


import argparse

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


UpperCAmelCase__   	=							16
UpperCAmelCase__   	=							32
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case = 16						)     ->			Any:







 """simple docstring"""


 _lowercase							      =AutoTokenizer.from_pretrained('''bert-base-cased'''						)
 _lowercase							      =load_dataset('''glue'''     ,						'''mrpc'''						)

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

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

 # Instantiate dataloaders.
 _lowercase							      =DataLoader(
     tokenized_datasets['''train''']     ,						shuffle=__snake_case     ,						collate_fn=__snake_case     ,						batch_size=__snake_case     ,						drop_last=__snake_case						)
 _lowercase							      =DataLoader(
     tokenized_datasets['''validation''']     ,						shuffle=__snake_case     ,						collate_fn=__snake_case     ,						batch_size=__snake_case     ,						drop_last=(accelerator.mixed_precision == '''fp8''')     ,						)

 return train_dataloader, eval_dataloader
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			List[str]:







 """simple docstring"""


 _lowercase							      =Accelerator(cpu=args.cpu     ,						mixed_precision=args.mixed_precision						)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _lowercase							      =config['''lr''']
 _lowercase							      =int(config['''num_epochs''']						)
 _lowercase							      =int(config['''seed''']						)
 _lowercase							      =int(config['''batch_size''']						)

 _lowercase							      =evaluate.load('''glue'''     ,						'''mrpc'''						)

 # If the batch size is too big we use gradient accumulation
 _lowercase							      =1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  _lowercase							      =batch_size // MAX_GPU_BATCH_SIZE
  _lowercase							      =MAX_GPU_BATCH_SIZE

 set_seed(__snake_case						)
 _lowercase      ,    _lowercase							      =get_dataloaders(__snake_case     ,						__snake_case						)
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _lowercase							      =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).
 _lowercase							      =model.to(accelerator.device						)
 # Instantiate optimizer
 _lowercase							      =AdamW(params=model.parameters()     ,						lr=__snake_case						)

 # Instantiate scheduler
 _lowercase							      =get_linear_schedule_with_warmup(
     optimizer=__snake_case     ,						num_warmup_steps=100     ,						num_training_steps=(len(__snake_case						) * 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							      =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						)
   _lowercase							      =model(**__snake_case						)
   _lowercase							      =outputs.loss
   _lowercase							      =loss / gradient_accumulation_steps
   accelerator.backward(__snake_case						)
   if step % gradient_accumulation_steps == 0:
    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():
    _lowercase							      =model(**__snake_case						)
   _lowercase							      =outputs.logits.argmax(dim=-1						)
   _lowercase      ,    _lowercase							      =accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
   metric.add_batch(
       predictions=__snake_case     ,						references=__snake_case     ,						)

  _lowercase							      =metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"     ,						__snake_case						)
def 				UpperCAmelCase_					(							)     ->			List[str]:







 """simple docstring"""


 _lowercase							      =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.'''     ,						)
 parser.add_argument('''--cpu'''     ,						action='''store_true'''     ,						help='''If passed, will train on the CPU.'''						)
 _lowercase							      =parser.parse_args()
 _lowercase							      ={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(__snake_case     ,						__snake_case						)


if __name__ == "__main__":
  main()








"""simple docstring"""




import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


lowerCamelCase_     :						Dict						=					get_logger(__name__)


lowerCamelCase_     :						List[str]						=					r'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'

class  _UpperCAmelCase  :



      '''simple docstring'''

      @add_start_docstrings(snake_case_  )
      def __call__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        raise NotImplementedError(
            F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""  )


class  _UpperCAmelCase  :



      '''simple docstring'''

      @add_start_docstrings(snake_case_  )
      def __call__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        raise NotImplementedError(
            F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""  )


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      @add_start_docstrings(snake_case_  )
      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      **snake_case_  ):

        """simple docstring"""

        for processor in self:
          A_  :       Tuple										=  inspect.signature(processor.__call__  ).parameters
          if len(snake_case_  ) > 3:
            if not all(arg in kwargs for arg in list(function_args.keys()  )[2:]  ):
              raise ValueError(
                  F"""Make sure that all the required parameters: {list(function_args.keys()  )} for """
                  F"""{processor.__class__} are passed to the logits processor."""  )
            A_  :       Tuple										=  processor(snake_case_					,      snake_case_					,      snake_case_					,      **snake_case_  )
          else:
            A_  :       Optional[Any]										=  processor(snake_case_					,      snake_case_					,      snake_case_  )
        return scores


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_  ):

        """simple docstring"""

        if not isinstance(snake_case_					,      snake_case_  ) or not (temperature > 0):
          raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}"""  )

