






import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


_lowerCamelCase      			=						16
_lowerCamelCase      			=						32




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Accelerator	,					__UpperCamelCase      :						int = 16	,					__UpperCamelCase      :						str = "bert-base-cased"				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					AutoTokenizer.from_pretrained(__UpperCamelCase				)
    UpperCAmelCase_										=					load_dataset('''glue'''	,					'''mrpc'''				)

    def tokenize_function(__UpperCamelCase      :						str				):
        # 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
    UpperCAmelCase_										=					datasets.map(
        __UpperCamelCase	,					batched=__UpperCamelCase	,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,					load_from_cache_file=__UpperCamelCase				)

    # 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      :						Any				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(__UpperCamelCase	,					padding='''max_length'''	,					max_length=128	,					return_tensors='''pt'''				)
        return tokenizer.pad(__UpperCamelCase	,					padding='''longest'''	,					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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						Optional[Any]				)  ->							Tuple:
    # Initialize accelerator
    UpperCAmelCase_										=					Accelerator()

    # 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_										=					args.model_name_or_path

    set_seed(__UpperCamelCase				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					get_dataloaders(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    UpperCAmelCase_										=					AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase	,					return_dict=__UpperCamelCase				)

    # Instantiate optimizer
    UpperCAmelCase_										=					(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    UpperCAmelCase_										=					optimizer_cls(params=model.parameters()	,					lr=__UpperCamelCase				)

    if accelerator.state.deepspeed_plugin is not None:
        UpperCAmelCase_										=					accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        UpperCAmelCase_										=					1
    UpperCAmelCase_										=					(len(__UpperCamelCase				) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        UpperCAmelCase_										=					get_linear_schedule_with_warmup(
            optimizer=__UpperCamelCase	,					num_warmup_steps=0	,					num_training_steps=__UpperCamelCase	,					)
    else:
        UpperCAmelCase_										=					DummyScheduler(__UpperCamelCase	,					total_num_steps=__UpperCamelCase	,					warmup_num_steps=0				)

    # 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				)

    # We need to keep track of how many total steps we have iterated over
    UpperCAmelCase_										=					0
    # We also need to keep track of the stating epoch so files are named properly
    UpperCAmelCase_										=					0

    # Now we train the model
    UpperCAmelCase_										=					evaluate.load('''glue'''	,					'''mrpc'''				)
    UpperCAmelCase_										=					0
    UpperCAmelCase_										=					{}
    for epoch in range(__UpperCamelCase	,					__UpperCamelCase				):
        model.train()
        for step, batch in enumerate(__UpperCamelCase				):
            UpperCAmelCase_										=					model(**__UpperCamelCase				)
            UpperCAmelCase_										=					outputs.loss
            UpperCAmelCase_										=					loss / gradient_accumulation_steps
            accelerator.backward(__UpperCamelCase				)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        UpperCAmelCase_										=					0
        for step, batch in enumerate(__UpperCamelCase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            with torch.no_grad():
                UpperCAmelCase_										=					model(**__UpperCamelCase				)
            UpperCAmelCase_										=					outputs.logits.argmax(dim=-1				)
            # It is slightly faster to call this once, than multiple times
            UpperCAmelCase_			,							UpperCAmelCase_										=					accelerator.gather(
                (predictions, batch['''labels'''])				)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(__UpperCamelCase				) - 1:
                    UpperCAmelCase_										=					predictions[: len(eval_dataloader.dataset				) - samples_seen]
                    UpperCAmelCase_										=					references[: len(eval_dataloader.dataset				) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            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				)
        UpperCAmelCase_										=					eval_metric['''accuracy''']

        if best_performance < eval_metric["accuracy"]:
            UpperCAmelCase_										=					eval_metric['''accuracy''']

