


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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


SCREAMING_SNAKE_CASE							    =							1_6
SCREAMING_SNAKE_CASE							    =							3_2




def  a   (lowerCAmelCase__		,				lowerCAmelCase__ = 16   ):
   __a					=  AutoTokenizer.from_pretrained("""bert-base-cased"""   )
   __a					=  load_dataset("""glue"""		,				"""mrpc"""   )

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

   def collate_fn(lowerCAmelCase__   ):
      # 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(
          lowercase_		,				padding="""longest"""		,				max_length=lowercase_		,				pad_to_multiple_of=lowercase_		,				return_tensors="""pt"""		,				)

   # Instantiate dataloaders.
   __a					=  DataLoader(
       tokenized_datasets["""train"""]		,				shuffle=lowercase_		,				collate_fn=lowercase_		,				batch_size=lowercase_   )
   __a					=  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

  SCREAMING_SNAKE_CASE							    =							mocked_dataloaders  # noqa: F811




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):

   if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,				lowercase_   ) == "1":
      __a					=  2
   # New Code #
   __a					=  int(args.gradient_accumulation_steps   )
   __a					=  int(args.local_sgd_steps   )
   # Initialize accelerator
   __a					=  Accelerator(
       cpu=args.cpu		,				mixed_precision=args.mixed_precision		,				gradient_accumulation_steps=lowercase_   )
   if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
      raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""   )
   # 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"""   )

   set_seed(lowercase_   )
   __a					=  get_dataloaders(lowercase_		,				lowercase_   )
   # 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=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).
   __a					=  model.to(accelerator.device   )

   # Instantiate optimizer
   __a					=  AdamW(params=model.parameters()		,				lr=lowercase_   )

   # Instantiate scheduler
   __a					=  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.
   __a					=  accelerator.prepare(
       lowercase_		,				lowercase_		,				lowercase_		,				lowercase_		,				lowercase_   )

   # Now we train the model
   for epoch in range(lowercase_   ):
      model.train()
      with LocalSGD(
          accelerator=lowercase_		,				model=lowercase_		,				local_sgd_steps=lowercase_		,				enabled=local_sgd_steps is not None   ) as local_sgd:
         for step, batch in enumerate(lowercase_   ):
            # 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(lowercase_   ):
               __a					=  model(**lowercase_   )
               __a					=  output.loss
               accelerator.backward(lowercase_   )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

      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():
            __a					=  model(**lowercase_   )
         __a					=  outputs.logits.argmax(dim=-1   )
         __a					=  accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
         metric.add_batch(
             predictions=lowercase_		,				references=lowercase_		,				)

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




def  a   ():
   __a					=  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."""		,				)
   # New Code #
   parser.add_argument(
       """--gradient_accumulation_steps"""		,				type=lowercase_		,				default=1		,				help="""The number of minibatches to be ran before gradients are accumulated."""		,				)
   parser.add_argument(
       """--local_sgd_steps"""		,				type=lowercase_		,				default=8		,				help="""Number of local SGD steps or None to disable local SGD"""   )
   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(lowercase_		,				lowercase_   )


if __name__ == "__main__":
  main()










"""simple docstring"""


import collections
import os
import re
from pathlib import Path


lowerCamelCase__						= "src/transformers"


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




def        lowercase__     (       lowercase_      )       ->		Optional[int]:




      """simple docstring"""

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




def        lowercase__     (       lowercase_      )       ->		Dict:




      """simple docstring"""

      with open(lowercase_		,"r"		,encoding="utf-8"		,newline="\n"      ) as f:
            _UpperCamelCase   :						Optional[Any]    =							f.readlines()

      _UpperCamelCase   :						str    =							0
      while line_index < len(lowercase_      ) and not lines[line_index].startswith("_import_structure = {"      ):
            line_index += 1

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

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

      _UpperCamelCase   :						str    =							{"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.
            _UpperCamelCase   :						Dict    =							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:
                  _UpperCamelCase   :						int    =							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

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

                  _UpperCamelCase   :						List[str]    =							objects
            else:
                  line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
      _UpperCamelCase   :						Optional[Any]    =							[]
      while (
          line_index < len(lowercase_      )
          and find_backend(lines[line_index]      ) is None
          and not lines[line_index].startswith("else"      )
      ):
            _UpperCamelCase   :						List[Any]    =							lines[line_index]
            _UpperCamelCase   :						Union[str, Any]    =							_re_import.search(lowercase_      )
            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

      _UpperCamelCase   :						List[str]    =							{"none": objects}
      # Let's continue with backend-specific objects
      while line_index < len(lowercase_      ):
            # If the line is an if is_backend_available, we grab all objects associated.
            _UpperCamelCase   :						Any    =							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:
                  _UpperCamelCase   :						List[Any]    =							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

                  _UpperCamelCase   :						Any    =							[]
                  # Until we unindent, add backend objects to the list
                  while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(" " * 8      ):
                        _UpperCamelCase   :						str    =							lines[line_index]
                        _UpperCamelCase   :						Optional[int]    =							_re_import.search(lowercase_      )
                        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

                  _UpperCamelCase   :						Dict    =							objects
            else:
                  line_index += 1

      return import_dict_objects, type_hint_objects




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

      def find_duplicates(lowercase_      ):
            return [k for k, v in collections.Counter(lowercase_      ).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!"]

      _UpperCamelCase   :						Optional[Any]    =							[]
      for key in import_dict_objects.keys():
            _UpperCamelCase   :						Optional[int]    =							find_duplicates(import_dict_objects[key]      )
            if duplicate_imports:
                  errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''      )
            _UpperCamelCase   :						int    =							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]      )      ):
                  _UpperCamelCase   :						Tuple    =							"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        lowercase__     (       )       ->		str:




      """simple docstring"""

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




def        lowercase__     (       )       ->		Dict:




      """simple docstring"""

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


lowerCamelCase__						= [
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
    "models.esm.openfold_utils",
]




def        lowercase__     (       )       ->		Tuple:




      """simple docstring"""

      from transformers.utils import direct_transformers_import

      _UpperCamelCase   :						List[str]    =							direct_transformers_import(lowercase_      )

      _UpperCamelCase   :						Any    =							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(lowercase_		,"__init__.py"      )		,"r"      ) as f:
            _UpperCamelCase   :						Union[str, Any]    =							f.read()
      import_structure_keys.update(set(re.findall(r"import_structure\[\"([^\"]*)\"\]"		,lowercase_      )      )      )

      _UpperCamelCase   :						Optional[Any]    =							[
          module
          for module in get_transformers_submodules()
          if module not in IGNORE_SUBMODULES and module not in import_structure_keys
      ]

      if len(lowercase_      ) > 0:
            _UpperCamelCase   :						List[str]    =							"\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()
