'''simple docstring'''




import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
  )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer




class 	__UpperCAmelCase				:





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=13    ,     _SCREAMING_SNAKE_CASE=7    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=99    ,     _SCREAMING_SNAKE_CASE=32    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=4    ,     _SCREAMING_SNAKE_CASE=37    ,     _SCREAMING_SNAKE_CASE="gelu"    ,     _SCREAMING_SNAKE_CASE=0.1    ,     _SCREAMING_SNAKE_CASE=0.1    ,     _SCREAMING_SNAKE_CASE=512    ,     _SCREAMING_SNAKE_CASE=16    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=0.02    ,     _SCREAMING_SNAKE_CASE=3    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=20    ,     _SCREAMING_SNAKE_CASE=30    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=10    ,     )		->     str:
           A_     =   parent
           A_     =   batch_size
           A_     =   seq_length
           A_     =   is_training
           A_     =   use_input_mask
           A_     =   use_token_type_ids
           A_     =   use_labels
           A_     =   vocab_size
           A_     =   hidden_size
           A_     =   num_hidden_layers
           A_     =   num_attention_heads
           A_     =   intermediate_size
           A_     =   hidden_act
           A_     =   hidden_dropout_prob
           A_     =   attention_probs_dropout_prob
           A_     =   max_position_embeddings
           A_     =   type_vocab_size
           A_     =   type_sequence_label_size
           A_     =   initializer_range
           A_     =   num_labels
           A_     =   scope
           A_     =   max_xpath_tag_unit_embeddings
           A_     =   max_xpath_subs_unit_embeddings
           A_     =   tag_pad_id
           A_     =   subs_pad_id
           A_     =   max_depth

       def 					__A       (      self )		->     str:
           A_     =   ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size )

           A_     =   ids_tensor(
               [self.batch_size, self.seq_length, self.max_depth]    ,     self.max_xpath_tag_unit_embeddings )

           A_     =   ids_tensor(
               [self.batch_size, self.seq_length, self.max_depth]    ,     self.max_xpath_subs_unit_embeddings )

           A_     =   None
           if self.use_input_mask:
               A_     =   ids_tensor([self.batch_size, self.seq_length]    ,     vocab_size=2 )

           A_     =   None
           if self.use_token_type_ids:
               A_     =   ids_tensor([self.batch_size, self.seq_length]    ,     self.type_vocab_size )

           A_     =   None
           A_     =   None
           if self.use_labels:
               A_     =   ids_tensor([self.batch_size]    ,     self.type_sequence_label_size )
               A_     =   ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels )

           A_     =   self.get_config()

           return (
               config,
               input_ids,
               xpath_tags_seq,
               xpath_subs_seq,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
           )

       def 					__A       (      self )		->     Optional[int]:
           return MarkupLMConfig(
               vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     num_hidden_layers=self.num_hidden_layers    ,     num_attention_heads=self.num_attention_heads    ,     intermediate_size=self.intermediate_size    ,     hidden_act=self.hidden_act    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     max_position_embeddings=self.max_position_embeddings    ,     type_vocab_size=self.type_vocab_size    ,     initializer_range=self.initializer_range    ,     max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings    ,     max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings    ,     tag_pad_id=self.tag_pad_id    ,     subs_pad_id=self.subs_pad_id    ,     max_depth=self.max_depth    ,     )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     )		->     int:
           A_     =   MarkupLMModel(config=_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           print('''Configs:'''    ,     model.config.tag_pad_id    ,     model.config.subs_pad_id )
           A_     =   model(_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE )
           A_     =   model(_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE )
           A_     =   model(_SCREAMING_SNAKE_CASE )
           self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size) )
           self.parent.assertEqual(result.pooler_output.shape    ,     (self.batch_size, self.hidden_size) )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     )		->     Tuple:
           A_     =   self.num_labels
           A_     =   MarkupLMForSequenceClassification(_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           A_     =   model(
               _SCREAMING_SNAKE_CASE    ,     xpath_tags_seq=_SCREAMING_SNAKE_CASE    ,     xpath_subs_seq=_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE    ,     labels=_SCREAMING_SNAKE_CASE    ,     )
           self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels) )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     )		->     str:
           A_     =   self.num_labels
           A_     =   MarkupLMForTokenClassification(config=_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           A_     =   model(
               _SCREAMING_SNAKE_CASE    ,     xpath_tags_seq=_SCREAMING_SNAKE_CASE    ,     xpath_subs_seq=_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE    ,     labels=_SCREAMING_SNAKE_CASE    ,     )
           self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels) )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     )		->     Tuple:
           A_     =   MarkupLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           A_     =   model(
               _SCREAMING_SNAKE_CASE    ,     xpath_tags_seq=_SCREAMING_SNAKE_CASE    ,     xpath_subs_seq=_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE    ,     start_positions=_SCREAMING_SNAKE_CASE    ,     end_positions=_SCREAMING_SNAKE_CASE    ,     )
           self.parent.assertEqual(result.start_logits.shape    ,     (self.batch_size, self.seq_length) )
           self.parent.assertEqual(result.end_logits.shape    ,     (self.batch_size, self.seq_length) )

