






'''simple docstring'''

import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
     import tensorflow as tf

if is_torch_available():
     import torch


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None   )						->   int:



        """simple docstring"""






        if not conversation_id:
              UpperCamelCase       						=      uuid.uuida()
        if past_user_inputs is None:
              UpperCamelCase       						=      []
        if generated_responses is None:
              UpperCamelCase       						=      []

        UpperCamelCase       						=      conversation_id
        UpperCamelCase       						=      past_user_inputs
        UpperCamelCase       						=      generated_responses
        UpperCamelCase       						=      text

  def __eq__(   self    ,	_SCREAMING_SNAKE_CASE   )						->   List[str]:



        """simple docstring"""






        if not isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              return False
        if self.uuid == other.uuid:
              return True
        return (
            self.new_user_input == other.new_user_input
            and self.past_user_inputs == other.past_user_inputs
            and self.generated_responses == other.generated_responses
        )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = False   )						->   str:



        """simple docstring"""






        if self.new_user_input:
              if overwrite:
                    logger.warning(
                        F"User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten "
                        F"with: \"{text}\"."   )
                    UpperCamelCase       						=      text
              else:
                    logger.warning(
                        F"User input added while unprocessed input was existing: \"{self.new_user_input}\" new input "
                        F"ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"   )
        else:
              UpperCamelCase       						=      text

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        if self.new_user_input:
              self.past_user_inputs.append(self.new_user_input   )
        UpperCamelCase       						=      None

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






        self.generated_responses.append(_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        for user_input, generated_response in zip(self.past_user_inputs    ,	self.generated_responses   ):
              yield True, user_input
              yield False, generated_response
        if self.new_user_input:
              yield True, self.new_user_input


  def __repr__(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      F"Conversation id: {self.uuid} \n"
        for is_user, text in self.iter_texts():
              UpperCamelCase       						=      """user""" if is_user else """bot"""
              output += F"{name} >> {text} \n"
        return output






@add_end_docstrings(
    lowerCamelCase			, r"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """			, )
class        a_		(    lowerCamelCase       ):

  def __init__(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






        super().__init__(*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
        if self.tokenizer.pad_token_id is None:
              UpperCamelCase       						=      self.tokenizer.eos_token

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	**_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      {}
        UpperCamelCase       						=      {}
        UpperCamelCase       						=      {}

        if min_length_for_response is not None:
              UpperCamelCase       						=      min_length_for_response
        if minimum_tokens is not None:
              UpperCamelCase       						=      minimum_tokens

        if "max_length" in generate_kwargs:
              UpperCamelCase       						=      generate_kwargs["""max_length"""]
              # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
        if clean_up_tokenization_spaces is not None:
              UpperCamelCase       						=      clean_up_tokenization_spaces

        if generate_kwargs:
              forward_params.update(_SCREAMING_SNAKE_CASE   )
        return preprocess_params, forward_params, postprocess_params

  def __call__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=0    ,	**_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      super().__call__(_SCREAMING_SNAKE_CASE    ,	num_workers=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
        if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ) and len(_SCREAMING_SNAKE_CASE   ) == 1:
              return outputs[0]
        return outputs

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=32   )						->   Dict[str, Any]:



        """simple docstring"""






        if not isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""   )
        if conversation.new_user_input is None:
              raise ValueError(
                  F"Conversation with UUID {type(conversation.uuid   )} does not contain new user input to process. "
                  """Add user inputs with the conversation's `add_user_input` method"""   )
        if hasattr(self.tokenizer    ,	"""_build_conversation_input_ids"""   ):
              UpperCamelCase       						=      self.tokenizer._build_conversation_input_ids(_SCREAMING_SNAKE_CASE   )
        else:
              # If the tokenizer cannot handle conversations, we default to only the old version
              UpperCamelCase       						=      self._legacy_parse_and_tokenize(_SCREAMING_SNAKE_CASE   )

        if self.framework == "pt":
              UpperCamelCase       						=      torch.LongTensor([input_ids]   )
        elif self.framework == "tf":
              UpperCamelCase       						=      tf.constant([input_ids]   )
        return {"input_ids": input_ids, "conversation": conversation}

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=10    ,	**_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      generate_kwargs.get("""max_length"""    ,	self.model.config.max_length   )

        UpperCamelCase       						=      model_inputs["""input_ids"""].shape[1]
        if max_length - minimum_tokens < n:
              logger.warning(F"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"   )
              UpperCamelCase       						=      max_length - minimum_tokens
              UpperCamelCase       						=      model_inputs["""input_ids"""][:, -trim:]
              if "attention_mask" in model_inputs:
                    UpperCamelCase       						=      model_inputs["""attention_mask"""][:, -trim:]
        UpperCamelCase       						=      model_inputs.pop("""conversation"""   )
        UpperCamelCase       						=      max_length
        UpperCamelCase       						=      self.model.generate(**_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
        if self.model.config.is_encoder_decoder:
              UpperCamelCase       						=      1
        else:
              UpperCamelCase       						=      n
        return {"output_ids": output_ids[:, start_position:], "conversation": conversation}

