



'''simple docstring'''





import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import (
      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  )

if is_tf_available() and is_tensorflow_probability_available():
  import tensorflow as tf
  import tensorflow_probability as tfp

  from ..models.auto.modeling_tf_auto import (
      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  )


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 def __call__(      self					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    **_lowerCamelCase  )    ->    List[Any]:
    # Returns tqa_pipeline_inputs of shape:
    # [
    #   {"table": pd.DataFrame, "query": List[str]},
    #   ...,
    #   {"table": pd.DataFrame, "query" : List[str]}
    # ]
    requires_backends(self					,    """pandas"""  )
    import pandas as pd

    if table is None:
       raise ValueError("""Keyword argument `table` cannot be None."""  )
    elif query is None:
       if isinstance(_lowerCamelCase					,    _lowerCamelCase  ) and table.get("""query"""  ) is not None and table.get("""table"""  ) is not None:
          A_  :		Any						=					[table]
       elif isinstance(_lowerCamelCase					,    _lowerCamelCase  ) and len(_lowerCamelCase  ) > 0:
          if not all(isinstance(_lowerCamelCase					,    _lowerCamelCase  ) for d in table  ):
             raise ValueError(
                 F"Keyword argument `table` should be a list of dict, but is {(type(_lowerCamelCase  ) for d in table)}"  )

          if table[0].get("""query"""  ) is not None and table[0].get("""table"""  ) is not None:
             A_  :		List[Any]						=					table
          else:
             raise ValueError(
                 """If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"""
                 F" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."  )
       elif Dataset is not None and isinstance(_lowerCamelCase					,    _lowerCamelCase  ) or isinstance(_lowerCamelCase					,    types.GeneratorType  ):
          return table
       else:
          raise ValueError(
              """Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but """
              F"is {type(_lowerCamelCase  )})"  )
    else:
       A_  :		Optional[Any]						=					[{"""table""": table, """query""": query}]

    for tqa_pipeline_input in tqa_pipeline_inputs:
       if not isinstance(tqa_pipeline_input["""table"""]					,    pd.DataFrame  ):
          if tqa_pipeline_input["table"] is None:
             raise ValueError("""Table cannot be None."""  )

          A_  :		Dict						=					pd.DataFrame(tqa_pipeline_input["""table"""]  )

    return tqa_pipeline_inputs


@add_end_docstrings(__A							)
class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     '''table,query'''







 def __init__(      self					,    _lowerCamelCase=TableQuestionAnsweringArgumentHandler()					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Any:
    super().__init__(*_lowerCamelCase					,    **_lowerCamelCase  )
    A_  :		int						=					args_parser

    self.check_model_type(
        dict(
            TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
            + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()  )
        if self.framework == """tf"""
        else dict(
            MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()  )  )

    A_  :		Any						=					bool(getattr(self.model.config					,    """aggregation_labels"""					,    _lowerCamelCase  )  ) and bool(
        getattr(self.model.config					,    """num_aggregation_labels"""					,    _lowerCamelCase  )  )
    A_  :		List[str]						=					"""tapas""" if hasattr(self.model.config					,    """aggregation_labels"""  ) else None







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    List[str]:
    return self.model(**_lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    List[Any]:
    if self.framework == "pt":
       A_  :		int						=					[]
       A_  :		str						=					[]
       A_  :		int						=					None
       A_  :		Dict						=					inputs["""input_ids"""].shape[0]

       A_  :		Any						=					inputs["""input_ids"""].to(self.device  )
       A_  :		List[str]						=					inputs["""attention_mask"""].to(self.device  )
       A_  :		Dict						=					inputs["""token_type_ids"""].to(self.device  )
       A_  :		Dict						=					None

       for index in range(_lowerCamelCase  ):
          # If sequences have already been processed, the token type IDs will be created according to the previous
          # answer.
          if prev_answers is not None:
             A_  :		int						=					token_type_ids_example[:, 3]  # shape (seq_len,)
             A_  :		List[str]						=					np.zeros_like(prev_labels_example.cpu().numpy()  )  # shape (seq_len,)

