


import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

A      = logging.get_logger(__name__)


A      = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

A      = {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

A      = {"facebook/blenderbot-3B": 128}


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__		=  VOCAB_FILES_NAMES
  A__		=  PRETRAINED_VOCAB_FILES_MAP
  A__		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__		=  ['input_ids', 'attention_mask']
  A__		=  BlenderbotTokenizer






  def __init__(					self      :					int		,						_lowercase      :					Tuple=None		,						_lowercase      :					List[str]=None		,						_lowercase      :					Optional[int]=None		,						_lowercase      :					Dict="replace"		,						_lowercase      :					int="<s>"		,						_lowercase      :					Optional[Any]="</s>"		,						_lowercase      :					Optional[int]="</s>"		,						_lowercase      :					Dict="<s>"		,						_lowercase      :					str="<unk>"		,						_lowercase      :					Union[str, Any]="<pad>"		,						_lowercase      :					Optional[Any]="<mask>"		,						_lowercase      :					List[Any]=False		,						_lowercase      :					int=True		,						**_lowercase      :					Optional[Any]		,						):




         """simple docstring"""







         super().__init__(
             _lowercase		,						_lowercase		,						tokenizer_file=_lowercase		,						errors=_lowercase		,						bos_token=_lowercase		,						eos_token=_lowercase		,						sep_token=_lowercase		,						cls_token=_lowercase		,						unk_token=_lowercase		,						pad_token=_lowercase		,						mask_token=_lowercase		,						add_prefix_space=_lowercase		,						trim_offsets=_lowercase		,						**_lowercase		,						)

         UpperCAmelCase__										=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get("add_prefix_space"		,						_lowercase ) != add_prefix_space:
                UpperCAmelCase__										=     getattr(_lowercase		,						pre_tok_state.pop("type" ) )
                UpperCAmelCase__										=     add_prefix_space
                UpperCAmelCase__										=     pre_tok_class(**_lowercase )

         UpperCAmelCase__										=     add_prefix_space

         UpperCAmelCase__										=     "post_processor"
         UpperCAmelCase__										=     getattr(self.backend_tokenizer		,						_lowercase		,						_lowercase )
         if tokenizer_component_instance:
                UpperCAmelCase__										=     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:
                       UpperCAmelCase__										=     tuple(state["sep"] )
                if "cls" in state:
                       UpperCAmelCase__										=     tuple(state["cls"] )

                UpperCAmelCase__										=     False

                if state.get("add_prefix_space"		,						_lowercase ) != add_prefix_space:
                       UpperCAmelCase__										=     add_prefix_space
                       UpperCAmelCase__										=     True

                if state.get("trim_offsets"		,						_lowercase ) != trim_offsets:
                       UpperCAmelCase__										=     trim_offsets
                       UpperCAmelCase__										=     True

                if changes_to_apply:
                       UpperCAmelCase__										=     getattr(_lowercase		,						state.pop("type" ) )
                       UpperCAmelCase__										=     component_class(**_lowercase )
                       setattr(self.backend_tokenizer		,						_lowercase		,						_lowercase )






  @property
  # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         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 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     AddedToken(_lowercase		,						lstrip=_lowercase		,						rstrip=_lowercase ) if isinstance(_lowercase		,						_lowercase ) else value
         UpperCAmelCase__										=     value






  def 			_UpperCAmelCase  (					self      :					Dict		,						*_lowercase      :					int		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     kwargs.get("is_split_into_words"		,						_lowercase )
         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(*_lowercase		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					Dict		,						*_lowercase      :					str		,						**_lowercase      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     kwargs.get("is_split_into_words"		,						_lowercase )

         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(*_lowercase		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					str		,						_lowercase      :					Optional[str] = None ):




         """simple docstring"""







         UpperCAmelCase__										=     self._tokenizer.model.save(_lowercase		,						name=_lowercase )
         return tuple(_lowercase )






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					List[int]		,						_lowercase      :					Optional[List[int]] = None ):




         """simple docstring"""







