




import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


lowercase  : Optional[Any]						   =							16
lowercase  : str						   =							32







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = 16 ,     SCREAMING_SNAKE_CASE__ = "bert-base-cased"    )			->   List[str]:
       lowercase							:					List[str]	     =				AutoTokenizer.from_pretrained(UpperCAmelCase__    )
       lowercase							:					List[Any]	     =				load_dataset("""glue""" ,     """mrpc"""    )

       def tokenize_function(SCREAMING_SNAKE_CASE__    ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase							:					Optional[Any]	     =				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
       lowercase							:					List[str]	     =				datasets.map(
           UpperCAmelCase__ ,     batched=UpperCAmelCase__ ,     remove_columns=["""idx""", """sentence1""", """sentence2"""] ,     load_from_cache_file=UpperCAmelCase__    )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       lowercase							:					Union[str, Any]	     =				tokenized_datasets.rename_column("""label""" ,     """labels"""    )

       def collate_fn(SCREAMING_SNAKE_CASE__    ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(UpperCAmelCase__ ,     padding="""max_length""" ,     max_length=128 ,     return_tensors="""pt"""    )
              return tokenizer.pad(UpperCAmelCase__ ,     padding="""longest""" ,     return_tensors="""pt"""    )

       # Instantiate dataloaders.
       lowercase							:					Any	     =				DataLoader(
           tokenized_datasets["""train"""] ,     shuffle=UpperCAmelCase__ ,     collate_fn=UpperCAmelCase__ ,     batch_size=UpperCAmelCase__    )
       lowercase							:					int	     =				DataLoader(
           tokenized_datasets["""validation"""] ,     shuffle=UpperCAmelCase__ ,     collate_fn=UpperCAmelCase__ ,     batch_size=UpperCAmelCase__    )

       return train_dataloader, eval_dataloader







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   str:
       # Initialize accelerator
       lowercase							:					Union[str, Any]	     =				Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase							:					Any	     =				config["""lr"""]
       lowercase							:					str	     =				int(config["""num_epochs"""]    )
       lowercase							:					Tuple	     =				int(config["""seed"""]    )
       lowercase							:					Optional[Any]	     =				int(config["""batch_size"""]    )
       lowercase							:					List[Any]	     =				args.model_name_or_path

       set_seed(UpperCAmelCase__    )
       lowercase							:					Dict	     =				get_dataloaders(UpperCAmelCase__ ,     UpperCAmelCase__ ,     UpperCAmelCase__    )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase							:					int	     =				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__ ,     return_dict=UpperCAmelCase__    )

       # Instantiate optimizer
       lowercase							:					Dict	     =				(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       lowercase							:					List[Any]	     =				optimizer_cls(params=model.parameters() ,     lr=UpperCAmelCase__    )

       if accelerator.state.deepspeed_plugin is not None:
              lowercase							:					Optional[Any]	     =				accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              lowercase							:					int	     =				1
       lowercase							:					Union[str, Any]	     =				(len(UpperCAmelCase__    ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              lowercase							:					List[str]	     =				get_linear_schedule_with_warmup(
                  optimizer=UpperCAmelCase__ ,     num_warmup_steps=0 ,     num_training_steps=UpperCAmelCase__ ,     )
       else:
              lowercase							:					Optional[int]	     =				DummyScheduler(UpperCAmelCase__ ,     total_num_steps=UpperCAmelCase__ ,     warmup_num_steps=0    )

       # 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.
       lowercase							:					List[str]	     =				accelerator.prepare(
           UpperCAmelCase__ ,     UpperCAmelCase__ ,     UpperCAmelCase__ ,     UpperCAmelCase__ ,     UpperCAmelCase__    )

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

       # Now we train the model
       lowercase							:					int	     =				evaluate.load("""glue""" ,     """mrpc"""    )
       lowercase							:					Tuple	     =				0
       lowercase							:					Tuple	     =				{}
       for epoch in range(UpperCAmelCase__ ,     UpperCAmelCase__    ):
              model.train()
              for step, batch in enumerate(UpperCAmelCase__    ):
                     lowercase							:					Optional[Any]	     =				model(**UpperCAmelCase__    )
                     lowercase							:					int	     =				outputs.loss
                     lowercase							:					List[str]	     =				loss / gradient_accumulation_steps
                     accelerator.backward(UpperCAmelCase__    )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              lowercase							:					Tuple	     =				0
              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():
                            lowercase							:					Tuple	     =				model(**UpperCAmelCase__    )
                     lowercase							:					int	     =				outputs.logits.argmax(dim=-1    )
                     # It is slightly faster to call this once, than multiple times
                     lowercase							:					Dict	     =				accelerator.gather(
                         (predictions, batch["""labels"""])    )  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(UpperCAmelCase__    ) - 1:
                                   lowercase							:					List[str]	     =				predictions[: len(eval_dataloader.dataset    ) - samples_seen]
                                   lowercase							:					List[str]	     =				references[: len(eval_dataloader.dataset    ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=UpperCAmelCase__ ,     references=UpperCAmelCase__ ,     )

