




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
# and perform 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
#
########################################################################


_snake_case										=       16
_snake_case										=       32







def 				lowerCAmelCase_	(     snake_case_,snake_case_ = 16     ):
      _A							:     List[Any]			=						AutoTokenizer.from_pretrained("""bert-base-cased"""     )
      _A							:     Any			=						load_dataset("""glue""","""mrpc"""     )

      def tokenize_function(snake_case_     ):
            # max_length=None => use the model max length (it's actually the default)
            _A							:     int			=						tokenizer(examples["""sentence1"""],examples["""sentence2"""],truncation=snake_case_,max_length=snake_case_     )
            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							:     Dict			=						datasets.map(
                snake_case_,batched=snake_case_,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							:     List[Any]			=						tokenized_datasets.rename_column("""label""","""labels"""     )

      def collate_fn(snake_case_     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            _A							:     Optional[Any]			=						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							:     Any			=						16
            elif accelerator.mixed_precision != "no":
                  _A							:     int			=						8
            else:
                  _A							:     str			=						None

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

      # Instantiate dataloaders.
      _A							:     Dict			=						DataLoader(
          tokenized_datasets["""train"""],shuffle=snake_case_,collate_fn=snake_case_,batch_size=snake_case_     )
      _A							:     str			=						DataLoader(
          tokenized_datasets["""validation"""],shuffle=snake_case_,collate_fn=snake_case_,batch_size=snake_case_     )

      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

  _snake_case										=       mocked_dataloaders  # noqa: F811







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS""",snake_case_     ) == "1":
            _A							:     Optional[Any]			=						2
      # New Code #
      _A							:     str			=						int(args.gradient_accumulation_steps     )
      # Initialize accelerator
      _A							:     Dict			=						Accelerator(
          cpu=args.cpu,mixed_precision=args.mixed_precision,gradient_accumulation_steps=snake_case_     )
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""     )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      _A							:     int			=						config["""lr"""]
      _A							:     str			=						int(config["""num_epochs"""]     )
      _A							:     Tuple			=						int(config["""seed"""]     )
      _A							:     Optional[Any]			=						int(config["""batch_size"""]     )

      _A							:     int			=						evaluate.load("""glue""","""mrpc"""     )

      set_seed(snake_case_     )
      _A			,   _A							:     Dict			=						get_dataloaders(snake_case_,snake_case_     )
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _A							:     Dict			=						AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""",return_dict=snake_case_     )

      # 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							:     List[Any]			=						model.to(accelerator.device     )

      # Instantiate optimizer
      _A							:     Any			=						AdamW(params=model.parameters(),lr=snake_case_     )

      # Instantiate scheduler
      _A							:     Optional[Any]			=						get_linear_schedule_with_warmup(
          optimizer=snake_case_,num_warmup_steps=100,num_training_steps=(len(snake_case_     ) * 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			,   _A			,   _A			,   _A			,   _A							:     List[Any]			=						accelerator.prepare(
          snake_case_,snake_case_,snake_case_,snake_case_,snake_case_     )

      # Now we train the model
      for epoch in range(snake_case_     ):
            model.train()
            for step, batch in enumerate(snake_case_     ):
                  # 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(snake_case_     ):
                        _A							:     List[Any]			=						model(**snake_case_     )
                        _A							:     Dict			=						output.loss
                        accelerator.backward(snake_case_     )
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(snake_case_     ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device     )
                  with torch.no_grad():
                        _A							:     Dict			=						model(**snake_case_     )
                  _A							:     Any			=						outputs.logits.argmax(dim=-1     )
                  _A			,   _A							:     Tuple			=						accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
                  metric.add_batch(
                      predictions=snake_case_,references=snake_case_,)

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







def 				lowerCAmelCase_	(     ):
      _A							:     Any			=						argparse.ArgumentParser(description="""Simple example of training script."""     )
      parser.add_argument(
          """--mixed_precision""",type=snake_case_,default=snake_case_,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=snake_case_,default=1,help="""The number of minibatches to be ran before gradients are accumulated.""",)
      parser.add_argument("""--cpu""",action="""store_true""",help="""If passed, will train on the CPU."""     )
      _A							:     Tuple			=						parser.parse_args()
      _A							:     List[str]			=						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(snake_case_,snake_case_     )


if __name__ == "__main__":
  main()






import argparse
import os
import re

import packaging.version


A__  :  Dict								=					'''examples/'''
A__  :  Any								=					{
    '''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''),
    '''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
A__  :  Any								=					{
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
A__  :  Any								=					'''README.md'''


