



import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter







class 							a_  (		TensorFormatter[Mapping, np.ndarray, Mapping]				):

						def __init__(    self	:Optional[Any]   ,						_lowercase	:str=None   ,						**_lowercase	:Optional[Any])		->					Optional[Any]:
												super().__init__(features=_lowercase)
												UpperCAmelCase_     							=     np_array_kwargs

						def 	__a       (    self	:Any   ,						_lowercase	:Optional[int])		->					Optional[int]:
												if isinstance(_lowercase   ,						_lowercase):
																		if column and all(
																		    isinstance(_lowercase   ,						np.ndarray) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
																								return np.stack(_lowercase)
																		else:
																								# don't use np.array(column, dtype=object)
																								# since it fails in certain cases
																								# see https://stackoverflow.com/q/51005699
																								UpperCAmelCase_     							=     np.empty(len(_lowercase)   ,						dtype=_lowercase)
																								UpperCAmelCase_     							=     column
																								return out
												return column

						def 	__a       (    self	:int   ,						_lowercase	:Optional[int])		->					Any:
												if isinstance(_lowercase   ,						(str, bytes, type(_lowercase))):
																		return value
												elif isinstance(_lowercase   ,						(np.character, np.ndarray)) and np.issubdtype(value.dtype   ,						np.character):
																		return value
												elif isinstance(_lowercase   ,						np.number):
																		return value

												UpperCAmelCase_     							=     {}

												if isinstance(_lowercase   ,						np.ndarray) and np.issubdtype(value.dtype   ,						np.integer):
																		UpperCAmelCase_     							=     {'''dtype''': np.intaa}
												elif isinstance(_lowercase   ,						np.ndarray) and np.issubdtype(value.dtype   ,						np.floating):
																		UpperCAmelCase_     							=     {'''dtype''': np.floataa}
												elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																		import PIL.Image

																		if isinstance(_lowercase   ,						PIL.Image.Image):
																								return np.asarray(_lowercase   ,						**self.np_array_kwargs)

												return np.asarray(_lowercase   ,						**{**default_dtype, **self.np_array_kwargs})

						def 	__a       (    self	:Dict   ,						_lowercase	:Union[str, Any])		->					Optional[int]:
												# support for torch, tf, jax etc.
												if config.TORCH_AVAILABLE and "torch" in sys.modules:
																		import torch

																		if isinstance(_lowercase   ,						torch.Tensor):
																								return self._tensorize(data_struct.detach().cpu().numpy()[()])
												if hasattr(_lowercase   ,						'''__array__''') and not isinstance(_lowercase   ,						(np.ndarray, np.character, np.number)):
																		UpperCAmelCase_     							=     data_struct.__array__()
												# support for nested types like struct of list of struct
												if isinstance(_lowercase   ,						np.ndarray):
																		if data_struct.dtype == object:
																								return self._consolidate([self.recursive_tensorize(_lowercase) for substruct in data_struct])
												if isinstance(_lowercase   ,						(list, tuple)):
																		return self._consolidate([self.recursive_tensorize(_lowercase) for substruct in data_struct])
												return self._tensorize(_lowercase)

						def 	__a       (    self	:Tuple   ,						_lowercase	:dict)		->					str:
												return map_nested(self._recursive_tensorize   ,						_lowercase   ,						map_list=_lowercase)

						def 	__a       (    self	:Optional[Any]   ,						_lowercase	:pa.Table)		->					Mapping:
												UpperCAmelCase_     							=     self.numpy_arrow_extractor().extract_row(_lowercase)
												UpperCAmelCase_     							=     self.python_features_decoder.decode_row(_lowercase)
												return self.recursive_tensorize(_lowercase)

						def 	__a       (    self	:Optional[Any]   ,						_lowercase	:pa.Table)		->					np.ndarray:
												UpperCAmelCase_     							=     self.numpy_arrow_extractor().extract_column(_lowercase)
												UpperCAmelCase_     							=     self.python_features_decoder.decode_column(_lowercase   ,						pa_table.column_names[0])
												UpperCAmelCase_     							=     self.recursive_tensorize(_lowercase)
												UpperCAmelCase_     							=     self._consolidate(_lowercase)
												return column




						def 	__a       (    self	:Optional[int]   ,						_lowercase	:pa.Table)		->					Mapping:
												UpperCAmelCase_     							=     self.numpy_arrow_extractor().extract_batch(_lowercase)
												UpperCAmelCase_     							=     self.python_features_decoder.decode_batch(_lowercase)
												UpperCAmelCase_     							=     self.recursive_tensorize(_lowercase)
												for column_name in batch:
																		UpperCAmelCase_     							=     self._consolidate(batch[column_name])
												return batch













"""simple docstring"""
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

A_			: Tuple				=			32






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

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

 def collate_fn(_lowerCamelCase				):
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   lowerCamelCase__     :			Any     				=						16
  elif accelerator.mixed_precision != "no":
   lowerCamelCase__     :			Any     				=						8
  else:
   lowerCamelCase__     :			Dict     				=						None