             A_  :		List[Any]						=					token_type_ids[index]  # shape (seq_len, 7)
             for i in range(model_labels.shape[0]  ):
                A_  :		Dict						=					token_type_ids_example[:, 0].tolist()[i]
                A_  :		int						=					token_type_ids_example[:, 1].tolist()[i] - 1
                A_  :		Tuple						=					token_type_ids_example[:, 2].tolist()[i] - 1

                if row_id >= 0 and col_id >= 0 and segment_id == 1:
                   A_  :		int						=					int(prev_answers[(col_id, row_id)]  )

             A_  :		int						=					torch.from_numpy(_lowerCamelCase  ).type(torch.long  ).to(self.device  )

          A_  :		str						=					input_ids[index]
          A_  :		str						=					attention_mask[index]  # shape (seq_len,)
          A_  :		str						=					token_type_ids[index]  # shape (seq_len, 7)
          A_  :		List[str]						=					self.model(
              input_ids=input_ids_example.unsqueeze(0  )					,    attention_mask=attention_mask_example.unsqueeze(0  )					,    token_type_ids=token_type_ids_example.unsqueeze(0  )					,    )
          A_  :		Optional[int]						=					outputs.logits

          if self.aggregate:
             all_aggregations.append(outputs.logits_aggregation  )

          all_logits.append(_lowerCamelCase  )

          A_  :		List[str]						=					torch.distributions.Bernoulli(logits=_lowerCamelCase  )
          A_  :		Any						=					dist_per_token.probs * attention_mask_example.type(torch.floataa  ).to(
              dist_per_token.probs.device  )

          A_  :		Optional[Any]						=					collections.defaultdict(_lowerCamelCase  )
          for i, p in enumerate(probabilities.squeeze().tolist()  ):
             A_  :		List[str]						=					token_type_ids_example[:, 0].tolist()[i]
             A_  :		List[Any]						=					token_type_ids_example[:, 1].tolist()[i] - 1
             A_  :		Union[str, Any]						=					token_type_ids_example[:, 2].tolist()[i] - 1
             if col >= 0 and row >= 0 and segment_id == 1:
                coords_to_probs[(col, row)].append(_lowerCamelCase  )

          A_  :		List[Any]						=					{key: np.array(coords_to_probs[key]  ).mean() > 0.5 for key in coords_to_probs}

       A_  :		Dict						=					torch.cat(tuple(_lowerCamelCase  )					,    0  )

       return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_lowerCamelCase  )					,    0  ))
    else:
       A_  :		List[Any]						=					[]
       A_  :		Any						=					[]
       A_  :		List[Any]						=					None
       A_  :		Optional[int]						=					inputs["""input_ids"""].shape[0]

       A_  :		List[str]						=					inputs["""input_ids"""]
       A_  :		Union[str, Any]						=					inputs["""attention_mask"""]
       A_  :		Tuple						=					inputs["""token_type_ids"""].numpy()
       A_  :		List[str]						=					None

       for index in range(_lowerCamelCase  ):
          # If sequences have already been processed, the token type IDs will be created according to the previous
          # answer.
          if prev_answers is not None:
             A_  :		List[Any]						=					token_type_ids_example[:, 3]  # shape (seq_len,)
             A_  :		Union[str, Any]						=					np.zeros_like(_lowerCamelCase					,    dtype=np.intaa  )  # shape (seq_len,)

             A_  :		Optional[int]						=					token_type_ids[index]  # shape (seq_len, 7)
             for i in range(model_labels.shape[0]  ):
                A_  :		Tuple						=					token_type_ids_example[:, 0].tolist()[i]
                A_  :		List[Any]						=					token_type_ids_example[:, 1].tolist()[i] - 1
                A_  :		List[str]						=					token_type_ids_example[:, 2].tolist()[i] - 1

                if row_id >= 0 and col_id >= 0 and segment_id == 1:
                   A_  :		Dict						=					int(prev_answers[(col_id, row_id)]  )

             A_  :		int						=					model_labels

          A_  :		Dict						=					input_ids[index]
          A_  :		Dict						=					attention_mask[index]  # shape (seq_len,)
          A_  :		Union[str, Any]						=					token_type_ids[index]  # shape (seq_len, 7)
          A_  :		Optional[int]						=					self.model(
              input_ids=np.expand_dims(_lowerCamelCase					,    axis=0  )					,    attention_mask=np.expand_dims(_lowerCamelCase					,    axis=0  )					,    token_type_ids=np.expand_dims(_lowerCamelCase					,    axis=0  )					,    )
          A_  :		Dict						=					outputs.logits

          if self.aggregate:
             all_aggregations.append(outputs.logits_aggregation  )

          all_logits.append(_lowerCamelCase  )

