import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


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




@dataclass
class        lowercase_  :



	"""simple docstring"""





	UpperCAmelCase_  : str
	UpperCAmelCase_  : str
	UpperCAmelCase_  : Optional[str]       =    None
	UpperCAmelCase_  : Optional[str]       =    None


	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								return json.dumps(dataclasses.asdict(self    )		,      indent=2    ) + "\n"




@dataclass(frozen=lowerCamelCase__						)
class        lowercase_  :



	"""simple docstring"""





	UpperCAmelCase_  : List[int]
	UpperCAmelCase_  : Optional[List[int]]       =    None
	UpperCAmelCase_  : Optional[List[int]]       =    None
	UpperCAmelCase_  : Optional[Union[int, float]]       =    None


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								return json.dumps(dataclasses.asdict(self    )    ) + "\n"




class        lowercase_  :



	"""simple docstring"""





	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->str:
								raise NotImplementedError()


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[Any]:
								raise NotImplementedError()


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->List[Any]:
								raise NotImplementedError()


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Any:
								raise NotImplementedError()


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Any:
								raise NotImplementedError()


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Dict:






								if len(self.get_labels()    ) > 1:
															lowerCAmelCase     		=		self.get_labels()[int(example.label    )]
								return example




	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=None    )						->Union[str, Any]:






								with open(lowercase_		,      '''r'''		,      encoding='''utf-8-sig'''    ) as f:
															return list(csv.reader(lowercase_		,      delimiter='''\t'''		,      quotechar=lowercase_    )    )




class        lowercase_  (  lowerCamelCase__						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE="classification"		,      __SCREAMING_SNAKE_CASE=False    )						->Tuple:
								lowerCAmelCase     		=		[] if labels is None else labels
								lowerCAmelCase     		=		[] if examples is None else examples
								lowerCAmelCase     		=		mode
								lowerCAmelCase     		=		verbose


	def __len__(							self    )						->List[str]:
								return len(self.examples    )


	def __getitem__(							self		,      __SCREAMING_SNAKE_CASE    )						->Tuple:






								if isinstance(lowercase_		,      lowercase_    ):
															return SingleSentenceClassificationProcessor(labels=self.labels		,      examples=self.examples[idx]    )
								return self.examples[idx]


	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=""		,      __SCREAMING_SNAKE_CASE=0		,      __SCREAMING_SNAKE_CASE=1		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=False		,      **__SCREAMING_SNAKE_CASE    )						->List[Any]:
								lowerCAmelCase     		=		cls(**lowercase_    )
								processor.add_examples_from_csv(
								    lowercase_		,      split_name=lowercase_		,      column_label=lowercase_		,      column_text=lowercase_		,      column_id=lowercase_		,      skip_first_row=lowercase_		,      overwrite_labels=lowercase_		,      overwrite_examples=lowercase_		,      )
								return processor


	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=None		,      **__SCREAMING_SNAKE_CASE    )						->int:
								lowerCAmelCase     		=		cls(**lowercase_    )
								processor.add_examples(lowercase_		,      labels=lowercase_    )
								return processor


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=""		,      __SCREAMING_SNAKE_CASE=0		,      __SCREAMING_SNAKE_CASE=1		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      )						->str:
								lowerCAmelCase     		=		self._read_tsv(lowercase_    )
								if skip_first_row:
															lowerCAmelCase     		=		lines[1:]
								lowerCAmelCase     		=		[]
								lowerCAmelCase     		=		[]
								lowerCAmelCase     		=		[]
								for i, line in enumerate(lowercase_    ):
															texts.append(line[column_text]    )
															labels.append(line[column_label]    )
															if column_id is not None:
																						ids.append(line[column_id]    )
															else:
																						lowerCAmelCase     		=		F"{split_name}-{i}" if split_name else str(lowercase_    )
																						ids.append(lowercase_    )

								return self.add_examples(
								    lowercase_		,      lowercase_		,      lowercase_		,      overwrite_labels=lowercase_		,      overwrite_examples=lowercase_    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False    )						->Dict:






								if labels is not None and len(lowercase_    ) != len(lowercase_    ):
															raise ValueError(
															    F"Text and labels have mismatched lengths {len(lowercase_    )} and {len(lowercase_    )}"    )
								if ids is not None and len(lowercase_    ) != len(lowercase_    ):
															raise ValueError(F"Text and ids have mismatched lengths {len(lowercase_    )} and {len(lowercase_    )}"    )
								if ids is None:
															lowerCAmelCase     		=		[None] * len(lowercase_    )
								if labels is None:
															lowerCAmelCase     		=		[None] * len(lowercase_    )
								lowerCAmelCase     		=		[]
								lowerCAmelCase     		=		set()
								for text_or_text_and_label, label, guid in zip(lowercase_		,      lowercase_		,      lowercase_    ):
															if isinstance(lowercase_		,      (tuple, list)    ) and label is None:
																						lowerCAmelCase		,				lowerCAmelCase     		=		text_or_text_and_label
															else:
																						lowerCAmelCase     		=		text_or_text_and_label
															added_labels.add(lowercase_    )
															examples.append(InputExample(guid=lowercase_		,      text_a=lowercase_		,      text_b=lowercase_		,      label=lowercase_    )    )

