code: 
  import tempfile
  import unittest
  
  from transformers import DPRConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  							import torch
  
  							from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
  							from transformers.models.dpr.modeling_dpr import (
  							    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
  							    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
  							    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
  							)
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:Optional[Any]      ,    lowercase			:Dict      ,    lowercase			:int=1_3      ,    lowercase			:Tuple=7      ,    lowercase			:str=False      ,    lowercase			:str=True      ,    lowercase			:Optional[Any]=True      ,    lowercase			:List[Any]=True      ,    lowercase			:int=9_9      ,    lowercase			:Optional[Any]=3_2      ,    lowercase			:Optional[int]=5      ,    lowercase			:int=4      ,    lowercase			:str=3_7      ,    lowercase			:Dict="gelu"      ,    lowercase			:str=0.1      ,    lowercase			:List[Any]=0.1      ,    lowercase			:Dict=5_1_2      ,    lowercase			:str=1_6      ,    lowercase			:str=2      ,    lowercase			:Union[str, Any]=0.02      ,    lowercase			:str=3      ,    lowercase			:str=4      ,    lowercase			:Optional[int]=None      ,    lowercase			:int=0      ,    )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   parent
  				SCREAMING_SNAKE_CASE    	=   batch_size
  				SCREAMING_SNAKE_CASE    	=   seq_length
  				SCREAMING_SNAKE_CASE    	=   is_training
  				SCREAMING_SNAKE_CASE    	=   use_input_mask
  				SCREAMING_SNAKE_CASE    	=   use_token_type_ids
  				SCREAMING_SNAKE_CASE    	=   use_labels
  				SCREAMING_SNAKE_CASE    	=   vocab_size
  				SCREAMING_SNAKE_CASE    	=   hidden_size
  				SCREAMING_SNAKE_CASE    	=   num_hidden_layers
  				SCREAMING_SNAKE_CASE    	=   num_attention_heads
  				SCREAMING_SNAKE_CASE    	=   intermediate_size
  				SCREAMING_SNAKE_CASE    	=   hidden_act
  				SCREAMING_SNAKE_CASE    	=   hidden_dropout_prob
  				SCREAMING_SNAKE_CASE    	=   attention_probs_dropout_prob
  				SCREAMING_SNAKE_CASE    	=   max_position_embeddings
  				SCREAMING_SNAKE_CASE    	=   type_vocab_size
  				SCREAMING_SNAKE_CASE    	=   type_sequence_label_size
  				SCREAMING_SNAKE_CASE    	=   initializer_range
  				SCREAMING_SNAKE_CASE    	=   num_labels
  				SCREAMING_SNAKE_CASE    	=   num_choices
  				SCREAMING_SNAKE_CASE    	=   scope
  				SCREAMING_SNAKE_CASE    	=   projection_dim
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str] )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size )
  
  				SCREAMING_SNAKE_CASE    	=   None
  				if self.use_input_mask:
  							SCREAMING_SNAKE_CASE    	=   random_attention_mask([self.batch_size, self.seq_length] )
  
  				SCREAMING_SNAKE_CASE    	=   None
  				if self.use_token_type_ids:
  							SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size, self.seq_length]      ,    self.type_vocab_size )
  
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   None
  				if self.use_labels:
  							SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size]      ,    self.type_sequence_label_size )
  							SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size, self.seq_length]      ,    self.num_labels )
  							SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size]      ,    self.num_choices )
  
  				SCREAMING_SNAKE_CASE    	=   self.get_config()
  
  				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return DPRConfig(
  				    projection_dim=self.projection_dim      ,    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      ,    )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[Any]      ,    lowercase			:Tuple      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:Any      ,    lowercase			:Optional[int]      ,    lowercase			:Dict      ,    lowercase			:List[str] )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   DPRContextEncoder(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase )
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    token_type_ids=lowercase )
  				SCREAMING_SNAKE_CASE    	=   model(lowercase )
  				self.parent.assertEqual(result.pooler_output.shape      ,    (self.batch_size, self.projection_dim or self.hidden_size) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:int      ,    lowercase			:Union[str, Any]      ,    lowercase			:Any      ,    lowercase			:Dict      ,    lowercase			:Tuple      ,    lowercase			:str      ,    lowercase			:Tuple )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   DPRQuestionEncoder(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase )
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    token_type_ids=lowercase )
  				SCREAMING_SNAKE_CASE    	=   model(lowercase )
  				self.parent.assertEqual(result.pooler_output.shape      ,    (self.batch_size, self.projection_dim or self.hidden_size) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:Optional[Any]      ,    lowercase			:int      ,    lowercase			:Optional[Any]      ,    lowercase			:List[Any]      ,    lowercase			:Tuple      ,    lowercase			:List[str]      ,    lowercase			:Tuple )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   DPRReader(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(
  				    lowercase      ,    attention_mask=lowercase      ,    )
  