       def 					__A       (      self )		->     Optional[int]:
           A_     =   self.prepare_config_and_inputs()
           (
               (
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,
           )     =   config_and_inputs
           A_     =   {
               '''input_ids''': input_ids,
               '''xpath_tags_seq''': xpath_tags_seq,
               '''xpath_subs_seq''': xpath_subs_seq,
               '''token_type_ids''': token_type_ids,
               '''attention_mask''': input_mask,
           }
           return config, inputs_dict




@require_torch
class 	__UpperCAmelCase				(				_UpperCamelCase						,      _UpperCamelCase						,      unittest.TestCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				(
           (
               MarkupLMModel,
               MarkupLMForSequenceClassification,
               MarkupLMForTokenClassification,
               MarkupLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       __lowercase					:					List[Any]    =				(
           {
               'feature-extraction': MarkupLMModel,
               'question-answering': MarkupLMForQuestionAnswering,
               'text-classification': MarkupLMForSequenceClassification,
               'token-classification': MarkupLMForTokenClassification,
               'zero-shot': MarkupLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     List[str]:
           # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
           # (batch of pretokenized examples).
           return True

       def 					__A       (      self )		->     Optional[Any]:
           A_     =   MarkupLMModelTester(self )
           A_     =   ConfigTester(self    ,     config_class=_SCREAMING_SNAKE_CASE    ,     hidden_size=37 )

       def 					__A       (      self )		->     Dict:
           self.config_tester.run_common_tests()

       def 					__A       (      self )		->     Optional[int]:
           A_     =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     List[Any]:
           A_     =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     Union[str, Any]:
           A_     =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     Tuple:
           A_     =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE )





def      _UpperCAmelCase    (							)	->      List[str]:
    A_     =   '''
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    '''

    return html_string




@require_torch
class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       @cached_property
       def 					__A       (      self )		->     Dict:
           # TODO use from_pretrained here
           A_     =   MarkupLMFeatureExtractor()
           A_     =   MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base''' )

           return MarkupLMProcessor(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )

       @slow
       def 					__A       (      self )		->     Any:
           A_     =   MarkupLMModel.from_pretrained('''microsoft/markuplm-base''' ).to(_SCREAMING_SNAKE_CASE )

           A_     =   self.default_processor

           A_     =   processor(prepare_html_string()    ,     return_tensors='''pt''' )
           A_     =   inputs.to(_SCREAMING_SNAKE_CASE )

           # forward pass
           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE )

           # verify the last hidden states
           A_     =   torch.Size([1, 14, 768] )
           self.assertEqual(outputs.last_hidden_state.shape    ,     _SCREAMING_SNAKE_CASE )

           A_     =   torch.tensor(
               [[0.0_675, -0.0_052, 0.5_001], [-0.2_281, 0.0_802, 0.2_192], [-0.0_583, -0.3_311, 0.1_185]] ).to(_SCREAMING_SNAKE_CASE )

           self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,     _SCREAMING_SNAKE_CASE    ,     atol=1E-4 ) )








