





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


a__      :     str         =		16
a__      :     Dict         =		32

def      UpperCAmelCase_    (    _UpperCAmelCase							:Accelerator  ,						_UpperCAmelCase							:int = 16				)	->				Dict:
     '''simple docstring'''


     A_          =       AutoTokenizer.from_pretrained('''bert-base-cased'''				)
     A_          =       load_dataset('''glue'''  ,						'''mrpc'''				)

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

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

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

     # Instantiate dataloaders.
     A_          =       DataLoader(
         tokenized_datasets['''train''']  ,						shuffle=_UpperCAmelCase  ,						collate_fn=_UpperCAmelCase  ,						batch_size=_UpperCAmelCase				)
     A_          =       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

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

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:Optional[int]				)	->				int:
     '''simple docstring'''


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

     A_          =       evaluate.load('''glue'''  ,						'''mrpc'''				)

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

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

     # Instantiate optimizer
     A_          =       AdamW(params=model.parameters()  ,						lr=_UpperCAmelCase				)

     # Instantiate scheduler
     A_          =       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.
     A_   ,    A_   ,    A_   ,    A_   ,    A_          =       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				)
               A_          =       model(**_UpperCAmelCase				)
               A_          =       outputs.loss
               A_          =       loss / gradient_accumulation_steps
               accelerator.backward(_UpperCAmelCase				)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          A_          =       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():
                    A_          =       model(**_UpperCAmelCase				)
               A_          =       outputs.logits.argmax(dim=-1				)
               A_   ,    A_          =       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
                         A_          =       predictions[: len(eval_dataloader.dataset				) - samples_seen]
                         A_          =       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  ,						)

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

def      UpperCAmelCase_    (    )	->				List[str]:
     '''simple docstring'''


     A_          =       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.'''				)
     A_          =       parser.parse_args()
     A_          =       {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_UpperCAmelCase  ,						_UpperCAmelCase				)


if __name__ == "__main__":
      main()










import argparse
import os
import re


a__      :     str         =		'src/transformers'

# Pattern that looks at the indentation in a line.
a__      :     Union[str, Any]         =		re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
a__      :     List[Any]         =		re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
a__      :     Dict         =		re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
a__      :     Optional[int]         =		re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
a__      :     Optional[Any]         =		re.compile(R'\[([^\]]+)\]')

def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple				)	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       _re_indent.search(_UpperCAmelCase				)
     return "" if search is None else search.groups()[0]

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]  ,						_UpperCAmelCase							:List[Any]=""  ,						_UpperCAmelCase							:Optional[Any]=None  ,						_UpperCAmelCase							:List[str]=None				)	->				Optional[Any]:
     '''simple docstring'''


     A_          =       0
     A_          =       code.split('''\n'''				)
     if start_prompt is not None:
          while not lines[index].startswith(_UpperCAmelCase				):
               index += 1
          A_          =       ['''\n'''.join(lines[:index]				)]
     else:
          A_          =       []

     # We split into blocks until we get to the `end_prompt` (or the end of the block).
     A_          =       [lines[index]]
     index += 1
     while index < len(_UpperCAmelCase				) and (end_prompt is None or not lines[index].startswith(_UpperCAmelCase				)):
          if len(lines[index]				) > 0 and get_indent(lines[index]				) == indent_level:
               if len(_UpperCAmelCase				) > 0 and get_indent(current_block[-1]				).startswith(indent_level + ''' '''				):
                    current_block.append(lines[index]				)
                    blocks.append('''\n'''.join(_UpperCAmelCase				)				)
                    if index < len(_UpperCAmelCase				) - 1:
                         A_          =       [lines[index + 1]]
                         index += 1
                    else:
                         A_          =       []
               else:
                    blocks.append('''\n'''.join(_UpperCAmelCase				)				)
                    A_          =       [lines[index]]
          else:
               current_block.append(lines[index]				)
          index += 1

     # Adds current block if it's nonempty.
     if len(_UpperCAmelCase				) > 0:
          blocks.append('''\n'''.join(_UpperCAmelCase				)				)

     # Add final block after end_prompt if provided.
     if end_prompt is not None and index < len(_UpperCAmelCase				):
          blocks.append('''\n'''.join(lines[index:]				)				)

     return blocks

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]				)	->				Dict:
     '''simple docstring'''


     def _inner(_UpperCAmelCase							:Dict				):
          return key(_UpperCAmelCase				).lower().replace('''_'''  ,						''''''				)

     return _inner

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[str]  ,						_UpperCAmelCase							:Dict=None				)	->				Union[str, Any]:
     '''simple docstring'''


     def noop(_UpperCAmelCase							:int				):
          return x

     if key is None:
          A_          =       noop
     # Constants are all uppercase, they go first.
     A_          =       [obj for obj in objects if key(_UpperCAmelCase				).isupper()]
     # Classes are not all uppercase but start with a capital, they go second.
     A_          =       [obj for obj in objects if key(_UpperCAmelCase				)[0].isupper() and not key(_UpperCAmelCase				).isupper()]
     # Functions begin with a lowercase, they go last.
     A_          =       [obj for obj in objects if not key(_UpperCAmelCase				)[0].isupper()]

     A_          =       ignore_underscore(_UpperCAmelCase				)
     return sorted(_UpperCAmelCase  ,						key=_UpperCAmelCase				) + sorted(_UpperCAmelCase  ,						key=_UpperCAmelCase				) + sorted(_UpperCAmelCase  ,						key=_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:int				)	->				str:
     '''simple docstring'''


     def _replace(_UpperCAmelCase							:List[Any]				):
          A_          =       match.groups()[0]
          if "," not in imports:
               return f'[{imports}]'
          A_          =       [part.strip().replace('''"'''  ,						''''''				) for part in imports.split(''','''				)]
          # We will have a final empty element if the line finished with a comma.
          if len(keys[-1]				) == 0:
               A_          =       keys[:-1]
          return "[" + ", ".join([f'"{k}"' for k in sort_objects(_UpperCAmelCase				)]				) + "]"