  				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) )
  				self.parent.assertEqual(result.relevance_logits.shape      ,    (self.batch_size,) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str] )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.prepare_config_and_inputs()
  				(
  				    (
  				    SCREAMING_SNAKE_CASE       
  				)	,       (
  				    SCREAMING_SNAKE_CASE       
  				)	,       (
  				    SCREAMING_SNAKE_CASE       
  				)	,       (
  				    SCREAMING_SNAKE_CASE       
  				)	,       (
  				    SCREAMING_SNAKE_CASE       
  				)	,       (
  				    SCREAMING_SNAKE_CASE       
  				)	,       (
  				    SCREAMING_SNAKE_CASE       
  				)	,              
  				)    	=   config_and_inputs
  				SCREAMING_SNAKE_CASE    	=   {'''input_ids''': input_ids}
  				return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 						lowerCamelCase			(					__lowerCamelCase   ,		__lowerCamelCase   ,		unittest.TestCase				):
  	UpperCamelCase_     :				int								=  (
  	    (
  	        DPRContextEncoder,
  	        DPRQuestionEncoder,
  	        DPRReader,
  	    )
  	    if is_torch_available()
  	    else ()
  	)
  	UpperCamelCase_     :				Optional[int]								=  {'feature-extraction': DPRQuestionEncoder} if is_torch_available() else {}
  
  	UpperCamelCase_     :				List[str]								=  False
  	UpperCamelCase_     :				Dict								=  False  # why?
  	UpperCamelCase_     :				List[str]								=  False
  	UpperCamelCase_     :				Dict								=  False
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str] )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   DPRModelTester(self )
  				SCREAMING_SNAKE_CASE    	=   ConfigTester(self      ,    config_class=lowercase      ,    hidden_size=3_7 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				self.config_tester.run_common_tests()
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int] )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_context_encoder(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_question_encoder(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[Any] )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_reader(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[Any] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()[0]
  
  				SCREAMING_SNAKE_CASE    	=   DPRQuestionEncoder(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  
  				with tempfile.TemporaryDirectory() as tmp_dirname:
  							model.save_pretrained(lowercase )
  							SCREAMING_SNAKE_CASE    	=   DPRQuestionEncoder.from_pretrained(lowercase      ,    projection_dim=5_1_2 )
  
  				self.assertIsNotNone(lowercase )
  
  
  
  
  
  
  
  	@slow
  	def    snake_case__    (    self			:Tuple )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  							SCREAMING_SNAKE_CASE    	=   DPRContextEncoder.from_pretrained(lowercase )
  							self.assertIsNotNone(lowercase )
  
  				for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  							SCREAMING_SNAKE_CASE    	=   DPRContextEncoder.from_pretrained(lowercase )
  							self.assertIsNotNone(lowercase )
  
  				for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  							SCREAMING_SNAKE_CASE    	=   DPRQuestionEncoder.from_pretrained(lowercase )
  							self.assertIsNotNone(lowercase )
  
  				for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  							SCREAMING_SNAKE_CASE    	=   DPRReader.from_pretrained(lowercase )
  							self.assertIsNotNone(lowercase )
  
  
  
  
  
  @require_torch
  class 						lowerCamelCase			(					unittest.TestCase				):
  
  
  
  
  
  
  
