

import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class 					__UpperCamelCase					:







	def __init__( self:			str	,			__UpperCamelCase:			Union[str, Any]	,			__UpperCamelCase:			str	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						__magic_name__				=     cuda
						__magic_name__				=     AutoConfig.from_pretrained(__UpperCamelCase   )
						__magic_name__				=     AutoModelForCTC.from_pretrained(__UpperCamelCase   )
						self.model.eval()
						if self.cuda:
											self.model.to(device='cuda'   )
						__magic_name__				=     AutoProcessor.from_pretrained(__UpperCamelCase   )
						__magic_name__				=     torchaudio.transforms.Resample(__UpperCamelCase	,			1_60_00   )
						__magic_name__				=     0
						__magic_name__				=     list(self.processor.tokenizer.get_vocab().keys()   )
						for i in range(len(__UpperCamelCase   )   ):
											if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																__magic_name__				=     i
						print('Blank Token id [PAD]/<pad>'	,			__UpperCamelCase   )
						__magic_name__				=     blank_id







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]	,			__UpperCamelCase:			Optional[Any]   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     torchaudio.load(__UpperCamelCase   )
						__magic_name__				=     self.resampler(__UpperCamelCase   ).squeeze().numpy()
						return speech







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						__magic_name__				=     self.blank_id
						__magic_name__				=     '|'.join(item['sent'].split(' '   )   )
						if not os.path.isfile(item['wav_path']   ):
											print(item['wav_path']	,			'not found in wavs directory'   )

						__magic_name__				=     self.speech_file_to_array_fn(item['wav_path']   )
						__magic_name__				=     self.processor(__UpperCamelCase	,			sampling_rate=1_60_00	,			return_tensors='pt'	,			padding=__UpperCamelCase   )
						if self.cuda:
											__magic_name__				=     inputs.to(device='cuda'   )

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

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

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

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

						def get_trellis(__UpperCamelCase:			Any	,			__UpperCamelCase:			Tuple	,			__UpperCamelCase:			Optional[int]=0   ):
											__magic_name__				=     emission.size(0   )
											__magic_name__				=     len(__UpperCamelCase   )

											# 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.
											__magic_name__				=     torch.full((num_frames + 1, num_tokens + 1)	,			-float('inf'   )   )
											__magic_name__				=     0
											for t in range(__UpperCamelCase   ):
																__magic_name__				=     torch.maximum(
																    # Score for staying at the same token
																    trellis[t, 1:] + emission[t, blank_id]	,			trellis[t, :-1] + emission[t, tokens]	,			)
											return trellis

						__magic_name__				=     get_trellis(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase   )

						@dataclass
						class 					__UpperCamelCase					:
							_lowercase	: int
							_lowercase	: int
							_lowercase	: float

						def backtrack(__UpperCamelCase:			int	,			__UpperCamelCase:			int	,			__UpperCamelCase:			Any	,			__UpperCamelCase:			Any=0   ):
											__magic_name__				=     trellis.size(1   ) - 1
											__magic_name__				=     torch.argmax(trellis[:, j]   ).item()

											__magic_name__				=     []
											for t in range(__UpperCamelCase	,			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.
																__magic_name__				=     trellis[t - 1, j] + emission[t - 1, blank_id]
																# Score for token changing from C-1 at T-1 to J at T.
																__magic_name__				=     trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																# 2. Store the path with frame-wise probability.
																__magic_name__				=     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	,			__UpperCamelCase   )   )

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

						__magic_name__				=     backtrack(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase   )

						@dataclass
						class 					__UpperCamelCase					:
							_lowercase	: str
							_lowercase	: int
							_lowercase	: int
							_lowercase	: float







							def __repr__( self:			Any   ):

												'''simple docstring'''





												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:			Any   ):

												'''simple docstring'''





												return self.end - self.start

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

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







	def 						_SCREAMING_SNAKE_CASE				( self:			str	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			Any	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						if not os.path.exists(__UpperCamelCase   ):
											os.makedirs(__UpperCamelCase   )

						# load text file
						__magic_name__				=     open(__UpperCamelCase	,			encoding='utf8'   ).readlines()

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

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

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

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




def        _lowercase  (					)					->						Any:




					'''simple docstring'''







					__magic_name__				=     argparse.ArgumentParser()

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

					__magic_name__				=     parser.parse_args()

					__magic_name__				=     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()







import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


A__							=				get_logger(__name__)




def        _lowercase  (					a_					:       int					)					->						Optional[int]:




					'''simple docstring'''







					return TestCommand(
					    args.dataset       ,args.name       ,args.cache_dir       ,args.data_dir       ,args.all_configs       ,args.save_info or args.save_infos       ,args.ignore_verifications       ,args.force_redownload       ,args.clear_cache       ,)


class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: List[Any]												=	False  # to tell pytest it's not a test class







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( __UpperCamelCase:			ArgumentParser   ):

						'''simple docstring'''





