





'''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


lowercase__		:     str			=							logging.get_logger(__name__)
@dataclass
class 							__lowerCAmelCase	:






       """simple docstring"""
       _snake_case      :	str									=       field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()					)}					)
       _snake_case      :	str									=       field(
           metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
       _snake_case      :	int									=       field(
           default=1_2_8		,		metadata={
               'help': (
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               )
           }		,		)
       _snake_case      :	bool									=       field(
           default=__magic_name__		,		metadata={'help': 'Overwrite the cached training and evaluation sets'}					)





       def snake_case__		(				self  :						Optional[int]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			self.task_name.lower()

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'train'
       _snake_case      :	Optional[int]									=       'dev'
       _snake_case      :	List[Any]									=       'test'

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	GlueDataTrainingArguments
       _snake_case      :	str
       _snake_case      :	List[InputFeatures]





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

            '''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__			,					)
            _UpperCamelCase							       =			args
            _UpperCamelCase							       =			glue_processors[args.task_name]()
            _UpperCamelCase							       =			glue_output_modes[args.task_name]
            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 try:
                      _UpperCamelCase							       =			Split[mode]
                 except KeyError:
                      raise KeyError('''mode is not a valid split name'''	)
        # Load data features from cache or dataset file
            _UpperCamelCase							       =			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}"""			,					)
            _UpperCamelCase							       =			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)
                 _UpperCamelCase       ,							_UpperCamelCase							       =			label_list[2], label_list[1]
            _UpperCamelCase							       =			label_list

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            _UpperCamelCase							       =			cached_features_file + '''.lock'''
            with FileLock(lowerCAmelCase__	):
                 if os.path.exists(lowerCAmelCase__	) and not args.overwrite_cache:
                      _UpperCamelCase							       =			time.time()
                      _UpperCamelCase							       =			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:
                           _UpperCamelCase							       =			self.processor.get_dev_examples(args.data_dir	)
                      elif mode == Split.test:
                           _UpperCamelCase							       =			self.processor.get_test_examples(args.data_dir	)
                      else:
                           _UpperCamelCase							       =			self.processor.get_train_examples(args.data_dir	)
                      if limit_length is not None:
                           _UpperCamelCase							       =			examples[:limit_length]
                      _UpperCamelCase							       =			glue_convert_examples_to_features(
                          lowerCAmelCase__			,					lowerCAmelCase__			,					max_length=args.max_seq_length			,					label_list=lowerCAmelCase__			,					output_mode=self.output_mode			,					)
                      _UpperCamelCase							       =			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  :						Union[str, Any]	)       ->	Dict:

            '''simple docstring'''




            return len(self.features	)





       def __getitem__(				self  :						Tuple			,					lowerCAmelCase__  :						List[str]	)       ->	InputFeatures:

            '''simple docstring'''




            return self.features[i]







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

            '''simple docstring'''




            return self.label_list











'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
 from ...feature_extraction_utils import FeatureExtractionMixin
 from ...tokenization_utils_base import PreTrainedTokenizerBase
 from ...utils import TensorType

__UpperCamelCase   :							List[str]         =   logging.get_logger(__name__)

__UpperCamelCase   :							Optional[int]         =   {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
__UpperCamelCase   :							Optional[Any]         =   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
__UpperCamelCase   :							List[str]         =   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="whisper"
      __a	       =["past_key_values"]
      __a	       ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




      def __init__(	self			, lowerCamelCase=5_1865			, lowerCamelCase=80			, lowerCamelCase=6			, lowerCamelCase=4			, lowerCamelCase=6			, lowerCamelCase=4			, lowerCamelCase=1536			, lowerCamelCase=1536			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=5_0257			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase="gelu"			, lowerCamelCase=256			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.02			, lowerCamelCase=False			, lowerCamelCase=1500			, lowerCamelCase=448			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=None			, lowerCamelCase=[220, 5_0256]			, lowerCamelCase=False			, lowerCamelCase=256			, lowerCamelCase=False			, lowerCamelCase=0.05			, lowerCamelCase=10			, lowerCamelCase=2			, lowerCamelCase=0.0			, lowerCamelCase=10			, lowerCamelCase=0			, lowerCamelCase=7			, **lowerCamelCase			, )				->Any:



         '''simple docstring'''
         __a					  =     vocab_size
         __a					  =     num_mel_bins
         __a					  =     d_model
         __a					  =     encoder_layers
         __a					  =     encoder_attention_heads
         __a					  =     decoder_layers
         __a					  =     decoder_attention_heads
         __a					  =     decoder_ffn_dim
         __a					  =     encoder_ffn_dim
         __a					  =     dropout
         __a					  =     attention_dropout
         __a					  =     activation_dropout
         __a					  =     activation_function
         __a					  =     init_std
         __a					  =     encoder_layerdrop
         __a					  =     decoder_layerdrop
         __a					  =     use_cache
         __a					  =     encoder_layers
         __a					  =     scale_embedding  # scale factor will be sqrt(d_model) if True
         __a					  =     max_source_positions
         __a					  =     max_target_positions

         # Audio Classification-specific parameters. Feel free to ignore for other classes.
         __a					  =     classifier_proj_size
         __a					  =     use_weighted_layer_sum

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __a					  =     apply_spec_augment
         __a					  =     mask_time_prob
         __a					  =     mask_time_length
         __a					  =     mask_time_min_masks
         __a					  =     mask_feature_prob
         __a					  =     mask_feature_length
         __a					  =     mask_feature_min_masks

         __a					  =     median_filter_width

         super().__init__(
             pad_token_id=lowerCamelCase			, bos_token_id=lowerCamelCase			, eos_token_id=lowerCamelCase			, is_encoder_decoder=lowerCamelCase			, decoder_start_token_id=lowerCamelCase			, suppress_tokens=lowerCamelCase			, begin_suppress_tokens=lowerCamelCase			, **lowerCamelCase			, )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      @property
      def 			__UpperCamelCase				(	self   )				->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         __a					  =     OrderedDict(
             [
                 ('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
             ]   )
         if self.use_past:
            __a					  =     {0: 'batch'}
         else:
            __a					  =     {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase			, direction='inputs'   )

         return common_inputs




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = -1			, lowerCamelCase = -1			, lowerCamelCase = False			, lowerCamelCase = None			, lowerCamelCase = 2_2050			, lowerCamelCase = 5.0			, lowerCamelCase = 220			, )				->Mapping[str, Any]:



         '''simple docstring'''
         __a					  =     OrderedDict()
         __a					  =     OnnxConfig.generate_dummy_inputs(
             self			, preprocessor=preprocessor.feature_extractor			, batch_size=lowerCamelCase			, framework=lowerCamelCase			, sampling_rate=lowerCamelCase			, time_duration=lowerCamelCase			, frequency=lowerCamelCase			, )
         __a					  =     encoder_inputs['input_features'].shape[2]
         __a					  =     encoder_sequence_length // 2 if self.use_past else seq_length

         __a					  =     super().generate_dummy_inputs(
             preprocessor.tokenizer			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )

         __a					  =     encoder_inputs.pop('input_features'   )
         __a					  =     decoder_inputs.pop('decoder_input_ids'   )

         if "past_key_values" in decoder_inputs:
            __a					  =     decoder_inputs.pop('past_key_values'   )

         return dummy_inputs






      @property
      def 			__UpperCamelCase				(	self   )				->float:



         '''simple docstring'''
         return 1e-3