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


A__           =							16
A__           =							32





def  _lowerCamelCase					(   a_				: Accelerator						,			a_				: int = 16						,			a_				: str = "bert-base-cased"):
   lowerCamelCase						:Union[str, Any]						  =     AutoTokenizer.from_pretrained(a_)
   lowerCamelCase						:Optional[int]						  =     load_dataset('''glue'''						,			'''mrpc''')

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

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

   def collate_fn(a_				: str):
      # 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(a_						,			padding='''max_length'''						,			max_length=1_28						,			return_tensors='''pt''')
      return tokenizer.pad(a_						,			padding='''longest'''						,			return_tensors='''pt''')

   # Instantiate dataloaders.
   lowerCamelCase						:Tuple						  =     DataLoader(
       tokenized_datasets['''train''']						,			shuffle=a_						,			collate_fn=a_						,			batch_size=a_)
   lowerCamelCase						:Any						  =     DataLoader(
       tokenized_datasets['''validation''']						,			shuffle=a_						,			collate_fn=a_						,			batch_size=a_)

   return train_dataloader, eval_dataloader





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

   lowerCamelCase						:Union[str, Any]						  =     metric.compute()
   return eval_metric["accuracy"]





def  _lowerCamelCase					(   a_				: Tuple						,			a_				: int):
   # Initialize accelerator
   lowerCamelCase						:Union[str, Any]						  =     Accelerator()

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

   set_seed(a_)
   lowerCamelCase						:Union[str, Any]						  =     get_dataloaders(a_						,			a_						,			a_)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCamelCase						:List[Any]						  =     AutoModelForSequenceClassification.from_pretrained(a_						,			return_dict=a_)

   # Instantiate optimizer
   lowerCamelCase						:Tuple						  =     (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   lowerCamelCase						:int						  =     optimizer_cls(params=model.parameters()						,			lr=a_)

   if accelerator.state.deepspeed_plugin is not None:
      lowerCamelCase						:str						  =     accelerator.state.deepspeed_plugin.deepspeed_config[
          '''gradient_accumulation_steps'''
      ]
   else:
      lowerCamelCase						:Tuple						  =     1
   lowerCamelCase						:List[Any]						  =     (len(a_) * num_epochs) // gradient_accumulation_steps

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

   # We need to keep track of how many total steps we have iterated over
   lowerCamelCase						:List[Any]						  =     0
   # We also need to keep track of the stating epoch so files are named properly
   lowerCamelCase						:int						  =     0
   lowerCamelCase						:List[Any]						  =     evaluate.load('''glue'''						,			'''mrpc''')
   lowerCamelCase						:Any						  =     num_epochs

   if args.partial_train_epoch is not None:
      lowerCamelCase						:int						  =     args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint)
      lowerCamelCase						:Tuple						  =     args.resume_from_checkpoint.split('''epoch_''')[1]
      lowerCamelCase						:str						  =     ''''''
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      lowerCamelCase						:Optional[Any]						  =     int(a_) + 1
      lowerCamelCase						:Optional[int]						  =     evaluation_loop(a_						,			a_						,			a_						,			a_)
      accelerator.print('''resumed checkpoint performance:'''						,			a_)
      accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''						,			lr_scheduler.get_lr()[0])
      accelerator.print('''resumed optimizers\'s lr:'''						,			optimizer.param_groups[0]['''lr'''])
      with open(os.path.join(args.output_dir						,			F"state_{starting_epoch-1}.json")						,			'''r''') as f:
         lowerCamelCase						:Any						  =     json.load(a_)
         assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
         assert (
             resumed_state["lr"] == lr_scheduler.get_lr()[0]
         ), "Scheduler learning rate mismatch, loading from checkpoint failed"
         assert (
             resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
         ), "Optimizer learning rate mismatch, loading from checkpoint failed"
         assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
         return

    # Now we train the model
   lowerCamelCase						:Optional[int]						  =     {}
   for epoch in range(a_						,			a_):
      model.train()
      for step, batch in enumerate(a_):
         lowerCamelCase						:List[str]						  =     model(**a_)
         lowerCamelCase						:int						  =     outputs.loss
         lowerCamelCase						:int						  =     loss / gradient_accumulation_steps
         accelerator.backward(a_)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1
      lowerCamelCase						:List[Any]						  =     F"epoch_{epoch}"
      lowerCamelCase						:Tuple						  =     os.path.join(args.output_dir						,			a_)
      accelerator.save_state(a_)
      lowerCamelCase						:Tuple						  =     evaluation_loop(a_						,			a_						,			a_						,			a_)
      lowerCamelCase						:int						  =     accuracy
      lowerCamelCase						:Union[str, Any]						  =     lr_scheduler.get_lr()[0]
      lowerCamelCase						:Any						  =     optimizer.param_groups[0]['''lr''']
      lowerCamelCase						:List[Any]						  =     epoch
      lowerCamelCase						:Optional[Any]						  =     overall_step
      accelerator.print(F"epoch {epoch}:"						,			a_)

