


import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_snake_case			 =	'''src/diffusers'''

# Matches is_xxx_available()
_snake_case			 =	re.compile(R'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
_snake_case			 =	re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')


_snake_case			 =	'''
{0} = None
'''

_snake_case			 =	'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
'''


_snake_case			 =	'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''






def 						lowercase_(    SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  lowerCamelCase					:   Tuple       =							_re_backend.findall(_lowercase      )
  if len(_lowercase      ) == 0:
    return None

  return "_and_".join(_lowercase      )






def 						lowercase_(    ):


  '''simple docstring'''

  with open(os.path.join(_lowercase ,		"__init__.py"      ) ,		"r" ,		encoding="utf-8" ,		newline="\n"      ) as f:
    lowerCamelCase					:   Tuple       =							f.readlines()

  # Get to the point we do the actual imports for type checking
  lowerCamelCase					:   List[str]       =							0
  lowerCamelCase					:   Optional[int]       =							{}
  # Go through the end of the file
  while line_index < len(_lowercase      ):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    lowerCamelCase					:   List[Any]       =							find_backend(lines[line_index]      )
    if backend is not None:
      while not lines[line_index].startswith("else:"      ):
        line_index += 1
      line_index += 1
      lowerCamelCase					:   Optional[int]       =							[]
      # Until we unindent, add backend objects to the list
      while line_index < len(_lowercase      ) and len(lines[line_index]      ) > 1:
        lowerCamelCase					:   Optional[Any]       =							lines[line_index]
        lowerCamelCase					:   Dict       =							_re_single_line_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

      if len(_lowercase      ) > 0:
        lowerCamelCase					:   List[str]       =							objects
    else:
      line_index += 1

  return backend_specific_objects






def 						lowercase_(    SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  if name.isupper():
    return DUMMY_CONSTANT.format(_lowercase      )
  elif name.islower():
    return DUMMY_FUNCTION.format(_lowercase ,		_lowercase      )
  else:
    return DUMMY_CLASS.format(_lowercase ,		_lowercase      )






def 						lowercase_(    SCREAMING_SNAKE_CASE_=None      ):


  '''simple docstring'''

  if backend_specific_objects is None:
    lowerCamelCase					:   str       =							read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  lowerCamelCase					:   List[str]       =							{}

  for backend, objects in backend_specific_objects.items():
    lowerCamelCase					:   Optional[Any]       =							"[" + ", ".join(f"""\"{b}\"""" for b in backend.split("_and_"      )      ) + "]"
    lowerCamelCase					:   Dict       =							"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(_lowercase ,		_lowercase      ) for o in objects]      )
    lowerCamelCase					:   Optional[int]       =							dummy_file

  return dummy_files






def 						lowercase_(    SCREAMING_SNAKE_CASE_=False      ):


  '''simple docstring'''

  lowerCamelCase					:   Any       =							create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  lowerCamelCase					:   List[Any]       =							{"torch": "pt"}

  # Locate actual dummy modules and read their content.
  lowerCamelCase					:   Dict       =							os.path.join(_lowercase ,		"utils"      )
  lowerCamelCase					:   List[Any]       =							{
      backend: os.path.join(_lowercase ,		f"""dummy_{short_names.get(_lowercase ,		_lowercase      )}_objects.py"""      )
      for backend in dummy_files.keys()
  }

  lowerCamelCase					:   List[Any]       =							{}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(_lowercase      ):
      with open(_lowercase ,		"r" ,		encoding="utf-8" ,		newline="\n"      ) as f:
        lowerCamelCase					:   Optional[int]       =							f.read()
    else:
      lowerCamelCase					:   Optional[int]       =							""

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            f"""Updating diffusers.utils.dummy_{short_names.get(_lowercase ,		_lowercase      )}_objects.py as the main """
            "__init__ has new objects."      )
        with open(dummy_file_paths[backend] ,		"w" ,		encoding="utf-8" ,		newline="\n"      ) as f:
          f.write(dummy_files[backend]      )
      else:
        raise ValueError(
            "The main __init__ has objects that are not present in "
            f"""diffusers.utils.dummy_{short_names.get(_lowercase ,		_lowercase      )}_objects.py. Run `make fix-copies` """
            "to fix this."      )


if __name__ == "__main__":
   _snake_case			 =	argparse.ArgumentParser()
   parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
   _snake_case			 =	parser.parse_args()

   check_dummies(args.fix_and_overwrite)







import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case   							=						1_6
__snake_case   							=						3_2






def 		_A		(		_lowercase					,  _lowercase = 16					,  _lowercase = "bert-base-cased" )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			AutoTokenizer.from_pretrained(_lowercase )
     __UpperCamelCase =			load_dataset('glue'					,  'mrpc' )

     def tokenize_function(_lowercase ):
          # max_length=None => use the model max length (it's actually the default)
          __UpperCamelCase =			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
     __UpperCamelCase =			datasets.map(
         _lowercase					,  batched=_lowercase					,  remove_columns=['idx', 'sentence1', 'sentence2']					,  load_from_cache_file=_lowercase )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     __UpperCamelCase =			tokenized_datasets.rename_column('label'					,  'labels' )

     def collate_fn(_lowercase ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(_lowercase					,  padding='max_length'					,  max_length=1_28					,  return_tensors='pt' )
          return tokenizer.pad(_lowercase					,  padding='longest'					,  return_tensors='pt' )

