
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowercase__						       =      logging.get_logger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						["""input_features""", """is_longer"""]



		def __init__(      self				:			Any  ,	UpperCamelCase__				:			int=64  ,	UpperCamelCase__				:			Optional[Any]=4_8000  ,	UpperCamelCase__				:			Optional[int]=480  ,	UpperCamelCase__				:			Optional[Any]=10  ,	UpperCamelCase__				:			Dict=1024  ,	UpperCamelCase__				:			int=0.0  ,	UpperCamelCase__				:			Tuple=False  ,	UpperCamelCase__				:			float = 0  ,	UpperCamelCase__				:			float = 1_4000  ,	UpperCamelCase__				:			int = None  ,	UpperCamelCase__				:			str = "fusion"  ,	UpperCamelCase__				:			str = "repeatpad"  ,	**UpperCamelCase__				:			Dict  ,	)						->			str:






				"""simple docstring"""







				super().__init__(
				    feature_size=UpperCamelCase__  ,	sampling_rate=UpperCamelCase__  ,	padding_value=UpperCamelCase__  ,	return_attention_mask=UpperCamelCase__  ,	**UpperCamelCase__  ,	)
				snake_case  :       Optional[int]     			=						top_db
				snake_case  :       Optional[Any]     			=						truncation
				snake_case  :       Tuple     			=						padding
				snake_case  :       List[Any]     			=						fft_window_size
				snake_case  :       List[str]     			=						(fft_window_size >> 1) + 1
				snake_case  :       Dict     			=						hop_length
				snake_case  :       str     			=						max_length_s
				snake_case  :       Optional[Any]     			=						max_length_s * sampling_rate
				snake_case  :       Union[str, Any]     			=						sampling_rate
				snake_case  :       int     			=						frequency_min
				snake_case  :       List[Any]     			=						frequency_max
				snake_case  :       List[Any]     			=						mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins  ,	num_mel_filters=UpperCamelCase__  ,	min_frequency=UpperCamelCase__  ,	max_frequency=UpperCamelCase__  ,	sampling_rate=UpperCamelCase__  ,	norm=UpperCamelCase__  ,	mel_scale='''htk'''  ,	)
				snake_case  :       Union[str, Any]     			=						mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins  ,	num_mel_filters=UpperCamelCase__  ,	min_frequency=UpperCamelCase__  ,	max_frequency=UpperCamelCase__  ,	sampling_rate=UpperCamelCase__  ,	norm='''slaney'''  ,	mel_scale='''slaney'''  ,	)



		def       lowerCAmelCase				(      self				:			Dict      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						copy.deepcopy(self.__dict__      )
				snake_case  :       str     			=						self.__class__.__name__
				if "mel_filters" in output:
						del output["mel_filters"]
				if "mel_filters_slaney" in output:
						del output["mel_filters_slaney"]
				return output



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			np.array  ,	UpperCamelCase__				:			Optional[np.array] = None      )						->			Dict:






				"""simple docstring"""







				snake_case  :       str     			=						spectrogram(
				    UpperCamelCase__  ,	window_function(self.fft_window_size  ,	'''hann'''      )  ,	frame_length=self.fft_window_size  ,	hop_length=self.hop_length  ,	power=2.0  ,	mel_filters=UpperCamelCase__  ,	log_mel='''dB'''  ,	)
				return log_mel_spectrogram.T



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			int      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						np.array_split(list(range(0  ,	total_frames - chunk_frames + 1      )      )  ,	3      )
				if len(ranges[1]      ) == 0:
						# if the audio is too short, we just use the first chunk
						snake_case  :       str     			=						[0]
				if len(ranges[2]      ) == 0:
						# if the audio is too short, we just use the first chunk
						snake_case  :       List[str]     			=						[0]
				# randomly choose index for each part
				snake_case  :       Union[str, Any]     			=						np.random.choice(ranges[0]      )
				snake_case  :       List[Any]     			=						np.random.choice(ranges[1]      )
				snake_case  :       Tuple     			=						np.random.choice(ranges[2]      )

				snake_case  :       Any     			=						mel[idx_front : idx_front + chunk_frames, :]
				snake_case  :       Optional[Any]     			=						mel[idx_middle : idx_middle + chunk_frames, :]
				snake_case  :       List[Any]     			=						mel[idx_back : idx_back + chunk_frames, :]

				snake_case  :       str     			=						torch.tensor(mel[None, None, :]      )
				snake_case  :       Optional[Any]     			=						torch.nn.functional.interpolate(
				    UpperCamelCase__  ,	size=[chunk_frames, 64]  ,	mode='''bilinear'''  ,	align_corners=UpperCamelCase__      )
				snake_case  :       Dict     			=						mel_shrink[0][0].numpy()
				snake_case  :       Optional[Any]     			=						np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]  ,	axis=0      )
				return mel_fusion



