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


lowerCamelCase__     :		Any        = logging.get_logger(__name__)

lowerCamelCase__     :		Optional[Any]        = {
    """edbeeching/decision-transformer-gym-hopper-medium""": (
        """https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      'decision_transformer'
 __lowerCAmelCase	:  Optional[Any]	   =      ['past_key_values']
 __lowerCAmelCase	:  int	   =      {
     'max_position_embeddings': 'n_positions',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=17       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=1_28       ,	SCREAMING_SNAKE_CASE_=40_96       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=10_24       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_="relu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=1E-5       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=5_02_56       ,	SCREAMING_SNAKE_CASE_=5_02_56       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=False       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Tuple     		=   state_dim
   lowercase__	: List[Any]     		=   act_dim
   lowercase__	: Optional[int]     		=   hidden_size
   lowercase__	: Tuple     		=   max_ep_len
   lowercase__	: Any     		=   action_tanh
   lowercase__	: Union[str, Any]     		=   vocab_size
   lowercase__	: Any     		=   n_positions
   lowercase__	: Any     		=   n_layer
   lowercase__	: List[Any]     		=   n_head
   lowercase__	: Optional[int]     		=   n_inner
   lowercase__	: int     		=   activation_function
   lowercase__	: Optional[int]     		=   resid_pdrop
   lowercase__	: List[str]     		=   embd_pdrop
   lowercase__	: Any     		=   attn_pdrop
   lowercase__	: str     		=   layer_norm_epsilon
   lowercase__	: Any     		=   initializer_range
   lowercase__	: Optional[int]     		=   scale_attn_weights
   lowercase__	: Optional[Any]     		=   use_cache
   lowercase__	: Union[str, Any]     		=   scale_attn_by_inverse_layer_idx
   lowercase__	: Tuple     		=   reorder_and_upcast_attn

   lowercase__	: Union[str, Any]     		=   bos_token_id
   lowercase__	: int     		=   eos_token_id

   super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_       ,	eos_token_id=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)








"""simple docstring"""




import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
   from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
   _A     :			List[str]  						=			True
   from torch.cuda.amp import autocast


_A     :			Any  						=			logging.getLogger(__name__)

@dataclass
class 						a__		:
   __lowerCAmelCase				=							field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},     )
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}		)
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Whether to log verbose messages or not."""},     )
   __lowerCAmelCase				=							field(
       default=2.0,     metadata={"""help""": """Maximum temperature for gumbel softmax."""}		)
   __lowerCAmelCase				=							field(
       default=0.5,     metadata={"""help""": """Minimum temperature for gumbel softmax."""}		)
   __lowerCAmelCase				=							field(
       default=0.999995,     metadata={"""help""": """Decay of gumbel temperature during training."""}		)


def     __magic_name__      ( __snake_case							:			ModelArguments	,					__snake_case							:			TrainingArguments    )					->							Any:
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,					datefmt="%m/%d/%Y %H:%M:%S"	,					handlers=[logging.StreamHandler(sys.stdout    )]	,					)
       lowercase       :							Optional[int]									=				logging.WARNING
       if model_args.verbose_logging:
              lowercase       :							Union[str, Any]									=				logging.DEBUG
       elif trainer_utils.is_main_process(training_args.local_rank    ):
              lowercase       :							Union[str, Any]									=				logging.INFO
       logger.setLevel(__snake_case    )

@dataclass
class 						a__		:
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}		)
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
   __lowerCAmelCase				=							field(
       default="""train""",     metadata={
           """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
       },     )
   __lowerCAmelCase				=							field(
       default="""validation""",     metadata={
           """help""": (
               """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
           )
       },     )
   __lowerCAmelCase				=							field(
       default="""file""",     metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""},     )
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}		)
   __lowerCAmelCase				=							field(
       default=1,     metadata={
           """help""": """The percentage of the train set used as validation set in case there's no validation split"""
       },     )
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """The number of processes to use for the preprocessing."""},     )
   __lowerCAmelCase				=							field(
       default=20.0,     metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}		)