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         with open(os.path.join(args.output_dir						,			F"state_{epoch}.json")						,			'''w''') as f:
            json.dump(a_						,			a_)





def  _lowerCamelCase					(   ):
   lowerCamelCase						:List[str]						  =     argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''')
   parser.add_argument(
       '''--model_name_or_path'''						,			type=a_						,			default='''bert-base-cased'''						,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''						,			required=a_						,			)
   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(
       '''--resume_from_checkpoint'''						,			type=a_						,			default=a_						,			help='''If the training should continue from a checkpoint folder.'''						,			)
   parser.add_argument(
       '''--partial_train_epoch'''						,			type=a_						,			default=a_						,			help='''If passed, the training will stop after this number of epochs.'''						,			)
   parser.add_argument(
       '''--num_epochs'''						,			type=a_						,			default=2						,			help='''Number of train epochs.'''						,			)
   lowerCamelCase						:str						  =     parser.parse_args()
   lowerCamelCase						:Tuple						  =     {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

   training_function(a_						,			a_)


if __name__ == "__main__":
   main()




import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     DebertaTokenizer
							_UpperCAmelCase						    =     True
							_UpperCAmelCase						    =     DebertaTokenizerFast







							def 				snake_case  (				self     :    List[str]		):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase						:Dict						  =     [
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''[UNK]''',
										]
										lowerCamelCase						:List[str]						  =     dict(zip(__snake_case	,					range(len(__snake_case		)		)		)		)
										lowerCamelCase						:Any						  =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowerCamelCase						:Dict						  =     {'''unk_token''': '''[UNK]'''}

										lowerCamelCase						:Any						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''vocab_file''']		)
										lowerCamelCase						:List[str]						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''merges_file''']		)
										with open(self.vocab_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write(json.dumps(__snake_case		) + '''\n'''		)
										with open(self.merges_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write('''\n'''.join(__snake_case		)		)







							def 				snake_case  (				self     :    str	,					**__snake_case     :    Dict		):
										kwargs.update(self.special_tokens_map		)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    int		):
										lowerCamelCase						:List[Any]						  =     '''lower newer'''
										lowerCamelCase						:List[str]						  =     '''lower newer'''
										return input_text, output_text







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[int]						  =     self.get_tokenizer()
										lowerCamelCase						:Union[str, Any]						  =     '''lower newer'''
										lowerCamelCase						:str						  =     ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										lowerCamelCase						:Optional[int]						  =     tokenizer.tokenize(__snake_case		)
										self.assertListEqual(__snake_case	,					__snake_case		)

										lowerCamelCase						:List[str]						  =     tokens + [tokenizer.unk_token]
										lowerCamelCase						:Optional[int]						  =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case		)	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:List[str]						  =     self.get_tokenizer()
										lowerCamelCase						:Optional[int]						  =     tokenizer('''Hello'''	,					'''World'''		)
										lowerCamelCase						:List[str]						  =     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd['''token_type_ids''']	,					__snake_case		)







							@slow
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Union[str, Any]						  =     self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

										lowerCamelCase						:Optional[Any]						  =     tokenizer.encode('''sequence builders'''	,					add_special_tokens=__snake_case		)
										lowerCamelCase						:Tuple						  =     tokenizer.encode('''multi-sequence build'''	,					add_special_tokens=__snake_case		)

										lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode(
										    '''sequence builders'''	,					add_special_tokens=__snake_case	,					add_prefix_space=__snake_case		)
										lowerCamelCase						:str						  =     tokenizer.encode(
										    '''sequence builders'''	,					'''multi-sequence build'''	,					add_special_tokens=__snake_case	,					add_prefix_space=__snake_case		)

										lowerCamelCase						:Any						  =     tokenizer.build_inputs_with_special_tokens(__snake_case		)
										lowerCamelCase						:Dict						  =     tokenizer.build_inputs_with_special_tokens(__snake_case	,					__snake_case		)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode







							@slow
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:List[str]						  =     [self.tokenizer_class]
										if self.test_rust_tokenizer:
													tokenizer_classes.append(self.rust_tokenizer_class		)

										for tokenizer_class in tokenizer_classes:
													lowerCamelCase						:int						  =     tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

													lowerCamelCase						:Tuple						  =     [
													    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
													    '''ALBERT incorporates two parameter reduction techniques''',
													    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
													    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
													    ''' vocabulary embedding.''',
													]

													lowerCamelCase						:List[Any]						  =     tokenizer(__snake_case	,					padding=__snake_case		)
													lowerCamelCase						:Union[str, Any]						  =     [tokenizer.decode(__snake_case	,					skip_special_tokens=__snake_case		) for seq in encoding['''input_ids''']]

													# fmt: off
													lowerCamelCase						:Any						  =     {
													    '''input_ids''': [
													        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
													    ],
													    '''token_type_ids''': [
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
													    ],
													    '''attention_mask''': [
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
													    ]
													}
													# fmt: on

													lowerCamelCase						:Optional[int]						  =     [
													    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
													    '''ALBERT incorporates two parameter reduction techniques''',
													    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
													    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
													    ''' vocabulary embedding.''',
													]

													self.assertDictEqual(encoding.data	,					__snake_case		)

													for expected, decoded in zip(__snake_case	,					__snake_case		):
																self.assertEqual(__snake_case	,					__snake_case		)



