

'''simple docstring'''




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_lowerCAmelCase         =						logging.getLogger(__name__)






@dataclass
class   _SCREAMING_SNAKE_CASE   :
	__SCREAMING_SNAKE_CASE					:str
	__SCREAMING_SNAKE_CASE					:List[str]
	__SCREAMING_SNAKE_CASE					:Optional[List[str]]






@dataclass
class   _SCREAMING_SNAKE_CASE   :
	__SCREAMING_SNAKE_CASE					:List[int]
	__SCREAMING_SNAKE_CASE					:List[int]
	__SCREAMING_SNAKE_CASE					:Optional[List[int]]      	=					None
	__SCREAMING_SNAKE_CASE					:Optional[List[int]]      	=					None






class   _SCREAMING_SNAKE_CASE   (   __lowercase     ):
	__SCREAMING_SNAKE_CASE					:Any      	=					"train"
	__SCREAMING_SNAKE_CASE					:Dict      	=					"dev"
	__SCREAMING_SNAKE_CASE					:Optional[int]      	=					"test"






class   _SCREAMING_SNAKE_CASE   :



	@staticmethod
	def 			snake_case__  (   a__		:   List[Any]			,				a__		:   str      ):
						raise NotImplementedError



	@staticmethod
	def 			snake_case__  (   a__		:   Union[str, Any]      ):
						raise NotImplementedError



	@staticmethod
	def 			snake_case__  (   a__		:   Any			,				a__		:   Dict			,				a__		:   str			,				a__		:   Dict			,				a__		:   Optional[int]=False			,				a__		:   List[str]="[CLS]"			,				a__		:   Union[str, Any]=1			,				a__		:   Any="[SEP]"			,				a__		:   List[Any]=False			,				a__		:   List[str]=False			,				a__		:   Tuple=0			,				a__		:   List[str]=0			,				a__		:   Any=-100			,				a__		:   Optional[Any]=0			,				a__		:   List[Any]=True			,				):
						__magic_name__						       =    {label: i for i, label in enumerate(_a      )}

						__magic_name__						       =    []
						for ex_index, example in enumerate(_a      ):
											if ex_index % 1_0000 == 0:
																logger.info('''Writing example %d of %d'''			,				_a			,				len(_a      )      )

											__magic_name__						       =    []
											__magic_name__						       =    []
											for word, label in zip(example.words			,				example.labels      ):
																__magic_name__						       =    tokenizer.tokenize(_a      )

																# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																if len(_a      ) > 0:
																					tokens.extend(_a      )
																					# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																					label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_a      ) - 1)      )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
											__magic_name__						       =    tokenizer.num_special_tokens_to_add()
											if len(_a      ) > max_seq_length - special_tokens_count:
																__magic_name__						       =    tokens[: (max_seq_length - special_tokens_count)]
																__magic_name__						       =    label_ids[: (max_seq_length - special_tokens_count)]

											# The convention in BERT is:
											# (a) For sequence pairs:
											#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
											#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
											# (b) For single sequences:
											#  tokens:   [CLS] the dog is hairy . [SEP]
											#  type_ids:   0   0   0   0  0     0   0
											#
											# Where "type_ids" are used to indicate whether this is the first
											# sequence or the second sequence. The embedding vectors for `type=0` and
											# `type=1` were learned during pre-training and are added to the wordpiece
											# embedding vector (and position vector). This is not *strictly* necessary
											# since the [SEP] token unambiguously separates the sequences, but it makes
											# it easier for the model to learn the concept of sequences.
											#
											# For classification tasks, the first vector (corresponding to [CLS]) is
											# used as the "sentence vector". Note that this only makes sense because
											# the entire model is fine-tuned.
											tokens += [sep_token]
											label_ids += [pad_token_label_id]
											if sep_token_extra:
																# roberta uses an extra separator b/w pairs of sentences
																tokens += [sep_token]
																label_ids += [pad_token_label_id]
											__magic_name__						       =    [sequence_a_segment_id] * len(_a      )

											if cls_token_at_end:
																tokens += [cls_token]
																label_ids += [pad_token_label_id]
																segment_ids += [cls_token_segment_id]
											else:
																__magic_name__						       =    [cls_token] + tokens
																__magic_name__						       =    [pad_token_label_id] + label_ids
																__magic_name__						       =    [cls_token_segment_id] + segment_ids

