






'''simple docstring'''

import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


SCREAMING_SNAKE_CASE__											=     getLogger(__name__)


SCREAMING_SNAKE_CASE__											=     'cuda' if torch.cuda.is_available() else 'cpu'







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase = 8	,		__UpperCamelCase = DEFAULT_DEVICE	,		__UpperCamelCase=False	,		__UpperCamelCase="summarization"	,		__UpperCamelCase=None	,		**__UpperCamelCase	,		)->				Dict:
						UpperCamelCase       						=      Path(__UpperCamelCase					).open("""w"""	,		encoding="""utf-8"""					)
						UpperCamelCase       						=      str(__UpperCamelCase					)
						UpperCamelCase       						=      AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase					).to(__UpperCamelCase					)
						if fpaa:
												UpperCamelCase       						=      model.half()

						UpperCamelCase       						=      AutoTokenizer.from_pretrained(__UpperCamelCase					)
						logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"					)  # if this is wrong, check config.model_type.

						UpperCamelCase       						=      time.time()
						# update config with task specific params
						use_task_specific_params(__UpperCamelCase	,		__UpperCamelCase					)
						if prefix is None:
												UpperCamelCase       						=      prefix or getattr(model.config	,		"""prefix"""	,		""""""					) or """"""
						for examples_chunk in tqdm(list(chunks(__UpperCamelCase	,		__UpperCamelCase					)					)					):
												UpperCamelCase       						=      [prefix + text for text in examples_chunk]
												UpperCamelCase       						=      tokenizer(__UpperCamelCase	,		return_tensors="""pt"""	,		truncation=__UpperCamelCase	,		padding="""longest"""					).to(__UpperCamelCase					)
												UpperCamelCase       						=      model.generate(
												    input_ids=batch.input_ids	,		attention_mask=batch.attention_mask	,		**__UpperCamelCase	,		)
												UpperCamelCase       						=      tokenizer.batch_decode(__UpperCamelCase	,		skip_special_tokens=__UpperCamelCase	,		clean_up_tokenization_spaces=__UpperCamelCase					)
												for hypothesis in dec:
																		fout.write(hypothesis + """\n"""					)
																		fout.flush()
						fout.close()
						UpperCamelCase       						=      int(time.time() - start_time					)  # seconds
						UpperCamelCase       						=      len(__UpperCamelCase					)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs	,		4					)}







def 					lowercase__				(   )->				Dict:
						return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""					)







def 					lowercase__				(   __UpperCamelCase=True					)->				Optional[int]:
						UpperCamelCase       						=      argparse.ArgumentParser()
						parser.add_argument("""model_name"""	,		type=__UpperCamelCase	,		help="""like facebook/bart-large-cnn,t5-base, etc."""					)
						parser.add_argument("""input_path"""	,		type=__UpperCamelCase	,		help="""like cnn_dm/test.source"""					)
						parser.add_argument("""save_path"""	,		type=__UpperCamelCase	,		help="""where to save summaries"""					)
						parser.add_argument("""--reference_path"""	,		type=__UpperCamelCase	,		required=__UpperCamelCase	,		help="""like cnn_dm/test.target"""					)
						parser.add_argument("""--score_path"""	,		type=__UpperCamelCase	,		required=__UpperCamelCase	,		default="""metrics.json"""	,		help="""where to save metrics"""					)
						parser.add_argument("""--device"""	,		type=__UpperCamelCase	,		required=__UpperCamelCase	,		default=__UpperCamelCase	,		help="""cuda, cuda:1, cpu etc."""					)
						parser.add_argument(
						    """--prefix"""	,		type=__UpperCamelCase	,		required=__UpperCamelCase	,		default=__UpperCamelCase	,		help="""will be added to the begininng of src examples"""					)
						parser.add_argument("""--task"""	,		type=__UpperCamelCase	,		default="""summarization"""	,		help="""used for task_specific_params + metrics"""					)
						parser.add_argument("""--bs"""	,		type=__UpperCamelCase	,		default=8	,		required=__UpperCamelCase	,		help="""batch size"""					)
						parser.add_argument(
						    """--n_obs"""	,		type=__UpperCamelCase	,		default=-1	,		required=__UpperCamelCase	,		help="""How many observations. Defaults to all."""					)
						parser.add_argument("""--fp16"""	,		action="""store_true"""					)
						parser.add_argument("""--dump-args"""	,		action="""store_true"""	,		help="""print the custom hparams with the results"""					)
						parser.add_argument(
						    """--info"""	,		nargs="""?"""	,		type=__UpperCamelCase	,		const=datetime_now()	,		help=(
						        """use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."""
						        """ lang=en-ru. If no value is passed, the current datetime string will be used."""
						    )	,		)
						# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
						UpperCamelCase						,UpperCamelCase       						=      parser.parse_known_args()
						UpperCamelCase       						=      parse_numeric_n_bool_cl_kwargs(__UpperCamelCase					)
						if parsed_args and verbose:
												print(F"parsed the following generate kwargs: {parsed_args}"					)
						UpperCamelCase       						=      [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path					).readlines()]
						if args.n_obs > 0:
												UpperCamelCase       						=      examples[: args.n_obs]
						Path(args.save_path					).parent.mkdir(exist_ok=__UpperCamelCase					)

