




'''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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


UpperCamelCase_						:      str       =						16
UpperCamelCase_						:      Optional[int]       =						32





def      __a     (       _UpperCamelCase:   Accelerator ,						_UpperCamelCase:   int = 16   )						->			int:

     """simple docstring"""




     _snake_case							=				AutoTokenizer.from_pretrained("bert-base-cased"   )
     _snake_case							=				load_dataset("glue" ,						"mrpc"   )

     def tokenize_function(_UpperCamelCase:   Optional[Any]   ):
          # max_length=None => use the model max length (it's actually the default)
          _snake_case							=				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():
          _snake_case							=				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
     _snake_case							=				tokenized_datasets.rename_column("label" ,						"labels"   )

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

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

     # Instantiate dataloaders.
     _snake_case							=				DataLoader(
         tokenized_datasets["train"] ,						shuffle=_UpperCamelCase ,						collate_fn=_UpperCamelCase ,						batch_size=_UpperCamelCase   )
     _snake_case							=				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

   UpperCamelCase_						:      Optional[Any]       =						mocked_dataloaders  # noqa: F811





def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   str   )						->			str:

     """simple docstring"""




     if os.environ.get("TESTING_MOCKED_DATALOADERS" ,						_UpperCamelCase   ) == "1":
          _snake_case							=				2
     # Initialize accelerator
     _snake_case							=				Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision   )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _snake_case							=				config["lr"]
     _snake_case							=				int(config["num_epochs"]   )
     _snake_case							=				int(config["seed"]   )
     _snake_case							=				int(config["batch_size"]   )

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

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

     set_seed(_UpperCamelCase   )
     _snake_case							, _snake_case							=				get_dataloaders(_UpperCamelCase ,						_UpperCamelCase   )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _snake_case							=				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).
     _snake_case							=				model.to(accelerator.device   )

     # Instantiate optimizer
     _snake_case							=				AdamW(params=model.parameters() ,						lr=_UpperCamelCase   )

     # Instantiate scheduler
     _snake_case							=				get_linear_schedule_with_warmup(
         optimizer=_UpperCamelCase ,						num_warmup_steps=100 ,						num_training_steps=(len(_UpperCamelCase   ) * 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.
     _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case							=				accelerator.prepare(
         _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )

     # Now we train the model
     for epoch in range(_UpperCamelCase   ):
          model.train()
          for step, batch in enumerate(_UpperCamelCase   ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device   )
               _snake_case							=				model(**_UpperCamelCase   )
               _snake_case							=				outputs.loss
               _snake_case							=				loss / gradient_accumulation_steps
               accelerator.backward(_UpperCamelCase   )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          _snake_case							=				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():
                    _snake_case							=				model(**_UpperCamelCase   )
               _snake_case							=				outputs.logits.argmax(dim=-1   )
               _snake_case							, _snake_case							=				accelerator.gather((predictions, batch["labels"])   )
               # New Code #
               # First we check if it's a distributed system
               if accelerator.use_distributed:
                    # Then see if we're on the last batch of our eval dataloader
                    if step == len(_UpperCamelCase   ) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         _snake_case							=				predictions[: len(eval_dataloader.dataset   ) - samples_seen]
                         _snake_case							=				references[: len(eval_dataloader.dataset   ) - samples_seen]
                    else:
                         # Otherwise we add the number of samples seen
                         samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
               metric.add_batch(
                   predictions=_UpperCamelCase ,						references=_UpperCamelCase ,						)

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





def      __a     (       )						->			int:

     """simple docstring"""




     _snake_case							=				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." ,						)
     parser.add_argument("--cpu" ,						action="store_true" ,						help="If passed, will train on the CPU."   )
     _snake_case							=				parser.parse_args()
     _snake_case							=				{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(_UpperCamelCase ,						_UpperCamelCase   )


if __name__ == "__main__":
   main()








'''simple docstring'''





def      __a     (       _UpperCamelCase:   int   )						->			None:

     """simple docstring"""




     _snake_case							=				generate_pascal_triangle(_UpperCamelCase   )
     for row_idx in range(_UpperCamelCase   ):
          # Print left spaces
          for _ in range(num_rows - row_idx - 1   ):
               print(end=" "   )
          # Print row values
          for col_idx in range(row_idx + 1   ):
               if col_idx != row_idx:
                    print(triangle[row_idx][col_idx] ,						end=" "   )
               else:
                    print(triangle[row_idx][col_idx] ,						end=""   )
          print()





def      __a     (       _UpperCamelCase:   int   )						->			list[list[int]]:

     """simple docstring"""




     if not isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
          raise TypeError("The input value of 'num_rows' should be 'int'"   )

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              "The input value of 'num_rows' should be greater than or equal to 0"   )

     _snake_case							=				[]
     for current_row_idx in range(_UpperCamelCase   ):
          _snake_case							=				populate_current_row(_UpperCamelCase ,						_UpperCamelCase   )
          triangle.append(_UpperCamelCase   )
     return triangle





def      __a     (       _UpperCamelCase:   list[list[int]] ,						_UpperCamelCase:   int   )						->			list[int]:

     """simple docstring"""




     _snake_case							=				[-1] * (current_row_idx + 1)
     # first and last elements of current row are equal to 1
     _snake_case							, _snake_case							=				1, 1
     for current_col_idx in range(1 ,						_UpperCamelCase   ):
          calculate_current_element(
              _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
     return current_row





def      __a     (       _UpperCamelCase:   list[list[int]] ,						_UpperCamelCase:   list[int] ,						_UpperCamelCase:   int ,						_UpperCamelCase:   int ,						)						->			None:

     """simple docstring"""




     _snake_case							=				triangle[current_row_idx - 1][current_col_idx - 1]
     _snake_case							=				triangle[current_row_idx - 1][current_col_idx]
     _snake_case							=				above_to_left_elt + above_to_right_elt





def      __a     (       _UpperCamelCase:   int   )						->			list[list[int]]:

     """simple docstring"""




     if not isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
          raise TypeError("The input value of 'num_rows' should be 'int'"   )

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              "The input value of 'num_rows' should be greater than or equal to 0"   )

     _snake_case							=				[[1]]

     for row_index in range(1 ,						_UpperCamelCase   ):
          _snake_case							=				[0] + result[-1] + [0]
          _snake_case							=				row_index + 1
          # Calculate the number of distinct elements in a row
          _snake_case							=				sum(divmod(_UpperCamelCase ,						2   )   )
          _snake_case							=				[
              temp_row[i - 1] + temp_row[i] for i in range(1 ,						distinct_elements + 1   )
          ]
          _snake_case							=				row_first_half[: (row_index + 1) // 2]
          row_second_half.reverse()
          _snake_case							=				row_first_half + row_second_half
          result.append(_UpperCamelCase   )

     return result





def      __a     (       )						->			None:

     """simple docstring"""




     from collections.abc import Callable
     from timeit import timeit

     def benchmark_a_function(_UpperCamelCase:   Callable ,						_UpperCamelCase:   int   ) -> None:
          _snake_case							=				F"""{func.__name__}({value})"""
          _snake_case							=				timeit(F"""__main__.{call}""" ,						setup="import __main__"   )
          # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
          print(F"""{call:38} -- {timing:.4f} seconds"""   )

     for value in range(15   ):  # (1, 7, 14):
          for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
               benchmark_a_function(_UpperCamelCase ,						_UpperCamelCase   )
          print()


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()


