





import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


lowercase				:	Dict      						=  logging.get_logger(__name__)

lowercase				:	Dict      						=  {
    """artists_file""": """artists.json""",
    """lyrics_file""": """lyrics.json""",
    """genres_file""": """genres.json""",
}

lowercase				:	Optional[Any]      						=  {
    """artists_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json""",
    },
    """genres_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json""",
    },
    """lyrics_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json""",
    },
}

lowercase				:	Any      						=  {
    """jukebox""": 5_1_2,
}





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    List[Any]	  =  VOCAB_FILES_NAMES
   __A       :    int	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    str	  =  PRETRAINED_LYRIC_TOKENS_SIZES
   __A       :    Tuple	  =  ['''input_ids''', '''attention_mask''']

   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase=["v3", "v2", "v2"] ,  lowercase=512 ,  lowercase=5 ,  lowercase="<|endoftext|>" ,  **lowercase ,  )      ->						Tuple:







          '''simple docstring'''

          a__						:     Dict			=    AddedToken(lowercase ,  lstrip=lowercase ,  rstrip=lowercase) if isinstance(lowercase ,  lowercase) else unk_token
          super().__init__(
              unk_token=lowercase ,  n_genres=lowercase ,  version=lowercase ,  max_n_lyric_tokens=lowercase ,  **lowercase ,  )
          a__						:     List[str]			=    version
          a__						:     Any			=    max_n_lyric_tokens
          a__						:     List[str]			=    n_genres

          with open(lowercase ,  encoding='utf-8') as vocab_handle:
                 a__						:     List[Any]			=    json.load(lowercase)

          with open(lowercase ,  encoding='utf-8') as vocab_handle:
                 a__						:     List[Any]			=    json.load(lowercase)

          with open(lowercase ,  encoding='utf-8') as vocab_handle:
                 a__						:     Tuple			=    json.load(lowercase)

          a__						:     Any			=    r'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
          # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
          if len(self.lyrics_encoder) == 79:
                 a__						:     List[str]			=    oov.replace(r'\-\'' ,  r'\-+\'')

          a__						:     List[str]			=    regex.compile(lowercase)
          a__						:     Dict			=    {v: k for k, v in self.artists_encoder.items()}
          a__						:     Optional[int]			=    {v: k for k, v in self.genres_encoder.items()}
          a__						:     Tuple			=    {v: k for k, v in self.lyrics_encoder.items()}

   @property
   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          return len(self.artists_encoder) + len(self.genres_encoder) + len(self.lyrics_encoder)

   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          return dict(self.artists_encoder ,  self.genres_encoder ,  self.lyrics_encoder)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    [self.artists_encoder.get(lowercase ,  0) for artist in list_artists]
          for genres in range(len(lowercase)):
                 a__						:     Dict			=    [self.genres_encoder.get(lowercase ,  0) for genre in list_genres[genres]]
                 a__						:     Any			=    list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]))

          a__						:     Dict			=    [[self.lyrics_encoder.get(lowercase ,  0) for character in list_lyrics[0]], [], []]
          return artists_id, list_genres, lyric_ids

   def 							__lowercase		( self ,  lowercase)      ->						Dict:







          '''simple docstring'''

          return list(lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  **lowercase)      ->						Dict:







          '''simple docstring'''

          a__			,							a__			,							a__						:     Tuple			=    self.prepare_for_tokenization(lowercase ,  lowercase ,  lowercase)
          a__						:     Optional[int]			=    self._tokenize(lowercase)
          return artist, genre, lyrics

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase = False)      ->						Tuple[str, str, str, Dict[str, Any]]:







          '''simple docstring'''

          for idx in range(len(self.version)):
                 if self.version[idx] == "v3":
                        a__						:     Union[str, Any]			=    artists[idx].lower()
                        a__						:     List[str]			=    [genres[idx].lower()]
                 else:
                        a__						:     Union[str, Any]			=    self._normalize(artists[idx]) + '.v2'
                        a__						:     int			=    [
                            self._normalize(lowercase) + '.v2' for genre in genres[idx].split('_')
                        ]  # split is for the full dictionary with combined genres

          if self.version[0] == "v2":
                 a__						:     Union[str, Any]			=    regex.compile(r'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+')
                 a__						:     Tuple			=    'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
                 a__						:     Union[str, Any]			=    {vocab[index]: index + 1 for index in range(len(lowercase))}
                 a__						:     Optional[int]			=    0
                 a__						:     Optional[int]			=    len(lowercase) + 1
                 a__						:     Union[str, Any]			=    self.vocab
                 a__						:     int			=    {v: k for k, v in self.vocab.items()}
                 a__						:     Union[str, Any]			=    ''
          else:
                 a__						:     List[Any]			=    regex.compile(r'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+')

          a__						:     Tuple			=    self._run_strip_accents(lowercase)
          a__						:     int			=    lyrics.replace('\\' ,  '\n')
          a__						:     str			=    self.out_of_vocab.sub('' ,  lowercase), [], []
          return artists, genres, lyrics

   def 							__lowercase		( self ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Optional[int]			=    unicodedata.normalize('NFD' ,  lowercase)
          a__						:     Optional[Any]			=    []
          for char in text:
                 a__						:     Tuple			=    unicodedata.category(lowercase)
                 if cat == "Mn":
                        continue
                 output.append(lowercase)
          return "".join(lowercase)