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

 # Instantiate dataloaders.
 lowerCamelCase__     :			List[str]     				=						DataLoader(
     tokenized_datasets['train']			,   shuffle=_lowerCamelCase			,   collate_fn=_lowerCamelCase			,   batch_size=_lowerCamelCase				)
 lowerCamelCase__     :			Tuple     				=						DataLoader(
     tokenized_datasets['validation']			,   shuffle=_lowerCamelCase			,   collate_fn=_lowerCamelCase			,   batch_size=_lowerCamelCase				)

 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

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






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 # For testing only
 if os.environ.get('TESTING_MOCKED_DATALOADERS'			,   _lowerCamelCase				) == "1":
  lowerCamelCase__     :			List[str]     				=						2
 # Initialize accelerator
 lowerCamelCase__     :			List[str]     				=						Accelerator(cpu=args.cpu			,   mixed_precision=args.mixed_precision				)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowerCamelCase__     :			Any     				=						config['lr']
 lowerCamelCase__     :			List[str]     				=						int(config['num_epochs']				)
 lowerCamelCase__     :			Optional[Any]     				=						int(config['seed']				)
 lowerCamelCase__     :			int     				=						int(config['batch_size']				)

 lowerCamelCase__     :			Union[str, Any]     				=						evaluate.load('glue'			,   'mrpc'				)

 # New Code #
 # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
 # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
 # half each time. From this, we can calculate the number of gradient accumulation steps needed
 # and modify the Accelerator object as a result
 @find_executable_batch_size(starting_batch_size=int(_lowerCamelCase				)				)
 def inner_training_loop(_lowerCamelCase				):
  # Since we need to modify the outside accelerator object, we need to bring it
  # to the local scope
  nonlocal accelerator

  # We can calculate the number of gradient accumulation steps based on the current
  # batch size vs the starting batch size
  lowerCamelCase__     :			List[str]     				=						observed_batch_size // batch_size

  # And then set it in the Accelerator directly:
  lowerCamelCase__     :			Any     				=						num_gradient_accumulation_steps

  # Next we need to free all of the stored model references in the Accelerator each time
  accelerator.free_memory()

  # And set the seed so our results are reproducable each reset
  set_seed(_lowerCamelCase				)

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

  # 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__     :			Optional[Any]     				=						model.to(accelerator.device				)

  # Instantiate optimizer
  lowerCamelCase__     :			int     				=						AdamW(params=model.parameters()			,   lr=_lowerCamelCase				)
  lowerCamelCase__				,     lowerCamelCase__     :			List[Any]     				=						get_dataloaders(_lowerCamelCase			,   _lowerCamelCase				)

  # Instantiate scheduler
  lowerCamelCase__     :			int     				=						get_linear_schedule_with_warmup(
      optimizer=_lowerCamelCase			,   num_warmup_steps=100			,   num_training_steps=(len(_lowerCamelCase				) * 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__     :			Optional[int]     				=						accelerator.prepare(
      _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)

  # Now we train the model
  for epoch in range(_lowerCamelCase				):
   model.train()
   for step, batch in enumerate(_lowerCamelCase				):
    # And perform gradient accumulation
    with accelerator.accumulate(_lowerCamelCase				):
     # We could avoid this line since we set the accelerator with `device_placement=True`.
     batch.to(accelerator.device				)
     lowerCamelCase__     :			Tuple     				=						model(**_lowerCamelCase				)
     lowerCamelCase__     :			Optional[Any]     				=						outputs.loss
     accelerator.backward(_lowerCamelCase				)
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

   model.eval()
   for step, batch in enumerate(_lowerCamelCase				):
    # 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(**_lowerCamelCase				)
    lowerCamelCase__     :			str     				=						outputs.logits.argmax(dim=-1				)
    lowerCamelCase__				,     lowerCamelCase__     :			Dict     				=						accelerator.gather_for_metrics((predictions, batch['labels'])				)
    metric.add_batch(
        predictions=_lowerCamelCase			,   references=_lowerCamelCase			,   )

   lowerCamelCase__     :			Dict     				=						metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f'''epoch {epoch}:'''			,   _lowerCamelCase				)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()






def        lowerCamelCase_  (						):
 lowerCamelCase__     :			Any     				=						argparse.ArgumentParser(description='Simple example of training script.'				)
 parser.add_argument(
     '--mixed_precision'			,   type=_lowerCamelCase			,   default=_lowerCamelCase			,   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.'				)
 lowerCamelCase__     :			List[Any]     				=						parser.parse_args()
 # New Code #
 # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
 lowerCamelCase__     :			Tuple     				=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
 training_function(_lowerCamelCase			,   _lowerCamelCase				)


if __name__ == "__main__":
      main()