              lowercase							:					int	     =				metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"epoch {epoch}:" ,     UpperCAmelCase__    )
              lowercase							:					Tuple	     =				eval_metric["""accuracy"""]

              if best_performance < eval_metric["accuracy"]:
                     lowercase							:					List[Any]	     =				eval_metric["""accuracy"""]

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir ,     """all_results.json"""    ) ,     """w"""    ) as f:
                     json.dump(UpperCAmelCase__ ,     UpperCAmelCase__    )







def 						_snake_case(  )			->   int:
       lowercase							:					int	     =				argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
       parser.add_argument(
           """--model_name_or_path""" ,     type=UpperCAmelCase__ ,     default="""bert-base-cased""" ,     help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,     required=UpperCAmelCase__ ,     )
       parser.add_argument(
           """--output_dir""" ,     type=UpperCAmelCase__ ,     default=""".""" ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,     )
       parser.add_argument(
           """--performance_lower_bound""" ,     type=UpperCAmelCase__ ,     default=UpperCAmelCase__ ,     help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" ,     )
       parser.add_argument(
           """--num_epochs""" ,     type=UpperCAmelCase__ ,     default=3 ,     help="""Number of train epochs.""" ,     )
       lowercase							:					Union[str, Any]	     =				parser.parse_args()
       lowercase							:					int	     =				{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(UpperCAmelCase__ ,     UpperCAmelCase__    )


if __name__ == "__main__":
  main()


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Optional[int]=    None
    _a		:						Optional[Any]=    BloomTokenizerFast
    _a		:						Tuple=    BloomTokenizerFast
    _a		:						str=    True
    _a		:						Optional[int]=    False
    _a		:						List[Any]=    "tokenizer_file"
    _a		:						List[Any]=    {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().setUp()
           lowercase							:					Optional[Any]	     =				BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""				)
           tokenizer.save_pretrained(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           kwargs.update(self.special_tokens_map				)
           return BloomTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.get_rust_tokenizer()

           lowercase							:					List[str]	     =				["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
           lowercase							:					Optional[int]	     =				[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

           lowercase							:					Any	     =				tokenizer.batch_encode_plus(snake_case				)["""input_ids"""]
           self.assertListEqual(snake_case		,snake_case				)

           lowercase							:					Optional[int]	     =				tokenizer.batch_decode(snake_case				)
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=6				):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                         lowercase							:					Dict	     =				self.rust_tokenizer_class.from_pretrained(snake_case		,**snake_case				)
                         # tokenizer_r.pad_token = None # Hotfixing padding = None
                         # Simple input
                         lowercase							:					Dict	     =				"""This is a simple input"""
                         lowercase							:					Tuple	     =				["""This is a simple input 1""", """This is a simple input 2"""]
                         lowercase							:					Dict	     =				("""This is a simple input""", """This is a pair""")
                         lowercase							:					Optional[Any]	     =				[
                             ("""This is a simple input 1""", """This is a simple input 2"""),
                             ("""This is a simple pair 1""", """This is a simple pair 2"""),
                         ]

                         # Simple input tests
                         try:
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode_plus(snake_case		,max_length=snake_case				)

                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                         except ValueError:
                                self.fail("""Bloom Tokenizer should be able to deal with padding"""				)

                         lowercase							:					Optional[int]	     =				None  # Hotfixing padding = None
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.get_rust_tokenizer()
           lowercase							:					List[str]	     =				load_dataset("""xnli"""		,"""all_languages"""		,split="""test"""		,streaming=snake_case				)

           lowercase							:					Optional[Any]	     =				next(iter(snake_case				)				)["""premise"""]  # pick up one data
           lowercase							:					str	     =				list(sample_data.values()				)

           lowercase							:					Optional[int]	     =				list(map(tokenizer.encode		,snake_case				)				)
           lowercase							:					Dict	     =				[tokenizer.decode(snake_case		,clean_up_tokenization_spaces=snake_case				) for x in output_tokens]
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)		,1				)
           self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)		,1				)
