




"""simple docstring"""





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				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ):
										snake_case__    :   List[str]							     =   cuda
										snake_case__    :   Union[str, Any]							     =   AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
										snake_case__    :   Tuple							     =   AutoModelForCTC.from_pretrained(SCREAMING_SNAKE_CASE_ )
										self.model.eval()
										if self.cuda:
																self.model.to(device='''cuda''' )
										snake_case__    :   int							     =   AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE_ )
										snake_case__    :   Any							     =   torchaudio.transforms.Resample(SCREAMING_SNAKE_CASE_ ,1_6_0_0_0 )
										snake_case__    :   Optional[int]							     =   0
										snake_case__    :   List[Any]							     =   list(self.processor.tokenizer.get_vocab().keys() )
										for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
																if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																						snake_case__    :   Tuple							     =   i
										print('''Blank Token id [PAD]/<pad>''' ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   Optional[Any]							     =   blank_id

				def  _lowerCamelCase   (				self				:       int ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ):
										snake_case__    :   Optional[Any]							     =   torchaudio.load(SCREAMING_SNAKE_CASE_ )
										snake_case__    :   str							     =   self.resampler(SCREAMING_SNAKE_CASE_ ).squeeze().numpy()
										return speech

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ):
										snake_case__    :   Optional[int]							     =   self.blank_id
										snake_case__    :   List[Any]							     =   '''|'''.join(item['''sent'''].split(''' ''' ) )
										if not os.path.isfile(item['''wav_path'''] ):
																print(item['''wav_path'''] ,'''not found in wavs directory''' )

										snake_case__    :   Dict							     =   self.speech_file_to_array_fn(item['''wav_path'''] )
										snake_case__    :   str							     =   self.processor(SCREAMING_SNAKE_CASE_ ,sampling_rate=1_6_0_0_0 ,return_tensors='''pt''' ,padding=SCREAMING_SNAKE_CASE_ )
										if self.cuda:
																snake_case__    :   Optional[int]							     =   inputs.to(device='''cuda''' )

										with torch.no_grad():
																snake_case__    :   Union[str, Any]							     =   self.model(inputs.input_values ).logits

										# get the emission probability at frame level
										snake_case__    :   int							     =   torch.log_softmax(SCREAMING_SNAKE_CASE_ ,dim=-1 )
										snake_case__    :   Optional[Any]							     =   emissions[0].cpu().detach()

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

										snake_case__    :   str							     =   {c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_ )}
										snake_case__    :   Dict							     =   []
										for c in transcript:
																if c in dictionary:
																						tokens.append(dictionary[c] )

										def get_trellis(SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Any=0 ):
																snake_case__    :   List[str]							     =   emission.size(0 )
																snake_case__    :   List[str]							     =   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.
																snake_case__    :   int							     =   torch.full((num_frames + 1, num_tokens + 1) ,-float('''inf''' ) )
																snake_case__    :   List[str]							     =   0
																for t in range(SCREAMING_SNAKE_CASE_ ):
																						snake_case__    :   int							     =   torch.maximum(
																						    # Score for staying at the same token
																						    trellis[t, 1:] + emission[t, blank_id] ,trellis[t, :-1] + emission[t, tokens] ,)
																return trellis

										snake_case__    :   Tuple							     =   get_trellis(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

										@dataclass
										class  __lowercase    :


														'''simple docstring'''


														_lowerCAmelCase		     =42
														_lowerCAmelCase		     =42
														_lowerCAmelCase		     =42

										def backtrack(SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       int=0 ):
																snake_case__    :   Optional[Any]							     =   trellis.size(1 ) - 1
																snake_case__    :   str							     =   torch.argmax(trellis[:, j] ).item()

																snake_case__    :   Optional[int]							     =   []
																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.
																						snake_case__    :   str							     =   trellis[t - 1, j] + emission[t - 1, blank_id]
																						# Score for token changing from C-1 at T-1 to J at T.
																						snake_case__    :   int							     =   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																						# 2. Store the path with frame-wise probability.
																						snake_case__    :   str							     =   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]

										snake_case__    :   Dict							     =   backtrack(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

										@dataclass
										class  __lowercase    :


														'''simple docstring'''


														_lowerCAmelCase		     =42
														_lowerCAmelCase		     =42
														_lowerCAmelCase		     =42
														_lowerCAmelCase		     =42

														def __repr__(				self				:       Optional[Any] ):
																				return F'''{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}'''





														@property
														def  _lowerCamelCase   (				self				:       List[str] ):
																				return self.end - self.start

										def merge_repeats(SCREAMING_SNAKE_CASE_				:       List[str] ):
																snake_case__    :   Tuple							     =   0, 0
																snake_case__    :   Union[str, Any]							     =   []
																while ia < len(SCREAMING_SNAKE_CASE_ ):
																						while ia < len(SCREAMING_SNAKE_CASE_ ) and path[ia].token_index == path[ia].token_index:
																												ia += 1
																						snake_case__    :   Dict							     =   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_ ,) )
																						snake_case__    :   int							     =   ia
																return segments

										snake_case__    :   List[Any]							     =   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  _lowerCamelCase   (				self				:       List[str] ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       Tuple ):
										if not os.path.exists(SCREAMING_SNAKE_CASE_ ):
																os.makedirs(SCREAMING_SNAKE_CASE_ )

