
'''simple docstring'''







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


__lowerCAmelCase         =    logging.get_logger(__name__)

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

__lowerCAmelCase         =    {
    """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""",
    },
}

__lowerCAmelCase         =    {
    """jukebox""": 5_1_2,
}







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				str   =					VOCAB_FILES_NAMES
  __UpperCAmelCase				:				Optional[int]   =					PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase				:				Union[str, Any]   =					PRETRAINED_LYRIC_TOKENS_SIZES
  __UpperCAmelCase				:				List[str]   =					['''input_ids''', '''attention_mask''']


  def __init__(		self      :     str							,_a      :     Any							,_a      :     Dict							,_a      :     str							,_a      :     Any=["v3", "v2", "v2"]							,_a      :     Tuple=512							,_a      :     int=5							,_a      :     Any="<|endoftext|>"							,**_a      :     List[Any]							,):


     '''simple docstring'''






     _a				:	Tuple			=   AddedToken(_a							,lstrip=_a							,rstrip=_a       ) if isinstance(_a							,_a       ) else unk_token
     super().__init__(
         unk_token=_a							,n_genres=_a							,version=_a							,max_n_lyric_tokens=_a							,**_a							,)
     _a				:	int			=   version
     _a				:	Dict			=   max_n_lyric_tokens
     _a				:	Dict			=   n_genres

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

     with open(_a							,encoding='utf-8'       ) as vocab_handle:
        _a				:	str			=   json.load(_a       )

     with open(_a							,encoding='utf-8'       ) as vocab_handle:
        _a				:	Union[str, Any]			=   json.load(_a       )

     _a				:	Optional[int]			=   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				:	Union[str, Any]			=   oov.replace(R'\-\''							,R'\-+\''       )

     _a				:	str			=   regex.compile(_a       )
     _a				:	Union[str, Any]			=   {v: k for k, v in self.artists_encoder.items()}
     _a				:	str			=   {v: k for k, v in self.genres_encoder.items()}
     _a				:	Dict			=   {v: k for k, v in self.lyrics_encoder.items()}


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Optional[int]							,_a      :     Union[str, Any]							,_a      :     Dict							,_a      :     Union[str, Any]       ):


     '''simple docstring'''






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

     _a				:	Optional[Any]			=   [[self.lyrics_encoder.get(_a							,0       ) for character in list_lyrics[0]], [], []]
     return artists_id, list_genres, lyric_ids


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     Union[str, Any]       ):


     '''simple docstring'''






     return list(_a       )


  def   __lowercase		(		self      :     str							,_a      :     int							,_a      :     Tuple							,_a      :     int							,**_a      :     List[str]       ):


     '''simple docstring'''






     _a,  _a,  _a				:	str			=   self.prepare_for_tokenization(_a							,_a							,_a       )
     _a				:	List[Any]			=   self._tokenize(_a       )
     return artist, genre, lyrics


  def   __lowercase		(		self      :     Any							,_a      :     str							,_a      :     str							,_a      :     str							,_a      :     bool = False       ):


     '''simple docstring'''






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

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

     _a				:	str			=   self._run_strip_accents(_a       )
     _a				:	List[Any]			=   lyrics.replace('\\'							,'\n'       )
     _a				:	List[str]			=   self.out_of_vocab.sub(''							,_a       ), [], []
     return artists, genres, lyrics


  def   __lowercase		(		self      :     Any							,_a      :     List[str]       ):


     '''simple docstring'''






     _a				:	Dict			=   unicodedata.normalize('NFD'							,_a       )
     _a				:	Union[str, Any]			=   []
     for char in text:
        _a				:	Optional[Any]			=   unicodedata.category(_a       )
        if cat == "Mn":
           continue
        output.append(_a       )
     return "".join(_a       )


  def   __lowercase		(		self      :     Any							,_a      :     str       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Tuple							,_a      :     List[str]       ):


     '''simple docstring'''






     return " ".join(_a       )


  def   __lowercase		(		self      :     str							,_a      :     Optional[int]							,_a      :     Optional[Union[str, TensorType]] = None							,_a      :     bool = False       ):


     '''simple docstring'''






     if not isinstance(_a							,_a       ):
        _a				:	List[str]			=   TensorType(_a       )

     # 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				:	Dict			=   tf.constant
        _a				:	str			=   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				:	Tuple			=   torch.tensor
        _a				:	List[Any]			=   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				:	Optional[Any]			=   jnp.array
        _a				:	Optional[Any]			=   _is_jax
     else:
        _a				:	Dict			=   np.asarray
        _a				:	Optional[int]			=   _is_numpy

     # Do the tensor conversion in batch

     try:
        if prepend_batch_axis:
           _a				:	Union[str, Any]			=   [inputs]

        if not is_tensor(_a       ):
           _a				:	str			=   as_tensor(_a       )
     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      :     int							,_a      :     int							,_a      :     Optional[int]							,_a      :     Dict=""							,_a      :     Any="pt"       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   [0, 0, 0]
     _a				:	Dict			=   [artist] * len(self.version       )
     _a				:	List[Any]			=   [genres] * len(self.version       )

     _a,  _a,  _a				:	int			=   self.tokenize(_a							,_a							,_a       )
     _a,  _a,  _a				:	Dict			=   self._convert_token_to_id(_a							,_a							,_a       )

     _a				:	Optional[Any]			=   [-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=_a       )
         for i in range(len(self.version       )       )
     ]
     return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks}       )


  def   __lowercase		(		self      :     int							,_a      :     str							,_a      :     Optional[str] = None       ):


     '''simple docstring'''






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

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

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

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

     return (artists_file, genres_file, lyrics_file)


  def   __lowercase		(		self      :     str							,_a      :     Dict							,_a      :     Dict							,_a      :     Optional[int]       ):


     '''simple docstring'''






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





'''simple docstring'''







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

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
  from fairscale.optim import OSS


__lowerCAmelCase         =    logging.get_logger(__name__)

__lowerCAmelCase         =    {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     Optional[int]							,_a      :     Optional[Any]=None							,_a      :     Dict=None							,*_a      :     int							,**_a      :     str       ):


     '''simple docstring'''






     super().__init__(*_a							,**_a       )

     if config is None:
        assert isinstance(self.model							,_a       ), (
            "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
            F""" {self.model.__class__}"""
        )
        _a				:	List[Any]			=   self.model.config
     else:
        _a				:	Optional[int]			=   config

     _a				:	List[str]			=   data_args
     _a				:	List[Any]			=   self.config.tgt_vocab_size if isinstance(self.config							,_a       ) else self.config.vocab_size

     if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
        assert self.config.pad_token_id is not None, (
            "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
            " calculation or doing label smoothing."
        )

     if self.config.pad_token_id is None and self.config.eos_token_id is not None:
        logger.warning(
            F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
            ' padding..'       )

     if self.args.label_smoothing == 0:
        _a				:	List[str]			=   torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
     else:
        # dynamically import label_smoothed_nll_loss
        from utils import label_smoothed_nll_loss

        _a				:	Tuple			=   label_smoothed_nll_loss


  def   __lowercase		(		self      :     List[str]							,_a      :     int       ):


     '''simple docstring'''






     if self.optimizer is None:
        _a				:	Union[str, Any]			=   ['bias', 'LayerNorm.weight']
        _a				:	Tuple			=   [
            {
                'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
                'weight_decay': self.args.weight_decay,
            },
            {
                'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
                'weight_decay': 0.0,
            },
        ]
        _a				:	Optional[int]			=   Adafactor if self.args.adafactor else AdamW
        if self.args.adafactor:
           _a				:	Any			=   Adafactor
           _a				:	Dict			=   {'scale_parameter': False, 'relative_step': False}
        else:
           _a				:	Union[str, Any]			=   AdamW
           _a				:	str			=   {
               'betas': (self.args.adam_betaa, self.args.adam_betaa),
               'eps': self.args.adam_epsilon,
           }
        _a				:	Union[str, Any]			=   self.args.learning_rate
        if self.sharded_ddp:
           _a				:	str			=   OSS(
               params=_a							,optim=_a							,**_a							,)
        else:
           _a				:	Tuple			=   optimizer_cls(_a							,**_a       )

     if self.lr_scheduler is None:
        _a				:	List[Any]			=   self._get_lr_scheduler(_a       )
     else:  # ignoring --lr_scheduler
        logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'       )


  def   __lowercase		(		self      :     List[Any]							,_a      :     List[Any]       ):


     '''simple docstring'''