     # Instantiate dataloaders.
     __UpperCamelCase =			DataLoader(
         tokenized_datasets['train']					,  shuffle=_lowercase					,  collate_fn=_lowercase					,  batch_size=_lowercase )
     __UpperCamelCase =			DataLoader(
         tokenized_datasets['validation']					,  shuffle=_lowercase					,  collate_fn=_lowercase					,  batch_size=_lowercase )

     return train_dataloader, eval_dataloader






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __UpperCamelCase =			config['lr']
     __UpperCamelCase =			int(config['num_epochs'] )
     __UpperCamelCase =			int(config['seed'] )
     __UpperCamelCase =			int(config['batch_size'] )
     __UpperCamelCase =			args.model_name_or_path

     set_seed(_lowercase )
     __UpperCamelCase,    __UpperCamelCase =			get_dataloaders(_lowercase					,  _lowercase					,  _lowercase )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __UpperCamelCase =			AutoModelForSequenceClassification.from_pretrained(_lowercase					,  return_dict=_lowercase )

     # Instantiate optimizer
     __UpperCamelCase =			(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     __UpperCamelCase =			optimizer_cls(params=model.parameters()					,  lr=_lowercase )

     if accelerator.state.deepspeed_plugin is not None:
          __UpperCamelCase =			accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          __UpperCamelCase =			1
     __UpperCamelCase =			(len(_lowercase ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          __UpperCamelCase =			get_linear_schedule_with_warmup(
              optimizer=_lowercase					,  num_warmup_steps=0					,  num_training_steps=_lowercase					,  )
     else:
          __UpperCamelCase =			DummyScheduler(_lowercase					,  total_num_steps=_lowercase					,  warmup_num_steps=0 )

     # 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 =			accelerator.prepare(
         _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )

     # We need to keep track of how many total steps we have iterated over
     __UpperCamelCase =			0
     # We also need to keep track of the stating epoch so files are named properly
     __UpperCamelCase =			0

     # Now we train the model
     __UpperCamelCase =			evaluate.load('glue'					,  'mrpc' )
     __UpperCamelCase =			0
     __UpperCamelCase =			{}
     for epoch in range(_lowercase					,  _lowercase ):
          model.train()
          for step, batch in enumerate(_lowercase ):
               __UpperCamelCase =			model(**_lowercase )
               __UpperCamelCase =			outputs.loss
               __UpperCamelCase =			loss / gradient_accumulation_steps
               accelerator.backward(_lowercase )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          __UpperCamelCase =			0
          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 =			model(**_lowercase )
               __UpperCamelCase =			outputs.logits.argmax(dim=-1 )
               # It is slightly faster to call this once, than multiple times
               __UpperCamelCase,    __UpperCamelCase =			accelerator.gather(
                   (predictions, batch['labels']) )  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(_lowercase ) - 1:
                         __UpperCamelCase =			predictions[: len(eval_dataloader.dataset ) - samples_seen]
                         __UpperCamelCase =			references[: len(eval_dataloader.dataset ) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=_lowercase					,  references=_lowercase					,  )

          __UpperCamelCase =			metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''					,  _lowercase )
          __UpperCamelCase =			eval_metric['accuracy']

          if best_performance < eval_metric["accuracy"]:
               __UpperCamelCase =			eval_metric['accuracy']

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir					,  'all_results.json' )					,  'w' ) as f:
               json.dump(_lowercase					,  _lowercase )






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
     parser.add_argument(
         '--model_name_or_path'					,  type=_lowercase					,  default='bert-base-cased'					,  help='Path to pretrained model or model identifier from huggingface.co/models.'					,  required=_lowercase					,  )
     parser.add_argument(
         '--output_dir'					,  type=_lowercase					,  default='.'					,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,  )
     parser.add_argument(
         '--performance_lower_bound'					,  type=_lowercase					,  default=_lowercase					,  help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'					,  )
     parser.add_argument(
         '--num_epochs'					,  type=_lowercase					,  default=3					,  help='Number of train epochs.'					,  )
     __UpperCamelCase =			parser.parse_args()
     __UpperCamelCase =			{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
     training_function(_lowercase					,  _lowercase )


if __name__ == "__main__":
    main()