'''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      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       str   ,     UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       Dict	)		->       List[Any]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		cuda
				snake_case   :    int			       =		AutoConfig.from_pretrained(UpperCamelCase__	)
				snake_case   :    int			       =		AutoModelForCTC.from_pretrained(UpperCamelCase__	)
				self.model.eval()
				if self.cuda:
						self.model.to(device='''cuda'''	)
				snake_case   :    Tuple			       =		AutoProcessor.from_pretrained(UpperCamelCase__	)
				snake_case   :    str			       =		torchaudio.transforms.Resample(UpperCamelCase__   ,     1_6000	)
				snake_case   :    Any			       =		0
				snake_case   :    int			       =		list(self.processor.tokenizer.get_vocab().keys()	)
				for i in range(len(UpperCamelCase__	)	):
						if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
								snake_case   :    Union[str, Any]			       =		i
				print('''Blank Token id [PAD]/<pad>'''   ,     UpperCamelCase__	)
				snake_case   :    List[Any]			       =		blank_id



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     UpperCamelCase__							:       str	)		->       List[str]:






				"""simple docstring"""







				snake_case       ,snake_case   :    Tuple			       =		torchaudio.load(UpperCamelCase__	)
				snake_case   :    Optional[Any]			       =		self.resampler(UpperCamelCase__	).squeeze().numpy()
				return speech



		def 	lowerCAmelCase							(	self							:       Optional[Any]   ,     UpperCamelCase__							:       int	)		->       Dict:






				"""simple docstring"""







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

				snake_case   :    List[Any]			       =		self.speech_file_to_array_fn(item['''wav_path''']	)
				snake_case   :    List[str]			       =		self.processor(UpperCamelCase__   ,     sampling_rate=1_6000   ,     return_tensors='''pt'''   ,     padding=UpperCamelCase__	)
				if self.cuda:
						snake_case   :    str			       =		inputs.to(device='''cuda'''	)

				with torch.no_grad():
						snake_case   :    List[Any]			       =		self.model(inputs.input_values	).logits

				# get the emission probability at frame level
				snake_case   :    str			       =		torch.log_softmax(UpperCamelCase__   ,     dim=-1	)
				snake_case   :    Optional[Any]			       =		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   :    List[str]			       =		{c: i for i, c in enumerate(UpperCamelCase__	)}
				snake_case   :    Any			       =		[]
				for c in transcript:
						if c in dictionary:
								tokens.append(dictionary[c]	)

				def get_trellis(UpperCamelCase__							:       str   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       str=0	):
						snake_case   :    List[str]			       =		emission.size(0	)
						snake_case   :    List[str]			       =		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.
						snake_case   :    List[Any]			       =		torch.full((num_frames + 1, num_tokens + 1)   ,     -float('''inf'''	)	)
						snake_case   :    Optional[int]			       =		0
						for t in range(UpperCamelCase__	):
								snake_case   :    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(UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__	)






				@dataclass
				class      snake_case__    :







						"""simple docstring"""

						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42

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

						snake_case   :    Dict			       =		[]
						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.
								snake_case   :    Union[str, Any]			       =		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   :    Optional[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   ,     UpperCamelCase__	)	)

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

				snake_case   :    Any			       =		backtrack(UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__	)






				@dataclass
				class      snake_case__    :







						"""simple docstring"""

						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42



						def __repr__(	self							:       Dict	)		->       int:






								"""simple docstring"""







								return f'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'



						@property
						def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Any:






								"""simple docstring"""







								return self.end - self.start

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

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



		def 	lowerCAmelCase							(	self							:       Dict   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       List[str]	)		->       List[str]:






				"""simple docstring"""







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

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

				snake_case   :    Optional[int]			       =		[]
				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   :    Dict			       =		line.strip().split('''\t'''	)
						snake_case   :    Tuple			       =		os.path.join(UpperCamelCase__   ,     wav_name + '''.wav'''	)
						snake_case   :    str			       =		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 					_UpperCamelCase		(    )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Optional[int]			       =		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=1_6000      ,    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   :    Tuple			       =		parser.parse_args()

		snake_case   :    Tuple			       =		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()