						if args.reference_path is None and Path(args.score_path					).exists():
												warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."					)

						if args.device == "cpu" and args.fpaa:
												# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
												raise ValueError("""Can't mix --fp16 and --device cpu"""					)

						UpperCamelCase       						=      generate_summaries_or_translations(
						    __UpperCamelCase	,		args.save_path	,		args.model_name	,		batch_size=args.bs	,		device=args.device	,		fpaa=args.fpaa	,		task=args.task	,		prefix=args.prefix	,		**__UpperCamelCase	,		)

						if args.reference_path is None:
												return {}

						# Compute scores
						UpperCamelCase       						=      calculate_bleu if """translation""" in args.task else calculate_rouge
						UpperCamelCase       						=      [x.rstrip() for x in open(args.save_path					).readlines()]
						UpperCamelCase       						=      [x.rstrip() for x in open(args.reference_path					).readlines()][: len(__UpperCamelCase					)]
						UpperCamelCase       						=      score_fn(__UpperCamelCase	,		__UpperCamelCase					)
						scores.update(__UpperCamelCase					)

						if args.dump_args:
												scores.update(__UpperCamelCase					)
						if args.info:
												UpperCamelCase       						=      args.info

						if verbose:
												print(__UpperCamelCase					)

						if args.score_path is not None:
												json.dump(__UpperCamelCase	,		open(args.score_path	,		"""w"""					)					)

						return scores


if __name__ == "__main__":
					# Usage for MT:
					# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
					run_generate(verbose=True)








'''simple docstring'''

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE__											=     1_6
SCREAMING_SNAKE_CASE__											=     3_2







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase = 16					)->				Dict:
      UpperCamelCase       						=      AutoTokenizer.from_pretrained("""bert-base-cased"""					)
      UpperCamelCase       						=      load_dataset("""glue"""	,		"""mrpc"""					)

      def tokenize_function(__UpperCamelCase					):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase       						=      tokenizer(examples["""sentence1"""]	,		examples["""sentence2"""]	,		truncation=__UpperCamelCase	,		max_length=__UpperCamelCase					)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            UpperCamelCase       						=      datasets.map(
                __UpperCamelCase	,		batched=__UpperCamelCase	,		remove_columns=["""idx""", """sentence1""", """sentence2"""]	,		)

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      UpperCamelCase       						=      tokenized_datasets.rename_column("""label"""	,		"""labels"""					)

      def collate_fn(__UpperCamelCase					):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCamelCase       						=      128 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  UpperCamelCase       						=      16
            elif accelerator.mixed_precision != "no":
                  UpperCamelCase       						=      8
            else:
                  UpperCamelCase       						=      None

            return tokenizer.pad(
                __UpperCamelCase	,		padding="""longest"""	,		max_length=__UpperCamelCase	,		pad_to_multiple_of=__UpperCamelCase	,		return_tensors="""pt"""	,		)

      # Instantiate dataloaders.
      UpperCamelCase       						=      DataLoader(
          tokenized_datasets["""train"""]	,		shuffle=__UpperCamelCase	,		collate_fn=__UpperCamelCase	,		batch_size=__UpperCamelCase					)
      UpperCamelCase       						=      DataLoader(
          tokenized_datasets["""validation"""]	,		shuffle=__UpperCamelCase	,		collate_fn=__UpperCamelCase	,		batch_size=__UpperCamelCase					)