     A_          =       import_statement.split('''\n'''				)
     if len(_UpperCAmelCase				) > 3:
          # Here we have to sort internal imports that are on several lines (one per name):
          # key: [
          #     "object1",
          #     "object2",
          #     ...
          # ]

          # We may have to ignore one or two lines on each side.
          A_          =       2 if lines[1].strip() == '''[''' else 1
          A_          =       [(i, _re_strip_line.search(_UpperCAmelCase				).groups()[0]) for i, line in enumerate(lines[idx:-idx]				)]
          A_          =       sort_objects(_UpperCAmelCase  ,						key=lambda _UpperCAmelCase				: x[1]				)
          A_          =       [lines[x[0] + idx] for x in sorted_indices]
          return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]				)
     elif len(_UpperCAmelCase				) == 3:
          # Here we have to sort internal imports that are on one separate line:
          # key: [
          #     "object1", "object2", ...
          # ]
          if _re_bracket_content.search(lines[1]				) is not None:
               A_          =       _re_bracket_content.sub(_replace  ,						lines[1]				)
          else:
               A_          =       [part.strip().replace('''"'''  ,						''''''				) for part in lines[1].split(''','''				)]
               # We will have a final empty element if the line finished with a comma.
               if len(keys[-1]				) == 0:
                    A_          =       keys[:-1]
               A_          =       get_indent(lines[1]				) + ''', '''.join([f'"{k}"' for k in sort_objects(_UpperCAmelCase				)]				)
          return "\n".join(_UpperCAmelCase				)
     else:
          # Finally we have to deal with imports fitting on one line
          A_          =       _re_bracket_content.sub(_replace  ,						_UpperCAmelCase				)
          return import_statement

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:int=True				)	->				str:
     '''simple docstring'''


     with open(_UpperCAmelCase  ,						encoding='''utf-8'''				) as f:
          A_          =       f.read()

     if "_import_structure" not in code:
          return

     # Blocks of indent level 0
     A_          =       split_code_in_indented_blocks(
         _UpperCAmelCase  ,						start_prompt='''_import_structure = {'''  ,						end_prompt='''if TYPE_CHECKING:'''				)

     # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
     for block_idx in range(1  ,						len(_UpperCAmelCase				) - 1				):
          # Check if the block contains some `_import_structure`s thingy to sort.
          A_          =       main_blocks[block_idx]
          A_          =       block.split('''\n'''				)

          # Get to the start of the imports.
          A_          =       0
          while line_idx < len(_UpperCAmelCase				) and "_import_structure" not in block_lines[line_idx]:
               # Skip dummy import blocks
               if "import dummy" in block_lines[line_idx]:
                    A_          =       len(_UpperCAmelCase				)
               else:
                    line_idx += 1
          if line_idx >= len(_UpperCAmelCase				):
               continue

          # Ignore beginning and last line: they don't contain anything.
          A_          =       '''\n'''.join(block_lines[line_idx:-1]				)
          A_          =       get_indent(block_lines[1]				)
          # Slit the internal block into blocks of indent level 1.
          A_          =       split_code_in_indented_blocks(_UpperCAmelCase  ,						indent_level=_UpperCAmelCase				)
          # We have two categories of import key: list or _import_structure[key].append/extend
          A_          =       _re_direct_key if '''_import_structure = {''' in block_lines[0] else _re_indirect_key
          # Grab the keys, but there is a trap: some lines are empty or just comments.
          A_          =       [(pattern.search(_UpperCAmelCase				).groups()[0] if pattern.search(_UpperCAmelCase				) is not None else None) for b in internal_blocks]
          # We only sort the lines with a key.
          A_          =       [(i, key) for i, key in enumerate(_UpperCAmelCase				) if key is not None]
          A_          =       [x[0] for x in sorted(_UpperCAmelCase  ,						key=lambda _UpperCAmelCase				: x[1]				)]

          # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
          A_          =       0
          A_          =       []
          for i in range(len(_UpperCAmelCase				)				):
               if keys[i] is None:
                    reorderded_blocks.append(internal_blocks[i]				)
               else:
                    A_          =       sort_objects_in_import(internal_blocks[sorted_indices[count]]				)
                    reorderded_blocks.append(_UpperCAmelCase				)
                    count += 1

        # And we put our main block back together with its first and last line.
          A_          =       '''\n'''.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]				)

     if code != "\n".join(_UpperCAmelCase				):
          if check_only:
               return True
          else:
               print(f'Overwriting {file}.'				)
               with open(_UpperCAmelCase  ,						'''w'''  ,						encoding='''utf-8'''				) as f:
                    f.write('''\n'''.join(_UpperCAmelCase				)				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[Any]=True				)	->				Tuple:
     '''simple docstring'''


     A_          =       []
     for root, _, files in os.walk(_UpperCAmelCase				):
          if "__init__.py" in files:
               A_          =       sort_imports(os.path.join(_UpperCAmelCase  ,						'''__init__.py'''				)  ,						check_only=_UpperCAmelCase				)
               if result:
                    A_          =       [os.path.join(_UpperCAmelCase  ,						'''__init__.py'''				)]
     if len(_UpperCAmelCase				) > 0:
          raise ValueError(f'Would overwrite {len(_UpperCAmelCase				)} files, run `make style`.'				)


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

      sort_imports_in_all_inits(check_only=args.check_only)



