
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__lowerCAmelCase			:     List[str]           =   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowerCAmelCase			:     List[Any]           =   """    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""




class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: int    )			->	str:







    """simple docstring"""






    __magic_name__				     =       tempfile.mkdtemp()
    os.makedirs(os.path.join(self.transformer_dir			,							"""models/bert/"""    )    )
    __magic_name__				     =       self.transformer_dir
    shutil.copy(
        os.path.join(__lowerCAmelCase			,							"""src/transformers/models/bert/modeling_bert.py"""    )			,							os.path.join(self.transformer_dir			,							"""models/bert/modeling_bert.py"""    )			,							)
  def 							_lowercase							( self			: Dict    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       '''src/transformers'''
    shutil.rmtree(self.transformer_dir    )
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: str=None    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
    if overwrite_result is not None:
      __magic_name__				     =       comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
    __magic_name__				     =       black.Mode(target_versions={black.TargetVersion.PYaa}			,							line_length=119    )
    __magic_name__				     =       black.format_str(__lowerCAmelCase			,							mode=__lowerCAmelCase    )
    __magic_name__				     =       os.path.join(self.transformer_dir			,							"""new_code.py"""    )
    with open(__lowerCAmelCase			,							"""w"""			,							newline="""\n"""    ) as f:
      f.write(__lowerCAmelCase    )
    if overwrite_result is None:
      self.assertTrue(len(check_copies.is_copy_consistent(__lowerCAmelCase    )    ) == 0    )
    else:
      check_copies.is_copy_consistent(f.name			,							overwrite=__lowerCAmelCase    )
      with open(__lowerCAmelCase			,							"""r"""    ) as f:
        self.assertTrue(f.read()			,							__lowerCAmelCase    )
  def 							_lowercase							( self			: Optional[int]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""    )
    self.assertEqual(__lowerCAmelCase			,							__lowerCAmelCase    )
  def 							_lowercase							( self			: List[Any]    )			->	str:







    """simple docstring"""






    self.check_copy_consistency(
        """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""			,							"""BertLMPredictionHead"""			,							REFERENCE_CODE + """\n"""			,							)

    # With no empty line at the end
    self.check_copy_consistency(
        """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""			,							"""BertLMPredictionHead"""			,							__lowerCAmelCase			,							)

    # Copy consistency with rename
    self.check_copy_consistency(
        """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""			,							"""TestModelLMPredictionHead"""			,							re.sub("""Bert"""			,							"""TestModel"""			,							__lowerCAmelCase    )			,							)

    # Copy consistency with a really long name
    __magic_name__				     =       '''TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
    self.check_copy_consistency(
        F'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''			,							F'''{long_class_name}LMPredictionHead'''			,							re.sub("""Bert"""			,							__lowerCAmelCase			,							__lowerCAmelCase    )			,							)

    # Copy consistency with overwrite
    self.check_copy_consistency(
        """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""			,							"""TestModelLMPredictionHead"""			,							__lowerCAmelCase			,							overwrite_result=re.sub("""Bert"""			,							"""TestModel"""			,							__lowerCAmelCase    )			,							)





  def 							_lowercase							( self			: List[str]    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

    __magic_name__				     =       (
        '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
        ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
        ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
        ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'''
        ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'''
        ''' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
        ''' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'''
        ''' method has been applied to compress GPT2 into'''
        ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
        ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
        ''' Multilingual BERT into'''
        ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
        ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'''
        ''' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'''
        ''' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'''
        ''' Luong, Quoc V. Le, Christopher D. Manning.'''
    )
    __magic_name__				     =       (
        '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
        ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
        ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
        ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
    )
    __magic_name__				     =       (
        '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
        ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
        ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
        ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'''
        ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'''
        ''' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
        ''' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'''
        ''' method has been applied to compress GPT2 into'''
        ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
        ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
        ''' Multilingual BERT into'''
        ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
        ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'''
        ''' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'''
        ''' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'''
        ''' Christopher D. Manning 发布。\n'''
    )

    __magic_name__				     =       check_copies.convert_to_localized_md(
        __lowerCAmelCase			,							__lowerCAmelCase			,							localized_readme["""format_model_list"""]    )

    self.assertFalse(__lowerCAmelCase    )
    self.assertEqual(__lowerCAmelCase			,							__lowerCAmelCase    )

    __magic_name__				     =       check_copies.convert_to_localized_md(
        __lowerCAmelCase			,							__lowerCAmelCase			,							localized_readme["""format_model_list"""]    )

    # Check whether the number of models is equal to README.md after conversion.
    self.assertTrue(__lowerCAmelCase    )

    __magic_name__				     =       (
        '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
        ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
        ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
        ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'''
    )
    __magic_name__				     =       (
        '''1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'''
        ''' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
        ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
        ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
    )
    __magic_name__				     =       (
        '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
        ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
        ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
        ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
    )

    __magic_name__				     =       check_copies.convert_to_localized_md(
        __lowerCAmelCase			,							__lowerCAmelCase			,							localized_readme["""format_model_list"""]    )

    # Check if the model link is synchronized.
    self.assertEqual(__lowerCAmelCase			,							__lowerCAmelCase    )


'''simple docstring'''

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,
# specifically showcasing the experiment tracking capability,
# 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
#
########################################################################

__a: Optional[Any]          =   16
__a: Any          =   32
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = 16     ):
 lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 lowercase__				:    Optional[int]  =					load_dataset('''glue'''		,     '''mrpc'''     )