								# Update examples
								if overwrite_examples:
															lowerCAmelCase     		=		examples
								else:
															self.examples.extend(lowercase_    )

								# Update labels
								if overwrite_labels:
															lowerCAmelCase     		=		list(lowercase_    )
								else:
															lowerCAmelCase     		=		list(set(self.labels    ).union(lowercase_    )    )

								return self.examples




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=0		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=None		,      )						->Optional[int]:






								if max_length is None:
															lowerCAmelCase     		=		tokenizer.max_len

								lowerCAmelCase     		=		{label: i for i, label in enumerate(self.labels    )}

								lowerCAmelCase     		=		[]
								for ex_index, example in enumerate(self.examples    ):
															if ex_index % 10000 == 0:
																						logger.info(F"Tokenizing example {ex_index}"    )

															lowerCAmelCase     		=		tokenizer.encode(
															    example.text_a		,      add_special_tokens=lowercase_		,      max_length=min(lowercase_		,      tokenizer.max_len    )		,      )
															all_input_ids.append(lowercase_    )

								lowerCAmelCase     		=		max(len(lowercase_    ) for input_ids in all_input_ids    )

								lowerCAmelCase     		=		[]
								for ex_index, (input_ids, example) in enumerate(zip(lowercase_		,      self.examples    )    ):
															if ex_index % 10000 == 0:
																						logger.info(F"Writing example {ex_index}/{len(self.examples    )}"    )
															# The mask has 1 for real tokens and 0 for padding tokens. Only real
															# tokens are attended to.
															lowerCAmelCase     		=		[1 if mask_padding_with_zero else 0] * len(lowercase_    )

															# Zero-pad up to the sequence length.
															lowerCAmelCase     		=		batch_length - len(lowercase_    )
															if pad_on_left:
																						lowerCAmelCase     		=		([pad_token] * padding_length) + input_ids
																						lowerCAmelCase     		=		([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
															else:
																						lowerCAmelCase     		=		input_ids + ([pad_token] * padding_length)
																						lowerCAmelCase     		=		attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

															if len(lowercase_    ) != batch_length:
																						raise ValueError(F"Error with input length {len(lowercase_    )} vs {batch_length}"    )
															if len(lowercase_    ) != batch_length:
																						raise ValueError(F"Error with input length {len(lowercase_    )} vs {batch_length}"    )

															if self.mode == "classification":
																						lowerCAmelCase     		=		label_map[example.label]
															elif self.mode == "regression":
																						lowerCAmelCase     		=		float(example.label    )
															else:
																						raise ValueError(self.mode    )

															if ex_index < 5 and self.verbose:
																						logger.info('''*** Example ***'''    )
																						logger.info(F"guid: {example.guid}"    )
																						logger.info(F"input_ids: {' '.join([str(lowercase_    ) for x in input_ids]    )}"    )
																						logger.info(F"attention_mask: {' '.join([str(lowercase_    ) for x in attention_mask]    )}"    )
																						logger.info(F"label: {example.label} (id = {label})"    )

															features.append(InputFeatures(input_ids=lowercase_		,      attention_mask=lowercase_		,      label=lowercase_    )    )

								if return_tensors is None:
															return features
								elif return_tensors == "tf":
															if not is_tf_available():
																						raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''    )
															import tensorflow as tf

															def gen():
																						for ex in features:
																													yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

															lowerCAmelCase     		=		tf.data.Dataset.from_generator(
															    lowercase_		,      ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)		,      ({'''input_ids''': tf.TensorShape([None]    ), '''attention_mask''': tf.TensorShape([None]    )}, tf.TensorShape([]    ))		,      )
															return dataset
								elif return_tensors == "pt":
															if not is_torch_available():
																						raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''    )
															import torch
															from torch.utils.data import TensorDataset