@dataclass
class 						a__		:
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							"longest"
   __lowerCAmelCase				=							None
   __lowerCAmelCase				=							None
   def __call__(     self      ,       _a							):
          # reformat list to dict and set to pytorch format
          lowercase       :							Tuple									=				self.feature_extractor.pad(
              _a      ,       max_length=self.max_length      ,       padding=self.padding      ,       pad_to_multiple_of=self.pad_to_multiple_of      ,       return_tensors="pt"      ,       )
          lowercase       :							str									=				self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]							)

          lowercase       :							Tuple									=				batch["input_values"].shape[0]

          # make sure that no loss is computed on padded inputs
          if batch["attention_mask"] is not None:
                 # compute real output lengths according to convolution formula
                 lowercase       :							Any									=				self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1							)							).to(
                     torch.long							)

                 lowercase       :							Dict									=				torch.zeros(
                     (batch_size, mask_indices_seq_length)      ,       dtype=torch.long      ,       device=batch["input_values"].device							)

                 # these two operations makes sure that all values
                 # before the output lengths indices are attended to
                 lowercase       :							int									=				1
                 lowercase       :							List[Any]									=				attention_mask.flip([-1]							).cumsum(-1							).flip([-1]							).bool()

          # sample randomly masked indices
          lowercase       :							int									=				_compute_mask_indices(
              (batch_size, mask_indices_seq_length)      ,       self.model.config.mask_time_prob      ,       self.model.config.mask_time_length      ,       attention_mask=_a      ,       min_masks=2      ,       )

          return batch


class 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       _a=1      ,       _a=0      ,       _a=1.0      ,       **_a							):
          super().__init__(*_a      ,       **_a							)
          lowercase       :							Tuple									=				0
          lowercase       :							Optional[Any]									=				max_gumbel_temp
          lowercase       :							Union[str, Any]									=				min_gumbel_temp
          lowercase       :							str									=				gumbel_temp_decay

   def      __magic_name__     (     self      ,       _a      ,       _a							):
          model.train()
          lowercase       :							Union[str, Any]									=				self._prepare_inputs(_a							)

          if self.use_amp:
                 with autocast():
                        lowercase       :							Optional[Any]									=				self.compute_loss(_a      ,       _a							)
          else:
                 lowercase       :							List[str]									=				self.compute_loss(_a      ,       _a							)

          if self.args.n_gpu > 1 or self.deepspeed:
                 if model.module.config.ctc_loss_reduction == "mean":
                        lowercase       :							Tuple									=				loss.mean()
                 elif model.module.config.ctc_loss_reduction == "sum":
                        lowercase       :							Any									=				loss.sum() / (inputs["mask_time_indices"]).sum()
                 else:
                        raise ValueError(f"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""							)

          if self.args.gradient_accumulation_steps > 1:
                 lowercase       :							List[str]									=				loss / self.args.gradient_accumulation_steps

          if self.use_amp:
                 self.scaler.scale(_a							).backward()
          elif self.use_apex:
                 with amp.scale_loss(_a      ,       self.optimizer							) as scaled_loss:
                        scaled_loss.backward()
          elif self.deepspeed:
                 self.deepspeed.backward(_a							)
          else:
                 loss.backward()

          self.num_update_step += 1
          # make sure gumbel softmax temperature is decayed
          if self.args.n_gpu > 1 or self.deepspeed:
                 model.module.set_gumbel_temperature(
                     max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step      ,       self.min_gumbel_temp							)							)
          else:
                 model.set_gumbel_temperature(
                     max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step      ,       self.min_gumbel_temp							)							)

          return loss.detach()


def     __magic_name__      ( )					->							List[Any]:
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.

       lowercase       :							Any									=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )

       lowercase						,       lowercase						,       lowercase       :							Union[str, Any]									=				parser.parse_args_into_dataclasses()
       configure_logger(__snake_case	,					__snake_case    )

