import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
class 	_a  :



	"""simple docstring"""



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


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   =torchaudio.load(lowerCAmelCase_       )
		_lowercase   =self.resampler(lowerCAmelCase_       ).squeeze().numpy()
		return speech


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =self.blank_id
		_lowercase   ="|".join(item["sent"].split(" "       )       )
		if not os.path.isfile(item["wav_path"]       ):
			print(item["wav_path"]       ,  "not found in wavs directory"       )

		_lowercase   =self.speech_file_to_array_fn(item["wav_path"]       )
		_lowercase   =self.processor(lowerCAmelCase_       ,  sampling_rate=16000       ,  return_tensors="pt"       ,  padding=lowerCAmelCase_       )
		if self.cuda:
			_lowercase   =inputs.to(device="cuda"       )

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

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

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

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

		def get_trellis(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=0       ):
			_lowercase   =emission.size(0       )
			_lowercase   =len(lowerCAmelCase_       )

			# 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.
			_lowercase   =torch.full((num_frames + 1, num_tokens + 1)       ,  -float("inf"       )       )
			_lowercase   =0
			for t in range(lowerCAmelCase_       ):
				_lowercase   =torch.maximum(
				    # Score for staying at the same token
				    trellis[t, 1:] + emission[t, blank_id]       ,  trellis[t, :-1] + emission[t, tokens]       ,  )
			return trellis

		_lowercase   =get_trellis(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		@dataclass
		class 	_a  :



			"""simple docstring"""



			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42

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

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

				# 2. Store the path with frame-wise probability.
				_lowercase   =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       ,  lowerCAmelCase_       )       )

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

		_lowercase   =backtrack(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		@dataclass
		class 	_a  :



			"""simple docstring"""



			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42


			def __repr__(   self       ):
				return F'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''


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

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

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


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if not os.path.exists(lowerCAmelCase_       ):
			os.makedirs(lowerCAmelCase_       )

		# load text file
		_lowercase   =open(lowerCAmelCase_       ,  encoding="utf8"       ).readlines()

		_lowercase   =[]
		for line in lines:
			if len(line.strip().split("\t"       )       ) != 2:
				print("Script must be in format: 00001  this is my sentence"       )
				exit()

			_lowercase   ,  _lowercase   =line.strip().split("\t"       )
			_lowercase   =os.path.join(lowerCAmelCase_       ,  wav_name + ".wav"       )
			_lowercase   =os.path.join(lowerCAmelCase_       ,  wav_name + ".txt"       )

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

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

def 	__lowerCamelCase							(				)      ->       Union[str, Any]:
	_lowercase   =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=16_000      ,							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"		)

	_lowercase   =parser.parse_args()

	_lowercase   =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()









from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__UpperCAmelCase    	=  logging.get_logger(__name__)

__UpperCAmelCase    	=  {
    '''google/bigbird-pegasus-large-arxiv''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-pubmed''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-bigpatent''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'''
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}


class 						lowerCamelCase__			(		_a ):
						_lowerCAmelCase             =			'''bigbird_pegasus'''
						_lowerCAmelCase             =			['''past_key_values''']
						_lowerCAmelCase             =			{
						    '''num_attention_heads''': '''encoder_attention_heads''',
						    '''hidden_size''': '''d_model''',
						    '''attention_probs_dropout_prob''': '''attention_dropout''',
						}


