


'''simple docstring'''

import collections
import os
import re
from pathlib import Path


lowerCamelCase							:   Optional[Any]  						=	'src/transformers'


# Matches is_xxx_available()
lowerCamelCase							:   Union[str, Any]  						=	re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
lowerCamelCase							:   int  						=	re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCamelCase							:   int  						=	re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
lowerCamelCase							:   int  						=	re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
lowerCamelCase							:   Optional[Any]  						=	re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCamelCase							:   Optional[Any]  						=	re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCamelCase							:   Any  						=	re.compile(R'^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCamelCase							:   List[Any]  						=	re.compile(R'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
lowerCamelCase							:   Union[str, Any]  						=	re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
lowerCamelCase							:   Union[str, Any]  						=	re.compile(R'^\s*try:')
# Catches a line with else:
lowerCamelCase							:   Tuple  						=	re.compile(R'^\s*else:')

def _SCREAMING_SNAKE_CASE      (A       )       ->       Union[str, Any]:






       """simple docstring"""







       if _re_test_backend.search(A       ) is None:
              return None
       lowercase__      			= [b[0] for b in _re_backend.findall(A       )]
       backends.sort()
       return "_and_".join(A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       Any:






       """simple docstring"""







       with open(A  , '''r'''  , encoding='''utf-8'''  , newline='''\n'''       ) as f:
              lowercase__      			= f.readlines()

       lowercase__      			= 0
       while line_index < len(A       ) and not lines[line_index].startswith('''_import_structure = {'''       ):
              line_index += 1

       # If this is a traditional init, just return.
       if line_index >= len(A       ):
              return None

       # First grab the objects without a specific backend in _import_structure
       lowercase__      			= []
       while not lines[line_index].startswith('''if TYPE_CHECKING'''       ) and find_backend(lines[line_index]       ) is None:
              lowercase__      			= lines[line_index]
              # If we have everything on a single line, let's deal with it.
              if _re_one_line_import_struct.search(A       ):
                     lowercase__      			= _re_one_line_import_struct.search(A       ).groups()[0]
                     lowercase__      			= re.findall(R'''\[([^\]]+)\]'''  , A       )
                     for imp in imports:
                            objects.extend([obj[1:-1] for obj in imp.split(''', '''       )]       )
                     line_index += 1
                     continue
              lowercase__      			= _re_import_struct_key_value.search(A       )
              if single_line_import_search is not None:
                     lowercase__      			= [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', '''       ) if len(A       ) > 0]
                     objects.extend(A       )
              elif line.startswith(''' ''' * 8 + '''"'''       ):
                     objects.append(line[9:-3]       )
              line_index += 1

       lowercase__      			= {'''none''': objects}
       # Let's continue with backend-specific objects in _import_structure
       while not lines[line_index].startswith('''if TYPE_CHECKING'''       ):
              # If the line is an if not is_backend_available, we grab all objects associated.
              lowercase__      			= find_backend(lines[line_index]       )
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]       ) is None:
                     lowercase__      			= None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]       ) is None:
                            line_index += 1

                     line_index += 1

                     lowercase__      			= []
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(''' ''' * 4       ):
                            lowercase__      			= lines[line_index]
                            if _re_import_struct_add_one.search(A       ) is not None:
                                   objects.append(_re_import_struct_add_one.search(A       ).groups()[0]       )
                            elif _re_import_struct_add_many.search(A       ) is not None:
                                   lowercase__      			= _re_import_struct_add_many.search(A       ).groups()[0].split(''', '''       )
                                   lowercase__      			= [obj[1:-1] for obj in imports if len(A       ) > 0]
                                   objects.extend(A       )
                            elif _re_between_brackets.search(A       ) is not None:
                                   lowercase__      			= _re_between_brackets.search(A       ).groups()[0].split(''', '''       )
                                   lowercase__      			= [obj[1:-1] for obj in imports if len(A       ) > 0]
                                   objects.extend(A       )
                            elif _re_quote_object.search(A       ) is not None:
                                   objects.append(_re_quote_object.search(A       ).groups()[0]       )
                            elif line.startswith(''' ''' * 8 + '''"'''       ):
                                   objects.append(line[9:-3]       )
                            elif line.startswith(''' ''' * 12 + '''"'''       ):
                                   objects.append(line[13:-3]       )
                            line_index += 1

                     lowercase__      			= objects
              else:
                     line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
       lowercase__      			= []
       while (
           line_index < len(A       )
           and find_backend(lines[line_index]       ) is None
           and not lines[line_index].startswith('''else'''       )
       ):
              lowercase__      			= lines[line_index]
              lowercase__      			= _re_import.search(A       )
              if single_line_import_search is not None:
                     objects.extend(single_line_import_search.groups()[0].split(''', '''       )       )
              elif line.startswith(''' ''' * 8       ):
                     objects.append(line[8:-2]       )
              line_index += 1