											__magic_name__						       =    tokenizer.convert_tokens_to_ids(_a      )

											# The mask has 1 for real tokens and 0 for padding tokens. Only real
											# tokens are attended to.
											__magic_name__						       =    [1 if mask_padding_with_zero else 0] * len(_a      )

											# Zero-pad up to the sequence length.
											__magic_name__						       =    max_seq_length - len(_a      )
											if pad_on_left:
																__magic_name__						       =    ([pad_token] * padding_length) + input_ids
																__magic_name__						       =    ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																__magic_name__						       =    ([pad_token_segment_id] * padding_length) + segment_ids
																__magic_name__						       =    ([pad_token_label_id] * padding_length) + label_ids
											else:
																input_ids += [pad_token] * padding_length
																input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																segment_ids += [pad_token_segment_id] * padding_length
																label_ids += [pad_token_label_id] * padding_length

											assert len(_a      ) == max_seq_length
											assert len(_a      ) == max_seq_length
											assert len(_a      ) == max_seq_length
											assert len(_a      ) == max_seq_length

											if ex_index < 5:
																logger.info('''*** Example ***'''      )
																logger.info('''guid: %s'''			,				example.guid      )
																logger.info('''tokens: %s'''			,				''' '''.join([str(_a      ) for x in tokens]      )      )
																logger.info('''input_ids: %s'''			,				''' '''.join([str(_a      ) for x in input_ids]      )      )
																logger.info('''input_mask: %s'''			,				''' '''.join([str(_a      ) for x in input_mask]      )      )
																logger.info('''segment_ids: %s'''			,				''' '''.join([str(_a      ) for x in segment_ids]      )      )
																logger.info('''label_ids: %s'''			,				''' '''.join([str(_a      ) for x in label_ids]      )      )

											if "token_type_ids" not in tokenizer.model_input_names:
																__magic_name__						       =    None

											features.append(
											    InputFeatures(
											        input_ids=_a			,				attention_mask=_a			,				token_type_ids=_a			,				label_ids=_a      )      )
						return features


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset






						class   _SCREAMING_SNAKE_CASE   (   __lowercase     ):
							__SCREAMING_SNAKE_CASE					:List[InputFeatures]
							__SCREAMING_SNAKE_CASE					:int      	=					nn.CrossEntropyLoss().ignore_index



							def __init__(   self		:   Optional[Any]			,				a__		:   str			,				a__		:   Tuple			,				a__		:   Dict			,				a__		:   Any			,				a__		:   Any			,				a__		:   int = None			,				a__		:   Optional[Any]=False			,				a__		:   int = Split.train			,				):
												# Load data features from cache or dataset file
												__magic_name__						       =    os.path.join(
												    _a			,				'''cached_{}_{}_{}'''.format(mode.value			,				tokenizer.__class__.__name__			,				str(_a      )      )			,				)

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												__magic_name__						       =    cached_features_file + '''.lock'''
												with FileLock(_a      ):
																	if os.path.exists(_a      ) and not overwrite_cache:
																						logger.info(F'''Loading features from cached file {cached_features_file}'''      )
																						__magic_name__						       =    torch.load(_a      )
																	else:
																						logger.info(F'''Creating features from dataset file at {data_dir}'''      )
																						__magic_name__						       =    token_classification_task.read_examples_from_file(_a			,				_a      )
																						# TODO clean up all this to leverage built-in features of tokenizers
																						__magic_name__						       =    token_classification_task.convert_examples_to_features(
																						    _a			,				_a			,				_a			,				_a			,				cls_token_at_end=bool(model_type in ['''xlnet''']      )			,				cls_token=tokenizer.cls_token			,				cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0			,				sep_token=tokenizer.sep_token			,				sep_token_extra=_a			,				pad_on_left=bool(tokenizer.padding_side == '''left'''      )			,				pad_token=tokenizer.pad_token_id			,				pad_token_segment_id=tokenizer.pad_token_type_id			,				pad_token_label_id=self.pad_token_label_id			,				)
																						logger.info(F'''Saving features into cached file {cached_features_file}'''      )
																						torch.save(self.features			,				_a      )



