



import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor



class     __snake_case						:




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




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	Union[str, Any]):
									lowerCAmelCase_ ,  lowerCAmelCase_      :					Tuple     =			torchaudio.load(A_)
									lowerCAmelCase_      :					Tuple     =			self.resampler(A_).squeeze().numpy()
									return speech




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	str):
									lowerCAmelCase_      :					Union[str, Any]     =			self.blank_id
									lowerCAmelCase_      :					Dict     =			'''|'''.join(item['''sent'''].split(''' '''))
									if not os.path.isfile(item['''wav_path''']):
											print(item['''wav_path''']    ,    '''not found in wavs directory''')

									lowerCAmelCase_      :					List[str]     =			self.speech_file_to_array_fn(item['''wav_path'''])
									lowerCAmelCase_      :					List[str]     =			self.processor(A_    ,    sampling_rate=1_6_0_0_0    ,    return_tensors='''pt'''    ,    padding=A_)
									if self.cuda:
											lowerCAmelCase_      :					Union[str, Any]     =			inputs.to(device='''cuda''')

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

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

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

									lowerCAmelCase_      :					Tuple     =			{c: i for i, c in enumerate(A_)}
									lowerCAmelCase_      :					Dict     =			[]
									for c in transcript:
											if c in dictionary:
													tokens.append(dictionary[c])

									def get_trellis(A_					:	List[Any]    ,    A_					:	Dict    ,    A_					:	Dict=0):
											lowerCAmelCase_      :					str     =			emission.size(0)
											lowerCAmelCase_      :					List[str]     =			len(A_)

											# 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_      :					str     =			torch.full((num_frames + 1, num_tokens + 1)    ,    -float('''inf'''))
											lowerCAmelCase_      :					str     =			0
											for t in range(A_):
													lowerCAmelCase_      :					List[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

									lowerCAmelCase_      :					Any     =			get_trellis(A_    ,    A_    ,    A_)



									@dataclass
									class     __snake_case						:
																_a      				=			42
																_a      				=			42
																_a      				=			42

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

											lowerCAmelCase_      :					Tuple     =			[]
											for t in range(A_    ,    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_      :					Tuple     =			trellis[t - 1, j] + emission[t - 1, blank_id]
													# Score for token changing from C-1 at T-1 to J at T.
													lowerCAmelCase_      :					str     =			trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

													# 2. Store the path with frame-wise probability.
													lowerCAmelCase_      :					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    ,    A_))

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

									lowerCAmelCase_      :					Optional[Any]     =			backtrack(A_    ,    A_    ,    A_    ,    A_)



									@dataclass
									class     __snake_case						:
																_a      				=			42
																_a      				=			42
																_a      				=			42
																_a      				=			42




																def __repr__(	self					:	int):
																		return F"""{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"""







																@property
																def        UpperCAmelCase__      (	self					:	Optional[int]):
																		return self.end - self.start

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

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







							def        UpperCAmelCase__      (	self					:	Any    ,    A_					:	Any    ,    A_					:	Dict    ,    A_					:	Optional[Any]):
									if not os.path.exists(A_):
											os.makedirs(A_)

									# load text file
									lowerCAmelCase_      :					Tuple     =			open(A_    ,    encoding='''utf8''').readlines()

									lowerCAmelCase_      :					List[str]     =			[]
									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_      :					str     =			line.strip().split('''\t''')
											lowerCAmelCase_      :					List[str]     =			os.path.join(A_    ,    wav_name + '''.wav''')
											lowerCAmelCase_      :					str     =			os.path.join(A_    ,    wav_name + '''.txt''')

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

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





def       UpperCamelCase(  ):
		lowerCAmelCase_      :					Union[str, Any]     =			argparse.ArgumentParser()

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

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

		lowerCAmelCase_      :					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()










import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


A__  :  Tuple       = logging.get_logger(__name__)

A__  :  List[str]       = [
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]



class     __snake_case						(					UpperCamelCase_						):
							_a      				=			RetriBertConfig
							_a      				=			None
							_a      				=			'''retribert'''




							def        UpperCAmelCase__      (	self					:	Optional[int]    ,    A_					:	Union[str, Any]):
									if isinstance(A_    ,    nn.Linear):
											module.weight.data.normal_(mean=0.0    ,    std=self.config.initializer_range)
											if module.bias is not None:
													module.bias.data.zero_()
									elif isinstance(A_    ,    nn.Embedding):
											module.weight.data.normal_(mean=0.0    ,    std=self.config.initializer_range)
											if module.padding_idx is not None:
													module.weight.data[module.padding_idx].zero_()
									elif isinstance(A_    ,    nn.LayerNorm):
											module.bias.data.zero_()
											module.weight.data.fill_(1.0)





A__  :  str       = R'''

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''