															lowerCAmelCase     		=		torch.tensor([f.input_ids for f in features]		,      dtype=torch.long    )
															lowerCAmelCase     		=		torch.tensor([f.attention_mask for f in features]		,      dtype=torch.long    )
															if self.mode == "classification":
																						lowerCAmelCase     		=		torch.tensor([f.label for f in features]		,      dtype=torch.long    )
															elif self.mode == "regression":
																						lowerCAmelCase     		=		torch.tensor([f.label for f in features]		,      dtype=torch.float    )

															lowerCAmelCase     		=		TensorDataset(lowercase_		,      lowercase_		,      lowercase_    )
															return dataset
								else:
															raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''    )




import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Any:
								lowerCAmelCase     		=		cuda
								lowerCAmelCase     		=		AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		AutoModelForCTC.from_pretrained(__SCREAMING_SNAKE_CASE    )
								self.model.eval()
								if self.cuda:
															self.model.to(device='''cuda'''    )
								lowerCAmelCase     		=		AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		torchaudio.transforms.Resample(__SCREAMING_SNAKE_CASE		,      16000    )
								lowerCAmelCase     		=		0
								lowerCAmelCase     		=		list(self.processor.tokenizer.get_vocab().keys()    )
								for i in range(len(__SCREAMING_SNAKE_CASE    )    ):
															if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																						lowerCAmelCase     		=		i
								print('''Blank Token id [PAD]/<pad>'''		,      __SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		blank_id


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								lowerCAmelCase		,				lowerCAmelCase     		=		torchaudio.load(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.resampler(__SCREAMING_SNAKE_CASE    ).squeeze().numpy()
								return speech


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->str:
								lowerCAmelCase     		=		self.blank_id
								lowerCAmelCase     		=		'''|'''.join(item['''sent'''].split(''' '''    )    )
								if not os.path.isfile(item['''wav_path''']    ):
															print(item['''wav_path''']		,      '''not found in wavs directory'''    )

								lowerCAmelCase     		=		self.speech_file_to_array_fn(item['''wav_path''']    )
								lowerCAmelCase     		=		self.processor(__SCREAMING_SNAKE_CASE		,      sampling_rate=16000		,      return_tensors='''pt'''		,      padding=__SCREAMING_SNAKE_CASE    )
								if self.cuda:
															lowerCAmelCase     		=		inputs.to(device='''cuda'''    )

								with torch.no_grad():
															lowerCAmelCase     		=		self.model(inputs.input_values    ).logits

								# get the emission probability at frame level
								lowerCAmelCase     		=		torch.log_softmax(__SCREAMING_SNAKE_CASE		,      dim=-1    )
								lowerCAmelCase     		=		emissions[0].cpu().detach()

								# get labels from vocab
								lowerCAmelCase     		=		([''''''] + list(self.processor.tokenizer.get_vocab().keys()    ))[
								    :-1
								]  # logits don't align with the tokenizer's vocab

								lowerCAmelCase     		=		{c: i for i, c in enumerate(__SCREAMING_SNAKE_CASE    )}
								lowerCAmelCase     		=		[]
								for c in transcript:
															if c in dictionary:
																						tokens.append(dictionary[c]    )

								def get_trellis(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=0    ):
															lowerCAmelCase     		=		emission.size(0    )
															lowerCAmelCase     		=		len(__SCREAMING_SNAKE_CASE    )

															# Trellis has extra diemsions for both time axis and tokens.
															# The extra dim for tokens represents <SoS> (start-of-sentence)
															# The extra dim for time axis is for simplification of the code.
															lowerCAmelCase     		=		torch.full((num_frames + 1, num_tokens + 1)		,      -float('''inf'''    )    )
															lowerCAmelCase     		=		0
															for t in range(__SCREAMING_SNAKE_CASE    ):
																						lowerCAmelCase     		=		torch.maximum(
																						    # Score for staying at the same token
																						    trellis[t, 1:] + emission[t, blank_id]		,      trellis[t, :-1] + emission[t, tokens]		,      )
															return trellis

								lowerCAmelCase     		=		get_trellis(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




								@dataclass
								class        lowercase_  :



									"""simple docstring"""





									UpperCAmelCase_  : int
									UpperCAmelCase_  : int
									UpperCAmelCase_  : float

								def backtrack(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=0    ):
															lowerCAmelCase     		=		trellis.size(1    ) - 1
															lowerCAmelCase     		=		torch.argmax(trellis[:, j]    ).item()

															lowerCAmelCase     		=		[]
															for t in range(__SCREAMING_SNAKE_CASE		,      0		,      -1    ):
																						# 1. Figure out if the current position was stay or change
																						# Note (again):
																						# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
																						# Score for token staying the same from time frame J-1 to T.
																						lowerCAmelCase     		=		trellis[t - 1, j] + emission[t - 1, blank_id]
																						# Score for token changing from C-1 at T-1 to J at T.
																						lowerCAmelCase     		=		trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																						# 2. Store the path with frame-wise probability.
																						lowerCAmelCase     		=		emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
																						# Return token index and time index in non-trellis coordinate.
																						path.append(Point(j - 1		,      t - 1		,      __SCREAMING_SNAKE_CASE    )    )

