




"""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							:			str     							=							logging.get_logger(__name__)

lowerCAmelCase							:			Union[str, Any]     							=							{
    """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     __magic_name__							(       snake_case__				):






				'''simple docstring'''





				def __init__(       self		, _a=None		, _a=None		, *_a		, **_a ):





							"""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__}'
										)
										lowerCamelCase			      =					self.model.config
							else:
										lowerCamelCase			      =					config

							lowerCamelCase			      =					data_args
							lowerCamelCase			      =					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:
										lowerCamelCase			      =					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
							else:
										# dynamically import label_smoothed_nll_loss
										from utils import label_smoothed_nll_loss

										lowerCamelCase			      =					label_smoothed_nll_loss





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							if self.optimizer is None:
										lowerCamelCase			      =					["bias", "LayerNorm.weight"]
										lowerCamelCase			      =					[
										    {
										        "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,
										    },
										]
										lowerCamelCase			      =					Adafactor if self.args.adafactor else AdamW
										if self.args.adafactor:
													lowerCamelCase			      =					Adafactor
													lowerCamelCase			      =					{"scale_parameter": False, "relative_step": False}
										else:
													lowerCamelCase			      =					AdamW
													lowerCamelCase			      =					{
													    "betas": (self.args.adam_betaa, self.args.adam_betaa),
													    "eps": self.args.adam_epsilon,
													}
										lowerCamelCase			      =					self.args.learning_rate
										if self.sharded_ddp:
													lowerCamelCase			      =					OSS(
													    params=_a		, optim=_a		, **_a		, )
										else:
													lowerCamelCase			      =					optimizer_cls(_a		, **_a )

							if self.lr_scheduler is None:
										lowerCamelCase			      =					self._get_lr_scheduler(_a )
							else:  # ignoring --lr_scheduler
										logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.""" )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					arg_to_scheduler[self.args.lr_scheduler]
							if self.args.lr_scheduler == "constant":
										lowerCamelCase			      =					schedule_func(self.optimizer )
							elif self.args.lr_scheduler == "constant_w_warmup":
										lowerCamelCase			      =					schedule_func(self.optimizer		, num_warmup_steps=self.args.warmup_steps )
							else:
										lowerCamelCase			      =					schedule_func(
										    self.optimizer		, num_warmup_steps=self.args.warmup_steps		, num_training_steps=_a )
							return scheduler





				def    _lowerCAmelCase				(       self ):





							"""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    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""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
													lowerCamelCase			      =					model(**_a		, use_cache=_a )[0]
													lowerCamelCase			      =					self.loss_fn(logits.view(-1		, logits.shape[-1] )		, labels.view(-1 ) )
										else:
													# compute usual loss via models
													lowerCamelCase			      =					model(**_a		, labels=_a		, use_cache=_a )[:2]
							else:
										# compute label smoothed loss
										lowerCamelCase			      =					model(**_a		, use_cache=_a )[0]
										lowerCamelCase			      =					torch.nn.functional.log_softmax(_a		, dim=-1 )
										lowerCamelCase			      =					self.loss_fn(_a		, _a		, self.args.label_smoothing		, ignore_index=self.config.pad_token_id )
							return loss, logits





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					inputs.pop("""labels""" )
							lowerCamelCase			      =					self._compute_loss(_a		, _a		, _a )
							return loss





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a = None		, ):





							"""simple docstring"""
							lowerCamelCase			      =					self._prepare_inputs(_a )

							lowerCamelCase			      =					{
							    "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:
										lowerCamelCase			      =					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"]:
													lowerCamelCase			      =					self._pad_tensors_to_max_len(_a		, gen_kwargs["""max_length"""] )

							lowerCamelCase			      =					inputs.pop("""labels""" )
							with torch.no_grad():
										# compute loss on predict data
										lowerCamelCase			      =					self._compute_loss(_a		, _a		, _a )

							lowerCamelCase			      =					loss.mean().detach()
							if self.args.prediction_loss_only:
										return (loss, None, None)

							lowerCamelCase			      =					generated_tokens if self.args.predict_with_generate else logits

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

							return (loss, logits, labels)





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							# If PAD token is not defined at least EOS token has to be defined
							lowerCamelCase			      =					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}' )

							lowerCamelCase			      =					pad_token_id * torch.ones(
							    (tensor.shape[0], max_length)		, dtype=tensor.dtype		, device=tensor.device )
							lowerCamelCase			      =					tensor
							return padded_tensor




'''simple docstring'''




import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     List[Any]     =  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  str						=						"encodec"




      def __init__(       self						:				Optional[int]						,					A						:				Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]						,					A						:				List[Any]=24000						,					A						:				Union[str, Any]=1						,					A						:				List[Any]=False						,					A						:				Optional[int]=None						,					A						:				int=None						,					A						:				str=128						,					A						:				List[Any]=32						,					A						:				List[Any]=1						,					A						:				int=[8, 5, 4, 2]						,					A						:				Optional[int]="weight_norm"						,					A						:				List[Any]=7						,					A						:				Any=7						,					A						:				Dict=3						,					A						:				Optional[int]=2						,					A						:				Dict=True						,					A						:				Dict="reflect"						,					A						:				Any=2						,					A						:				Dict=2						,					A						:				str=1.0						,					A						:				Optional[int]=1024						,					A						:				Any=None						,					A						:				Any=True						,					**A						:				str						,					):
             _UpperCAmelCase     :  Optional[int]							      =    target_bandwidths
             _UpperCAmelCase     :  List[str]							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    audio_channels
             _UpperCAmelCase     :  str							      =    normalize
             _UpperCAmelCase     :  int							      =    chunk_length_s
             _UpperCAmelCase     :  str							      =    overlap
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_filters
             _UpperCAmelCase     :  Optional[Any]							      =    num_residual_layers
             _UpperCAmelCase     :  Optional[int]							      =    upsampling_ratios
             _UpperCAmelCase     :  int							      =    norm_type
             _UpperCAmelCase     :  List[Any]							      =    kernel_size
             _UpperCAmelCase     :  List[Any]							      =    last_kernel_size
             _UpperCAmelCase     :  List[Any]							      =    residual_kernel_size
             _UpperCAmelCase     :  List[str]							      =    dilation_growth_rate
             _UpperCAmelCase     :  Dict							      =    use_causal_conv
             _UpperCAmelCase     :  Tuple							      =    pad_mode
             _UpperCAmelCase     :  Tuple							      =    compress
             _UpperCAmelCase     :  List[str]							      =    num_lstm_layers
             _UpperCAmelCase     :  List[Any]							      =    trim_right_ratio
             _UpperCAmelCase     :  int							      =    codebook_size
             _UpperCAmelCase     :  Optional[Any]							      =    codebook_dim if codebook_dim is not None else hidden_size
             _UpperCAmelCase     :  Optional[int]							      =    use_conv_shortcut

             if self.norm_type not in ["weight_norm", "time_group_norm"]:
                    raise ValueError(
                        F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""       )

             super().__init__(**A       )




      @property
      def      _A	(       self						:				Any       ):
             if self.chunk_length_s is None:
                    return None
             else:
                    return int(self.chunk_length_s * self.sampling_rate       )




      @property
      def      _A	(       self						:				Union[str, Any]       ):
             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       )       )




      @property
      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Dict							      =    np.prod(self.upsampling_ratios       )
             return math.ceil(self.sampling_rate / hop_length       )





      @property
      def      _A	(       self						:				str       ):
             return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )