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			np.array  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			int      )						->			str:






				"""simple docstring"""







				if waveform.shape[0] > max_length:
						if truncation == "rand_trunc":
								snake_case  :       Union[str, Any]     			=						True
								# random crop to max_length (for compatibility) -> this should be handled by self.pad
								snake_case  :       List[str]     			=						len(UpperCamelCase__      ) - max_length
								snake_case  :       Optional[Any]     			=						np.random.randint(0  ,	overflow + 1      )
								snake_case  :       Optional[int]     			=						waveform[idx : idx + max_length]
								snake_case  :       str     			=						self._np_extract_fbank_features(UpperCamelCase__  ,	self.mel_filters_slaney      )[None, :]
						elif truncation == "fusion":
								snake_case  :       Any     			=						self._np_extract_fbank_features(UpperCamelCase__  ,	self.mel_filters      )
								snake_case  :       int     			=						max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
								snake_case  :       str     			=						mel.shape[0]
								if chunk_frames == total_frames:
										# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
										# In this case, we just use the whole audio.
										snake_case  :       List[Any]     			=						np.stack([mel, mel, mel, mel]  ,	axis=0      )
										snake_case  :       List[str]     			=						False
								else:
										snake_case  :       List[str]     			=						self._random_mel_fusion(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
										snake_case  :       Tuple     			=						True
						else:
								raise NotImplementedError(f'data_truncating {truncation} not implemented'      )

				else:
						snake_case  :       Dict     			=						False
						# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
						if waveform.shape[0] < max_length:
								if padding == "repeat":
										snake_case  :       Union[str, Any]     			=						int(max_length / len(UpperCamelCase__      )      )
										snake_case  :       Union[str, Any]     			=						np.stack(np.tile(UpperCamelCase__  ,	n_repeat + 1      )      )[:max_length]
								if padding == "repeatpad":
										snake_case  :       Union[str, Any]     			=						int(max_length / len(UpperCamelCase__      )      )
										snake_case  :       str     			=						np.stack(np.tile(UpperCamelCase__  ,	UpperCamelCase__      )      )
								snake_case  :       Dict     			=						np.pad(UpperCamelCase__  ,	(0, max_length - waveform.shape[0])  ,	mode='''constant'''  ,	constant_values=0      )

						if truncation == "fusion":
								snake_case  :       List[Any]     			=						self._np_extract_fbank_features(UpperCamelCase__  ,	self.mel_filters      )
								snake_case  :       Optional[int]     			=						np.stack([input_mel, input_mel, input_mel, input_mel]  ,	axis=0      )
						else:
								snake_case  :       Union[str, Any]     			=						self._np_extract_fbank_features(UpperCamelCase__  ,	self.mel_filters_slaney      )[None, :]

				return input_mel, longer



		def __call__(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,	UpperCamelCase__				:			str = None  ,	UpperCamelCase__				:			Optional[str] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Optional[Union[str, TensorType]] = None  ,	**UpperCamelCase__				:			int  ,	)						->			Any:






				"""simple docstring"""







				snake_case  :       Tuple     			=						truncation if truncation is not None else self.truncation
				snake_case  :       List[Any]     			=						padding if padding else self.padding

				if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
								raise ValueError(
								    f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
								    f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
								    f' was sampled with {self.sampling_rate} and not {sampling_rate}.'      )
				else:
						logger.warning(
						    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
						    '''Failing to do so can result in silent errors that might be hard to debug.'''      )

				snake_case  :       str     			=						isinstance(UpperCamelCase__  ,	np.ndarray      ) and len(raw_speech.shape      ) > 1
				if is_batched_numpy and len(raw_speech.shape      ) > 2:
						raise ValueError(f'Only mono-channel audio is supported for input to {self}'      )
				snake_case  :       Dict     			=						is_batched_numpy or (
				    isinstance(UpperCamelCase__  ,	(list, tuple)      ) and (isinstance(raw_speech[0]  ,	(np.ndarray, tuple, list)      ))
				)

				if is_batched:
						snake_case  :       List[Any]     			=						[np.asarray(UpperCamelCase__  ,	dtype=np.floataa      ) for speech in raw_speech]
				elif not is_batched and not isinstance(UpperCamelCase__  ,	np.ndarray      ):
						snake_case  :       Any     			=						np.asarray(UpperCamelCase__  ,	dtype=np.floataa      )
				elif isinstance(UpperCamelCase__  ,	np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
						snake_case  :       Optional[int]     			=						raw_speech.astype(np.floataa      )

				# always return batch
				if not is_batched:
						snake_case  :       List[Any]     			=						[np.asarray(UpperCamelCase__      )]

