






"""simple docstring"""



import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
						from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
						_snake_case							:   Optional[int]	  =       True
						from torch.cuda.amp import autocast


_snake_case							:   List[Any]	  =       logging.getLogger(__name__)




@dataclass
class 							__lowercase:
						lowerCAmelCase_		:				str=							field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	)
						lowerCAmelCase_		:				Optional[str]=							field(
						    default=_UpperCamelCase    ,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,				)
						lowerCAmelCase_		:				Optional[bool]=							field(
						    default=_UpperCamelCase    ,				metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}	)
						lowerCAmelCase_		:				Optional[bool]=							field(
						    default=_UpperCamelCase    ,				metadata={"""help""": """Whether to log verbose messages or not."""}    ,				)




def     lowerCamelCase_					(	__A	:		ModelArguments      ,						__A	:		TrainingArguments      ):
					logging.basicConfig(
					    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'      ,						datefmt='%m/%d/%Y %H:%M:%S'      ,						handlers=[logging.StreamHandler(sys.stdout      )]      ,						)
					lowerCamelCase__					=logging.WARNING
					if model_args.verbose_logging:
										lowerCamelCase__					=logging.DEBUG
					elif trainer_utils.is_main_process(training_args.local_rank      ):
										lowerCamelCase__					=logging.INFO
					logger.setLevel(__A      )




@dataclass
class 							__lowercase:
						lowerCAmelCase_		:				str=							field(
						    default=_UpperCamelCase    ,				metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}	)
						lowerCAmelCase_		:				Optional[str]=							field(
						    default=_UpperCamelCase    ,				metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}	)
						lowerCAmelCase_		:				Optional[str]=							field(
						    default="""train"""    ,				metadata={
						        """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
						    }    ,				)
						lowerCAmelCase_		:				Optional[str]=							field(
						    default="""validation"""    ,				metadata={
						        """help""": (
						            """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
						        )
						    }    ,				)
						lowerCAmelCase_		:				Optional[str]=							field(
						    default="""text"""    ,				metadata={"""help""": """Column in the dataset that contains label (target text). Defaults to 'text'"""}    ,				)
						lowerCAmelCase_		:				Optional[str]=							field(
						    default="""file"""    ,				metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""}    ,				)
						lowerCAmelCase_		:				Optional[bool]=							field(
						    default=_UpperCamelCase    ,				metadata={"""help""": """Resample loaded audio to target feature extractor's sampling rate or not."""}    ,				)
						lowerCAmelCase_		:				Optional[float]=							field(
						    default=_UpperCamelCase    ,				metadata={"""help""": """Filters out examples longer than specified. Defaults to no filtering."""}    ,				)
						lowerCAmelCase_		:				Optional[str]=							field(
						    default="""librispeech"""    ,				metadata={
						        """help""": (
						            """Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or"""
						            """ 'buckwalter'."""
						        )
						    }    ,				)
						lowerCAmelCase_		:				bool=							field(
						    default=_UpperCamelCase    ,				metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}	)
						lowerCAmelCase_		:				Optional[int]=							field(
						    default=_UpperCamelCase    ,				metadata={"""help""": """The number of processes to use for the preprocessing."""}    ,				)




@dataclass
class 							__lowercase:
						lowerCAmelCase_		:				bool=							False
						lowerCAmelCase_		:				Optional[str]=							None
						lowerCAmelCase_		:				Optional[str]=							"|"
						lowerCAmelCase_		:				Optional[Dict[str, str]]=							field(default_factory=_UpperCamelCase	)
						lowerCAmelCase_		:				Optional[Set[str]]=							field(default_factory=_UpperCamelCase	)
						lowerCAmelCase_		:				Optional[Callable[[str], str]]=							None






						@classmethod
						def 		lowerCAmelCase (       cls     ,    _SCREAMING_SNAKE_CASE)      ->	Optional[Any]:




