



"""simple docstring"""







import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


__snake_case	:				Any  					=			-1 if torch_device == 'cpu' else 0

@is_pipeline_test
class       UpperCamelCase  (					unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: Any				   =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _lowerCamelCase					: Tuple				   =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





     def 						A__  (					self :							Any	,      _lowerCamelCase :							Dict	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							Optional[int] ):
          A__				=   SummarizationPipeline(model=_lowerCamelCase	,      tokenizer=_lowerCamelCase )
          return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]





     def 						A__  (					self :							List[str]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Union[str, Any] ):
          A__				=   summarizer.model

          A__				=   summarizer('''(CNN)The Palestinian Authority officially became''' )
          self.assertEqual(_lowerCamelCase	,      [{'''summary_text''': ANY(_lowerCamelCase )}] )

          A__				=   summarizer(
              '''(CNN)The Palestinian Authority officially became '''	,      num_beams=2	,      min_length=2	,      max_length=5	,      )
          self.assertEqual(_lowerCamelCase	,      [{'''summary_text''': ANY(_lowerCamelCase )}] )

          # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
          A__				=   [
              '''SwitchTransformersConfig''',
              '''T5Config''',
              '''LongT5Config''',
              '''LEDConfig''',
              '''PegasusXConfig''',
              '''FSMTConfig''',
              '''M2M100Config''',
              '''ProphetNetConfig''',  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
          ]
          if model.config.__class__.__name__ not in model_can_handle_longer_seq:
               # Too long and exception is expected.
               # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
               # the embedding layer.
               if not (
                   isinstance(_lowerCamelCase	,      _lowerCamelCase )
                   and get_gpu_count() > 0
                   and len(summarizer.model.trainable_weights ) > 0
               ):
                    with self.assertRaises(_lowerCamelCase ):
                         A__				=   summarizer('''This ''' * 1_0_0_0 )
          A__				=   summarizer('''This ''' * 1_0_0_0	,      truncation=TruncationStrategy.ONLY_FIRST )





     @require_torch
     def 						A__  (					self :							Optional[int] ):
          A__				=   pipeline(task='''summarization'''	,      model='''sshleifer/tiny-mbart'''	,      framework='''pt''' )
          A__				=   summarizer('''This is a small test''' )
          self.assertEqual(
              _lowerCamelCase	,      [
                  {
                      '''summary_text''': '''เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'''
                  }
              ]	,      )





     @require_tf
     def 						A__  (					self :							Optional[Any] ):
          A__				=   pipeline(task='''summarization'''	,      model='''sshleifer/tiny-mbart'''	,      framework='''tf''' )
          A__				=   summarizer('''This is a small test''' )
          self.assertEqual(
              _lowerCamelCase	,      [
                  {
                      '''summary_text''': '''เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'''
                  }
              ]	,      )





     @require_torch
     @slow
     def 						A__  (					self :							Union[str, Any] ):
          A__				=   pipeline(task='''summarization'''	,      device=_lowerCamelCase )
          A__				=   (
              ''' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on'''
              ''' Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The'''
              ''' formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.'''
              ''' The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its'''
              ''' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'''
              ''' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'''
              ''' situation in Palestinian territories, paving the way for possible war crimes investigations against'''
              ''' Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and'''
              ''' the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the'''
              ''' body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a'''
              ''' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'''
              ''' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'''
              ''' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'''
              ''' Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the'''
              ''' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'''
              ''' acquires all the rights as well as responsibilities that come with being a State Party to the Statute.'''
              ''' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'''
              ''' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'''
              ''' immediately end their pressure, and countries that support universal acceptance of the court\'s treaty'''
              ''' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'''
              ''' group. \"What\'s objectionable is the attempts to undermine international justice, not Palestine\'s'''
              ''' decision to join a treaty to which over 100 countries around the world are members." In January, when'''
              ''' the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an'''
              ''' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'''
              ''' disagreed with the court\'s decision. \"As we have said repeatedly, we do not believe that Palestine is a'''
              ''' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'''
              ''' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'''
              ''' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'''
              ''' it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the'''
              ''' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'''
              ''' court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou'''
              ''' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'''
              ''' between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry'''
              ''' will include alleged war crimes committed since June. The International Criminal Court was set up in'''
              ''' 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder'''
              ''' and Faith Karimi contributed to this report.'''
          )
          A__				=   (
              ''' The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives'''
              ''' the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States'''
              ''' opposed the Palestinians\' efforts to join the court . Rights group Human Rights Watch welcomes the move,'''
              ''' says governments seeking to penalize Palestine should end pressure .'''
          )
          A__				=   summarizer(_lowerCamelCase )
          self.assertEqual(result[0]['''summary_text''']	,      _lowerCamelCase )








"""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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


__snake_case	:				Tuple  					=			16
__snake_case	:				Any  					=			32



def   a_	(			__a		,							__a      ):
     # Initialize accelerator
     if args.with_tracking:
          A__				=   Accelerator(
              cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with='''all'''		,							project_dir=args.project_dir      )
     else:
          A__				=   Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision      )

     if hasattr(args.checkpointing_steps		,							'''isdigit'''      ):
          if args.checkpointing_steps == "epoch":
               A__				=   args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               A__				=   int(args.checkpointing_steps      )
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''      )
     else:
          A__				=   None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     A__				=   config['''lr''']
     A__				=   int(config['''num_epochs''']      )
     A__				=   int(config['''seed''']      )
     A__				=   int(config['''batch_size''']      )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          A__				=   os.path.split(__a      )[-1].split('''.'''      )[0]
          accelerator.init_trackers(__a		,							__a      )

