

'''simple docstring'''




import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int  ):
  UpperCAmelCase  							=		filter(lambda SCREAMING_SNAKE_CASE  : p.requires_grad    ,				model.parameters()  )
  UpperCAmelCase  							=		sum([np.prod(p.size()  ) for p in model_parameters]  )
  return params


_a			:	str								=  logging.getLogger(__name__)



class     lowercase_		(						pl.Callback	):

  '''simple docstring'''





  def   snake_case_     (       self				,	a_				,	a_       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		{F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
    pl_module.logger.log_metrics(a_       )
  @rank_zero_only
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_=True       )							->			None:


    """simple docstring"""
    logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''       )
    UpperCAmelCase  							=		trainer.callback_metrics
    trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}       )
    # Log results
    UpperCAmelCase  							=		Path(pl_module.hparams.output_dir       )
    if type_path == "test":
      UpperCAmelCase  							=		od / 'test_results.txt'
      UpperCAmelCase  							=		od / 'test_generations.txt'
    else:
      # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
      # If people want this it will be easy enough to add back.
      UpperCAmelCase  							=		od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
      UpperCAmelCase  							=		od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
      results_file.parent.mkdir(exist_ok=a_       )
      generations_file.parent.mkdir(exist_ok=a_       )
    with open(a_				,	'a+'       ) as writer:
      for key in sorted(a_       ):
        if key in ["log", "progress_bar", "preds"]:
          continue
        UpperCAmelCase  							=		metrics[key]
        if isinstance(a_				,	torch.Tensor       ):
          UpperCAmelCase  							=		val.item()
        UpperCAmelCase  							=		F'''{key}: {val:.6f}\n'''
        writer.write(a_       )

    if not save_generations:
      return

    if "preds" in metrics:
      UpperCAmelCase  							=		'\n'.join(metrics['preds']       )
      generations_file.open('w+'       ).write(a_       )
  @rank_zero_only
  def   snake_case_     (       self				,	a_				,	a_       )							->			Optional[Any]:


    """simple docstring"""
    try:
      UpperCAmelCase  							=		pl_module.model.model.num_parameters()
    except AttributeError:
      UpperCAmelCase  							=		pl_module.model.num_parameters()

    UpperCAmelCase  							=		count_trainable_parameters(a_       )
    # mp stands for million parameters
    trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}       )
  @rank_zero_only
  def   snake_case_     (       self				,	a_				,	a_       )							->			List[Any]:


    """simple docstring"""
    save_json(pl_module.metrics				,	pl_module.metrics_save_path       )
    return self._write_logs(a_				,	a_				,	'test'       )





  @rank_zero_only
  def   snake_case_     (       self				,	a_				,	a_       )							->			Dict:


    """simple docstring"""
    save_json(pl_module.metrics				,	pl_module.metrics_save_path       )
    # Uncommenting this will save val generations
    # return self._write_logs(trainer, pl_module, "valid")




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Union[str, Any]    ,				SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				int=1    ,				SCREAMING_SNAKE_CASE :				List[Any]=False  ):
  if metric == "rouge2":
    UpperCAmelCase  							=		'{val_avg_rouge2:.4f}-{step_count}'
  elif metric == "bleu":
    UpperCAmelCase  							=		'{val_avg_bleu:.4f}-{step_count}'
  elif metric == "loss":
    UpperCAmelCase  							=		'{val_avg_loss:.4f}-{step_count}'
  else:
    raise NotImplementedError(
        f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
        ' this function.'  )

  UpperCAmelCase  							=		ModelCheckpoint(
      dirpath=SCREAMING_SNAKE_CASE    ,				filename=SCREAMING_SNAKE_CASE    ,				monitor=f'''val_{metric}'''    ,				mode='min' if 'loss' in metric else 'max'    ,				save_top_k=SCREAMING_SNAKE_CASE    ,				)
  return checkpoint_callback



def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Union[str, Any]    ,				SCREAMING_SNAKE_CASE :				Tuple  ):
  return EarlyStopping(
      monitor=f'''val_{metric}'''    ,				mode='min' if 'loss' in metric else 'max'    ,				patience=SCREAMING_SNAKE_CASE    ,				verbose=SCREAMING_SNAKE_CASE    ,				)





'''simple docstring'''




import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
  np.random.seed(args.seed  )
  torch.manual_seed(args.seed  )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed  )


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		argparse.ArgumentParser()
  parser.add_argument(
      '--model_name_or_path'    ,				default=SCREAMING_SNAKE_CASE    ,				type=SCREAMING_SNAKE_CASE    ,				required=SCREAMING_SNAKE_CASE    ,				)
  parser.add_argument('--prompt'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''  )
  parser.add_argument('--length'    ,				type=SCREAMING_SNAKE_CASE    ,				default=20  )
  parser.add_argument('--stop_token'    ,				type=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				help='Token at which text generation is stopped'  )
  parser.add_argument(
      '--temperature'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='temperature of 1.0 has no effect, lower tend toward greedy sampling'    ,				)
  parser.add_argument(
      '--repetition_penalty'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='primarily useful for CTRL model; in that case, use 1.2'  )
  parser.add_argument('--k'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0  )
  parser.add_argument('--penalty_alpha'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.0  )
  parser.add_argument('--p'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.9  )

  parser.add_argument('--prefix'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Text added prior to input.'  )
  parser.add_argument('--padding_text'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Deprecated, the use of `--prefix` is preferred.'  )
  parser.add_argument('--xlm_language'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Optional language when used with the XLM model.'  )

  parser.add_argument('--seed'    ,				type=SCREAMING_SNAKE_CASE    ,				default=42    ,				help='random seed for initialization'  )
  parser.add_argument('--no_cuda'    ,				action='store_true'    ,				help='Avoid using CUDA when available'  )
  parser.add_argument(
      '--fp16'    ,				action='store_true'    ,				help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'    ,				)
  UpperCAmelCase  							=		parser.parse_args()

  UpperCAmelCase  							=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )
  UpperCAmelCase  							=		0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''  )

  set_seed(SCREAMING_SNAKE_CASE  )

  # Initialize the model and tokenizer
  UpperCAmelCase  							=		AutoTokenizer.from_pretrained(args.model_name_or_path  )
  UpperCAmelCase  							=		AutoModelForCausalLM.from_pretrained(args.model_name_or_path  )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device  )

  if args.fpaa:
    model.half()

  logger.info(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		args.prompt if args.prompt else input('Model prompt >>> '  )

  UpperCAmelCase  							=		tokenizer(SCREAMING_SNAKE_CASE    ,				return_tensors='pt'    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		{key: value.to(args.device  ) for key, value in inputs.items()}

  UpperCAmelCase  							=		model.generate(
      **SCREAMING_SNAKE_CASE    ,				max_length=args.length + len(inputs['input_ids'][0]  )    ,				penalty_alpha=args.penalty_alpha    ,				top_k=args.k    ,				)

  UpperCAmelCase  							=		[]
  for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE  ):
    print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''  )
    UpperCAmelCase  							=		generated_sequence.tolist()

    # Decode text
    UpperCAmelCase  							=		tokenizer.decode(SCREAMING_SNAKE_CASE    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )

    # Remove all text after the stop token
    UpperCAmelCase  							=		text[: text.find(args.stop_token  ) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    UpperCAmelCase  							=		(
        prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE  )  ) :]
    )

    generated_sequences.append(SCREAMING_SNAKE_CASE  )
    print(SCREAMING_SNAKE_CASE  )

  return generated_sequences


if __name__ == "__main__":
  main()