        A_  :       Optional[int]										=  temperature

      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       int										=  scores / self.temperature
        return scores


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_ = -float('Inf'  )					,      snake_case_ = 1  ):

        """simple docstring"""

        if not isinstance(snake_case_					,      snake_case_  ) or (top_p < 0 or top_p > 1.0):
          raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}"""  )
        if not isinstance(snake_case_					,      snake_case_  ) or (min_tokens_to_keep < 1):
          raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"""  )

        A_  :       str										=  top_p
        A_  :       Union[str, Any]										=  filter_value
        A_  :       int										=  min_tokens_to_keep

      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_						,       A_  :       Tuple										=  lax.top_k(snake_case_					,      scores.shape[-1]  )

        A_  :       List[Any]										=  jnp.full_like(snake_case_					,      self.filter_value  )
        A_  :       List[str]										=  jax.nn.softmax(snake_case_					,      axis=-1  ).cumsum(axis=-1  )
        A_  :       Optional[int]										=  cumulative_probs < self.top_p

        # include the token that is higher than top_p as well
        A_  :       Union[str, Any]										=  jnp.roll(snake_case_					,      1  )
        score_mask |= score_mask.at[:, 0].set(snake_case_  )

        # min tokens to keep
        A_  :       int										=  score_mask.at[:, : self.min_tokens_to_keep].set(snake_case_  )

        A_  :       Optional[Any]										=  jnp.where(snake_case_					,      snake_case_					,      snake_case_  )
        A_  :       List[Any]										=  jax.lax.sort_key_val(snake_case_					,      snake_case_  )[-1]

        return next_scores


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_ = -float('Inf'  )					,      snake_case_ = 1  ):

        """simple docstring"""

        if not isinstance(snake_case_					,      snake_case_  ) or top_k <= 0:
          raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}"""  )

        A_  :       str										=  max(snake_case_					,      snake_case_  )
        A_  :       Union[str, Any]										=  filter_value

      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_						,       A_  :       int										=  scores.shape
        A_  :       Tuple										=  jnp.full(batch_size * vocab_size					,      self.filter_value  )

        A_  :       Union[str, Any]										=  min(self.top_k					,      scores.shape[-1]  )  # Safety check
        A_						,       A_  :       Dict										=  lax.top_k(snake_case_					,      snake_case_  )
        A_  :       Optional[int]										=  jnp.broadcast_to((jnp.arange(snake_case_  ) * vocab_size)[:, None]					,      (batch_size, topk)  ).flatten()
        A_  :       int										=  topk_scores.flatten()
        A_  :       Any										=  topk_indices.flatten() + shift

        A_  :       List[str]										=  next_scores_flat.at[topk_indices_flat].set(snake_case_  )
        A_  :       Union[str, Any]										=  next_scores_flat.reshape(snake_case_					,      snake_case_  )
        return next_scores


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  bos_token_id

      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  jnp.full(scores.shape					,      -float('inf'  )  )

        A_  :       Union[str, Any]										=  1 - jnp.bool_(cur_len - 1  )

        A_  :       str										=  jnp.where(snake_case_					,      new_scores.at[:, self.bos_token_id].set(0  )					,      snake_case_  )

        return scores


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Dict										=  max_length
        A_  :       Optional[int]										=  eos_token_id

      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  jnp.full(scores.shape					,      -float('inf'  )  )

        A_  :       Dict										=  1 - jnp.bool_(cur_len - self.max_length + 1  )

        A_  :       Dict										=  jnp.where(snake_case_					,      new_scores.at[:, self.eos_token_id].set(0  )					,      snake_case_  )

        return scores


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        if not isinstance(snake_case_					,      snake_case_  ) or min_length < 0:
          raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}"""  )

        if not isinstance(snake_case_					,      snake_case_  ) or eos_token_id < 0:
          raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}"""  )

        A_  :       Any										=  min_length
        A_  :       List[Any]										=  eos_token_id

      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       int										=  1 - jnp.clip(cur_len - self.min_length					,      0					,      1  )

        A_  :       Optional[Any]										=  jnp.where(snake_case_					,      scores.at[:, self.eos_token_id].set(-float('inf'  )  )					,      snake_case_  )

        return scores


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[Any]										=  list(snake_case_  )
        A_  :       Tuple										=  begin_index

      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Dict										=  1 - jnp.bool_(cur_len - self.begin_index  )

        A_  :       int										=  jnp.where(snake_case_					,      scores.at[:, self.begin_suppress_tokens].set(-float('inf'  )  )					,      snake_case_  )

        return scores


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[Any]										=  list(snake_case_  )