       # Downloading and loading a dataset from the hub.
       lowercase       :							Any									=				load_dataset(data_args.dataset_name	,					data_args.dataset_config_name	,					cache_dir=model_args.cache_dir    )

       if "validation" not in datasets.keys():
              # make sure only "validation" and "train" keys remain"
              lowercase       :							Union[str, Any]									=				DatasetDict()
              lowercase       :							Dict									=				load_dataset(
                  data_args.dataset_name	,					data_args.dataset_config_name	,					split=f"""{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"""	,					cache_dir=model_args.cache_dir	,					)
              lowercase       :							Any									=				load_dataset(
                  data_args.dataset_name	,					data_args.dataset_config_name	,					split=f"""{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"""	,					cache_dir=model_args.cache_dir	,					)
       else:
              # make sure only "validation" and "train" keys remain"
              lowercase       :							Tuple									=				DatasetDict()
              lowercase       :							Dict									=				load_dataset(
                  data_args.dataset_name	,					data_args.dataset_config_name	,					split="validation"	,					cache_dir=model_args.cache_dir	,					)
              lowercase       :							int									=				load_dataset(
                  data_args.dataset_name	,					data_args.dataset_config_name	,					split=f"""{data_args.train_split_name}"""	,					cache_dir=model_args.cache_dir	,					)

       # only normalized-inputs-training is supported
       lowercase       :							Optional[int]									=				WavaVecaFeatureExtractor.from_pretrained(
           model_args.model_name_or_path	,					cache_dir=model_args.cache_dir	,					do_normalize=__snake_case    )

       def prepare_dataset(__snake_case							:			Tuple    ):
              # check that all files have the correct sampling rate
              lowercase						,       lowercase       :							Union[str, Any]									=				librosa.load(batch[data_args.speech_file_column]	,					sr=feature_extractor.sampling_rate    )
              return batch

       # load audio files into numpy arrays
       lowercase       :							str									=				datasets.map(
           __snake_case	,					num_proc=data_args.preprocessing_num_workers	,					remove_columns=datasets["train"].column_names    )

       # filter audio files that are too long
       lowercase       :							Optional[Any]									=				vectorized_datasets.filter(
           lambda __snake_case    : len(data["speech"]    ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate    )    )

       def normalize(__snake_case							:			Union[str, Any]    ):
              return feature_extractor(batch["speech"]	,					sampling_rate=feature_extractor.sampling_rate    )

       # normalize and transform to `BatchFeatures`
       lowercase       :							Any									=				vectorized_datasets.map(
           __snake_case	,					batched=__snake_case	,					num_proc=data_args.preprocessing_num_workers	,					load_from_cache_file=not data_args.overwrite_cache	,					remove_columns=vectorized_datasets["train"].column_names	,					)

       # pretraining is only supported for "newer" stable layer norm architecture
       # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
       lowercase       :							Union[str, Any]									=				WavaVecaConfig.from_pretrained(
           model_args.model_name_or_path	,					cache_dir=model_args.cache_dir	,					gradient_checkpointing=training_args.gradient_checkpointing	,					)

       if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
              raise ValueError(
                  "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
                  " ``config.feat_extract_norm='layer'"    )

       lowercase       :							Optional[Any]									=				WavaVecaForPreTraining(__snake_case    )

       lowercase       :							Optional[int]									=				DataCollatorForWavaVecaPretraining(model=__snake_case	,					feature_extractor=__snake_case    )

       lowercase       :							List[Any]									=				WavaVecaPreTrainer(
           model=__snake_case	,					data_collator=__snake_case	,					args=__snake_case	,					train_dataset=vectorized_datasets["train"]	,					eval_dataset=vectorized_datasets["validation"]	,					tokenizer=__snake_case	,					max_gumbel_temp=model_args.max_gumbel_temperature	,					min_gumbel_temp=model_args.min_gumbel_temperature	,					gumbel_temp_decay=model_args.gumbel_temperature_decay	,					)
       trainer.train()


if __name__ == "__main__":
   main()