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     SCREAMING_SNAKE_CASE__											=     mocked_dataloaders  # noqa: F811







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List[Any]:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,		__UpperCamelCase					) == "1":
            UpperCamelCase       						=      2
      # New Code #
      UpperCamelCase       						=      int(args.gradient_accumulation_steps					)
      UpperCamelCase       						=      int(args.local_sgd_steps					)
      # Initialize accelerator
      UpperCamelCase       						=      Accelerator(
          cpu=args.cpu	,		mixed_precision=args.mixed_precision	,		gradient_accumulation_steps=__UpperCamelCase					)
      if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
            raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""					)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCamelCase       						=      config["""lr"""]
      UpperCamelCase       						=      int(config["""num_epochs"""]					)
      UpperCamelCase       						=      int(config["""seed"""]					)
      UpperCamelCase       						=      int(config["""batch_size"""]					)

      UpperCamelCase       						=      evaluate.load("""glue"""	,		"""mrpc"""					)

      set_seed(__UpperCamelCase					)
      UpperCamelCase						,UpperCamelCase       						=      get_dataloaders(__UpperCamelCase	,		__UpperCamelCase					)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCamelCase       						=      AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""	,		return_dict=__UpperCamelCase					)

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      UpperCamelCase       						=      model.to(accelerator.device					)

      # Instantiate optimizer
      UpperCamelCase       						=      AdamW(params=model.parameters()	,		lr=__UpperCamelCase					)

      # Instantiate scheduler
      UpperCamelCase       						=      get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase	,		num_warmup_steps=100	,		num_training_steps=(len(__UpperCamelCase					) * num_epochs)	,		)

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      accelerator.prepare(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)

      # Now we train the model
      for epoch in range(__UpperCamelCase					):
            model.train()
            with LocalSGD(
                accelerator=__UpperCamelCase	,		model=__UpperCamelCase	,		local_sgd_steps=__UpperCamelCase	,		enabled=local_sgd_steps is not None					) as local_sgd:
                  for step, batch in enumerate(__UpperCamelCase					):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device					)
                        # New code #
                        # We use the new `accumulate` context manager to perform gradient accumulation
                        # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                        with accelerator.accumulate(__UpperCamelCase					):
                              UpperCamelCase       						=      model(**__UpperCamelCase					)
                              UpperCamelCase       						=      output.loss
                              accelerator.backward(__UpperCamelCase					)
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                              # LocalSGD-specific line
                              local_sgd.step()

            model.eval()
            for step, batch in enumerate(__UpperCamelCase					):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device					)
                  with torch.no_grad():
                        UpperCamelCase       						=      model(**__UpperCamelCase					)
                  UpperCamelCase       						=      outputs.logits.argmax(dim=-1					)
                  UpperCamelCase						,UpperCamelCase       						=      accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
                  metric.add_batch(
                      predictions=__UpperCamelCase	,		references=__UpperCamelCase	,		)

            UpperCamelCase       						=      metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"epoch {epoch}:"	,		__UpperCamelCase					)







def 					lowercase__				(   )->				List[Any]:
      UpperCamelCase       						=      argparse.ArgumentParser(description="""Simple example of training script."""					)
      parser.add_argument(
          """--mixed_precision"""	,		type=__UpperCamelCase	,		default=__UpperCamelCase	,		choices=["""no""", """fp16""", """bf16""", """fp8"""]	,		help="""Whether to use mixed precision. Choose"""
          """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
          """and an Nvidia Ampere GPU."""	,		)
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""	,		type=__UpperCamelCase	,		default=1	,		help="""The number of minibatches to be ran before gradients are accumulated."""	,		)
      parser.add_argument(
          """--local_sgd_steps"""	,		type=__UpperCamelCase	,		default=8	,		help="""Number of local SGD steps or None to disable local SGD"""					)
      parser.add_argument("""--cpu"""	,		action="""store_true"""	,		help="""If passed, will train on the CPU."""					)
      UpperCamelCase       						=      parser.parse_args()
      UpperCamelCase       						=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(__UpperCamelCase	,		__UpperCamelCase					)


if __name__ == "__main__":
     main()