											"""simple docstring"""





											if name == "librispeech":
																return cls()
											if name == "timit":
																return cls(
																    do_lower_case=_SCREAMING_SNAKE_CASE     ,    translation_table=str.maketrans({'-': ' '})     ,    )
											if name == "buckwalter":
																lowerCamelCase__					={
																    '-': ' ',  # sometimes used to represent pauses
																    '^': 'v',  # fixing "tha" in arabic_speech_corpus dataset
																}
																return cls(
																    vocab_file=pathlib.Path(_SCREAMING_SNAKE_CASE).parent.joinpath('vocab/buckwalter.json')     ,    word_delimiter_token='/'     ,    translation_table=str.maketrans(_SCREAMING_SNAKE_CASE)     ,    words_to_remove={'sil'}     ,    untransliterator=arabic.buckwalter.untransliterate     ,    )
											raise ValueError(F"Unsupported orthography: '{name}'.")






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	str:




											"""simple docstring"""





											if len(self.translation_table) > 0:
																lowerCamelCase__					=text.translate(self.translation_table)
											if len(self.words_to_remove) == 0:
																lowerCamelCase__					=' '.join(text.split())  # clean up whitespaces
											else:
																lowerCamelCase__					=' '.join(w for w in text.split() if w not in self.words_to_remove)  # and clean up whilespaces
											return text






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	WavaVecaProcessor:




											"""simple docstring"""





											lowerCamelCase__					=WavaVecaFeatureExtractor.from_pretrained(
											    model_args.model_name_or_path     ,    cache_dir=model_args.cache_dir)
											if self.vocab_file:
																lowerCamelCase__					=WavaVecaCTCTokenizer(
																    self.vocab_file     ,    cache_dir=model_args.cache_dir     ,    do_lower_case=self.do_lower_case     ,    word_delimiter_token=self.word_delimiter_token     ,    )
											else:
																lowerCamelCase__					=WavaVecaCTCTokenizer.from_pretrained(
																    model_args.model_name_or_path     ,    cache_dir=model_args.cache_dir     ,    do_lower_case=self.do_lower_case     ,    word_delimiter_token=self.word_delimiter_token     ,    )
											return WavaVecaProcessor(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)




@dataclass
class 							__lowercase:
						lowerCAmelCase_		:				WavaVecaProcessor
						lowerCAmelCase_		:				Union[bool, str]=							True
						lowerCAmelCase_		:				Optional[int]=							None
						lowerCAmelCase_		:				Optional[int]=							None
						lowerCAmelCase_		:				Optional[int]=							None
						lowerCAmelCase_		:				Optional[int]=							None






						def __call__(       self     ,    _SCREAMING_SNAKE_CASE)      ->	Dict[str, torch.Tensor]:




											"""simple docstring"""





											lowerCamelCase__					=[{'input_values': feature['input_values']} for feature in features]
											lowerCamelCase__					=[{'input_ids': feature['labels']} for feature in features]

											lowerCamelCase__					=self.processor.pad(
											    _SCREAMING_SNAKE_CASE     ,    padding=self.padding     ,    max_length=self.max_length     ,    pad_to_multiple_of=self.pad_to_multiple_of     ,    return_tensors='pt'     ,    )
											lowerCamelCase__					=self.processor.pad(
											    labels=_SCREAMING_SNAKE_CASE     ,    padding=self.padding     ,    max_length=self.max_length_labels     ,    pad_to_multiple_of=self.pad_to_multiple_of_labels     ,    return_tensors='pt'     ,    )

											# replace padding with -100 to ignore loss correctly
											lowerCamelCase__					=labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1)     ,    -1_00)

											lowerCamelCase__					=labels

											return batch




class 							__lowercase(							_UpperCamelCase	):






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	torch.Tensor:




