import argparse
import gc
import json
import os

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


_lowercase							:      Any      							=1_6
_lowercase							:      int      							=3_2



def A__  (			lowercase:     Union[str, Any]					)				->		List[Any]:
       return int(x / 2**20					)



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __enter__(				self    :					Tuple     )    ->				Tuple:
         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         A						:     Optional[int]         =torch.cuda.memory_allocated()
         return self







  def __exit__(				self    :					Optional[int]      ,	*SCREAMING_SNAKE_CASE__    :					int     )    ->				Tuple:
         gc.collect()
         torch.cuda.empty_cache()
         A						:     Dict         =torch.cuda.memory_allocated()
         A						:     Any         =torch.cuda.max_memory_allocated()
         A						:     List[str]         =bamb(self.end - self.begin     )
         A						:     Optional[Any]         =bamb(self.peak - self.begin     )
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def A__  (			lowercase:     Accelerator,		lowercase:     int = 16,		lowercase:     str = "bert-base-cased",		lowercase:     int = 320,		lowercase:     int = 160,		)				->		int:
       A						:     Any         =AutoTokenizer.from_pretrained(lowercase					)
       A						:     Any         =load_dataset(
           'glue',		'mrpc',		split={'train': F'train[:{n_train}]', 'validation': F'validation[:{n_val}]'}					)

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

       def collate_fn(lowercase:     List[str]					):
              # 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=128,		return_tensors='pt'					)
              return tokenizer.pad(lowercase,		padding='longest',		return_tensors='pt'					)

       # Instantiate dataloaders.
       A						:     Optional[int]         =DataLoader(
           tokenized_datasets['train'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)
       A						:     Optional[Any]         =DataLoader(
           tokenized_datasets['validation'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)

       return train_dataloader, eval_dataloader



def A__  (			lowercase:     Tuple,		lowercase:     Optional[Any]					)				->		List[str]:
       # Initialize accelerator
       A						:     Optional[Any]         =Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A						:     Optional[Any]         =config['lr']
       A						:     Union[str, Any]         =int(config['num_epochs']					)
       A						:     Dict         =int(config['seed']					)
       A						:     Optional[Any]         =int(config['batch_size']					)
       A						:     Optional[Any]         =args.model_name_or_path

       set_seed(lowercase					)
       A  ,  A						:     List[Any]         =get_dataloaders(lowercase,		lowercase,		lowercase,		args.n_train,		args.n_val					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A						:     Optional[int]         =AutoModelForSequenceClassification.from_pretrained(lowercase,		return_dict=lowercase					)

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

       if accelerator.state.deepspeed_plugin is not None:
              A						:     List[Any]         =accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              A						:     Tuple         =1
       A						:     str         =(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
       ):
              A						:     str         =get_linear_schedule_with_warmup(
                  optimizer=lowercase,		num_warmup_steps=0,		num_training_steps=lowercase,		)
       else:
              A						:     Optional[int]         =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.
       A  ,  A  ,  A  ,  A  ,  A						:     Optional[int]         =accelerator.prepare(
           lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)

       # We need to keep track of how many total steps we have iterated over
       A						:     str         =0
       # We also need to keep track of the stating epoch so files are named properly
       A						:     Optional[Any]         =0

       # Now we train the model
       A						:     Tuple         ={}
       for epoch in range(lowercase,		lowercase					):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(lowercase					):
                            A						:     Dict         =model(**lowercase					)
                            A						:     Optional[Any]         =outputs.loss
                            A						:     str         =loss / gradient_accumulation_steps
                            accelerator.backward(lowercase					)
                            if step % gradient_accumulation_steps == 0:
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
              accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin					)					)					)
              accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used					)					)
              accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked					)					)
              accelerator.print(
                  'Total Peak Memory consumed during the train (max): {}'.format(
                      tracemalloc.peaked + bamb(tracemalloc.begin					)					)					)
              A						:     List[Any]         =tracemalloc.peaked + bamb(tracemalloc.begin					)
              if args.peak_memory_upper_bound is not None:
                     assert (
                         train_total_peak_memory[F'epoch-{epoch}'] <= args.peak_memory_upper_bound
                     ), "Peak memory usage exceeded the upper bound"

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



def A__  (			)				->		List[str]:
       A						:     Optional[int]         =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(
           '--peak_memory_upper_bound',		type=lowercase,		default=lowercase,		help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.',		)
       parser.add_argument(
           '--n_train',		type=lowercase,		default=320,		help='Number of training examples to use.',		)
       parser.add_argument(
           '--n_val',		type=lowercase,		default=160,		help='Number of validation examples to use.',		)
       parser.add_argument(
           '--num_epochs',		type=lowercase,		default=1,		help='Number of train epochs.',		)
       A						:     List[Any]         =parser.parse_args()
       A						:     Any         ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(lowercase,		lowercase					)


if __name__ == "__main__":
    main()







import collections
import importlib.util
import os
import re
from pathlib import Path


_lowercase							:      List[str]      							='''src/transformers'''


