
"""simple docstring"""






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


lowerCamelCase__ :       Optional[int]						     =			logging.get_logger(__name__)




@dataclass
class 				lowercase__:
	'''simple docstring'''

	UpperCamelCase				=  field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()   )}   )
	UpperCamelCase				=  field(
	    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
	UpperCamelCase				=  field(
	    default=1_28		,						metadata={
	        """help""": (
	            """The maximum total input sequence length after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    }		,						)
	UpperCamelCase				=  field(
	    default=_UpperCAmelCase		,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )





	def   __lowerCAmelCase (						self    :Any					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.task_name.lower()





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """train"""
	UpperCamelCase				=  """dev"""
	UpperCamelCase				=  """test"""





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  42
	UpperCamelCase				=  42
	UpperCamelCase				=  42





	def __init__(						self    :List[str]		, lowerCamelCase_    :GlueDataTrainingArguments		, lowerCamelCase_    :PreTrainedTokenizerBase		, lowerCamelCase_    :Optional[int] = None		, lowerCamelCase_    :Union[str, Split] = Split.train		, lowerCamelCase_    :Optional[str] = None		, )							->			int:







			'''simple docstring'''




			warnings.warn(
			    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
			    '''library. You can have a look at this example script for pointers: '''
			    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''		, lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     int				= args
			SCREAMING_SNAKE_CASE       :     Any				= glue_processors[args.task_name]()
			SCREAMING_SNAKE_CASE       :     int				= glue_output_modes[args.task_name]
			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					try:
							SCREAMING_SNAKE_CASE       :     List[str]				= Split[mode]
					except KeyError:
							raise KeyError('''mode is not a valid split name'''					)
        # Load data features from cache or dataset file
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= os.path.join(
			    cache_dir if cache_dir is not None else args.data_dir		, f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"		, )
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.processor.get_labels()
			if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
			    "RobertaTokenizer",
			    "RobertaTokenizerFast",
			    "XLMRobertaTokenizer",
			    "BartTokenizer",
			    "BartTokenizerFast",
			):
					# HACK(label indices are swapped in RoBERTa pretrained model)
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= label_list[2], label_list[1]
			SCREAMING_SNAKE_CASE       :     List[Any]				= label_list

			# Make sure only the first process in distributed training processes the dataset,
			# and the others will use the cache.
			SCREAMING_SNAKE_CASE       :     Dict				= cached_features_file + '''.lock'''
			with FileLock(lowerCamelCase_					):
					if os.path.exists(lowerCamelCase_					) and not args.overwrite_cache:
							SCREAMING_SNAKE_CASE       :     Dict				= time.time()
							SCREAMING_SNAKE_CASE       :     List[Any]				= torch.load(lowerCamelCase_					)
							logger.info(
							    f"Loading features from cached file {cached_features_file} [took %.3f s]"		, time.time() - start					)
					else:
							logger.info(f"Creating features from dataset file at {args.data_dir}"					)

							if mode == Split.dev:
									SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.processor.get_dev_examples(args.data_dir					)
							elif mode == Split.test:
									SCREAMING_SNAKE_CASE       :     List[str]				= self.processor.get_test_examples(args.data_dir					)
							else:
									SCREAMING_SNAKE_CASE       :     Optional[int]				= self.processor.get_train_examples(args.data_dir					)
							if limit_length is not None:
									SCREAMING_SNAKE_CASE       :     Tuple				= examples[:limit_length]
							SCREAMING_SNAKE_CASE       :     Optional[int]				= glue_convert_examples_to_features(
							    lowerCamelCase_		, lowerCamelCase_		, max_length=args.max_seq_length		, label_list=lowerCamelCase_		, output_mode=self.output_mode		, )
							SCREAMING_SNAKE_CASE       :     List[Any]				= time.time()
							torch.save(self.features		, lowerCamelCase_					)
							# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
							logger.info(
							    f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"					)





	def __len__(						self    :List[Any]					)							->			Any:







			'''simple docstring'''




			return len(self.features					)





	def __getitem__(						self    :List[Any]		, lowerCamelCase_    :Union[str, Any]					)							->			InputFeatures:







			'''simple docstring'''




			return self.features[i]







	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Dict:







			'''simple docstring'''




			return self.label_list







"""simple docstring"""






import math
from typing import Optional

import numpy as np

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


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       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 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """encodec"""





	def __init__(						self    :List[str]		, lowerCamelCase_    :Tuple=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]		, lowerCamelCase_    :str=2_40_00		, lowerCamelCase_    :Any=1		, lowerCamelCase_    :List[Any]=False		, lowerCamelCase_    :Optional[int]=None		, lowerCamelCase_    :Optional[Any]=None		, lowerCamelCase_    :str=1_28		, lowerCamelCase_    :Any=32		, lowerCamelCase_    :int=1		, lowerCamelCase_    :Dict=[8, 5, 4, 2]		, lowerCamelCase_    :List[Any]="weight_norm"		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :Tuple=7		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :int=2		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :Optional[int]="reflect"		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Dict=1.0		, lowerCamelCase_    :Any=10_24		, lowerCamelCase_    :str=None		, lowerCamelCase_    :Union[str, Any]=True		, **lowerCamelCase_    :Optional[int]		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= target_bandwidths
			SCREAMING_SNAKE_CASE       :     List[str]				= sampling_rate
			SCREAMING_SNAKE_CASE       :     Tuple				= audio_channels
			SCREAMING_SNAKE_CASE       :     Tuple				= normalize
			SCREAMING_SNAKE_CASE       :     str				= chunk_length_s
			SCREAMING_SNAKE_CASE       :     List[str]				= overlap
			SCREAMING_SNAKE_CASE       :     int				= hidden_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_filters
			SCREAMING_SNAKE_CASE       :     Tuple				= num_residual_layers
			SCREAMING_SNAKE_CASE       :     List[Any]				= upsampling_ratios
			SCREAMING_SNAKE_CASE       :     Optional[int]				= norm_type
			SCREAMING_SNAKE_CASE       :     Any				= kernel_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= last_kernel_size
			SCREAMING_SNAKE_CASE       :     Tuple				= residual_kernel_size
			SCREAMING_SNAKE_CASE       :     Any				= dilation_growth_rate
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_causal_conv
			SCREAMING_SNAKE_CASE       :     str				= pad_mode
			SCREAMING_SNAKE_CASE       :     List[Any]				= compress
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_lstm_layers
			SCREAMING_SNAKE_CASE       :     Dict				= trim_right_ratio
			SCREAMING_SNAKE_CASE       :     List[Any]				= codebook_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= codebook_dim if codebook_dim is not None else hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 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__(**lowerCamelCase_					)





	@property
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			if self.chunk_length_s is None:
					return None
			else:
					return int(self.chunk_length_s * self.sampling_rate					)





	@property
	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[int]:







			'''simple docstring'''




			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   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= np.prod(self.upsampling_ratios					)
			return math.ceil(self.sampling_rate / hop_length					)







	@property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)