  	@slow
  	def    snake_case__    (    self			:Optional[Any] )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   DPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''      ,    return_dict=lowercase )
  				model.to(lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   torch.tensor(
  				    [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]      ,    dtype=torch.long      ,    device=lowercase )  # [CLS] hello, is my dog cute? [SEP]
  				SCREAMING_SNAKE_CASE    	=   model(lowercase )[0]  # embedding shape = (1, 768)
  				# compare the actual values for a slice.
  				SCREAMING_SNAKE_CASE    	=   torch.tensor(
  				    [
  				        [
  				            0.03_23_62_53,
  				            0.12_75_33_35,
  				            0.16_81_85_09,
  				            0.00_27_97_86,
  				            0.3_89_69_33,
  				            0.24_26_49_45,
  				            0.2_17_89_71,
  				            -0.02_33_52_27,
  				            -0.08_48_19_59,
  				            -0.14_32_41_17,
  				        ]
  				    ]      ,    dtype=torch.float      ,    device=lowercase      ,    )
  				self.assertTrue(torch.allclose(output[:, :1_0]      ,    lowercase      ,    atol=1e-4 ) )
  
  
  
  
  
  
  
  	@slow
  	def    snake_case__    (    self			:Dict )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   DPRReaderTokenizer.from_pretrained('''facebook/dpr-reader-single-nq-base''' )
  				SCREAMING_SNAKE_CASE    	=   DPRReader.from_pretrained('''facebook/dpr-reader-single-nq-base''' )
  				model.to(lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   tokenizer(
  				    questions='''What is love ?'''      ,    titles='''Haddaway'''      ,    texts='''What Is Love is a song recorded by the artist Haddaway'''      ,    padding=lowercase      ,    return_tensors='''pt'''      ,    )
  				encoded_inputs.to(lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   model(**lowercase )
  
  				# compare the actual values for a slice.
  				SCREAMING_SNAKE_CASE    	=   torch.tensor(
  				    [[-10.30_05, -10.77_65, -11.48_72, -11.68_41, -11.93_12, -10.30_02, -9.85_44, -11.73_78, -12.08_21, -10.29_75]]      ,    dtype=torch.float      ,    device=lowercase      ,    )
  
  				SCREAMING_SNAKE_CASE    	=   torch.tensor(
  				    [[-11.06_84, -11.70_41, -11.53_97, -10.34_65, -10.87_91, -6.84_43, -11.99_59, -11.03_64, -10.00_96, -6.84_05]]      ,    dtype=torch.float      ,    device=lowercase      ,    )
  				self.assertTrue(torch.allclose(outputs.start_logits[:, :1_0]      ,    lowercase      ,    atol=1e-4 ) )
  				self.assertTrue(torch.allclose(outputs.end_logits[:, :1_0]      ,    lowercase      ,    atol=1e-4 ) )
style_context: 
  import argparse
  import os
  from dataclasses import dataclass
  
  import torch
  import torchaudio
  from tqdm import tqdm
  
  from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:Optional[int]      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:int )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   cuda
  				SCREAMING_SNAKE_CASE    	=   AutoConfig.from_pretrained(lowercase )
  				SCREAMING_SNAKE_CASE    	=   AutoModelForCTC.from_pretrained(lowercase )
  				self.model.eval()
  				if self.cuda:
  							self.model.to(device='''cuda''' )
  				SCREAMING_SNAKE_CASE    	=   AutoProcessor.from_pretrained(lowercase )
  				SCREAMING_SNAKE_CASE    	=   torchaudio.transforms.Resample(lowercase      ,    1_6_0_0_0 )
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   list(self.processor.tokenizer.get_vocab().keys() )
  				for i in range(len(lowercase ) ):
  							if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
  										SCREAMING_SNAKE_CASE    	=   i
  				print('''Blank Token id [PAD]/<pad>'''      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   blank_id
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any      ,    lowercase			:int )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   torchaudio.load(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.resampler(lowercase ).squeeze().numpy()
  				return speech
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:Optional[Any] )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.blank_id
  				SCREAMING_SNAKE_CASE    	=   '''|'''.join(item['''sent'''].split(''' ''' ) )
  				if not os.path.isfile(item['''wav_path'''] ):
  							print(item['''wav_path''']      ,    '''not found in wavs directory''' )
  
