
'''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     a :




				def __init__(				self						:					int,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										snake_case							:  Optional[int]			=     cuda
										snake_case							:  List[Any]			=     AutoConfig.from_pretrained(lowercase__   )
										snake_case							:  Union[str, Any]			=     AutoModelForCTC.from_pretrained(lowercase__   )
										self.model.eval()
										if self.cuda:
																self.model.to(device='''cuda'''   )
										snake_case							:  Tuple			=     AutoProcessor.from_pretrained(lowercase__   )
										snake_case							:  int			=     torchaudio.transforms.Resample(lowercase__,     1_60_00   )
										snake_case							:  Optional[Any]			=     0
										snake_case							:  int			=     list(self.processor.tokenizer.get_vocab().keys()   )
										for i in range(len(lowercase__   )   ):
																if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																						snake_case							:  List[str]			=     i
										print('''Blank Token id [PAD]/<pad>''',     lowercase__   )
										snake_case							:  List[Any]			=     blank_id




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					str   ):
										snake_case,       snake_case							:  int			=     torchaudio.load(lowercase__   )
										snake_case							:  str			=     self.resampler(lowercase__   ).squeeze().numpy()
										return speech




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

										snake_case							:  str			=     self.speech_file_to_array_fn(item['''wav_path''']   )
										snake_case							:  Optional[int]			=     self.processor(lowercase__,     sampling_rate=1_60_00,     return_tensors='''pt''',     padding=lowercase__   )
										if self.cuda:
																snake_case							:  Union[str, Any]			=     inputs.to(device='''cuda'''   )

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

										# get the emission probability at frame level
										snake_case							:  List[str]			=     torch.log_softmax(lowercase__,     dim=-1   )
										snake_case							:  List[str]			=     emissions[0].cpu().detach()

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

										snake_case							:  Union[str, Any]			=     {c: i for i, c in enumerate(lowercase__   )}
										snake_case							:  List[str]			=     []
										for c in transcript:
																if c in dictionary:
																						tokens.append(dictionary[c]   )

										def get_trellis(SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					List[str]=0   ):
																snake_case							:  Union[str, Any]			=     emission.size(0   )
																snake_case							:  List[Any]			=     len(lowercase__   )

																# 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							:  Union[str, Any]			=     torch.full((num_frames + 1, num_tokens + 1),     -float('''inf'''   )   )
																snake_case							:  Dict			=     0
																for t in range(lowercase__   ):
																						snake_case							:  Union[str, Any]			=     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							:  Optional[Any]			=     get_trellis(lowercase__,     lowercase__,     lowercase__   )

										@dataclass
										class     a :
														_snake_case		     =			42
														_snake_case		     =			42
														_snake_case		     =			42

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

																snake_case							:  Dict			=     []
																for t in range(lowercase__,     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							:  Optional[int]			=     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							:  Optional[int]			=     trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																						# 2. Store the path with frame-wise probability.
																						snake_case							:  Union[str, Any]			=     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,     lowercase__   )   )

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

										snake_case							:  Union[str, Any]			=     backtrack(lowercase__,     lowercase__,     lowercase__,     lowercase__   )

										@dataclass
										class     a :
														_snake_case		     =			42
														_snake_case		     =			42
														_snake_case		     =			42
														_snake_case		     =			42




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




														@property
														def 						__snake_case				(				self						:					int   ):
																				return self.end - self.start

										def merge_repeats(SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
																snake_case,       snake_case							:  List[str]			=     0, 0
																snake_case							:  Tuple			=     []
																while ia < len(lowercase__   ):
																						while ia < len(lowercase__   ) and path[ia].token_index == path[ia].token_index:
																												ia += 1
																						snake_case							:  int			=     sum(path[k].score for k in range(lowercase__,     lowercase__   )   ) / (ia - ia)
																						segments.append(
																						    Segment(
																						        transcript[path[ia].token_index],     path[ia].time_index,     path[ia - 1].time_index + 1,     lowercase__,     )   )
																						snake_case							:  Any			=     ia
																return segments

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




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					int   ):