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=True   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      model_outputs["""output_ids"""]
        UpperCamelCase       						=      self.tokenizer.decode(
            output_ids[0]    ,	skip_special_tokens=_SCREAMING_SNAKE_CASE    ,	clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE    ,	)
        UpperCamelCase       						=      model_outputs["""conversation"""]
        conversation.mark_processed()
        conversation.append_response(_SCREAMING_SNAKE_CASE   )
        return conversation


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      self.tokenizer.eos_token_id
        UpperCamelCase       						=      []
        for is_user, text in conversation.iter_texts():
              if eos_token_id is not None:
                    input_ids.extend(self.tokenizer.encode(_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   ) + [eos_token_id]   )
              else:
                    input_ids.extend(self.tokenizer.encode(_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )   )

        if len(_SCREAMING_SNAKE_CASE   ) > self.tokenizer.model_max_length:
              UpperCamelCase       						=      input_ids[-self.tokenizer.model_max_length :]
        return input_ids








'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


SCREAMING_SNAKE_CASE__											=     1_6
SCREAMING_SNAKE_CASE__											=     3_2







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase = 16					)->				Dict:
      UpperCamelCase       						=      AutoTokenizer.from_pretrained("""bert-base-cased"""					)
      UpperCamelCase       						=      load_dataset("""glue"""	,		"""mrpc"""					)

      def tokenize_function(__UpperCamelCase					):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase       						=      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():
            UpperCamelCase       						=      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
      UpperCamelCase       						=      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.
            UpperCamelCase       						=      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":
                  UpperCamelCase       						=      16
            elif accelerator.mixed_precision != "no":
                  UpperCamelCase       						=      8
            else:
                  UpperCamelCase       						=      None

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

      # Instantiate dataloaders.
      UpperCamelCase       						=      DataLoader(
          tokenized_datasets["""train"""]	,		shuffle=__UpperCamelCase	,		collate_fn=__UpperCamelCase	,		batch_size=__UpperCamelCase					)
      UpperCamelCase       						=      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

     SCREAMING_SNAKE_CASE__											=     mocked_dataloaders  # noqa: F811







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List[Any]:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,		__UpperCamelCase					) == "1":
            UpperCamelCase       						=      2
      # New Code #
      UpperCamelCase       						=      int(args.gradient_accumulation_steps					)
      UpperCamelCase       						=      int(args.local_sgd_steps					)
      # Initialize accelerator
      UpperCamelCase       						=      Accelerator(
          cpu=args.cpu	,		mixed_precision=args.mixed_precision	,		gradient_accumulation_steps=__UpperCamelCase					)
      if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
            raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""					)
      # 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(__UpperCamelCase					)
      UpperCamelCase						,UpperCamelCase       						=      get_dataloaders(__UpperCamelCase	,		__UpperCamelCase					)
      # 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=__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).
      UpperCamelCase       						=      model.to(accelerator.device					)

      # Instantiate optimizer
      UpperCamelCase       						=      AdamW(params=model.parameters()	,		lr=__UpperCamelCase					)

      # Instantiate scheduler
      UpperCamelCase       						=      get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase	,		num_warmup_steps=100	,		num_training_steps=(len(__UpperCamelCase					) * 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(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)

      # Now we train the model
      for epoch in range(__UpperCamelCase					):
            model.train()
            with LocalSGD(
                accelerator=__UpperCamelCase	,		model=__UpperCamelCase	,		local_sgd_steps=__UpperCamelCase	,		enabled=local_sgd_steps is not None					) as local_sgd:
                  for step, batch in enumerate(__UpperCamelCase					):
                        # 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(__UpperCamelCase					):
                              UpperCamelCase       						=      model(**__UpperCamelCase					)
                              UpperCamelCase       						=      output.loss
                              accelerator.backward(__UpperCamelCase					)
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                              # LocalSGD-specific line
                              local_sgd.step()

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

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







def 					lowercase__				(   )->				List[Any]:
      UpperCamelCase       						=      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."""	,		)
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""	,		type=__UpperCamelCase	,		default=1	,		help="""The number of minibatches to be ran before gradients are accumulated."""	,		)
      parser.add_argument(
          """--local_sgd_steps"""	,		type=__UpperCamelCase	,		default=8	,		help="""Number of local SGD steps or None to disable local SGD"""					)
      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""": 42, """batch_size""": 16}
      training_function(__UpperCamelCase	,		__UpperCamelCase					)


if __name__ == "__main__":
     main()
