'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__a: str          =   logging.get_logger(__name__)

__a: Optional[int]          =   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__a: Tuple          =   {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

__a: List[Any]          =   {
    """roberta-base""": 5_12,
    """roberta-large""": 5_12,
    """roberta-large-mnli""": 5_12,
    """distilroberta-base""": 5_12,
    """roberta-base-openai-detector""": 5_12,
    """roberta-large-openai-detector""": 5_12,
}



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE							=    PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE							=    ["input_ids", "attention_mask"]
       SCREAMING_SNAKE_CASE							=    RobertaTokenizer

       def __init__(						self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase="replace"	,   __lowerCAmelCase="<s>"	,   __lowerCAmelCase="</s>"	,   __lowerCAmelCase="</s>"	,   __lowerCAmelCase="<s>"	,   __lowerCAmelCase="<unk>"	,   __lowerCAmelCase="<pad>"	,   __lowerCAmelCase="<mask>"	,   __lowerCAmelCase=False	,   __lowerCAmelCase=True	,   **__lowerCAmelCase	,   )  ->  str:
        super().__init__(
            __lowerCAmelCase	,   __lowerCAmelCase	,   tokenizer_file=__lowerCAmelCase	,   errors=__lowerCAmelCase	,   bos_token=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   sep_token=__lowerCAmelCase	,   cls_token=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   mask_token=__lowerCAmelCase	,   add_prefix_space=__lowerCAmelCase	,   trim_offsets=__lowerCAmelCase	,   **__lowerCAmelCase	,   )

        lowercase__				:    Tuple  =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
        if pre_tok_state.get('''add_prefix_space'''	,   __lowerCAmelCase ) != add_prefix_space:
         lowercase__				:    Optional[Any]  =					getattr(__lowerCAmelCase	,   pre_tok_state.pop('''type''' ) )
         lowercase__				:    Optional[int]  =					add_prefix_space
         lowercase__				:    Any  =					pre_tok_class(**__lowerCAmelCase )

        lowercase__				:    Optional[Any]  =					add_prefix_space

        lowercase__				:    Union[str, Any]  =					'''post_processor'''
        lowercase__				:    List[Any]  =					getattr(self.backend_tokenizer	,   __lowerCAmelCase	,   __lowerCAmelCase )
        if tokenizer_component_instance:
         lowercase__				:    List[str]  =					json.loads(tokenizer_component_instance.__getstate__() )

         # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
         if "sep" in state:
          lowercase__				:    List[str]  =					tuple(state['''sep'''] )
         if "cls" in state:
          lowercase__				:    Tuple  =					tuple(state['''cls'''] )

         lowercase__				:    List[Any]  =					False

         if state.get('''add_prefix_space'''	,   __lowerCAmelCase ) != add_prefix_space:
          lowercase__				:    Union[str, Any]  =					add_prefix_space
          lowercase__				:    Dict  =					True

         if state.get('''trim_offsets'''	,   __lowerCAmelCase ) != trim_offsets:
          lowercase__				:    Optional[int]  =					trim_offsets
          lowercase__				:    Optional[int]  =					True

         if changes_to_apply:
          lowercase__				:    str  =					getattr(__lowerCAmelCase	,   state.pop('''type''' ) )
          lowercase__				:    str  =					component_class(**__lowerCAmelCase )
          setattr(self.backend_tokenizer	,   __lowerCAmelCase	,   __lowerCAmelCase )

       @property
       def _lowerCAmelCase(						self )  ->  str:
        if self._mask_token is None:
         if self.verbose:
          logger.error('''Using mask_token, but it is not set yet.''' )
         return None
        return str(self._mask_token )

       @mask_token.setter
       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  str:
        lowercase__				:    str  =					AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase ) else value
        lowercase__				:    str  =					value

       def _lowerCAmelCase(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  BatchEncoding:
        lowercase__				:    List[Any]  =					kwargs.get('''is_split_into_words'''	,   __lowerCAmelCase )
        assert self.add_prefix_space or not is_split_into_words, (
            F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  BatchEncoding:
        lowercase__				:    str  =					kwargs.get('''is_split_into_words'''	,   __lowerCAmelCase )

        assert self.add_prefix_space or not is_split_into_words, (
            F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  Tuple[str]:
        lowercase__				:    Optional[Any]  =					self._tokenizer.model.save(__lowerCAmelCase	,   name=__lowerCAmelCase )
        return tuple(__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=None )  ->  Union[str, Any]:
        lowercase__				:    List[Any]  =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
         return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[int]:
        lowercase__				:    List[Any]  =					[self.sep_token_id]
        lowercase__				:    Tuple  =					[self.cls_token_id]

        if token_ids_a is None:
         return len(cls + token_ids_a + sep ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]


'''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()