    if args.performance_lower_bound is not None:
        assert (
            args.performance_lower_bound <= best_performance
        ), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir	,					'''all_results.json'''				)	,					'''w'''				) as f:
            json.dump(__UpperCamelCase	,					__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	)  ->							List[Any]:
    UpperCAmelCase_										=					argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''				)
    parser.add_argument(
        '''--model_name_or_path'''	,					type=__UpperCamelCase	,					default='''bert-base-cased'''	,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,					required=__UpperCamelCase	,					)
    parser.add_argument(
        '''--output_dir'''	,					type=__UpperCamelCase	,					default='''.'''	,					help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,					)
    parser.add_argument(
        '''--performance_lower_bound'''	,					type=__UpperCamelCase	,					default=__UpperCamelCase	,					help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''	,					)
    parser.add_argument(
        '''--num_epochs'''	,					type=__UpperCamelCase	,					default=3	,					help='''Number of train epochs.'''	,					)
    UpperCAmelCase_										=					parser.parse_args()
    UpperCAmelCase_										=					{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(__UpperCamelCase	,					__UpperCamelCase				)


if __name__ == "__main__":
 main()











from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  ['input_values', 'padding_mask']
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      int = 1 ,   __snake_case       :      int = 2_40_00 ,   __snake_case       :      float = 0.0 ,   __snake_case       :      float = None ,   __snake_case       :      float = None ,   **__snake_case       :      Dict ,   ):
         super().__init__(feature_size=__snake_case ,   sampling_rate=__snake_case ,   padding_value=__snake_case ,   **__snake_case    )
         UpperCAmelCase_										=					chunk_length_s
         UpperCAmelCase_										=					overlap
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None:
             return None
         else:
             return int(self.chunk_length_s * self.sampling_rate    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None or self.overlap is None:
             return None
         else:
             return max(1 ,   int((1.0 - self.overlap) * self.chunk_length    )    )




     def __call__(     self       :      List[str] ,   __snake_case       :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,   __snake_case       :      Optional[Union[bool, str, PaddingStrategy]] = None ,   __snake_case       :      Optional[bool] = False ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[Union[str, TensorType]] = None ,   __snake_case       :      Optional[int] = None ,   ):
         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                     F' {self.sampling_rate} and not {sampling_rate}.'    )
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''    )

         if padding and truncation:
             raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''    )
         elif padding is None:
             # by default let's pad the inputs
             UpperCAmelCase_										=					True

         UpperCAmelCase_										=					bool(
             isinstance(__snake_case ,   (list, tuple)    ) and (isinstance(raw_audio[0] ,   (np.ndarray, tuple, list)    ))    )

         if is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case ,   dtype=np.floataa    ).T for audio in raw_audio]
         elif not is_batched and not isinstance(__snake_case ,   np.ndarray    ):
             UpperCAmelCase_										=					np.asarray(__snake_case ,   dtype=np.floataa    )
         elif isinstance(__snake_case ,   np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
             UpperCAmelCase_										=					raw_audio.astype(np.floataa    )

         # always return batch
         if not is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case    ).T]

         # verify inputs are valid
         for idx, example in enumerate(__snake_case    ):
             if example.ndim > 2:
                 raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'    )
             if self.feature_size == 1 and example.ndim != 1:
                 raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'    )
             if self.feature_size == 2 and example.shape[-1] != 2:
                 raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'    )

         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					BatchFeature({'''input_values''': raw_audio}    )
         if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
             if truncation:
                 UpperCAmelCase_										=					min(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.floor(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                 UpperCAmelCase_										=					max(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.ceil(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
                 UpperCAmelCase_										=					'''max_length'''
             else:
                 UpperCAmelCase_										=					input_values

        # normal padding on batch
         if padded_inputs is None:
             UpperCAmelCase_										=					self.pad(
                 __snake_case ,   max_length=__snake_case ,   truncation=__snake_case ,   padding=__snake_case ,   return_attention_mask=__snake_case ,   )
             if padding:
                 UpperCAmelCase_										=					padded_inputs.pop('''attention_mask'''    )

         UpperCAmelCase_										=					[]
         for example in padded_inputs.pop('''input_values'''    ):
             if self.feature_size == 1:
                 UpperCAmelCase_										=					example[..., None]
             input_values.append(example.T    )

         UpperCAmelCase_										=					input_values
         if return_tensors is not None:
             UpperCAmelCase_										=					padded_inputs.convert_to_tensors(__snake_case    )

         return padded_inputs