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



def A__  (			lowercase:     Dict					)				->		int:
       if _re_test_backend.search(lowercase					) is None:
              return None
       A						:     Any         =[b[0] for b in _re_backend.findall(lowercase					)]
       backends.sort()
       return "_and_".join(lowercase					)



def A__  (			lowercase:     Any					)				->		List[Any]:
       with open(lowercase,		'r',		encoding='utf-8',		newline='\n'					) as f:
              A						:     Optional[Any]         =f.readlines()

       A						:     Dict         =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
       A						:     Optional[int]         =[]
       while not lines[line_index].startswith('if TYPE_CHECKING'					) and find_backend(lines[line_index]					) is None:
              A						:     int         =lines[line_index]
              # If we have everything on a single line, let's deal with it.
              if _re_one_line_import_struct.search(lowercase					):
                     A						:     int         =_re_one_line_import_struct.search(lowercase					).groups()[0]
                     A						:     int         =re.findall('\[([^\]]+)\]',		lowercase					)
                     for imp in imports:
                            objects.extend([obj[1:-1] for obj in imp.split(', '					)]					)
                     line_index += 1
                     continue
              A						:     Optional[int]         =_re_import_struct_key_value.search(lowercase					)
              if single_line_import_search is not None:
                     A						:     Dict         =[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

       A						:     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.
              A						:     Optional[int]         =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:
                     A						:     str         =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

                     A						:     List[str]         =[]
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]					) <= 1 or lines[line_index].startswith(' ' * 4					):
                            A						:     Optional[Any]         =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:
                                   A						:     Optional[Any]         =_re_import_struct_add_many.search(lowercase					).groups()[0].split(', '					)
                                   A						:     int         =[obj[1:-1] for obj in imports if len(lowercase					) > 0]
                                   objects.extend(lowercase					)
                            elif _re_between_brackets.search(lowercase					) is not None:
                                   A						:     Optional[int]         =_re_between_brackets.search(lowercase					).groups()[0].split(', '					)
                                   A						:     Optional[int]         =[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

                     A						:     Optional[Any]         =objects
              else:
                     line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
       A						:     Optional[Any]         =[]
       while (
           line_index < len(lowercase					)
           and find_backend(lines[line_index]					) is None
           and not lines[line_index].startswith('else'					)
       ):
              A						:     Any         =lines[line_index]
              A						:     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(' ' * 8					):
                     objects.append(line[8:-2]					)
              line_index += 1

       A						:     Optional[Any]         ={'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.
              A						:     str         =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:
                     A						:     Optional[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

                     A						:     List[str]         =[]
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]					) <= 1 or lines[line_index].startswith(' ' * 8					):
                            A						:     Any         =lines[line_index]
                            A						:     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(' ' * 12					):
                                   objects.append(line[12:-2]					)
                            line_index += 1

                     A						:     Dict         =objects
              else:
                     line_index += 1

       return import_dict_objects, type_hint_objects



def A__  (			lowercase:     Any,		lowercase:     int					)				->		Dict:

       def find_duplicates(lowercase:     List[str]					):
              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!"]

       A						:     List[Any]         =[]
       for key in import_dict_objects.keys():
              A						:     List[Any]         =find_duplicates(import_dict_objects[key]					)
              if duplicate_imports:
                     errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}'					)
              A						:     Tuple         =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]					)					):
                     A						:     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 A__  (			)				->		List[str]:
       A						:     Dict         =[]
       for root, _, files in os.walk(lowercase					):
              if "__init__.py" in files:
                     A						:     Any         =os.path.join(lowercase,		'__init__.py'					)
                     A						:     Union[str, Any]         =parse_init(lowercase					)
                     if objects is not None:
                            A						:     str         =analyze_results(*lowercase					)
                            if len(lowercase					) > 0:
                                   A						:     Any         =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 A__  (			)				->		int:
       A						:     List[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
                     A						:     Any         =str((Path(lowercase					) / folder).relative_to(lowercase					)					)
                     A						:     List[str]         =short_path.replace(os.path.sep,		'.'					)
                     submodules.append(lowercase					)
              for fname in files:
                     if fname == "__init__.py":
                            continue
                     A						:     Optional[Any]         =str((Path(lowercase					) / fname).relative_to(lowercase					)					)
                     A						:     Dict         =short_path.replace('.py',		''					).replace(os.path.sep,		'.'					)
                     if len(submodule.split('.'					)					) == 1:
                            submodules.append(lowercase					)
       return submodules


_lowercase							:      Tuple      							=[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
]



def A__  (			)				->		Tuple:
       # This is to make sure the transformers module imported is the one in the repo.
       A						:     str         =importlib.util.spec_from_file_location(
           'transformers',		os.path.join(lowercase,		'__init__.py'					),		submodule_search_locations=[PATH_TO_TRANSFORMERS],		)
       A						:     Any         =spec.loader.load_module()

       A						:     Any         =[
           module
           for module in get_transformers_submodules()
           if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
       ]
       if len(lowercase					) > 0:
              A						:     Dict         ='\n'.join(F'- {module}' for module in module_not_registered					)
              raise ValueError(
                  'The following submodules are not properly registered 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()