											"""simple docstring"""





											model.train()
											lowerCamelCase__					=self._prepare_inputs(_SCREAMING_SNAKE_CASE)

											if self.use_amp:
																with autocast():
																					lowerCamelCase__					=self.compute_loss(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											else:
																lowerCamelCase__					=self.compute_loss(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

											if self.args.n_gpu > 1:
																if model.module.config.ctc_loss_reduction == "mean":
																					lowerCamelCase__					=loss.mean()
																elif model.module.config.ctc_loss_reduction == "sum":
																					lowerCamelCase__					=loss.sum() / (inputs['labels'] >= 0).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:
																lowerCamelCase__					=loss / self.args.gradient_accumulation_steps

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

											return loss.detach()




def     lowerCamelCase_					(	):
					# 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.

					lowerCamelCase__					=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

					lowerCamelCase__					,  lowerCamelCase__					,  lowerCamelCase__					=parser.parse_args_into_dataclasses()
					configure_logger(__A      ,						__A      )

					lowerCamelCase__					=Orthography.from_name(data_args.orthography.lower()      )
					lowerCamelCase__					=orthography.create_processor(__A      )
					lowerCamelCase__					=WavaVecaForCTC.from_pretrained(
					    model_args.model_name_or_path      ,						cache_dir=model_args.cache_dir      ,						gradient_checkpointing=training_args.gradient_checkpointing      ,						vocab_size=len(processor.tokenizer      )      ,						)

					lowerCamelCase__					=datasets.load_dataset(
					    data_args.dataset_name      ,						data_args.dataset_config_name      ,						split=data_args.train_split_name      )
					lowerCamelCase__					=datasets.load_dataset(
					    data_args.dataset_name      ,						data_args.dataset_config_name      ,						split=data_args.validation_split_name      )

					lowerCamelCase__					=datasets.load_metric('wer'      )
					lowerCamelCase__					=processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
					lowerCamelCase__					=''.join(t for t in processor.tokenizer.get_vocab().keys() if len(__A      ) == 1      )
					lowerCamelCase__					=re.compile(  # remove characters not in vocabulary
					    rf"[^\s{re.escape(__A      )}]"      ,						flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0      ,						)
					lowerCamelCase__					=[]

					def prepare_example(__A	:		Union[str, Any]      ):  # TODO(elgeish) make use of multiprocessing?
										lowerCamelCase__					,  lowerCamelCase__					=librosa.load(example[data_args.speech_file_column]      ,						sr=__A      )
										if data_args.max_duration_in_seconds is not None:
															lowerCamelCase__					=len(example['speech']      ) / example['sampling_rate']
										# Normalize and clean up text; order matters!
										lowerCamelCase__					=orthography.preprocess_for_training(example[data_args.target_text_column]      )
										lowerCamelCase__					=vocabulary_text_cleaner.sub(''      ,						__A      )
										if updated_text != example[data_args.target_text_column]:
															text_updates.append((example[data_args.target_text_column], updated_text)      )
															lowerCamelCase__					=updated_text
										return example

					lowerCamelCase__					=train_dataset.map(__A      ,						remove_columns=[data_args.speech_file_column]      )
					lowerCamelCase__					=val_dataset.map(__A      ,						remove_columns=[data_args.speech_file_column]      )

					if data_args.max_duration_in_seconds is not None:

										def filter_by_max_duration(__A	:		str      ):
															return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

										lowerCamelCase__					=len(__A      )
										lowerCamelCase__					=len(__A      )
										lowerCamelCase__					=train_dataset.filter(__A      ,						remove_columns=['duration_in_seconds']      )
										lowerCamelCase__					=val_dataset.filter(__A      ,						remove_columns=['duration_in_seconds']      )
										if len(__A      ) > old_train_size:
															logger.warning(
															    f"Filtered out {len(__A      ) - old_train_size} train example(s) longer than"
															    f" {data_args.max_duration_in_seconds} second(s)."      )
										if len(__A      ) > old_val_size:
															logger.warning(
															    f"Filtered out {len(__A      ) - old_val_size} validation example(s) longer than"
															    f" {data_args.max_duration_in_seconds} second(s)."      )
					logger.info(f"Split sizes: {len(__A      )} train and {len(__A      )} validation."      )