       lowercase__      			= {'''none''': objects}
       # Let's continue with backend-specific objects
       while line_index < len(A       ):
              # If the line is an if is_backend_available, we grab all objects associated.
              lowercase__      			= find_backend(lines[line_index]       )
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]       ) is None:
                     lowercase__      			= None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]       ) is None:
                            line_index += 1

                     line_index += 1

                     lowercase__      			= []
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(''' ''' * 8       ):
                            lowercase__      			= lines[line_index]
                            lowercase__      			= _re_import.search(A       )
                            if single_line_import_search is not None:
                                   objects.extend(single_line_import_search.groups()[0].split(''', '''       )       )
                            elif line.startswith(''' ''' * 12       ):
                                   objects.append(line[12:-2]       )
                            line_index += 1

                     lowercase__      			= objects
              else:
                     line_index += 1

       return import_dict_objects, type_hint_objects

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       List[str]:






       """simple docstring"""







       def find_duplicates(A       ):
              return [k for k, v in collections.Counter(A       ).items() if v > 1]

       if list(import_dict_objects.keys()       ) != list(type_hint_objects.keys()       ):
              return ["Both sides of the init do not have the same backends!"]

       lowercase__      			= []
       for key in import_dict_objects.keys():
              lowercase__      			= find_duplicates(import_dict_objects[key]       )
              if duplicate_imports:
                     errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}"       )
              lowercase__      			= find_duplicates(type_hint_objects[key]       )
              if duplicate_type_hints:
                     errors.append(f"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"       )

              if sorted(set(import_dict_objects[key]       )       ) != sorted(set(type_hint_objects[key]       )       ):
                     lowercase__      			= '''base imports''' if key == '''none''' else f"{key} backend"
                     errors.append(f"Differences for {name}:"       )
                     for a in type_hint_objects[key]:
                            if a not in import_dict_objects[key]:
                                   errors.append(f"  {a} in TYPE_HINT but not in _import_structure."       )
                     for a in import_dict_objects[key]:
                            if a not in type_hint_objects[key]:
                                   errors.append(f"  {a} in _import_structure but not in TYPE_HINT."       )
       return errors

def _SCREAMING_SNAKE_CASE      ()       ->       int:






       """simple docstring"""







       lowercase__      			= []
       for root, _, files in os.walk(A       ):
              if "__init__.py" in files:
                     lowercase__      			= os.path.join(A  , '''__init__.py'''       )
                     lowercase__      			= parse_init(A       )
                     if objects is not None:
                            lowercase__      			= analyze_results(*A       )
                            if len(A       ) > 0:
                                   lowercase__      			= f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
                                   failures.append('''\n'''.join(A       )       )
       if len(A       ) > 0:
              raise ValueError('''\n\n'''.join(A       )       )

def _SCREAMING_SNAKE_CASE      ()       ->       Dict:






       """simple docstring"""







       lowercase__      			= []
       for path, directories, files in os.walk(A       ):
              for folder in directories:
                     # Ignore private modules
                     if folder.startswith('''_'''       ):
                            directories.remove(A       )
                            continue
                     # Ignore leftovers from branches (empty folders apart from pycache)
                     if len(list((Path(A       ) / folder).glob('''*.py'''       )       )       ) == 0:
                            continue
                     lowercase__      			= str((Path(A       ) / folder).relative_to(A       )       )
                     lowercase__      			= short_path.replace(os.path.sep  , '''.'''       )
                     submodules.append(A       )
              for fname in files:
                     if fname == "__init__.py":
                            continue
                     lowercase__      			= str((Path(A       ) / fname).relative_to(A       )       )
                     lowercase__      			= short_path.replace('''.py'''  , ''''''       ).replace(os.path.sep  , '''.'''       )
                     if len(submodule.split('''.'''       )       ) == 1:
                            submodules.append(A       )
       return submodules


lowerCamelCase							:   List[Any]  						=	[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
    'models.esm.openfold_utils',
]

def _SCREAMING_SNAKE_CASE      ()       ->       str:






       """simple docstring"""







       from transformers.utils import direct_transformers_import

       lowercase__      			= direct_transformers_import(A       )

       lowercase__      			= set(transformers._import_structure.keys()       )
       # This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
       # some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
       # (potentiall re-) add them.
       with open(os.path.join(A  , '''__init__.py'''       )  , '''r'''       ) as f:
              lowercase__      			= f.read()
       import_structure_keys.update(set(re.findall(R'''import_structure\[\"([^\"]*)\"\]'''  , A       )       )       )

       lowercase__      			= [
           module
           for module in get_transformers_submodules()
           if module not in IGNORE_SUBMODULES and module not in import_structure_keys
       ]

       if len(A       ) > 0:
              lowercase__      			= '''\n'''.join(f"- {module}" for module in module_not_registered       )
              raise ValueError(
                  '''The following submodules are not properly registed in the main init of Transformers:\n'''
                  f"{list_of_modules}\n"
                  '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'''       )


if __name__ == "__main__":
 check_all_inits()
 check_submodules()






import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


__lowerCAmelCase			:     List[Any]           =   16
__lowerCAmelCase			:     Any           =   32

def a__    (       A_,     A_,     A_,     A_,     A_ = 16					):