          A_  :		Optional[int]						=					tfp.distributions.Bernoulli(logits=_lowerCamelCase  )
          A_  :		Tuple						=					dist_per_token.probs_parameter() * tf.cast(_lowerCamelCase					,    tf.floataa  )

          A_  :		Any						=					collections.defaultdict(_lowerCamelCase  )
          A_  :		Dict						=					token_type_ids_example
          for i, p in enumerate(tf.squeeze(_lowerCamelCase  ).numpy().tolist()  ):
             A_  :		Tuple						=					token_type_ids_example[:, 0].tolist()[i]
             A_  :		Tuple						=					token_type_ids_example[:, 1].tolist()[i] - 1
             A_  :		Tuple						=					token_type_ids_example[:, 2].tolist()[i] - 1
             if col >= 0 and row >= 0 and segment_id == 1:
                coords_to_probs[(col, row)].append(_lowerCamelCase  )

          A_  :		Any						=					{key: np.array(coords_to_probs[key]  ).mean() > 0.5 for key in coords_to_probs}

       A_  :		Union[str, Any]						=					tf.concat(tuple(_lowerCamelCase  )					,    0  )

       return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(_lowerCamelCase  )					,    0  ))







 def __call__(      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Tuple:
    A_  :		Optional[int]						=					self._args_parser(*_lowerCamelCase					,    **_lowerCamelCase  )

    A_  :		Dict						=					super().__call__(_lowerCamelCase					,    **_lowerCamelCase  )
    if len(_lowerCamelCase  ) == 1:
       return results[0]
    return results







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    **_lowerCamelCase  )    ->    Optional[Any]:
    A_  :		Tuple						=					{}
    if padding is not None:
       A_  :		Any						=					padding
    if truncation is not None:
       A_  :		List[str]						=					truncation

    A_  :		Optional[int]						=					{}
    if sequential is not None:
       A_  :		Any						=					sequential
    return preprocess_params, forward_params, {}







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=None					,    _lowerCamelCase=True					,    _lowerCamelCase=None  )    ->    Optional[Any]:
    if truncation is None:
       if self.type == "tapas":
          A_  :		str						=					"""drop_rows_to_fit"""
       else:
          A_  :		Any						=					"""do_not_truncate"""

    A_						,  A_  :		Tuple						=					pipeline_input["""table"""], pipeline_input["""query"""]
    if table.empty:
       raise ValueError("""table is empty"""  )
    if query is None or query == "":
       raise ValueError("""query is empty"""  )
    A_  :		Optional[int]						=					self.tokenizer(_lowerCamelCase					,    _lowerCamelCase					,    return_tensors=self.framework					,    truncation=_lowerCamelCase					,    padding=_lowerCamelCase  )
    A_  :		Dict						=					table
    return inputs







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=False  )    ->    Tuple:
    A_  :		Any						=					model_inputs.pop("""table"""  )

    if self.type == "tapas":
       if sequential:
          A_  :		Optional[Any]						=					self.sequential_inference(**_lowerCamelCase  )
       else:
          A_  :		Optional[Any]						=					self.batch_inference(**_lowerCamelCase  )
    else:
       A_  :		List[str]						=					self.model.generate(**_lowerCamelCase  )
    A_  :		str						=					{"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
    return model_outputs







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Dict:
    A_  :		Optional[int]						=					model_outputs["""model_inputs"""]
    A_  :		Any						=					model_outputs["""table"""]
    A_  :		Any						=					model_outputs["""outputs"""]
    if self.type == "tapas":
       if self.aggregate:
          A_						,  A_  :		Union[str, Any]						=					outputs[:2]
          A_  :		Optional[int]						=					self.tokenizer.convert_logits_to_predictions(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )
          A_						,  A_  :		str						=					predictions
          A_  :		Dict						=					{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_lowerCamelCase  )}