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

										# load text file
										snake_case							:  List[str]			=     open(lowercase__,     encoding='''utf8'''   ).readlines()

										snake_case							:  Dict			=     []
										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,       snake_case							:  List[str]			=     line.strip().split('''\t'''   )
																snake_case							:  Any			=     os.path.join(lowercase__,     wav_name + '''.wav'''   )
																snake_case							:  Optional[Any]			=     os.path.join(lowercase__,     wav_name + '''.txt'''   )

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

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




def   A			(							):
						snake_case							:  List[str]			=     argparse.ArgumentParser()

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

						snake_case							:  Any			=     parser.parse_args()

						snake_case							:  Dict			=     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"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case					  =						logging.get_logger(__name__)

snake_case					  =						{
    # pcqm4mv1 now deprecated
    'graphormer-base': 'https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							"graphormer"
				UpperCAmelCase_							: Optional[int]		    =							["past_key_values"]


				def __init__(							self						,      lowercase__ = 1						,      lowercase__ = 512 * 9						,      lowercase__ = 512 * 3						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 128						,      lowercase__ = 5						,      lowercase__ = 1024						,      lowercase__ = "multi_hop"						,      lowercase__ = 512						,      lowercase__ = False						,      lowercase__ = 12						,      lowercase__ = 768						,      lowercase__ = 768						,      lowercase__ = 32						,      lowercase__ = 0.1						,      lowercase__ = 0.1						,      lowercase__ = 0.0						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = "gelu"						,      lowercase__ = None						,      lowercase__ = False						,      lowercase__ = 0						,      lowercase__ = False						,      lowercase__ = 0.0						,      lowercase__ = 8						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = True						,      lowercase__ = True						,      lowercase__=0						,      lowercase__=1						,      lowercase__=2						,      **lowercase__						,      )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      num_classes
									SCREAMING_SNAKE_CASE           =      num_atoms
									SCREAMING_SNAKE_CASE           =      num_in_degree
									SCREAMING_SNAKE_CASE           =      num_out_degree
									SCREAMING_SNAKE_CASE           =      num_edges
									SCREAMING_SNAKE_CASE           =      num_spatial
									SCREAMING_SNAKE_CASE           =      num_edge_dis
									SCREAMING_SNAKE_CASE           =      edge_type
									SCREAMING_SNAKE_CASE           =      multi_hop_max_dist
									SCREAMING_SNAKE_CASE           =      spatial_pos_max
									SCREAMING_SNAKE_CASE           =      max_nodes
									SCREAMING_SNAKE_CASE           =      num_hidden_layers
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      ffn_embedding_dim
									SCREAMING_SNAKE_CASE           =      num_attention_heads
									SCREAMING_SNAKE_CASE           =      dropout
									SCREAMING_SNAKE_CASE           =      attention_dropout
									SCREAMING_SNAKE_CASE           =      layerdrop
									SCREAMING_SNAKE_CASE           =      encoder_normalize_before
									SCREAMING_SNAKE_CASE           =      pre_layernorm
									SCREAMING_SNAKE_CASE           =      apply_graphormer_init
									SCREAMING_SNAKE_CASE           =      activation_fn
									SCREAMING_SNAKE_CASE           =      embed_scale
									SCREAMING_SNAKE_CASE           =      freeze_embeddings
									SCREAMING_SNAKE_CASE           =      num_trans_layers_to_freeze
									SCREAMING_SNAKE_CASE           =      share_input_output_embed
									SCREAMING_SNAKE_CASE           =      traceable
									SCREAMING_SNAKE_CASE           =      q_noise
									SCREAMING_SNAKE_CASE           =      qn_block_size

									# These parameters are here for future extensions
									# atm, the model only supports self attention
									SCREAMING_SNAKE_CASE           =      kdim
									SCREAMING_SNAKE_CASE           =      vdim
									SCREAMING_SNAKE_CASE           =      self_attention
									SCREAMING_SNAKE_CASE           =      bias

									super().__init__(
									    pad_token_id=lowercase__						,      bos_token_id=lowercase__						,      eos_token_id=lowercase__						,      **lowercase__						,      )