  '''simple docstring'''





  __magic_name__				     =       AutoTokenizer.from_pretrained("""bert-base-cased"""					)
  __magic_name__				     =       DatasetDict(
      {
          """train""": dataset["""train"""].select(A_					),
          """validation""": dataset["""train"""].select(A_					),
          """test""": dataset["""validation"""],
      }					)

  def tokenize_function(A_					):
    # max_length=None => use the model max length (it's actually the default)
    __magic_name__				     =       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():
    __magic_name__				     =       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
  __magic_name__				     =       tokenized_datasets.rename_column("""label""",     """labels"""					)

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

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

  # Instantiate dataloaders.
  __magic_name__				     =       DataLoader(
      tokenized_datasets["""train"""],     shuffle=A_,     collate_fn=A_,     batch_size=A_					)
  __magic_name__				     =       DataLoader(
      tokenized_datasets["""validation"""],     shuffle=A_,     collate_fn=A_,     batch_size=A_					)

  __magic_name__				     =       DataLoader(
      tokenized_datasets["""test"""],     shuffle=A_,     collate_fn=A_,     batch_size=A_					)

  return train_dataloader, eval_dataloader, test_dataloader

def a__    (       A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       []
  # Download the dataset
  __magic_name__				     =       load_dataset("""glue""",     """mrpc"""					)
  # Create our splits
  __magic_name__				     =       StratifiedKFold(n_splits=int(args.num_folds					)					)
  # Initialize accelerator
  __magic_name__				     =       Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision					)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __magic_name__				     =       config["""lr"""]
  __magic_name__				     =       int(config["""num_epochs"""]					)
  __magic_name__				     =       int(config["""seed"""]					)
  __magic_name__				     =       int(config["""batch_size"""]					)

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

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

  set_seed(A_					)

  # New Code #
  # Create our folds:
  __magic_name__				     =       kfold.split(np.zeros(datasets["""train"""].num_rows					),     datasets["""train"""]["""label"""]					)
  __magic_name__				     =       []
  # Iterate over them
  for i, (train_idxs, valid_idxs) in enumerate(A_					):
    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       get_fold_dataloaders(
        A_,     A_,     A_,     A_,     )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __magic_name__				     =       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).
    __magic_name__				     =       model.to(accelerator.device					)

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

    # Instantiate scheduler
    __magic_name__				     =       get_linear_schedule_with_warmup(
        optimizer=A_,     num_warmup_steps=100,     num_training_steps=(len(A_					) * 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.
    __magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       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					)
        __magic_name__				     =       model(**A_					)
        __magic_name__				     =       outputs.loss
        __magic_name__				     =       loss / gradient_accumulation_steps
        accelerator.backward(A_					)
        if step % gradient_accumulation_steps == 0:
          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():
          __magic_name__				     =       model(**A_					)
        __magic_name__				     =       outputs.logits.argmax(dim=-1					)
        __magic_name__     ,    __magic_name__				     =       accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
        metric.add_batch(
            predictions=A_,     references=A_,     )

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

    # New Code #
    # We also run predictions on the test set at the very end
    __magic_name__				     =       []
    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():
        __magic_name__				     =       model(**A_					)
      __magic_name__				     =       outputs.logits
      __magic_name__     ,    __magic_name__				     =       accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
      fold_predictions.append(predictions.cpu()					)
      if i == 0:
        # We need all of the test predictions
        test_references.append(references.cpu()					)
        # Use accelerator.print to print only on the main process.
    test_predictions.append(torch.cat(A_,     dim=0					)					)
    # We now need to release all our memory and get rid of the current model, optimizer, etc
    accelerator.free_memory()
  # New Code #
  # Finally we check the accuracy of our folded results:
  __magic_name__				     =       torch.cat(A_,     dim=0					)
  __magic_name__				     =       torch.stack(A_,     dim=0					).sum(dim=0					).div(int(args.num_folds					)					).argmax(dim=-1					)
  __magic_name__				     =       metric.compute(predictions=A_,     references=A_					)
  accelerator.print("""Average test metrics from all folds:""",     A_					)

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       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.""",     )
  parser.add_argument("""--cpu""",     action="""store_true""",     help="""If passed, will train on the CPU."""					)
  # New Code #
  parser.add_argument("""--num_folds""",     type=A_,     default=3,     help="""The number of splits to perform across the dataset"""					)
  __magic_name__				     =       parser.parse_args()
  __magic_name__				     =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(A_,     A_					)


if __name__ == "__main__":
    main()