				# convert to mel spectrogram, truncate and pad if needed.
				snake_case  :       str     			=						[
				    self._get_input_mel(UpperCamelCase__  ,	max_length if max_length else self.nb_max_samples  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				    for waveform in raw_speech
				]

				snake_case  :       Tuple     			=						[]
				snake_case  :       Optional[int]     			=						[]
				for mel, longer in padded_inputs:
						input_mel.append(UpperCamelCase__      )
						is_longer.append(UpperCamelCase__      )

				if truncation == "fusion" and sum(UpperCamelCase__      ) == 0:
						# if no audio is longer than 10s, then randomly select one audio to be longer
						snake_case  :       Optional[Any]     			=						np.random.randint(0  ,	len(UpperCamelCase__      )      )
						snake_case  :       Union[str, Any]     			=						True

				if isinstance(input_mel[0]  ,	UpperCamelCase__      ):
						snake_case  :       Union[str, Any]     			=						[np.asarray(UpperCamelCase__  ,	dtype=np.floataa      ) for feature in input_mel]

				# is_longer is a list of bool
				snake_case  :       Optional[int]     			=						[[longer] for longer in is_longer]

				snake_case  :       Dict     			=						{"input_features": input_mel, "is_longer": is_longer}
				snake_case  :       Optional[Any]     			=						BatchFeature(UpperCamelCase__      )

				if return_tensors is not None:
						snake_case  :       Optional[int]     			=						input_features.convert_to_tensors(UpperCamelCase__      )

				return input_features






import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


SCREAMING_SNAKE_CASE__     					=			logging.getLogger(__name__)



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Optional[int]   ,  UpperCAmelCase				:     Any=-1):
				# in NER datasets, the last column is usually reserved for NER label
				SCREAMING_SNAKE_CASE_				:Tuple				     =						label_idx






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						1
				SCREAMING_SNAKE_CASE_				:str				     =						[]
				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
						SCREAMING_SNAKE_CASE_				:int				     =						[]
						for line in f:
								if line.startswith("-DOCSTART-") or line == "" or line == "\n":
										if words:
												examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
												guid_index += 1
												SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
												SCREAMING_SNAKE_CASE_				:Any				     =						[]
								else:
										SCREAMING_SNAKE_CASE_				:int				     =						line.split(" ")
										words.append(splits[0])
										if len(UpperCAmelCase) > 1:
												labels.append(splits[self.label_idx].replace("\n"   ,  ""))
										else:
												# Examples could have no label for mode = "test"
												labels.append("O")
						if words:
								examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
				return examples






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						0
				for line in test_input_reader:
						if line.startswith("-DOCSTART-") or line == "" or line == "\n":
								writer.write(UpperCAmelCase)
								if not preds_list[example_id]:
										example_id += 1
						elif preds_list[example_id]:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
								writer.write(UpperCAmelCase)
						else:
								logger.warning("Maximum sequence length exceeded: No prediction for '%s'."   ,  line.split()[0])







		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Any				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						["O"] + labels
						return labels
				else:
						return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Dict):
				# in CONLL2003 dataset chunk column is second-to-last
				super().__init__(label_idx=-2)







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Dict				     =						["O"] + labels
						return labels
				else:
						return [
						    "O",
						    "B-ADVP",
						    "B-INTJ",
						    "B-LST",
						    "B-PRT",
						    "B-NP",
						    "B-SBAR",
						    "B-VP",
						    "B-ADJP",
						    "B-CONJP",
						    "B-PP",
						    "I-ADVP",
						    "I-INTJ",
						    "I-LST",
						    "I-PRT",
						    "I-NP",
						    "I-SBAR",
						    "I-VP",
						    "I-ADJP",
						    "I-CONJP",
						    "I-PP",
						]




class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:List[str]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Dict				     =						1
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[]

				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						for sentence in parse_incr(UpperCAmelCase):
								SCREAMING_SNAKE_CASE_				:List[str]				     =						[]
								SCREAMING_SNAKE_CASE_				:int				     =						[]
								for token in sentence:
										words.append(token["form"])
										labels.append(token["upos"])
								assert len(UpperCAmelCase) == len(UpperCAmelCase)
								if words:
										examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
										guid_index += 1
				return examples






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						0
				for sentence in parse_incr(UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:str				     =						preds_list[example_id]
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						""
						for token in sentence:
								out += F"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
						out += "\n"
						writer.write(UpperCAmelCase)
						example_id += 1







		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								return f.read().splitlines()
				else:
						return [
						    "ADJ",
						    "ADP",
						    "ADV",
						    "AUX",
						    "CCONJ",
						    "DET",
						    "INTJ",
						    "NOUN",
						    "NUM",
						    "PART",
						    "PRON",
						    "PROPN",
						    "PUNCT",
						    "SCONJ",
						    "SYM",
						    "VERB",
						    "X",
						]