     _a				:	str			=   arg_to_scheduler[self.args.lr_scheduler]
     if self.args.lr_scheduler == "constant":
        _a				:	int			=   schedule_func(self.optimizer       )
     elif self.args.lr_scheduler == "constant_w_warmup":
        _a				:	List[str]			=   schedule_func(self.optimizer							,num_warmup_steps=self.args.warmup_steps       )
     else:
        _a				:	Optional[int]			=   schedule_func(
            self.optimizer							,num_warmup_steps=self.args.warmup_steps							,num_training_steps=_a       )
     return scheduler


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     if isinstance(self.train_dataset							,torch.utils.data.IterableDataset       ):
        return None
     elif is_torch_tpu_available():
        return get_tpu_sampler(self.train_dataset       )
     else:
        if self.args.sortish_sampler:
           self.train_dataset.make_sortish_sampler(
               self.args.per_device_train_batch_size							,distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)							,)

        return (
            RandomSampler(self.train_dataset       )
            if self.args.local_rank == -1
            else DistributedSampler(self.train_dataset       )
        )


  def   __lowercase		(		self      :     Dict							,_a      :     Dict							,_a      :     Any							,_a      :     Dict       ):


     '''simple docstring'''






     if self.args.label_smoothing == 0:
        if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
           # force training to ignore pad token
           _a				:	List[Any]			=   model(**_a							,use_cache=_a       )[0]
           _a				:	Union[str, Any]			=   self.loss_fn(logits.view(-1							,logits.shape[-1]       )							,labels.view(-1       )       )
        else:
           # compute usual loss via models
           _a,  _a				:	Union[str, Any]			=   model(**_a							,labels=_a							,use_cache=_a       )[:2]
     else:
        # compute label smoothed loss
        _a				:	List[Any]			=   model(**_a							,use_cache=_a       )[0]
        _a				:	Any			=   torch.nn.functional.log_softmax(_a							,dim=-1       )
        _a,  _a				:	List[str]			=   self.loss_fn(_a							,_a							,self.args.label_smoothing							,ignore_index=self.config.pad_token_id       )
     return loss, logits


  def   __lowercase		(		self      :     Optional[int]							,_a      :     Union[str, Any]							,_a      :     List[Any]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   inputs.pop('labels'       )
     _a,  _a				:	int			=   self._compute_loss(_a							,_a							,_a       )
     return loss


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     nn.Module							,_a      :     Dict[str, Union[torch.Tensor, Any]]							,_a      :     bool							,_a      :     Optional[List[str]] = None							,):


     '''simple docstring'''






     _a				:	int			=   self._prepare_inputs(_a       )

     _a				:	Any			=   {
         'max_length': self.data_args.val_max_target_length
         if self.data_args is not None
         else self.config.max_length,
         'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
     }

     if self.args.predict_with_generate and not self.args.prediction_loss_only:
        _a				:	int			=   self.model.generate(
            inputs['input_ids']							,attention_mask=inputs['attention_mask']							,**_a							,)
        # in case the batch is shorter than max length, the output should be padded
        if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
           _a				:	int			=   self._pad_tensors_to_max_len(_a							,gen_kwargs['max_length']       )

     _a				:	Union[str, Any]			=   inputs.pop('labels'       )
     with torch.no_grad():
        # compute loss on predict data
        _a,  _a				:	Optional[int]			=   self._compute_loss(_a							,_a							,_a       )

     _a				:	Optional[Any]			=   loss.mean().detach()
     if self.args.prediction_loss_only:
        return (loss, None, None)

     _a				:	Optional[Any]			=   generated_tokens if self.args.predict_with_generate else logits

     if labels.shape[-1] < gen_kwargs["max_length"]:
        _a				:	Dict			=   self._pad_tensors_to_max_len(_a							,gen_kwargs['max_length']       )

     return (loss, logits, labels)


  def   __lowercase		(		self      :     str							,_a      :     Tuple							,_a      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[Any]			=   self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

     if pad_token_id is None:
        raise ValueError(
            'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
            F""" padded to `max_length`={max_length}"""       )

     _a				:	int			=   pad_token_id * torch.ones(
         (tensor.shape[0], max_length)							,dtype=tensor.dtype							,device=tensor.device       )
     _a				:	Union[str, Any]			=   tensor
     return padded_tensor