      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  scores.at[..., self.suppress_tokens].set(-float('inf'  )  )

        return scores


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Any										=  dict(snake_case_  )
        # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
        # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
        # Indexes without forced tokens will have a negative value.
        A_  :       Tuple										=  jnp.ones((max(force_token_map.keys()  ) + 1)					,      dtype=jnp.intaa  ) * -1
        for index, token in force_token_map.items():
          if token is not None:
            A_  :       Tuple										=  force_token_array.at[index].set(snake_case_  )
        A_  :       Any										=  jnp.intaa(snake_case_  )

      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        def _force_token(snake_case_  ):
          A_  :       List[Any]										=  scores.shape[0]
          A_  :       Any										=  self.force_token_array[generation_idx]

          A_  :       Tuple										=  jnp.ones_like(snake_case_					,      dtype=scores.dtype  ) * -float('inf'  )
          A_  :       List[Any]										=  jnp.zeros((batch_size, 1)					,      dtype=scores.dtype  )
          A_  :       int										=  lax.dynamic_update_slice(snake_case_					,      snake_case_					,      (0, current_token)  )
          return new_scores

        A_  :       int										=  lax.cond(
            cur_len >= self.force_token_array.shape[0]					,      lambda: scores					,      lambda: lax.cond(
                self.force_token_array[cur_len] >= 0					,      lambda: _force_token(snake_case_  )					,      lambda: scores					,      )					,      )
        return scores


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  generate_config.eos_token_id
        A_  :       Optional[int]										=  generate_config.no_timestamps_token_id
        A_  :       List[str]										=  generate_config.no_timestamps_token_id + 1

        A_  :       Any										=  decoder_input_length + 1

        if generate_config.is_multilingual:
          # room for language token and task token
          self.begin_index += 2
        if hasattr(snake_case_					,      'max_initial_timestamp_index'  ):
          A_  :       List[Any]										=  generate_config.max_initial_timestamp_index
        else:
          A_  :       Any										=  model_config.vocab_size
        if self.max_initial_timestamp_index is None:
          A_  :       Optional[Any]										=  model_config.vocab_size

      def __call__(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  scores.at[:, self.no_timestamps_token_id].set(-float('inf'  )  )

        def handle_pairs(snake_case_					,      snake_case_  ):
          A_  :       Any										=  jnp.where((cur_len - self.begin_index) >= 1					,      snake_case_					,      snake_case_  )
          A_  :       Tuple										=  jnp.where(
              input_ids_k[cur_len - 1] >= self.timestamp_begin					,      True and last_was_timestamp					,      snake_case_					,      )

          A_  :       Tuple										=  jnp.where((cur_len - self.begin_index) < 2					,      snake_case_					,      snake_case_  )
          A_  :       Any										=  jnp.where(
              input_ids_k[cur_len - 2] >= self.timestamp_begin					,      snake_case_					,      snake_case_					,      )

          return jnp.where(
              snake_case_					,      jnp.where(
                  penultimate_was_timestamp > 0					,      scores_k.at[self.timestamp_begin :].set(-float('inf'  )  )					,      scores_k.at[: self.eos_token_id].set(-float('inf'  )  )					,      )					,      snake_case_					,      )

        A_  :       Tuple										=  jax.vmap(snake_case_  )(snake_case_					,      snake_case_  )

        A_  :       Optional[Any]										=  jnp.where(cur_len == self.begin_index					,      snake_case_					,      snake_case_  )
        A_  :       Tuple										=  jnp.where(
            self.max_initial_timestamp_index is not None					,      True and apply_max_initial_timestamp					,      snake_case_					,      )

        A_  :       int										=  self.timestamp_begin + self.max_initial_timestamp_index

        A_  :       List[Any]										=  jnp.where(
            snake_case_					,      scores.at[:, last_allowed + 1 :].set(-float('inf'  )  )					,      snake_case_					,      )

        # if sum of probability over timestamps is above any other token, sample timestamp
        A_  :       Any										=  jax.nn.log_softmax(snake_case_					,      axis=-1  )

        def handle_cumulative_probs(snake_case_					,      snake_case_  ):
          A_  :       Dict										=  jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]					,      axis=-1  )
          A_  :       Optional[Any]										=  jnp.max(logprobs_k[: self.timestamp_begin]  )
          return jnp.where(
              timestamp_logprob > max_text_token_logprob					,      scores_k.at[: self.timestamp_begin].set(-float('inf'  )  )					,      snake_case_					,      )

        A_  :       Union[str, Any]										=  jax.vmap(snake_case_  )(snake_case_					,      snake_case_  )

        return scores