().numpy()
		lowercase       =    inputs['labels'].detach().cpu().numpy()

		return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase       =    torch.stack([x['val_loss'] for x in outputs]       ).mean().detach().cpu().item()
		lowercase       =    np.concatenate([x['pred'] for x in outputs]		,    axis=0       )

		if self.hparams.glue_output_mode == "classification":
			lowercase       =    np.argmax(snake_case		,    axis=1       )
		elif self.hparams.glue_output_mode == "regression":
			lowercase       =    np.squeeze(snake_case       )

		lowercase       =    np.concatenate([x['target'] for x in outputs]		,    axis=0       )
		lowercase       =    [[] for _ in range(out_label_ids.shape[0]       )]
		lowercase       =    [[] for _ in range(out_label_ids.shape[0]       )]

		lowercase       =    {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,    snake_case		,    snake_case       )}

		lowercase       =    dict(results.items()       )
		lowercase       =    results
		return ret, preds_list, out_label_list





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase  ,					lowercase  ,					lowercase       =    self._eval_end(snake_case       )
		lowercase       =    ret['log']
		return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase  ,					lowercase  ,					lowercase       =    self._eval_end(snake_case       )
		lowercase       =    ret['log']
		# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
		return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	@staticmethod
	def 	SCREAMING_SNAKE_CASE__						(       snake_case		,    snake_case       ):
		BaseTransformer.add_model_specific_args(snake_case		,    snake_case       )
		parser.add_argument(
		    '--max_seq_length'		,    default=128		,    type=snake_case		,    help=(
		        'The maximum total input sequence length after tokenization. Sequences longer '
		        'than this will be truncated, sequences shorter will be padded.'
		    )		,    )

		parser.add_argument(
		    '--task'		,    default=''		,    type=snake_case		,    required=snake_case		,    help='The GLUE task to run'		,    )
		parser.add_argument(
		    '--gpus'		,    default=0		,    type=snake_case		,    help='The number of GPUs allocated for this, it is by default 0 meaning none'		,    )

		parser.add_argument(
		    '--overwrite_cache'		,    action='store_true'		,    help='Overwrite the cached training and evaluation sets'       )

		return parser






def 							UpperCAmelCase_		(							):
	lowercase       =    argparse.ArgumentParser()
	add_generic_args(__SCREAMING_SNAKE_CASE ,							os.getcwd()							)
	lowercase       =    GLUETransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE ,							os.getcwd()							)
	lowercase       =    parser.parse_args()

	# If output_dir not provided, a folder will be generated in pwd
	if args.output_dir is None:
		lowercase       =    os.path.join(
		    './results' ,							F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'							)}''' ,							)
		os.makedirs(args.output_dir							)

	lowercase       =    GLUETransformer(__SCREAMING_SNAKE_CASE							)
	lowercase       =    generic_train(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	# Optionally, predict on dev set and write to output_dir
	if args.do_predict:
		lowercase       =    sorted(glob.glob(os.path.join(args.output_dir ,							'checkpoint-epoch=*.ckpt'							) ,							recursive=__SCREAMING_SNAKE_CASE							)							)
		lowercase       =    model.load_from_checkpoint(checkpoints[-1]							)
		return trainer.test(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	main()