@add_start_docstrings(
    '''Bert Based model to embed queries or document for document retrieval.'''						,UpperCamelCase_						,)
class     __snake_case						(					UpperCamelCase_						):




							def __init__(	self					:	Optional[int]    ,    A_					:	RetriBertConfig):
									super().__init__(A_)
									lowerCAmelCase_      :					Optional[int]     =			config.projection_dim

									lowerCAmelCase_      :					int     =			BertModel(A_)
									lowerCAmelCase_      :					int     =			None if config.share_encoders else BertModel(A_)
									lowerCAmelCase_      :					str     =			nn.Dropout(config.hidden_dropout_prob)
									lowerCAmelCase_      :					Dict     =			nn.Linear(config.hidden_size    ,    config.projection_dim    ,    bias=A_)
									lowerCAmelCase_      :					Any     =			nn.Linear(config.hidden_size    ,    config.projection_dim    ,    bias=A_)

									lowerCAmelCase_      :					int     =			nn.CrossEntropyLoss(reduction='''mean''')

									# Initialize weights and apply final processing
									self.post_init()




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	Any    ,    A_					:	Optional[int]    ,    A_					:	Optional[int]    ,    A_					:	Optional[Any]=-1    ,    ):
									# reproduces BERT forward pass with checkpointing
									if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
											return sent_encoder(A_    ,    attention_mask=A_)[1]
									else:
											# prepare implicit variables
											lowerCAmelCase_      :					Any     =			input_ids.device
											lowerCAmelCase_      :					str     =			input_ids.size()
											lowerCAmelCase_      :					Dict     =			torch.zeros(A_    ,    dtype=torch.long    ,    device=A_)
											lowerCAmelCase_      :					List[str]     =			[None] * sent_encoder.config.num_hidden_layers
											lowerCAmelCase_      :					torch.Tensor     =			sent_encoder.get_extended_attention_mask(
											    A_    ,    A_)

											# define function for checkpointing
											def partial_encode(*A_					:	List[Any]):
													lowerCAmelCase_      :					Optional[Any]     =			sent_encoder.encoder(
													    inputs[0]    ,    attention_mask=inputs[1]    ,    head_mask=A_    ,    )
													lowerCAmelCase_      :					Any     =			encoder_outputs[0]
													lowerCAmelCase_      :					Optional[Any]     =			sent_encoder.pooler(A_)
													return pooled_output

											# run embedding layer on everything at once
											lowerCAmelCase_      :					str     =			sent_encoder.embeddings(
											    input_ids=A_    ,    position_ids=A_    ,    token_type_ids=A_    ,    inputs_embeds=A_)
											# run encoding and pooling on one mini-batch at a time
											lowerCAmelCase_      :					str     =			[]
											for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)):
													lowerCAmelCase_      :					Optional[Any]     =			embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
													lowerCAmelCase_      :					int     =			extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
													lowerCAmelCase_      :					str     =			checkpoint.checkpoint(A_    ,    A_    ,    A_)
													pooled_output_list.append(A_)
											return torch.cat(A_    ,    dim=0)




							def        UpperCAmelCase__      (	self					:	List[str]    ,    A_					:	List[Any]    ,    A_					:	Union[str, Any]=None    ,    A_					:	Dict=-1    ,    ):
									lowerCAmelCase_      :					Optional[int]     =			self.embed_sentences_checkpointed(
									    A_    ,    A_    ,    self.bert_query    ,    A_    ,    )
									return self.project_query(A_)




							def        UpperCAmelCase__      (	self					:	Optional[Any]    ,    A_					:	str    ,    A_					:	int=None    ,    A_					:	Any=-1    ,    ):
									lowerCAmelCase_      :					Union[str, Any]     =			self.embed_sentences_checkpointed(
									    A_    ,    A_    ,    self.bert_query if self.bert_doc is None else self.bert_doc    ,    A_    ,    )
									return self.project_doc(A_)







							def        UpperCAmelCase__      (	self					:	List[Any]    ,    A_					:	torch.LongTensor    ,    A_					:	Optional[torch.FloatTensor]    ,    A_					:	torch.LongTensor    ,    A_					:	Optional[torch.FloatTensor]    ,    A_					:	int = -1    ,    ):
									lowerCAmelCase_      :					List[Any]     =			input_ids_query.device
									lowerCAmelCase_      :					List[str]     =			self.embed_questions(A_    ,    A_    ,    A_)
									lowerCAmelCase_      :					Union[str, Any]     =			self.embed_answers(A_    ,    A_    ,    A_)
									lowerCAmelCase_      :					Any     =			torch.mm(A_    ,    a_reps.t())
									lowerCAmelCase_      :					int     =			self.ce_loss(A_    ,    torch.arange(compare_scores.shape[1]).to(A_))
									lowerCAmelCase_      :					str     =			self.ce_loss(compare_scores.t()    ,    torch.arange(compare_scores.shape[0]).to(A_))
									lowerCAmelCase_      :					List[Any]     =			(loss_qa + loss_aq) / 2
									return loss