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     List[str]			=    (
              [chr(lowercase) for i in range(ord('a') ,  ord('z') + 1)]
              + [chr(lowercase) for i in range(ord('A') ,  ord('Z') + 1)]
              + [chr(lowercase) for i in range(ord('0') ,  ord('9') + 1)]
              + ['.']
          )
          a__						:     int			=    frozenset(lowercase)
          a__						:     Union[str, Any]			=    re.compile(r'_+')
          a__						:     Optional[int]			=    ''.join([c if c in accepted else '_' for c in text.lower()])
          a__						:     Union[str, Any]			=    pattern.sub('_' ,  lowercase).strip('_')
          return text

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          return " ".join(lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase = None ,  lowercase = False)      ->						Union[str, Any]:







          '''simple docstring'''

          if not isinstance(lowercase ,  lowercase):
                 a__						:     Optional[Any]			=    TensorType(lowercase)

          # Get a function reference for the correct framework
          if tensor_type == TensorType.TENSORFLOW:
                 if not is_tf_available():
                        raise ImportError(
                            'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.')
                 import tensorflow as tf

                 a__						:     List[str]			=    tf.constant
                 a__						:     Optional[int]			=    tf.is_tensor
          elif tensor_type == TensorType.PYTORCH:
                 if not is_torch_available():
                        raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.')
                 import torch

                 a__						:     List[str]			=    torch.tensor
                 a__						:     List[str]			=    torch.is_tensor
          elif tensor_type == TensorType.JAX:
                 if not is_flax_available():
                        raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.')
                 import jax.numpy as jnp  # noqa: F811

                 a__						:     List[str]			=    jnp.array
                 a__						:     Dict			=    _is_jax
          else:
                 a__						:     Union[str, Any]			=    np.asarray
                 a__						:     Union[str, Any]			=    _is_numpy

          # Do the tensor conversion in batch

          try:
                 if prepend_batch_axis:
                        a__						:     Any			=    [inputs]

                 if not is_tensor(lowercase):
                        a__						:     Tuple			=    as_tensor(lowercase)
          except:  # noqa E722
                 raise ValueError(
                     'Unable to create tensor, you should probably activate truncation and/or padding '
                     'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.')

          return inputs

   def __call__( self ,  lowercase ,  lowercase ,  lowercase="" ,  lowercase="pt")      ->						BatchEncoding:







          '''simple docstring'''

          a__						:     int			=    [0, 0, 0]
          a__						:     Tuple			=    [artist] * len(self.version)
          a__						:     Tuple			=    [genres] * len(self.version)

          a__			,							a__			,							a__						:     Dict			=    self.tokenize(lowercase ,  lowercase ,  lowercase)
          a__			,							a__			,							a__						:     Any			=    self._convert_token_to_id(lowercase ,  lowercase ,  lowercase)

          a__						:     List[str]			=    [-INFINITY] * len(full_tokens[-1])
          a__						:     List[Any]			=    [
              self.convert_to_tensors(
                  [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] ,  tensor_type=lowercase)
              for i in range(len(self.version))
          ]
          return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks})

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						Tuple[str]:







          '''simple docstring'''

          if not os.path.isdir(lowercase):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory')
                 return

          a__						:     Any			=    os.path.join(
              lowercase ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file'])
          with open(lowercase ,  'w' ,  encoding='utf-8') as f:
                 f.write(json.dumps(self.artists_encoder ,  ensure_ascii=lowercase))

          a__						:     Union[str, Any]			=    os.path.join(
              lowercase ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file'])
          with open(lowercase ,  'w' ,  encoding='utf-8') as f:
                 f.write(json.dumps(self.genres_encoder ,  ensure_ascii=lowercase))

          a__						:     Tuple			=    os.path.join(
              lowercase ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file'])
          with open(lowercase ,  'w' ,  encoding='utf-8') as f:
                 f.write(json.dumps(self.lyrics_encoder ,  ensure_ascii=lowercase))

          return (artists_file, genres_file, lyrics_file)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Optional[Any]			=    self.artists_decoder.get(lowercase)
          a__						:     List[Any]			=    [self.genres_decoder.get(lowercase) for genre in genres_index]
          a__						:     str			=    [self.lyrics_decoder.get(lowercase) for character in lyric_index]
          return artist, genres, lyrics









import argparse
import gc
import json
import os

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


lowercase				:	Optional[int]      						=  1_6
lowercase				:	Optional[int]      						=  3_2



def A_     (		A__    )  ->		Tuple:
       return int(x / 2**20    )





class 			A__   :




   """simple docstring"""



   def __enter__( self)      ->						Dict:







          '''simple docstring'''

          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          a__						:     Union[str, Any]			=    torch.cuda.memory_allocated()
          return self

   def __exit__( self ,  *lowercase)      ->						List[Any]:







          '''simple docstring'''

          gc.collect()
          torch.cuda.empty_cache()
          a__						:     Optional[int]			=    torch.cuda.memory_allocated()
          a__						:     int			=    torch.cuda.max_memory_allocated()
          a__						:     Optional[int]			=    bamb(self.end - self.begin)
          a__						:     int			=    bamb(self.peak - self.begin)
          # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")



def A_     (		A__		,				A__ = 16		,				A__ = "bert-base-cased"		,				A__ = 320		,				A__ = 160		,				)  ->		Dict:
       a__						:     int			=    AutoTokenizer.from_pretrained(A__    )
       a__						:     str			=    load_dataset(
           'glue'		,				'mrpc'		,				split={'train': F'train[:{n_train}]', 'validation': F'validation[:{n_val}]'}    )

       def tokenize_function(A__    ):
              # max_length=None => use the model max length (it's actually the default)
              a__						:     int			=    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
       a__						:     List[str]			=    datasets.map(
           A__		,				batched=A__		,				remove_columns=['idx', 'sentence1', 'sentence2']		,				load_from_cache_file=A__    )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       a__						:     Optional[int]			=    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.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(A__		,				padding='max_length'		,				max_length=128		,				return_tensors='pt'    )
              return tokenizer.pad(A__		,				padding='longest'		,				return_tensors='pt'    )

       # Instantiate dataloaders.
       a__						:     Dict			=    DataLoader(
           tokenized_datasets['train']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )
       a__						:     Optional[int]			=    DataLoader(
           tokenized_datasets['validation']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )

       return train_dataloader, eval_dataloader



def A_     (		A__		,				A__    )  ->		Any:
       # Initialize accelerator
       a__						:     Optional[int]			=    Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       a__						:     Union[str, Any]			=    config['lr']
       a__						:     List[Any]			=    int(config['num_epochs']    )
       a__						:     Union[str, Any]			=    int(config['seed']    )
       a__						:     Tuple			=    int(config['batch_size']    )
       a__						:     int			=    args.model_name_or_path

       set_seed(A__    )
       a__			,							a__						:     Any			=    get_dataloaders(A__		,				A__		,				A__		,				args.n_train		,				args.n_val    )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a__						:     Optional[int]			=    AutoModelForSequenceClassification.from_pretrained(A__		,				return_dict=A__    )

       # Instantiate optimizer
       a__						:     Optional[Any]			=    (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       a__						:     Any			=    optimizer_cls(params=model.parameters()		,				lr=A__    )

       if accelerator.state.deepspeed_plugin is not None:
              a__						:     str			=    accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              a__						:     str			=    1
       a__						:     Optional[int]			=    (len(A__    ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              a__						:     List[Any]			=    get_linear_schedule_with_warmup(
                  optimizer=A__		,				num_warmup_steps=0		,				num_training_steps=A__		,				)
       else:
              a__						:     Any			=    DummyScheduler(A__		,				total_num_steps=A__		,				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.
       a__			,							a__			,							a__			,							a__			,							a__						:     Optional[int]			=    accelerator.prepare(
           A__		,				A__		,				A__		,				A__		,				A__    )

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

       # Now we train the model
       a__						:     List[str]			=    {}
       for epoch in range(A__		,				A__    ):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(A__    ):
                            a__						:     Optional[Any]			=    model(**A__    )
                            a__						:     List[Any]			=    outputs.loss
                            a__						:     Union[str, Any]			=    loss / gradient_accumulation_steps
                            accelerator.backward(A__    )
                            if step % gradient_accumulation_steps == 0:
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
              accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin    )    )    )
              accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used    )    )
              accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked    )    )
              accelerator.print(
                  'Total Peak Memory consumed during the train (max): {}'.format(
                      tracemalloc.peaked + bamb(tracemalloc.begin    )    )    )
              a__						:     List[str]			=    tracemalloc.peaked + bamb(tracemalloc.begin    )
              if args.peak_memory_upper_bound is not None:
                     assert (
                         train_total_peak_memory[F'epoch-{epoch}'] <= args.peak_memory_upper_bound
                     ), "Peak memory usage exceeded the upper bound"

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



def A_     (		)  ->		int:
       a__						:     Union[str, Any]			=    argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
       parser.add_argument(
           '--model_name_or_path'		,				type=A__		,				default='bert-base-cased'		,				help='Path to pretrained model or model identifier from huggingface.co/models.'		,				required=A__		,				)
       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(
           '--peak_memory_upper_bound'		,				type=A__		,				default=A__		,				help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'		,				)
       parser.add_argument(
           '--n_train'		,				type=A__		,				default=320		,				help='Number of training examples to use.'		,				)
       parser.add_argument(
           '--n_val'		,				type=A__		,				default=160		,				help='Number of validation examples to use.'		,				)
       parser.add_argument(
           '--num_epochs'		,				type=A__		,				default=1		,				help='Number of train epochs.'		,				)
       a__						:     Dict			=    parser.parse_args()
       a__						:     List[str]			=    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(A__		,				A__    )


if __name__ == "__main__":
    main()