						__magic_name__				=     parser.add_parser('test'	,			help='Test dataset implementation.'   )
						test_parser.add_argument('--name'	,			type=__UpperCamelCase	,			default=__UpperCamelCase	,			help='Dataset processing name'   )
						test_parser.add_argument(
						    '--cache_dir'	,			type=__UpperCamelCase	,			default=__UpperCamelCase	,			help='Cache directory where the datasets are stored.'	,			)
						test_parser.add_argument(
						    '--data_dir'	,			type=__UpperCamelCase	,			default=__UpperCamelCase	,			help='Can be used to specify a manual directory to get the files from.'	,			)
						test_parser.add_argument('--all_configs'	,			action='store_true'	,			help='Test all dataset configurations'   )
						test_parser.add_argument(
						    '--save_info'	,			action='store_true'	,			help='Save the dataset infos in the dataset card (README.md)'   )
						test_parser.add_argument(
						    '--ignore_verifications'	,			action='store_true'	,			help='Run the test without checksums and splits checks.'	,			)
						test_parser.add_argument('--force_redownload'	,			action='store_true'	,			help='Force dataset redownload'   )
						test_parser.add_argument(
						    '--clear_cache'	,			action='store_true'	,			help='Remove downloaded files and cached datasets after each config test'	,			)
						# aliases
						test_parser.add_argument('--save_infos'	,			action='store_true'	,			help='alias to save_info'   )
						test_parser.add_argument('dataset'	,			type=__UpperCamelCase	,			help='Name of the dataset to download'   )
						test_parser.set_defaults(func=_test_command_factory   )







	def __init__( self:			List[str]	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			):

						'''simple docstring'''





						__magic_name__				=     dataset
						__magic_name__				=     name
						__magic_name__				=     cache_dir
						__magic_name__				=     data_dir
						__magic_name__				=     all_configs
						__magic_name__				=     save_infos
						__magic_name__				=     ignore_verifications
						__magic_name__				=     force_redownload
						__magic_name__				=     clear_cache
						if clear_cache and not cache_dir:
											print(
											    'When --clear_cache is used, specifying a cache directory is mandatory.\n'
											    'The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'
											    'Please provide a --cache_dir that will be used to test the dataset script.'   )
											exit(1   )
						if save_infos:
											__magic_name__				=     True







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						fl_logger().setLevel(__UpperCamelCase   )
						if self._name is not None and self._all_configs:
											print('Both parameters `config` and `all_configs` can\'t be used at once.'   )
											exit(1   )
						__magic_name__,						__magic_name__				=     self._dataset, self._name
						__magic_name__				=     dataset_module_factory(__UpperCamelCase   )
						__magic_name__				=     import_main_class(module.module_path   )
						__magic_name__				=     len(builder_cls.BUILDER_CONFIGS   ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

						def get_builders() -> Generator[DatasetBuilder, None, None]:
											if self._all_configs and builder_cls.BUILDER_CONFIGS:
																for i, config in enumerate(builder_cls.BUILDER_CONFIGS   ):
																					if "config_name" in module.builder_kwargs:
																										yield builder_cls(
																										    cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs	,			)
																					else:
																										yield builder_cls(
																										    config_name=config.name	,			cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs	,			)
											else:
																if "config_name" in module.builder_kwargs:
																					yield builder_cls(cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs   )
																else:
																					yield builder_cls(
																					    config_name=__UpperCamelCase	,			cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs	,			)

						for j, builder in enumerate(get_builders()   ):
											print(F'Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'   )
											__magic_name__				=     os.path.exists(
											    os.path.join(builder.get_imported_module_dir()	,			datasets.config.DATASETDICT_INFOS_FILENAME   )   )  # record checksums only if we need to update a (deprecated) dataset_infos.json
											builder.download_and_prepare(
											    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
											    if not self._force_redownload
											    else DownloadMode.FORCE_REDOWNLOAD	,			verification_mode=VerificationMode.NO_CHECKS
											    if self._ignore_verifications
											    else VerificationMode.ALL_CHECKS	,			try_from_hf_gcs=__UpperCamelCase	,			)
											builder.as_dataset()
											if self._save_infos:
																builder._save_infos()

											# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
											# The dataset_infos are saved in the YAML part of the README.md

											# Let's move it to the original directory of the dataset script, to allow the user to
											# upload them on S3 at the same time afterwards.
											if self._save_infos:
																__magic_name__				=     os.path.join(builder_cls.get_imported_module_dir()	,			'README.md'   )
																__magic_name__				=     Path(__UpperCamelCase   ).name + '.py'
																__magic_name__				=     os.path.join(__UpperCamelCase	,			__UpperCamelCase   )
																if os.path.isfile(__UpperCamelCase   ):
																					__magic_name__				=     os.path.dirname(__UpperCamelCase   )
																elif os.path.isfile(__UpperCamelCase   ):
																					__magic_name__				=     path
																elif os.path.isdir(__UpperCamelCase   ):  # for local directories containing only data files
																					__magic_name__				=     path
																else:  # in case of a remote dataset
																					__magic_name__				=     None
																					print(F'Dataset card saved at {dataset_readme_path}'   )

																# Move dataset_info back to the user
																if dataset_dir is not None:
																					__magic_name__				=     os.path.join(__UpperCamelCase	,			'README.md'   )
																					copyfile(__UpperCamelCase	,			__UpperCamelCase   )
																					print(F'Dataset card saved at {user_dataset_readme_path}'   )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
											if self._clear_cache:
																if os.path.isdir(builder._cache_dir   ):
																					logger.warning(F'Clearing cache at {builder._cache_dir}'   )
																					rmtree(builder._cache_dir   )
																__magic_name__				=     os.path.join(self._cache_dir	,			datasets.config.DOWNLOADED_DATASETS_DIR   )
																if os.path.isdir(__UpperCamelCase   ):
																					logger.warning(F'Clearing cache at {download_dir}'   )
																					rmtree(__UpperCamelCase   )

						print('Test successful.'   )