										# load text file
										snake_case__    :   Optional[int]							     =   open(SCREAMING_SNAKE_CASE_ ,encoding='''utf8''' ).readlines()

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

																snake_case__    :   Any							     =   line.strip().split('''\t''' )
																snake_case__    :   int							     =   os.path.join(SCREAMING_SNAKE_CASE_ ,wav_name + '''.wav''' )
																snake_case__    :   Union[str, Any]							     =   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 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   str							     =   argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_name''' ,     type=lowerCamelCase ,     default='''arijitx/wav2vec2-xls-r-300m-bengali''' ,     help='''wav2vec model name'''						)
						parser.add_argument('''--wav_dir''' ,     type=lowerCamelCase ,     default='''./wavs''' ,     help='''directory containing wavs'''						)
						parser.add_argument('''--text_file''' ,     type=lowerCamelCase ,     default='''script.txt''' ,     help='''file containing text'''						)
						parser.add_argument('''--input_wavs_sr''' ,     type=lowerCamelCase ,     default=16000 ,     help='''sampling rate of input audios'''						)
						parser.add_argument(
						    '''--output_dir''' ,     type=lowerCamelCase ,     default='''./out_alignment''' ,     help='''output directory containing the alignment files'''						)
						parser.add_argument('''--cuda''' ,     action='''store_true'''						)

						snake_case__    :   Union[str, Any]							     =   parser.parse_args()

						snake_case__    :   Union[str, Any]							     =   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()





"""simple docstring"""





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						if "model" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''model.''' ,     ''''''						)
						if "norm1" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''norm1''' ,     '''attention.output.LayerNorm'''						)
						if "norm2" in orig_key:
												snake_case__    :   Dict							     =   orig_key.replace('''norm2''' ,     '''output.LayerNorm'''						)
						if "norm" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''norm''' ,     '''LayerNorm'''						)
						if "transformer" in orig_key:
												snake_case__    :   Optional[Any]							     =   orig_key.split('''.'''						)[0].split('''_'''						)[-1]
												snake_case__    :   Optional[Any]							     =   orig_key.replace(f'''transformer_{layer_num}''' ,     f'''encoder.layer.{layer_num}'''						)
						if "mha.attn" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''mha.attn''' ,     '''attention.self'''						)
						if "mha" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''mha''' ,     '''attention'''						)
						if "W_q" in orig_key:
												snake_case__    :   Optional[int]							     =   orig_key.replace('''W_q''' ,     '''self.query'''						)
						if "W_k" in orig_key:
												snake_case__    :   Tuple							     =   orig_key.replace('''W_k''' ,     '''self.key'''						)
						if "W_v" in orig_key:
												snake_case__    :   Dict							     =   orig_key.replace('''W_v''' ,     '''self.value'''						)
						if "ff.0" in orig_key:
												snake_case__    :   str							     =   orig_key.replace('''ff.0''' ,     '''intermediate.dense'''						)
						if "ff.2" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''ff.2''' ,     '''output.dense'''						)
						if "ff" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''ff''' ,     '''output.dense'''						)
						if "mlm_class" in orig_key:
												snake_case__    :   str							     =   orig_key.replace('''mlm.mlm_class''' ,     '''cls.predictions.decoder'''						)
						if "mlm" in orig_key:
												snake_case__    :   List[Any]							     =   orig_key.replace('''mlm''' ,     '''cls.predictions.transform'''						)
						if "backbone.backbone.encoders" in orig_key:
												snake_case__    :   Optional[Any]							     =   orig_key.replace('''backbone.backbone.encoders''' ,     '''encoder.layer'''						)
						if "cls" not in orig_key:
												snake_case__    :   Union[str, Any]							     =   '''mra.''' + orig_key

						return orig_key

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case__    :   int							     =   orig_state_dict.pop(lowerCamelCase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		snake_case__    :   Optional[int]							     =   val

						snake_case__    :   Union[str, Any]							     =   orig_state_dict['''cls.predictions.decoder.bias''']
						snake_case__    :   Any							     =   torch.arange(lowerCamelCase						).expand((1, -1)						) + 2

						return orig_state_dict

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   List[Any]							     =   torch.load(lowerCamelCase ,     map_location='''cpu'''						)['''model_state_dict''']
						snake_case__    :   Optional[Any]							     =   MraConfig.from_json_file(lowerCamelCase						)
						snake_case__    :   List[Any]							     =   MraForMaskedLM(lowerCamelCase						)

						snake_case__    :   List[Any]							     =   convert_checkpoint_helper(config.max_position_embeddings ,     lowerCamelCase						)

						print(model.load_state_dict(lowerCamelCase						)						)
						model.eval()
						model.save_pretrained(lowerCamelCase						)

						print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''						)


if __name__ == "__main__":
							_lowercase		 =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The json file for Mra model config.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							_lowercase		 =      parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


