


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
# and perform 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
#
########################################################################


snake_case__				:	List[Any]					       =					16
snake_case__				:	Tuple					       =					32


def    __lowerCamelCase   (      A__						: Accelerator						,						A__						: int = 16					)     ->    int:
   lowerCamelCase_						:   Union[str, Any]		= AutoTokenizer.from_pretrained("""bert-base-cased"""					)
   lowerCamelCase_						:   Optional[int]		= load_dataset("""glue"""						,						"""mrpc"""					)

   def tokenize_function(A__						: List[Any]					):
      # max_length=None => use the model max length (it's actually the default)
      lowerCamelCase_						:   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
   # starting with the main process first:
   with accelerator.main_process_first():
      lowerCamelCase_						:   Any		= 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
   lowerCamelCase_						:   Tuple		= tokenized_datasets.rename_column("""label"""						,						"""labels"""					)

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

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

   # Instantiate dataloaders.
   lowerCamelCase_						:   Optional[int]		= DataLoader(
       tokenized_datasets["""train"""]						,						shuffle=A__						,						collate_fn=A__						,						batch_size=A__					)
   lowerCamelCase_						:   int		= DataLoader(
       tokenized_datasets["""validation"""]						,						shuffle=A__						,						collate_fn=A__						,						batch_size=A__					)

   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

      snake_case__				:	List[str]					       =					mocked_dataloaders  # noqa: F811


def    __lowerCamelCase   (      A__						: Any						,						A__						: str					)     ->    int:
   # For testing only
   if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,						A__					) == "1":
      lowerCamelCase_						:   List[str]		= 2
   # New Code #
   lowerCamelCase_						:   List[str]		= int(args.gradient_accumulation_steps					)
   # Initialize accelerator
   lowerCamelCase_						:   Optional[Any]		= Accelerator(
       cpu=args.cpu						,						mixed_precision=args.mixed_precision						,						gradient_accumulation_steps=A__					)
   if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
      raise NotImplementedError(
          """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""					)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCamelCase_						:   Tuple		= config["""lr"""]
   lowerCamelCase_						:   Tuple		= int(config["""num_epochs"""]					)
   lowerCamelCase_						:   Dict		= int(config["""seed"""]					)
   lowerCamelCase_						:   List[str]		= int(config["""batch_size"""]					)

   lowerCamelCase_						:   Dict		= evaluate.load("""glue"""						,						"""mrpc"""					)

   set_seed(A__					)
   lowerCamelCase_,	lowerCamelCase_						:   Any		= get_dataloaders(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("""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).
   lowerCamelCase_						:   Any		= model.to(accelerator.device					)

   # Instantiate optimizer
   lowerCamelCase_						:   List[str]		= AdamW(params=model.parameters()						,						lr=A__					)

   # Instantiate scheduler
   lowerCamelCase_						:   List[str]		= get_linear_schedule_with_warmup(
       optimizer=A__						,						num_warmup_steps=100						,						num_training_steps=(len(A__					) * 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.
   lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   List[Any]		= accelerator.prepare(
       A__						,						A__						,						A__						,						A__						,						A__					)

   # Now we train the model
   for epoch in range(A__					):
      model.train()
      for step, batch in enumerate(A__					):
         # 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(A__					):
            lowerCamelCase_						:   Any		= model(**A__					)
            lowerCamelCase_						:   Tuple		= output.loss
            accelerator.backward(A__					)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      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():
            lowerCamelCase_						:   int		= model(**A__					)
         lowerCamelCase_						:   List[str]		= outputs.logits.argmax(dim=-1					)
         lowerCamelCase_,	lowerCamelCase_						:   Union[str, Any]		= accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
         metric.add_batch(
             predictions=A__						,						references=A__						,						)

      lowerCamelCase_						:   str		= metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''						,						A__					)


def    __lowerCamelCase   (      )     ->    Optional[int]:
   lowerCamelCase_						:   List[Any]		= 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."""						,						)
   # New Code #
   parser.add_argument(
       """--gradient_accumulation_steps"""						,						type=A__						,						default=1						,						help="""The number of minibatches to be ran before gradients are accumulated."""						,						)
   parser.add_argument("""--cpu"""						,						action="""store_true"""						,						help="""If passed, will train on the CPU."""					)
   lowerCamelCase_						:   Any		= parser.parse_args()
   lowerCamelCase_						:   Optional[Any]		= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(A__						,						A__					)


if __name__ == "__main__":
      main()









class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				List[Any]							)					->Tuple:
          lowerCamelCase_						:   Optional[Any]		= """"""
          lowerCamelCase_						:   Dict		= """"""
          lowerCamelCase_						:   Optional[Any]		= []



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				int		,				__a				:				int							)					->int:
          if m == -1:
             return n + 1
          elif n == -1:
             return m + 1
          elif self.dp[m][n] > -1:
             return self.dp[m][n]
          else:
             if self.worda[m] == self.worda[n]:
                lowerCamelCase_						:   Optional[Any]		= self.__min_dist_top_down_dp(m - 1		,				n - 1							)
             else:
                lowerCamelCase_						:   List[Any]		= self.__min_dist_top_down_dp(__a		,				n - 1							)
                lowerCamelCase_						:   Union[str, Any]		= self.__min_dist_top_down_dp(m - 1		,				__a							)
                lowerCamelCase_						:   Any		= self.__min_dist_top_down_dp(m - 1		,				n - 1							)
                lowerCamelCase_						:   Tuple		= 1 + min(__a		,				__a		,				__a							)

             return self.dp[m][n]



       def 				_lowerCAmelCase							(						self				:				Any		,				__a				:				str		,				__a				:				str							)					->int:
          lowerCamelCase_						:   Dict		= worda
          lowerCamelCase_						:   str		= worda
          lowerCamelCase_						:   Union[str, Any]		= [[-1 for _ in range(len(__a							)							)] for _ in range(len(__a							)							)]

          return self.__min_dist_top_down_dp(len(__a							) - 1		,				len(__a							) - 1							)




       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				str		,				__a				:				str							)					->int:
          lowerCamelCase_						:   Tuple		= worda
          lowerCamelCase_						:   Optional[Any]		= worda
          lowerCamelCase_						:   Tuple		= len(__a							)
          lowerCamelCase_						:   Optional[Any]		= len(__a							)
          lowerCamelCase_						:   List[Any]		= [[0 for _ in range(n + 1							)] for _ in range(m + 1							)]

          for i in range(m + 1							):
             for j in range(n + 1							):
                if i == 0:  # first string is empty
                   lowerCamelCase_						:   Optional[Any]		= j
                elif j == 0:  # second string is empty
                   lowerCamelCase_						:   Any		= i
                elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                   lowerCamelCase_						:   Tuple		= self.dp[i - 1][j - 1]
                else:
                   lowerCamelCase_						:   Dict		= self.dp[i][j - 1]
                   lowerCamelCase_						:   Union[str, Any]		= self.dp[i - 1][j]
                   lowerCamelCase_						:   Any		= self.dp[i - 1][j - 1]
                   lowerCamelCase_						:   Optional[int]		= 1 + min(__a		,				__a		,				__a							)
          return self.dp[m][n]


if __name__ == "__main__":
      snake_case__				:	Optional[Any]					       =					EditDistance()

      print('****************** Testing Edit Distance DP Algorithm ******************')
      print()

      snake_case__				:	Union[str, Any]					       =					input('Enter the first string: ').strip()
      snake_case__				:	List[Any]					       =					input('Enter the second string: ').strip()

      print()
      print(F'The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}')
      print(F'The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}')
      print()
      print('*************** End of Testing Edit Distance DP Algorithm ***************')