							def __len__(   self		:   Tuple      ):
												return len(self.features      )



							def __getitem__(   self		:   Dict			,				a__		:   int      ):
												return self.features[i]


if is_tf_available():
						import tensorflow as tf






						class   _SCREAMING_SNAKE_CASE   :
							__SCREAMING_SNAKE_CASE					:List[InputFeatures]
							__SCREAMING_SNAKE_CASE					:int      	=					-100



							def __init__(   self		:   int			,				a__		:   str			,				a__		:   Union[str, Any]			,				a__		:   Dict			,				a__		:   Dict			,				a__		:   Dict			,				a__		:   int = None			,				a__		:   Optional[Any]=False			,				a__		:   Union[str, Any] = Split.train			,				):
												__magic_name__						       =    token_classification_task.read_examples_from_file(_a			,				_a      )
												# TODO clean up all this to leverage built-in features of tokenizers
												__magic_name__						       =    token_classification_task.convert_examples_to_features(
												    _a			,				_a			,				_a			,				_a			,				cls_token_at_end=bool(model_type in ['''xlnet''']      )			,				cls_token=tokenizer.cls_token			,				cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0			,				sep_token=tokenizer.sep_token			,				sep_token_extra=_a			,				pad_on_left=bool(tokenizer.padding_side == '''left'''      )			,				pad_token=tokenizer.pad_token_id			,				pad_token_segment_id=tokenizer.pad_token_type_id			,				pad_token_label_id=self.pad_token_label_id			,				)

												def gen():
																	for ex in self.features:
																						if ex.token_type_ids is None:
																											yield (
																											    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																											    ex.label_ids,
																											)
																						else:
																											yield (
																											    {
																											        "input_ids": ex.input_ids,
																											        "attention_mask": ex.attention_mask,
																											        "token_type_ids": ex.token_type_ids,
																											    },
																											    ex.label_ids,
																											)

												if "token_type_ids" not in tokenizer.model_input_names:
																	__magic_name__						       =    tf.data.Dataset.from_generator(
																	    _a			,				({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)			,				(
																	        {'''input_ids''': tf.TensorShape([None]      ), '''attention_mask''': tf.TensorShape([None]      )},
																	        tf.TensorShape([None]      ),
																	    )			,				)
												else:
																	__magic_name__						       =    tf.data.Dataset.from_generator(
																	    _a			,				({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)			,				(
																	        {
																	            '''input_ids''': tf.TensorShape([None]      ),
																	            '''attention_mask''': tf.TensorShape([None]      ),
																	            '''token_type_ids''': tf.TensorShape([None]      ),
																	        },
																	        tf.TensorShape([None]      ),
																	    )			,				)



							def 			snake_case__  (   self		:   List[Any]      ):
												__magic_name__						       =    self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

												return self.dataset



							def __len__(   self		:   Union[str, Any]      ):
												return len(self.features      )



							def __getitem__(   self		:   Optional[Any]			,				a__		:   str      ):
												return self.features[i]









'''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

_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{
    "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}