						def __init__(			self    :	int   ,							_a    :	List[Any]=9_6_1_0_3   ,							_a    :	Tuple=4_0_9_6   ,							_a    :	int=1_6   ,							_a    :	Dict=4_0_9_6   ,							_a    :	List[str]=1_6   ,							_a    :	Tuple=1_6   ,							_a    :	Union[str, Any]=4_0_9_6   ,							_a    :	int=1_6   ,							_a    :	Any=0.0   ,							_a    :	Union[str, Any]=0.0   ,							_a    :	Tuple=True   ,							_a    :	List[str]=True   ,							_a    :	Union[str, Any]="gelu_new"   ,							_a    :	Tuple=1_0_2_4   ,							_a    :	List[str]=0.1   ,							_a    :	Tuple=0.0   ,							_a    :	Any=0.0   ,							_a    :	int=0.0_2   ,							_a    :	str=2   ,							_a    :	Dict=0.0   ,							_a    :	Dict=True   ,							_a    :	List[str]=0   ,							_a    :	Dict=2   ,							_a    :	Any=1   ,							_a    :	Union[str, Any]="block_sparse"   ,							_a    :	Tuple=6_4   ,							_a    :	Tuple=3   ,							_a    :	List[str]=False   ,							**_a    :	Optional[Any]   ,							):
													a__:							Any									=vocab_size
													a__:							str									=max_position_embeddings
													a__:							str									=d_model
													a__:							int									=encoder_ffn_dim
													a__:							Optional[Any]									=encoder_layers
													a__:							Any									=encoder_attention_heads
													a__:							int									=decoder_ffn_dim
													a__:							Dict									=decoder_layers
													a__:							Dict									=decoder_attention_heads
													a__:							Union[str, Any]									=dropout
													a__:							List[Any]									=attention_dropout
													a__:							int									=activation_dropout
													a__:							Union[str, Any]									=activation_function
													a__:							Optional[int]									=init_std
													a__:							List[Any]									=encoder_layerdrop
													a__:							Dict									=decoder_layerdrop
													a__:							int									=classifier_dropout
													a__:							List[Any]									=use_cache
													a__:							int									=encoder_layers
													a__:							List[Any]									=scale_embedding  # scale factor will be sqrt(d_model) if True

													# extra config
													a__:							Any									=attention_type
													a__:							Any									=block_size
													a__:							int									=num_random_blocks
													a__:							List[str]									=use_bias

													super().__init__(
													    pad_token_id=_a   ,							bos_token_id=_a   ,							eos_token_id=_a   ,							is_encoder_decoder=_a   ,							decoder_start_token_id=_a   ,							**_a   ,							)


class 						lowerCamelCase__			(		_a ):


						@property
						def 	_lowerCamelCase      (			self    :	int      ):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							Optional[Any]									=OrderedDict(
																				    [
																				        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																				        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																				    ]      )

																				if self.use_past:
																											a__:							List[Any]									={0: "batch"}
																											a__:							int									={0: "batch", 1: "past_decoder_sequence + sequence"}
																				else:
																											a__:							List[str]									={0: "batch", 1: "decoder_sequence"}
																											a__:							Optional[int]									={0: "batch", 1: "decoder_sequence"}

																				if self.use_past:
																											self.fill_with_past_key_values_(_a   ,							direction="inputs"      )
													elif self.task == "causal-lm":
																				# TODO: figure this case out.
																				a__:							List[str]									=OrderedDict(
																				    [
																				        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																				        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																				    ]      )
																				if self.use_past:
																											a__				,      a__:							str									=self.num_layers
																											for i in range(_a      ):
																																		a__:							Any									={0: "batch", 2: "past_sequence + sequence"}
																																		a__:							Any									={0: "batch", 2: "past_sequence + sequence"}
													else:
																				a__:							int									=OrderedDict(
																				    [
																				        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																				        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																				        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
																				        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
																				    ]      )

													return common_inputs


						@property
						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							str									=super().outputs
													else:
																				a__:							Dict									=super(_a   ,							self      ).outputs
																				if self.use_past:
																											a__				,      a__:							Tuple									=self.num_layers
																											for i in range(_a      ):
																																		a__:							str									={0: "batch", 2: "past_sequence + sequence"}
																																		a__:							List[str]									={0: "batch", 2: "past_sequence + sequence"}
													return common_outputs


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													a__:							Union[str, Any]									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _a   ,							_a   ,							_a   ,							_a   ,							_a      )

													# Generate decoder inputs
													a__:							str									=seq_length if not self.use_past else 1
													a__:							Dict									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _a   ,							_a   ,							_a   ,							_a   ,							_a      )
													a__:							List[str]									={F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
													a__:							str									=dict(**_a   ,							**_a      )

													if self.use_past:
																				if not is_torch_available():
																											raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
																				else:
																											import torch
																				a__				,      a__:							str									=common_inputs["input_ids"].shape
																				a__:							List[str]									=common_inputs["decoder_input_ids"].shape[1]
																				a__				,      a__:							Any									=self.num_attention_heads
																				a__:							int									=(
																				    batch,
																				    num_encoder_attention_heads,
																				    encoder_seq_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)
																				a__:							str									=decoder_seq_length + 3
																				a__:							List[str]									=(
																				    batch,
																				    num_decoder_attention_heads,
																				    decoder_past_length,
																				    self._config.hidden_size // num_decoder_attention_heads,
																				)