         UpperCAmelCase__										=     [self.sep_token_id]
         UpperCAmelCase__										=     [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]






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					List[int]		,						_lowercase      :					Optional[List[int]] = None ):




         """simple docstring"""







         return token_ids_a + [self.eos_token_id]






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					"Conversation" ):




         """simple docstring"""







         UpperCAmelCase__										=     []
         for is_user, text in conversation.iter_texts():
                if is_user:
                       # We need to space prefix as it's being done within blenderbot
                       inputs.append(" " + text )
                else:
                       # Generated responses should contain them already.
                       inputs.append(_lowercase )

         UpperCAmelCase__										=     "  ".join(_lowercase )
         UpperCAmelCase__										=     self.encode(_lowercase )
         if len(_lowercase ) > self.model_max_length:
                UpperCAmelCase__										=     input_ids[-self.model_max_length :]
                logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
         return input_ids









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
#
########################################################################


A      = 16
A      = 32





def __UpperCAmelCase       (		__A ,						__A = 1_6			)						->					Tuple:



       '''simple docstring'''





       UpperCAmelCase__										=     AutoTokenizer.from_pretrained("bert-base-cased"			)
       UpperCAmelCase__										=     load_dataset("glue" ,						"mrpc"			)

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

       def collate_fn(__A			):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCAmelCase__										=     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCAmelCase__										=     1_6
              elif accelerator.mixed_precision != "no":
                     UpperCAmelCase__										=     8
              else:
                     UpperCAmelCase__										=     None

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

       # Instantiate dataloaders.
       UpperCAmelCase__										=     DataLoader(
           tokenized_datasets["train"] ,						shuffle=__A ,						collate_fn=__A ,						batch_size=__A			)
       UpperCAmelCase__										=     DataLoader(
           tokenized_datasets["validation"] ,						shuffle=__A ,						collate_fn=__A ,						batch_size=__A			)

       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      = mocked_dataloaders  # noqa: F811





def __UpperCAmelCase       (		__A ,						__A			)						->					Dict:



       '''simple docstring'''





       if os.environ.get("TESTING_MOCKED_DATALOADERS" ,						__A			) == "1":
              UpperCAmelCase__										=     2
       # New Code #
       UpperCAmelCase__										=     int(args.gradient_accumulation_steps			)
       # Initialize accelerator
       UpperCAmelCase__										=     Accelerator(
           cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						gradient_accumulation_steps=__A			)
       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
       UpperCAmelCase__										=     config["lr"]
       UpperCAmelCase__										=     int(config["num_epochs"]			)
       UpperCAmelCase__										=     int(config["seed"]			)
       UpperCAmelCase__										=     int(config["batch_size"]			)

       UpperCAmelCase__										=     evaluate.load("glue" ,						"mrpc"			)

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

       # 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).
       UpperCAmelCase__										=     model.to(accelerator.device			)

       # Instantiate optimizer
       UpperCAmelCase__										=     AdamW(params=model.parameters() ,						lr=__A			)

       # Instantiate scheduler
       UpperCAmelCase__										=     get_linear_schedule_with_warmup(
           optimizer=__A ,						num_warmup_steps=1_0_0 ,						num_training_steps=(len(__A			) * 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.
       UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     accelerator.prepare(
           __A ,						__A ,						__A ,						__A ,						__A			)

       # Now we train the model
       for epoch in range(__A			):
              model.train()
              for step, batch in enumerate(__A			):
                     # 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(__A			):
                            UpperCAmelCase__										=     model(**__A			)
                            UpperCAmelCase__										=     output.loss
                            accelerator.backward(__A			)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

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





def __UpperCAmelCase       (		)						->					Dict:



       '''simple docstring'''





       UpperCAmelCase__										=     argparse.ArgumentParser(description="Simple example of training script."			)
       parser.add_argument(
           "--mixed_precision" ,						type=__A ,						default=__A ,						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=__A ,						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."			)
       UpperCAmelCase__										=     parser.parse_args()
       UpperCAmelCase__										=     {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
       training_function(__A ,						__A			)


if __name__ == "__main__":
    main()