# fmt: off
_lowerCAmelCase         =						[
    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
]
_lowerCAmelCase         =						[
    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   (   __a     ):
 __SCREAMING_SNAKE_CASE					:str      	=					"""whisper"""
 __SCREAMING_SNAKE_CASE					:str      	=					["""past_key_values"""]
 __SCREAMING_SNAKE_CASE					:Tuple      	=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



 def __init__(   self		:   Dict			,				a__		:   Optional[int]=5_1865			,				a__		:   str=80			,				a__		:   List[str]=6			,				a__		:   List[str]=4			,				a__		:   List[Any]=6			,				a__		:   Union[str, Any]=4			,				a__		:   Tuple=1536			,				a__		:   Optional[int]=1536			,				a__		:   List[str]=0.0			,				a__		:   Union[str, Any]=0.0			,				a__		:   Union[str, Any]=5_0257			,				a__		:   Dict=True			,				a__		:   Optional[Any]=True			,				a__		:   Union[str, Any]="gelu"			,				a__		:   Tuple=256			,				a__		:   Dict=0.0			,				a__		:   str=0.0			,				a__		:   Optional[Any]=0.0			,				a__		:   int=0.02			,				a__		:   Any=False			,				a__		:   List[Any]=1500			,				a__		:   Optional[int]=448			,				a__		:   Dict=5_0256			,				a__		:   str=5_0256			,				a__		:   Tuple=5_0256			,				a__		:   List[str]=None			,				a__		:   List[Any]=[220, 5_0256]			,				a__		:   Any=False			,				a__		:   Dict=256			,				a__		:   Optional[Any]=False			,				a__		:   str=0.05			,				a__		:   List[Any]=10			,				a__		:   List[Any]=2			,				a__		:   Optional[int]=0.0			,				a__		:   List[Any]=10			,				a__		:   Union[str, Any]=0			,				a__		:   int=7			,				**a__		:   Any			,				):
      __magic_name__						       =    vocab_size
      __magic_name__						       =    num_mel_bins
      __magic_name__						       =    d_model
      __magic_name__						       =    encoder_layers
      __magic_name__						       =    encoder_attention_heads
      __magic_name__						       =    decoder_layers
      __magic_name__						       =    decoder_attention_heads
      __magic_name__						       =    decoder_ffn_dim
      __magic_name__						       =    encoder_ffn_dim
      __magic_name__						       =    dropout
      __magic_name__						       =    attention_dropout
      __magic_name__						       =    activation_dropout
      __magic_name__						       =    activation_function
      __magic_name__						       =    init_std
      __magic_name__						       =    encoder_layerdrop
      __magic_name__						       =    decoder_layerdrop
      __magic_name__						       =    use_cache
      __magic_name__						       =    encoder_layers
      __magic_name__						       =    scale_embedding  # scale factor will be sqrt(d_model) if True
      __magic_name__						       =    max_source_positions
      __magic_name__						       =    max_target_positions

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

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

      __magic_name__						       =    median_filter_width

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






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    OrderedDict(
          [
              ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
          ]      )
      if self.use_past:
           __magic_name__						       =    {0: '''batch'''}
      else:
           __magic_name__						       =    {0: '''batch''', 1: '''decoder_sequence'''}

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

      return common_inputs



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]			,				a__		:   int = -1			,				a__		:   int = -1			,				a__		:   bool = False			,				a__		:   Optional["TensorType"] = None			,				a__		:   int = 2_2050			,				a__		:   float = 5.0			,				a__		:   int = 220			,				):
      __magic_name__						       =    OrderedDict()
      __magic_name__						       =    OnnxConfig.generate_dummy_inputs(
          self			,				preprocessor=preprocessor.feature_extractor			,				batch_size=a__			,				framework=a__			,				sampling_rate=a__			,				time_duration=a__			,				frequency=a__			,				)
      __magic_name__						       =    encoder_inputs['''input_features'''].shape[2]
      __magic_name__						       =    encoder_sequence_length // 2 if self.use_past else seq_length

      __magic_name__						       =    super().generate_dummy_inputs(
          preprocessor.tokenizer			,				a__			,				a__			,				a__			,				a__      )

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

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

      return dummy_inputs



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return 1E-3