					logger.warning(f"Updated {len(__A      )} transcript(s) using '{data_args.orthography}' orthography rules."      )
					if logger.isEnabledFor(logging.DEBUG      ):
										for original_text, updated_text in text_updates:
															logger.debug(f"Updated text: \"{original_text}\" -> \"{updated_text}\""      )
					lowerCamelCase__					=None

					def prepare_dataset(__A	:		List[str]      ):
										# check that all files have the correct sampling rate
										assert (
										    len(set(batch['sampling_rate']      )      ) == 1
										), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."

										lowerCamelCase__					=processor(
										    audio=batch['speech']      ,						text=batch[data_args.target_text_column]      ,						sampling_rate=batch['sampling_rate'][0]      )
										batch.update(__A      )
										return batch

					lowerCamelCase__					=train_dataset.map(
					    __A      ,						batch_size=training_args.per_device_train_batch_size      ,						batched=__A      ,						num_proc=data_args.preprocessing_num_workers      ,						)
					lowerCamelCase__					=val_dataset.map(
					    __A      ,						batch_size=training_args.per_device_train_batch_size      ,						batched=__A      ,						num_proc=data_args.preprocessing_num_workers      ,						)

					lowerCamelCase__					=DataCollatorCTCWithPadding(processor=__A      ,						padding=__A      )

					def compute_metrics(__A	:		int      ):
										lowerCamelCase__					=pred.predictions
										lowerCamelCase__					=np.argmax(__A      ,						axis=-1      )

										lowerCamelCase__					=processor.tokenizer.pad_token_id

										lowerCamelCase__					=processor.batch_decode(__A      )
										# we do not want to group tokens when computing the metrics
										lowerCamelCase__					=processor.batch_decode(pred.label_ids      ,						group_tokens=__A      )
										if logger.isEnabledFor(logging.DEBUG      ):
															for reference, predicted in zip(__A      ,						__A      ):
																				logger.debug(f"reference: \"{reference}\""      )
																				logger.debug(f"predicted: \"{predicted}\""      )
																				if orthography.untransliterator is not None:
																									logger.debug(f"reference (untransliterated): \"{orthography.untransliterator(__A      )}\""      )
																									logger.debug(f"predicted (untransliterated): \"{orthography.untransliterator(__A      )}\""      )

										lowerCamelCase__					=wer_metric.compute(predictions=__A      ,						references=__A      )

										return {"wer": wer}

					if model_args.freeze_feature_extractor:
										model.freeze_feature_extractor()

					lowerCamelCase__					=CTCTrainer(
					    model=__A      ,						data_collator=__A      ,						args=__A      ,						compute_metrics=__A      ,						train_dataset=__A      ,						eval_dataset=__A      ,						tokenizer=processor.feature_extractor      ,						)

					trainer.train()


if __name__ == "__main__":
						main()







"""simple docstring"""



import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.




def     lowerCamelCase_					(	__A	:		Tuple      ,						__A	:		Optional[int]      ,						__A	:		str      ,						__A	:		Optional[Any]      ,						__A	:		Optional[int]      ,						__A	:		Union[str, Any]      ):
					# we don't care about the past rewards in this model

					lowerCamelCase__					=states.reshape(1      ,						-1      ,						model.config.state_dim      )
					lowerCamelCase__					=actions.reshape(1      ,						-1      ,						model.config.act_dim      )
					lowerCamelCase__					=returns_to_go.reshape(1      ,						-1      ,						1      )
					lowerCamelCase__					=timesteps.reshape(1      ,						-1      )

					if model.config.max_length is not None:
										lowerCamelCase__					=states[:, -model.config.max_length :]
										lowerCamelCase__					=actions[:, -model.config.max_length :]
										lowerCamelCase__					=returns_to_go[:, -model.config.max_length :]
										lowerCamelCase__					=timesteps[:, -model.config.max_length :]