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

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

  return tokenizer.pad(
      UpperCAmelCase		,     padding='''longest'''		,     max_length=UpperCAmelCase		,     pad_to_multiple_of=UpperCAmelCase		,     return_tensors='''pt'''		,     )

 # Instantiate dataloaders.
 lowercase__				:    str  =					DataLoader(
     tokenized_datasets['''train''']		,     shuffle=UpperCAmelCase		,     collate_fn=UpperCAmelCase		,     batch_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					DataLoader(
     tokenized_datasets['''validation''']		,     shuffle=UpperCAmelCase		,     collate_fn=UpperCAmelCase		,     batch_size=UpperCAmelCase     )

 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

  __a: Tuple          =   mocked_dataloaders  # noqa: F811
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,     UpperCAmelCase     ) == "1":
  lowercase__				:    Optional[int]  =					2
 # Initialize Accelerator

 # New Code #
 # We pass in "all" to `log_with` to grab all available trackers in the environment
 # Note: If using a custom `Tracker` class, should be passed in here such as:
 # >>> log_with = ["all", MyCustomTrackerClassInstance()]
 if args.with_tracking:
  lowercase__				:    Union[str, Any]  =					Accelerator(
      cpu=args.cpu		,     mixed_precision=args.mixed_precision		,     log_with='''all'''		,     project_dir=args.project_dir     )
 else:
  lowercase__				:    Any  =					Accelerator(cpu=args.cpu		,     mixed_precision=args.mixed_precision     )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowercase__				:    int  =					config['''lr''']
 lowercase__				:    Optional[int]  =					int(config['''num_epochs''']     )
 lowercase__				:    Optional[Any]  =					int(config['''seed''']     )
 lowercase__				:    int  =					int(config['''batch_size''']     )
 set_seed(UpperCAmelCase     )

 lowercase__ ,							lowercase__				:    str  =					get_dataloaders(UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    Union[str, Any]  =					evaluate.load('''glue'''		,     '''mrpc'''     )

 # If the batch size is too big we use gradient accumulation
 lowercase__				:    Any  =					1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  lowercase__				:    Union[str, Any]  =					batch_size // MAX_GPU_BATCH_SIZE
  lowercase__				:    Any  =					MAX_GPU_BATCH_SIZE

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowercase__				:    Optional[int]  =					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,     return_dict=UpperCAmelCase     )

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

 # Instantiate optimizer
 lowercase__				:    List[Any]  =					AdamW(params=model.parameters()		,     lr=UpperCAmelCase     )

 # Instantiate scheduler
 lowercase__				:    List[str]  =					get_linear_schedule_with_warmup(
     optimizer=UpperCAmelCase		,     num_warmup_steps=100		,     num_training_steps=(len(UpperCAmelCase     ) * 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.
 lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    List[str]  =					accelerator.prepare(
     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )

 # New Code #
 # We need to initialize the trackers we use. Overall configurations can also be stored
 if args.with_tracking:
  lowercase__				:    Optional[Any]  =					os.path.split(UpperCAmelCase     )[-1].split('''.'''     )[0]
  accelerator.init_trackers(UpperCAmelCase		,     UpperCAmelCase     )

 # Now we train the model
 for epoch in range(UpperCAmelCase     ):
  model.train()
  # New Code #
  # For our tracking example, we will log the total loss of each epoch
  if args.with_tracking:
   lowercase__				:    str  =					0
  for step, batch in enumerate(UpperCAmelCase     ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device     )
   lowercase__				:    List[str]  =					model(**UpperCAmelCase     )
   lowercase__				:    List[str]  =					outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   lowercase__				:    List[str]  =					loss / gradient_accumulation_steps
   accelerator.backward(UpperCAmelCase     )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  for step, batch in enumerate(UpperCAmelCase     ):
   # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
   batch.to(accelerator.device     )
   with torch.no_grad():
    lowercase__				:    List[str]  =					model(**UpperCAmelCase     )
   lowercase__				:    Optional[int]  =					outputs.logits.argmax(dim=-1     )
   lowercase__ ,							lowercase__				:    Optional[int]  =					accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
   metric.add_batch(
       predictions=UpperCAmelCase		,     references=UpperCAmelCase		,     )

  lowercase__				:    int  =					metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""		,     UpperCAmelCase     )

  # New Code #
  # To actually log, we call `Accelerator.log`
  # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': eval_metric['''accuracy'''],
           '''f1''': eval_metric['''f1'''],
           '''train_loss''': total_loss.item() / len(UpperCAmelCase     ),
           '''epoch''': epoch,
       }		,     step=UpperCAmelCase		,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
 if args.with_tracking:
  accelerator.end_training()
def 			__UpperCamelCase       (   ):
 lowercase__				:    Any  =					argparse.ArgumentParser(description='''Simple example of training script.'''     )
 parser.add_argument(
     '''--mixed_precision'''		,     type=UpperCAmelCase		,     default=UpperCAmelCase		,     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.'''     )
 parser.add_argument(
     '''--with_tracking'''		,     action='''store_true'''		,     help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,     )
 parser.add_argument(
     '''--project_dir'''		,     type=UpperCAmelCase		,     default='''logs'''		,     help='''Location on where to store experiment tracking logs` and relevent project information'''		,     )
 lowercase__				:    str  =					parser.parse_args()
 lowercase__				:    Tuple  =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(UpperCAmelCase		,     UpperCAmelCase     )


if __name__ == "__main__":
  main()