																				a__:							List[str]									=torch.cat(
																				    [common_inputs["decoder_attention_mask"], torch.ones(_a   ,							_a      )]   ,							dim=1      )

																				a__:							List[str]									=[]
																				# If the number of encoder and decoder layers are present in the model configuration, both are considered
																				a__				,      a__:							int									=self.num_layers
																				a__:							List[Any]									=min(_a   ,							_a      )
																				a__:							str									=max(_a   ,							_a      ) - min_num_layers
																				a__:							Optional[Any]									="encoder" if num_encoder_layers > num_decoder_layers else "decoder"

																				for _ in range(_a      ):
																											common_inputs["past_key_values"].append(
																											    (
																											        torch.zeros(_a      ),
																											        torch.zeros(_a      ),
																											        torch.zeros(_a      ),
																											        torch.zeros(_a      ),
																											    )      )
																				# TODO: test this.
																				a__:							Any									=encoder_shape if remaining_side_name == "encoder" else decoder_shape
																				for _ in range(_a   ,							_a      ):
																											common_inputs["past_key_values"].append((torch.zeros(_a      ), torch.zeros(_a      ))      )
													return common_inputs


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													a__:							Tuple									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _a   ,							_a   ,							_a   ,							_a   ,							_a      )

													if self.use_past:
																				if not is_torch_available():
																											raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
																				else:
																											import torch
																				a__				,      a__:							Tuple									=common_inputs["input_ids"].shape
																				# Not using the same length for past_key_values
																				a__:							str									=seqlen + 2
																				a__				,      a__:							List[str]									=self.num_layers
																				a__				,      a__:							Any									=self.num_attention_heads
																				a__:							Optional[int]									=(
																				    batch,
																				    num_encoder_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)

																				a__:							int									=common_inputs["attention_mask"].dtype
																				a__:							str									=torch.cat(
																				    [common_inputs["attention_mask"], torch.ones(_a   ,							_a   ,							dtype=_a      )]   ,							dim=1      )
																				a__:							int									=[
																				    (torch.zeros(_a      ), torch.zeros(_a      )) for _ in range(_a      )
																				]
													return common_inputs


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													# Copied from OnnxConfig.generate_dummy_inputs
													# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
													# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
													a__:							List[str]									=compute_effective_axis_dimension(
													    _a   ,							fixed_dimension=OnnxConfig.default_fixed_batch   ,							num_token_to_add=0      )

													# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
													a__:							Optional[int]									=tokenizer.num_special_tokens_to_add(_a      )
													a__:							Any									=compute_effective_axis_dimension(
													    _a   ,							fixed_dimension=OnnxConfig.default_fixed_sequence   ,							num_token_to_add=_a      )

													# Generate dummy inputs according to compute batch and sequence
													a__:							Any									=[" ".join([tokenizer.unk_token]      ) * seq_length] * batch_size
													a__:							Optional[Any]									=dict(tokenizer(_a   ,							return_tensors=_a      )      )
													return common_inputs


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							Any									=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																				    _a   ,							batch_size=_a   ,							seq_length=_a   ,							is_pair=_a   ,							framework=_a      )

													elif self.task == "causal-lm":
																				a__:							Dict									=self._generate_dummy_inputs_for_causal_lm(
																				    _a   ,							batch_size=_a   ,							seq_length=_a   ,							is_pair=_a   ,							framework=_a      )
													else:
																				a__:							int									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																				    _a   ,							batch_size=_a   ,							seq_length=_a   ,							is_pair=_a   ,							framework=_a      )

													return common_inputs







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	int   ,							_a    :	str   ,							_a    :	Any   ,							_a    :	str      ):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							Tuple									=super()._flatten_past_key_values_(_a   ,							_a   ,							_a   ,							_a      )
													else:
																				a__:							Tuple									=super(_a   ,							self      )._flatten_past_key_values_(
																				    _a   ,							_a   ,							_a   ,							_a      )