										# pad all tokens to sequence length
										lowerCamelCase__					=torch.cat(
										    [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
										lowerCamelCase__					=attention_mask.to(dtype=torch.long      ,						device=states.device      ).reshape(1      ,						-1      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)      ,						device=states.device      ,						),
										        states,
										    ]      ,						dim=1      ,						).to(dtype=torch.floataa      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)      ,						device=actions.device      ,						),
										        actions,
										    ]      ,						dim=1      ,						).to(dtype=torch.floataa      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)      ,						device=returns_to_go.device      ,						),
										        returns_to_go,
										    ]      ,						dim=1      ,						).to(dtype=torch.floataa      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])      ,						device=timesteps.device      ),
										        timesteps,
										    ]      ,						dim=1      ,						).to(dtype=torch.long      )
					else:
										lowerCamelCase__					=None

					lowerCamelCase__					,  lowerCamelCase__					,  lowerCamelCase__					=model(
					    states=__A      ,						actions=__A      ,						rewards=__A      ,						returns_to_go=__A      ,						timesteps=__A      ,						attention_mask=__A      ,						return_dict=__A      ,						)

					return action_preds[0, -1]


# build the environment

_snake_case							:   List[str]	  =       gym.make("Hopper-v3")
_snake_case							:   Optional[int]	  =       env.observation_space.shape[0]
_snake_case							:   Tuple	  =       env.action_space.shape[0]
_snake_case							:   Union[str, Any]	  =       1_000
_snake_case							:   Dict	  =       "cuda"
_snake_case							:   Dict	  =       1000.0  # normalization for rewards/returns
_snake_case							:   Optional[int]	  =       3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_snake_case							:   Optional[Any]	  =       np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
_snake_case							:   Tuple	  =       np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
_snake_case							:   int	  =       torch.from_numpy(state_mean).to(device=device)
_snake_case							:   int	  =       torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_snake_case							:   List[Any]	  =       DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
_snake_case							:   List[str]	  =       model.to(device)
model.eval()

for ep in range(10):
						_snake_case  ,    _snake_case							:   List[str]	  =       0, 0
						_snake_case							:   Any	  =       env.reset()
						_snake_case							:   int	  =       torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
						_snake_case							:   Optional[Any]	  =       torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
						_snake_case							:   Tuple	  =       torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
						_snake_case							:   Tuple	  =       torch.zeros(0, device=device, dtype=torch.floataa)

						_snake_case							:   Tuple	  =       torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
						for t in range(max_ep_len):
												env.render()
												# add padding
												_snake_case							:   List[Any]	  =       torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
												_snake_case							:   str	  =       torch.cat([rewards, torch.zeros(1, device=device)])

												_snake_case							:   List[str]	  =       get_action(
												    model,
												    (states.to(dtype=torch.floataa) - state_mean) / state_std,
												    actions.to(dtype=torch.floataa),
												    rewards.to(dtype=torch.floataa),
												    target_return.to(dtype=torch.floataa),
												    timesteps.to(dtype=torch.long),
												)
												_snake_case							:   List[str]	  =       action
												_snake_case							:   Optional[int]	  =       action.detach().cpu().numpy()

												_snake_case  ,    _snake_case  ,    _snake_case  ,    _snake_case							:   List[str]	  =       env.step(action)

												_snake_case							:   Optional[int]	  =       torch.from_numpy(state).to(device=device).reshape(1, state_dim)
												_snake_case							:   Optional[int]	  =       torch.cat([states, cur_state], dim=0)
												_snake_case							:   Tuple	  =       reward

												_snake_case							:   List[str]	  =       target_return[0, -1] - (reward / scale)
												_snake_case							:   Optional[int]	  =       torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
												_snake_case							:   Any	  =       torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

												episode_return += reward
												episode_length += 1

												if done:
																		break