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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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_           =				1_6
UpperCAmelCase_           =				3_2




def    lowerCAmelCase_  (		lowercase:				Accelerator	,       lowercase:				int = 16		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			AutoTokenizer.from_pretrained('''bert-base-cased'''		)
 _UpperCamelCase:  Dict    						=			load_dataset('''glue'''	,       '''mrpc'''		)

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

 def collate_fn(lowercase:				Union[str, Any]		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  _UpperCamelCase:  List[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":
   _UpperCamelCase:  str    						=			16
  elif accelerator.mixed_precision != "no":
   _UpperCamelCase:  int    						=			8
  else:
   _UpperCamelCase:  Optional[Any]    						=			None

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

 # Instantiate dataloaders.
 _UpperCamelCase:  Optional[int]    						=			DataLoader(
     tokenized_datasets['''train''']	,       shuffle=lowercase	,       collate_fn=lowercase	,       batch_size=lowercase		)
 _UpperCamelCase:  List[Any]    						=			DataLoader(
     tokenized_datasets['''validation''']	,       shuffle=lowercase	,       collate_fn=lowercase	,       batch_size=lowercase		)

 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_           =				mocked_dataloaders  # noqa: F811




def    lowerCAmelCase_  (		lowercase:				Dict	,       lowercase:				List[str]		)     ->					Dict:







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

 _UpperCamelCase:  Any    						=			evaluate.load('''glue'''	,       '''mrpc'''		)

 # New Code #
 # We now can define an inner training loop function. It should take a batch size as the only parameter,
 # and build the dataloaders in there.
 # It also gets our decorator
 @find_executable_batch_size(starting_batch_size=lowercase		)
 def inner_training_loop(lowercase:				Optional[int]		):
  # And now just move everything below under this function
  # We need to bring in the Accelerator object from earlier
  nonlocal accelerator
  # And reset all of its attributes that could hold onto any memory:
  accelerator.free_memory()

  # Then we can declare the model, optimizer, and everything else:
  set_seed(lowercase		)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _UpperCamelCase:  Optional[int]    						=			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,       return_dict=lowercase		)

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

  # Instantiate optimizer
  _UpperCamelCase:  Optional[int]    						=			AdamW(params=model.parameters()	,       lr=lowercase		)
  _UpperCamelCase						,				_UpperCamelCase:  int    						=			get_dataloaders(lowercase	,       lowercase		)

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

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

   model.eval()
   for step, batch in enumerate(lowercase		):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device		)
    with torch.no_grad():
     _UpperCamelCase:  str    						=			model(**lowercase		)
    _UpperCamelCase:  Optional[int]    						=			outputs.logits.argmax(dim=-1		)
    _UpperCamelCase						,				_UpperCamelCase:  int    						=			accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
    metric.add_batch(
        predictions=lowercase	,       references=lowercase	,       )

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

    # 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_  (		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			argparse.ArgumentParser(description='''Simple example of training script.'''		)
 parser.add_argument(
     '''--mixed_precision'''	,       type=lowercase	,       default=lowercase	,       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.'''		)
 _UpperCamelCase:  Dict    						=			parser.parse_args()
 _UpperCamelCase:  Dict    						=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(lowercase	,       lowercase		)


if __name__ == "__main__":
  main()
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


UpperCAmelCase_           =				'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
  import torch

  print('''Torch version:''', torch.__version__)
  print('''Cuda available:''', torch.cuda.is_available())
  print('''Cuda version:''', torch.version.cuda)
  print('''CuDNN version:''', torch.backends.cudnn.version())
  print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
  print('''Torch version:''', None)

try:
  import transformers

  print('''transformers version:''', transformers.__version__)
except ImportError:
  print('''transformers version:''', None)