     A__				=   AutoTokenizer.from_pretrained('''bert-base-cased'''      )
     A__				=   load_dataset('''glue'''		,							'''mrpc'''      )
     A__				=   evaluate.load('''glue'''		,							'''mrpc'''      )

     def tokenize_function(__a      ):
          # max_length=None => use the model max length (it's actually the default)
          A__				=   tokenizer(examples['''sentence1''']		,							examples['''sentence2''']		,							truncation=__a		,							max_length=__a      )
          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():
          A__				=   datasets.map(
              __a		,							batched=__a		,							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
     A__				=   tokenized_datasets.rename_column('''label'''		,							'''labels'''      )

     # If the batch size is too big we use gradient accumulation
     A__				=   1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          A__				=   batch_size // MAX_GPU_BATCH_SIZE
          A__				=   MAX_GPU_BATCH_SIZE

     def collate_fn(__a      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          A__				=   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":
               A__				=   16
          elif accelerator.mixed_precision != "no":
               A__				=   8
          else:
               A__				=   None

          return tokenizer.pad(
              __a		,							padding='''longest'''		,							max_length=__a		,							pad_to_multiple_of=__a		,							return_tensors='''pt'''		,							)

     # Instantiate dataloaders.
     A__				=   DataLoader(
         tokenized_datasets['''train''']		,							shuffle=__a		,							collate_fn=__a		,							batch_size=__a      )
     A__				=   DataLoader(
         tokenized_datasets['''validation''']		,							shuffle=__a		,							collate_fn=__a		,							batch_size=__a      )

     set_seed(__a      )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     A__				=   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,							return_dict=__a      )

     # 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).
     A__				=   model.to(accelerator.device      )

     # Instantiate optimizer
     A__				=   AdamW(params=model.parameters()		,							lr=__a      )

     # Instantiate scheduler
     A__				=   get_linear_schedule_with_warmup(
         optimizer=__a		,							num_warmup_steps=100		,							num_training_steps=(len(__a      ) * num_epochs) // gradient_accumulation_steps		,							)

     # 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.
     A__	,     A__	,     A__	,     A__	,     A__				=   accelerator.prepare(
         __a		,							__a		,							__a		,							__a		,							__a      )

     # We need to keep track of how many total steps we have iterated over
     A__				=   0
     # We also need to keep track of the stating epoch so files are named properly
     A__				=   0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''      )
               accelerator.load_state(args.resume_from_checkpoint      )
               A__				=   os.path.basename(args.resume_from_checkpoint      )
          else:
               # Get the most recent checkpoint
               A__				=   [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
               dirs.sort(key=os.path.getctime      )
               A__				=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          A__				=   os.path.splitext(__a      )[0]

          if "epoch" in training_difference:
               A__				=   int(training_difference.replace('''epoch_'''		,							''''''      )      ) + 1
               A__				=   None
          else:
               A__				=   int(training_difference.replace('''step_'''		,							''''''      )      )
               A__				=   resume_step // len(__a      )
               resume_step -= starting_epoch * len(__a      )

    # Now we train the model
     for epoch in range(__a		,							__a      ):
          model.train()
          if args.with_tracking:
               A__				=   0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               A__				=   accelerator.skip_first_batches(__a		,							__a      )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               A__				=   train_dataloader
          for step, batch in enumerate(__a      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               A__				=   model(**__a      )
               A__				=   outputs.loss
               A__				=   loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__a      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(__a		,							__a      ):
                    A__				=   f'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              A__				=   os.path.join(args.output_dir		,							__a      )
                         accelerator.save_state(__a      )

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

          A__				=   metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''		,							__a      )
          if args.with_tracking:
               accelerator.log(
                   {
                       '''accuracy''': eval_metric['''accuracy'''],
                       '''f1''': eval_metric['''f1'''],
                       '''train_loss''': total_loss.item() / len(__a      ),
                       '''epoch''': epoch,
                   }		,							step=__a		,							)

          if checkpointing_steps == "epoch":
               A__				=   f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    A__				=   os.path.join(args.output_dir		,							__a      )
               accelerator.save_state(__a      )

     if args.with_tracking:
          accelerator.end_training()



def   a_	(			):
     A__				=   argparse.ArgumentParser(description='''Simple example of training script.'''      )
     parser.add_argument(
         '''--mixed_precision'''		,							type=__a		,							default=__a		,							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.'''		,							)
     parser.add_argument('''--cpu'''		,							action='''store_true'''		,							help='''If passed, will train on the CPU.'''      )
     parser.add_argument(
         '''--checkpointing_steps'''		,							type=__a		,							default=__a		,							help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''		,							)
     parser.add_argument(
         '''--resume_from_checkpoint'''		,							type=__a		,							default=__a		,							help='''If the training should continue from a checkpoint folder.'''		,							)
     parser.add_argument(
         '''--with_tracking'''		,							action='''store_true'''		,							help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,							)
     parser.add_argument(
         '''--output_dir'''		,							type=__a		,							default='''.'''		,							help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''		,							)
     parser.add_argument(
         '''--project_dir'''		,							type=__a		,							default='''logs'''		,							help='''Location on where to store experiment tracking logs` and relevent project information'''		,							)
     A__				=   parser.parse_args()
     A__				=   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(__a		,							__a      )


if __name__ == "__main__":
 main()