          A_  :		int						=					self.model.config.no_aggregation_label_index
          A_  :		List[str]						=					{
              i: aggregators[i] + """ > """ for i, pred in enumerate(_lowerCamelCase  ) if pred != no_agg_label_index
          }
       else:
          A_  :		List[Any]						=					outputs[0]
          A_  :		Optional[Any]						=					self.tokenizer.convert_logits_to_predictions(_lowerCamelCase					,    _lowerCamelCase  )
          A_  :		str						=					predictions[0]
          A_  :		List[str]						=					{}
          A_  :		Optional[int]						=					{}
       A_  :		Union[str, Any]						=					[]
       for index, coordinates in enumerate(_lowerCamelCase  ):
          A_  :		str						=					[table.iat[coordinate] for coordinate in coordinates]
          A_  :		int						=					aggregators.get(_lowerCamelCase					,    """"""  )
          A_  :		Union[str, Any]						=					aggregators_prefix.get(_lowerCamelCase					,    """"""  )
          A_  :		Union[str, Any]						=					{
              """answer""": aggregator_prefix + """, """.join(_lowerCamelCase  ),
              """coordinates""": coordinates,
              """cells""": [table.iat[coordinate] for coordinate in coordinates],
          }
          if aggregator:
             A_  :		Union[str, Any]						=					aggregator

          answers.append(_lowerCamelCase  )
       if len(_lowerCamelCase  ) == 0:
          raise PipelineException("""Empty answer"""  )
    else:
       A_  :		Union[str, Any]						=					[{"""answer""": answer} for answer in self.tokenizer.batch_decode(_lowerCamelCase					,    skip_special_tokens=_lowerCamelCase  )]

    return answers if len(_lowerCamelCase  ) > 1 else answers[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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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__    :     Union[str, Any]			  =   16
UpperCamelCase__    :     Optional[int]			  =   32



def     UpperCAmelCase     (    a_      ,		a_		)   ->       List[Any]:



   """simple docstring"""






   if args.with_tracking:
      A_  :		Union[str, Any]						=					Accelerator(
          cpu=args.cpu      ,		mixed_precision=args.mixed_precision      ,		log_with="""all"""      ,		project_dir=args.project_dir		)
   else:
      A_  :		Optional[Any]						=					Accelerator(cpu=args.cpu      ,		mixed_precision=args.mixed_precision		)

   if hasattr(args.checkpointing_steps      ,		"""isdigit"""		):
      if args.checkpointing_steps == "epoch":
         A_  :		Tuple						=					args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         A_  :		List[str]						=					int(args.checkpointing_steps		)
      else:
         raise ValueError(
             F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."		)
   else:
      A_  :		Optional[Any]						=					None
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A_  :		str						=					config["""lr"""]
   A_  :		List[str]						=					int(config["""num_epochs"""]		)
   A_  :		Dict						=					int(config["""seed"""]		)
   A_  :		str						=					int(config["""batch_size"""]		)

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      A_  :		str						=					os.path.split(a_		)[-1].split("""."""		)[0]
      accelerator.init_trackers(a_      ,		a_		)

   A_  :		Union[str, Any]						=					AutoTokenizer.from_pretrained("""bert-base-cased"""		)
   A_  :		Optional[int]						=					load_dataset("""glue"""      ,		"""mrpc"""		)
   A_  :		List[str]						=					evaluate.load("""glue"""      ,		"""mrpc"""		)

   def tokenize_function(a_		):
      # max_length=None => use the model max length (it's actually the default)
      A_  :		Tuple						=					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():
      A_  :		Dict						=					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
   A_  :		int						=					tokenized_datasets.rename_column("""label"""      ,		"""labels"""		)

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

   def collate_fn(a_		):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A_  :		List[Any]						=					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":
         A_  :		Any						=					1_6
      elif accelerator.mixed_precision != "no":
         A_  :		Optional[int]						=					8
      else:
         A_  :		Optional[Any]						=					None

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

   # Instantiate dataloaders.
   A_  :		str						=					DataLoader(
       tokenized_datasets["""train"""]      ,		shuffle=a_      ,		collate_fn=a_      ,		batch_size=a_		)
   A_  :		Any						=					DataLoader(
       tokenized_datasets["""validation"""]      ,		shuffle=a_      ,		collate_fn=a_      ,		batch_size=a_		)

   set_seed(a_		)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   A_  :		List[Any]						=					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).
   A_  :		Union[str, Any]						=					model.to(accelerator.device		)