																						# 3. Update the token
																						if changed > stayed:
																													j -= 1
																													if j == 0:
																																				break
															else:
																						raise ValueError('''Failed to align'''    )
															return path[::-1]

								lowerCAmelCase     		=		backtrack(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




								@dataclass
								class        lowercase_  :



									"""simple docstring"""





									UpperCAmelCase_  : str
									UpperCAmelCase_  : int
									UpperCAmelCase_  : int
									UpperCAmelCase_  : float


									def __repr__(							self    )						->int:
																return F"{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"




									@property
									def       SCREAMING_SNAKE_CASE_    (							self    )						->str:
																return self.end - self.start

								def merge_repeats(__SCREAMING_SNAKE_CASE    ):
															lowerCAmelCase		,				lowerCAmelCase     		=		0, 0
															lowerCAmelCase     		=		[]
															while ia < len(__SCREAMING_SNAKE_CASE    ):
																						while ia < len(__SCREAMING_SNAKE_CASE    ) and path[ia].token_index == path[ia].token_index:
																													ia += 1
																						lowerCAmelCase     		=		sum(path[k].score for k in range(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )    ) / (ia - ia)
																						segments.append(
																						    Segment(
																						        transcript[path[ia].token_index]		,      path[ia].time_index		,      path[ia - 1].time_index + 1		,      __SCREAMING_SNAKE_CASE		,      )    )
																						lowerCAmelCase     		=		ia
															return segments

								lowerCAmelCase     		=		merge_repeats(__SCREAMING_SNAKE_CASE    )
								with open(item['''out_path''']		,      '''w'''    ) as out_align:
															for seg in segments:
																						out_align.write(str(__SCREAMING_SNAKE_CASE    ) + '''\n'''    )




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								if not os.path.exists(__SCREAMING_SNAKE_CASE    ):
															os.makedirs(__SCREAMING_SNAKE_CASE    )

								# load text file
								lowerCAmelCase     		=		open(__SCREAMING_SNAKE_CASE		,      encoding='''utf8'''    ).readlines()

								lowerCAmelCase     		=		[]
								for line in lines:
															if len(line.strip().split('''\t'''    )    ) != 2:
																						print('''Script must be in format: 00001  this is my sentence'''    )
																						exit()

															lowerCAmelCase		,				lowerCAmelCase     		=		line.strip().split('''\t'''    )
															lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      wav_name + '''.wav'''    )
															lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      wav_name + '''.txt'''    )

															items.append({'''sent''': sentence, '''wav_path''': wav_path, '''out_path''': out_path}    )
								print('''Number of samples found in script file'''		,      len(__SCREAMING_SNAKE_CASE    )    )

								for item in tqdm(__SCREAMING_SNAKE_CASE    ):
															self.align_single_sample(__SCREAMING_SNAKE_CASE    )

def        SCREAMING_SNAKE_CASE_					(						)					->     Optional[Any]:
							lowerCAmelCase     		=		argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name'''       ,  type=snake_case__       ,  default='''arijitx/wav2vec2-xls-r-300m-bengali'''       ,  help='''wav2vec model name'''							)
							parser.add_argument('''--wav_dir'''       ,  type=snake_case__       ,  default='''./wavs'''       ,  help='''directory containing wavs'''							)
							parser.add_argument('''--text_file'''       ,  type=snake_case__       ,  default='''script.txt'''       ,  help='''file containing text'''							)
							parser.add_argument('''--input_wavs_sr'''       ,  type=snake_case__       ,  default=1_6_0_0_0       ,  help='''sampling rate of input audios'''							)
							parser.add_argument(
							    '''--output_dir'''       ,  type=snake_case__       ,  default='''./out_alignment'''       ,  help='''output directory containing the alignment files'''							)
							parser.add_argument('''--cuda'''       ,  action='''store_true'''							)

							lowerCAmelCase     		=		parser.parse_args()

							lowerCAmelCase     		=		WavaVecaAligner(args.model_name       ,  args.input_wavs_sr       ,  args.cuda							)
							aligner.align_data(args.wav_dir       ,  args.text_file       ,  args.output_dir							)


if __name__ == "__main__":
	main()



