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
#
########################################################################


UpperCAmelCase_           =				1_6
UpperCAmelCase_           =				3_2




def    lowerCAmelCase_  (		lowercase:				List[Any]	,       lowercase:				Optional[Any] = 16		)     ->					Tuple:







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

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

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

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

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

  UpperCAmelCase_           =				mocked_dataloaders  # noqa: F811




def    lowerCAmelCase_  (		lowercase:				Dict	,       lowercase:				Dict		)     ->					int:







 '''simple docstring'''





 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,       __a		) == "1":
  _UpperCamelCase:  Tuple    						=			2
 # New Code #
 _UpperCamelCase:  str    						=			int(args.gradient_accumulation_steps		)
 # Initialize accelerator
 _UpperCamelCase:  Tuple    						=			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
 _UpperCamelCase:  Optional[Any]    						=			config['''lr''']
 _UpperCamelCase:  List[str]    						=			int(config['''num_epochs''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''seed''']		)
 _UpperCamelCase:  int    						=			int(config['''batch_size''']		)

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

 set_seed(__a		)
 _UpperCamelCase						,				_UpperCamelCase:  str    						=			get_dataloaders(__a	,       __a		)
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase:  Union[str, 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).
 _UpperCamelCase:  Dict    						=			model.to(accelerator.device		)

 # Instantiate optimizer
 _UpperCamelCase:  Any    						=			AdamW(params=model.parameters()	,       lr=__a		)

 # Instantiate scheduler
 _UpperCamelCase:  Any    						=			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.
 _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  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		):
    _UpperCamelCase:  List[str]    						=			model(**__a		)
    _UpperCamelCase:  List[str]    						=			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():
    _UpperCamelCase:  List[Any]    						=			model(**__a		)
   _UpperCamelCase:  str    						=			outputs.logits.argmax(dim=-1		)
   _UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
   metric.add_batch(
       predictions=__a	,       references=__a	,       )

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




def    lowerCAmelCase_  (		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  str    						=			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.'''		)
 _UpperCamelCase:  Dict    						=			parser.parse_args()
 _UpperCamelCase:  str    						=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(__a	,       __a		)


if __name__ == "__main__":
  main()

"""simple docstring"""





import argparse
import os
import re


UpperCamelCase_       :    Any						      =			'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_       :    Optional[int]						      =			re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
UpperCamelCase_       :    Tuple						      =			re.compile(R'''\s*\(\s*"(\S[^"]+)"''')
def 				A_  (__a					, __a = False					):

					'''simple docstring'''





					with open(__a					, "r"					, encoding="utf-8"					) as f:
										A_								=    f.read()

					A_								=    content.split("\n"					)
					A_								=    []
					A_								=    0
					while line_idx < len(__a					):
										if _re_intro_mapping.search(lines[line_idx]					) is not None:
															A_								=    len(re.search(R"^(\s*)\S"					, lines[line_idx]					).groups()[0]					) + 8
															# Start of a new mapping!
															while not lines[line_idx].startswith(" " * indent + "("					):
																				new_lines.append(lines[line_idx]					)
																				line_idx += 1

															A_								=    []
															while lines[line_idx].strip() != "]":
																				# Blocks either fit in one line or not
																				if lines[line_idx].strip() == "(":
																									A_								=    line_idx
																									while not lines[line_idx].startswith(" " * indent + ")"					):
																														line_idx += 1
																									blocks.append("\n".join(lines[start_idx : line_idx + 1]					)					)
																				else:
																									blocks.append(lines[line_idx]					)
																				line_idx += 1

															# Sort blocks by their identifiers
															A_								=    sorted(__a					, key=lambda __a					: _re_identifier.search(__a					).groups()[0]					)
															new_lines += blocks
										else:
															new_lines.append(lines[line_idx]					)
															line_idx += 1

					if overwrite:
										with open(__a					, "w"					, encoding="utf-8"					) as f:
															f.write("\n".join(__a					)					)
					elif "\n".join(__a					) != content:
										return True
def 				A_  (__a = False					):

					'''simple docstring'''





					A_								=    [os.path.join(__a					, __a					) for f in os.listdir(__a					) if f.endswith(".py"					)]
					A_								=    [sort_auto_mapping(__a					, overwrite=__a					) for fname in fnames]

					if not overwrite and any(__a					):
										A_								=    [f for f, d in zip(__a					, __a					) if d]
										raise ValueError(
										    f'The following files have auto mappings that need sorting: {", ".join(__a					)}. Run `make style` to fix'
										    " this."					)


if __name__ == "__main__":
		UpperCamelCase_       :    str						      =			argparse.ArgumentParser()
		parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
		UpperCamelCase_       :    List[Any]						      =			parser.parse_args()

		sort_all_auto_mappings(not args.check_only)