   # Instantiate optimizer
   A_  :		Any						=					AdamW(params=model.parameters()      ,		lr=a_		)

   # Instantiate scheduler
   A_  :		Dict						=					get_linear_schedule_with_warmup(
       optimizer=a_      ,		num_warmup_steps=1_0_0      ,		num_training_steps=(len(a_		) * 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.
   A_						,  A_						,  A_						,  A_						,  A_  :		List[str]						=					accelerator.prepare(
       a_      ,		a_      ,		a_      ,		a_      ,		a_		)

   # We need to keep track of how many total steps we have iterated over
   A_  :		Optional[int]						=					0
   # We also need to keep track of the stating epoch so files are named properly
   A_  :		Union[str, Any]						=					0

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"		)
         accelerator.load_state(args.resume_from_checkpoint		)
         A_  :		int						=					os.path.basename(args.resume_from_checkpoint		)
      else:
         # Get the most recent checkpoint
         A_  :		int						=					[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
         dirs.sort(key=os.path.getctime		)
         A_  :		Optional[Any]						=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      A_  :		Optional[Any]						=					os.path.splitext(a_		)[0]

      if "epoch" in training_difference:
         A_  :		List[Any]						=					int(training_difference.replace("""epoch_"""      ,		""""""		)		) + 1
         A_  :		List[str]						=					None
      else:
         A_  :		Optional[int]						=					int(training_difference.replace("""step_"""      ,		""""""		)		)
         A_  :		Optional[int]						=					resume_step // len(a_		)
         resume_step -= starting_epoch * len(a_		)

    # Now we train the model
   for epoch in range(a_      ,		a_		):
      model.train()
      if args.with_tracking:
         A_  :		List[str]						=					0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         A_  :		List[Any]						=					accelerator.skip_first_batches(a_      ,		a_		)
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         A_  :		Tuple						=					train_dataloader
      for step, batch in enumerate(a_		):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device		)
         A_  :		List[Any]						=					model(**a_		)
         A_  :		str						=					outputs.loss
         A_  :		Tuple						=					loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(a_		)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(a_      ,		a_		):
            A_  :		str						=					F"step_{overall_step}"
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  A_  :		Dict						=					os.path.join(args.output_dir      ,		a_		)
               accelerator.save_state(a_		)

      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():
            A_  :		str						=					model(**a_		)
         A_  :		Any						=					outputs.logits.argmax(dim=-1		)
         A_						,  A_  :		Dict						=					accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
         metric.add_batch(
             predictions=a_      ,		references=a_      ,		)

      A_  :		Union[str, Any]						=					metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}:"      ,		a_		)
      if args.with_tracking:
         accelerator.log(
             {
                 """accuracy""": eval_metric["""accuracy"""],
                 """f1""": eval_metric["""f1"""],
                 """train_loss""": total_loss.item() / len(a_		),
                 """epoch""": epoch,
             }      ,		step=a_      ,		)

      if checkpointing_steps == "epoch":
         A_  :		str						=					F"epoch_{epoch}"
         if args.output_dir is not None:
            A_  :		Dict						=					os.path.join(args.output_dir      ,		a_		)
         accelerator.save_state(a_		)

   if args.with_tracking:
      accelerator.end_training()



def     UpperCAmelCase     (    )   ->       str:



   """simple docstring"""






   A_  :		List[Any]						=					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."""      ,		)
   parser.add_argument("""--cpu"""      ,		action="""store_true"""      ,		help="""If passed, will train on the CPU."""		)
   parser.add_argument(
       """--checkpointing_steps"""      ,		type=a_      ,		default=a_      ,		help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""      ,		)
   parser.add_argument(
       """--resume_from_checkpoint"""      ,		type=a_      ,		default=a_      ,		help="""If the training should continue from a checkpoint folder."""      ,		)
   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(
       """--output_dir"""      ,		type=a_      ,		default="""."""      ,		help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,		)
   parser.add_argument(
       """--project_dir"""      ,		type=a_      ,		default="""logs"""      ,		help="""Location on where to store experiment tracking logs` and relevent project information"""      ,		)
   A_  :		Tuple						=					parser.parse_args()
   A_  :		Union[str, Any]						=					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
   training_function(a_      ,		a_		)


if __name__ == "__main__":
  main()