def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		List[Any]     ,__UpperCamelCase							:		List[Any] ):
		with open(__UpperCamelCase     ,'''r'''     ,encoding='''utf-8'''     ,newline='''\n''' ) as f:
				lowerCAmelCase_			:     Tuple									= f.read()
		lowerCAmelCase_					,  lowerCAmelCase_			:     Dict									= REPLACE_PATTERNS[pattern]
		lowerCAmelCase_			:     Tuple									= replace.replace('''VERSION'''     ,__UpperCamelCase )
		lowerCAmelCase_			:     Optional[int]									= re_pattern.sub(__UpperCamelCase     ,__UpperCamelCase )
		with open(__UpperCamelCase     ,'''w'''     ,encoding='''utf-8'''     ,newline='''\n''' ) as f:
				f.write(__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any] ):
		for folder, directories, fnames in os.walk(__UpperCamelCase ):
				# Removing some of the folders with non-actively maintained examples from the walk
				if "research_projects" in directories:
						directories.remove('''research_projects''' )
				if "legacy" in directories:
						directories.remove('''legacy''' )
				for fname in fnames:
						if fname.endswith('''.py''' ):
								update_version_in_file(os.path.join(__UpperCamelCase     ,__UpperCamelCase )     ,__UpperCamelCase     ,pattern='''examples''' )


def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		List[Any]=False ):
		for pattern, fname in REPLACE_FILES.items():
				update_version_in_file(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase )
		if not patch:
				update_version_in_examples(__UpperCamelCase )


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     List[str]									= '''🤗 Transformers currently provides the following architectures'''
		lowerCAmelCase_			:     List[Any]									= '''1. Want to contribute a new model?'''
		with open(__UpperCamelCase     ,'''r'''     ,encoding='''utf-8'''     ,newline='''\n''' ) as f:
				lowerCAmelCase_			:     Union[str, Any]									= f.readlines()

		# Find the start of the list.
		lowerCAmelCase_			:     int									= 0
		while not lines[start_index].startswith(_start_prompt ):
				start_index += 1
		start_index += 1

		lowerCAmelCase_			:     str									= start_index
		# Update the lines in the model list.
		while not lines[index].startswith(_end_prompt ):
				if lines[index].startswith('''1.''' ):
						lowerCAmelCase_			:     int									= lines[index].replace(
						    '''https://huggingface.co/docs/transformers/main/model_doc'''     ,'''https://huggingface.co/docs/transformers/model_doc'''     ,)
				index += 1

		with open(__UpperCamelCase     ,'''w'''     ,encoding='''utf-8'''     ,newline='''\n''' ) as f:
				f.writelines(__UpperCamelCase )


def 			UpperCamelCase(  ):
		with open(REPLACE_FILES['''init''']     ,'''r''' ) as f:
				lowerCAmelCase_			:     Optional[Any]									= f.read()
		lowerCAmelCase_			:     Dict									= REPLACE_PATTERNS['''init'''][0].search(__UpperCamelCase ).groups()[0]
		return packaging.version.parse(__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		Dict=False ):
		lowerCAmelCase_			:     Union[str, Any]									= get_version()
		if patch and default_version.is_devrelease:
				raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' )
		if default_version.is_devrelease:
				lowerCAmelCase_			:     List[str]									= default_version.base_version
		elif patch:
				lowerCAmelCase_			:     int									= f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
		else:
				lowerCAmelCase_			:     int									= f"""{default_version.major}.{default_version.minor + 1}.0"""

		# Now let's ask nicely if that's the right one.
		lowerCAmelCase_			:     Optional[Any]									= input(f"""Which version are you releasing? [{default_version}]""" )
		if len(__UpperCamelCase ) == 0:
				lowerCAmelCase_			:     List[str]									= default_version

		print(f"""Updating version to {version}.""" )
		global_version_update(__UpperCamelCase     ,patch=__UpperCamelCase )
		if not patch:
				print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
				clean_main_ref_in_model_list()


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Any									= get_version()
		lowerCAmelCase_			:     int									= f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
		lowerCAmelCase_			:     Optional[Any]									= current_version.base_version

		# Check with the user we got that right.
		lowerCAmelCase_			:     Optional[Any]									= input(f"""Which version are we developing now? [{dev_version}]""" )
		if len(__UpperCamelCase ) == 0:
				lowerCAmelCase_			:     int									= dev_version

		print(f"""Updating version to {version}.""" )
		global_version_update(__UpperCamelCase )
		print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
		clean_main_ref_in_model_list()


if __name__ == "__main__":
						A__  :  Dict								=					argparse.ArgumentParser()
						parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
						parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
						A__  :  Optional[int]								=					parser.parse_args()
						if not args.post_release:
												pre_release_work(patch=args.patch)
						elif args.patch:
												print('''Nothing to do after a patch :-)''')
						else:
												post_release_work()