  				SCREAMING_SNAKE_CASE    	=   self.speech_file_to_array_fn(item['''wav_path'''] )
  				SCREAMING_SNAKE_CASE    	=   self.processor(lowercase      ,    sampling_rate=1_6_0_0_0      ,    return_tensors='''pt'''      ,    padding=lowercase )
  				if self.cuda:
  							SCREAMING_SNAKE_CASE    	=   inputs.to(device='''cuda''' )
  
  				with torch.no_grad():
  							SCREAMING_SNAKE_CASE    	=   self.model(inputs.input_values ).logits
  
  				# get the emission probability at frame level
  				SCREAMING_SNAKE_CASE    	=   torch.log_softmax(lowercase      ,    dim=-1 )
  				SCREAMING_SNAKE_CASE    	=   emissions[0].cpu().detach()
  
  				# get labels from vocab
  				SCREAMING_SNAKE_CASE    	=   ([''''''] + list(self.processor.tokenizer.get_vocab().keys() ))[
  				    :-1
  				]  # logits don't align with the tokenizer's vocab
  
  				SCREAMING_SNAKE_CASE    	=   {c: i for i, c in enumerate(lowercase )}
  				SCREAMING_SNAKE_CASE    	=   []
  				for c in transcript:
  							if c in dictionary:
  										tokens.append(dictionary[c] )
  
  				def get_trellis(lowercase			:List[Any]      ,    lowercase			:Any      ,    lowercase			:int=0 ):
  							SCREAMING_SNAKE_CASE    	=   emission.size(0 )
  							SCREAMING_SNAKE_CASE    	=   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.
  							SCREAMING_SNAKE_CASE    	=   torch.full((num_frames + 1, num_tokens + 1)      ,    -float('''inf''' ) )
  							SCREAMING_SNAKE_CASE    	=   0
  							for t in range(lowercase ):
  										SCREAMING_SNAKE_CASE    	=   torch.maximum(
  										    # Score for staying at the same token
  										    trellis[t, 1:] + emission[t, blank_id]      ,    trellis[t, :-1] + emission[t, tokens]      ,    )
  							return trellis
  
  				SCREAMING_SNAKE_CASE    	=   get_trellis(lowercase      ,    lowercase      ,    lowercase )
  
  
  
  
  
  				@dataclass
  				class 						lowerCamelCase			:
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				float
  
  				def backtrack(lowercase			:Optional[int]      ,    lowercase			:List[str]      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Any]=0 ):
  							SCREAMING_SNAKE_CASE    	=   trellis.size(1 ) - 1
  							SCREAMING_SNAKE_CASE    	=   torch.argmax(trellis[:, j] ).item()
  
  							SCREAMING_SNAKE_CASE    	=   []
  							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.
  										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j] + emission[t - 1, blank_id]
  										# Score for token changing from C-1 at T-1 to J at T.
  										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]
  
  										# 2. Store the path with frame-wise probability.
  										SCREAMING_SNAKE_CASE    	=   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]
  
  				SCREAMING_SNAKE_CASE    	=   backtrack(lowercase      ,    lowercase      ,    lowercase      ,    lowercase )
  
  
  
  
  
  				@dataclass
  				class 						lowerCamelCase			:
  					UpperCamelCase_     :				str
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				float
  
  
  
  
  
  
  
  					def __repr__(    self			:Optional[Any] )		->			str:
  
  
  
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return f"""{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"""
  
  
  
  
  
  
  
  					@property
  					def    snake_case__    (    self			:List[str] )		->			Any:
  
  
  
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.end - self.start
  
  				def merge_repeats(lowercase			:List[str] ):
  							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   0, 0
  							SCREAMING_SNAKE_CASE    	=   []
  							while ia < len(lowercase ):
  										while ia < len(lowercase ) and path[ia].token_index == path[ia].token_index:
  													ia += 1
  										SCREAMING_SNAKE_CASE    	=   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      ,    ) )
  										SCREAMING_SNAKE_CASE    	=   ia
  							return segments
  
  				SCREAMING_SNAKE_CASE    	=   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			:List[Any]      ,    lowercase			:Dict      ,    lowercase			:Tuple      ,    lowercase			:str )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if not os.path.exists(lowercase ):
  							os.makedirs(lowercase )
  
  				# load text file
  				SCREAMING_SNAKE_CASE    	=   open(lowercase      ,    encoding='''utf8''' ).readlines()
  
  				SCREAMING_SNAKE_CASE    	=   []
  				for line in lines:
  							if len(line.strip().split('''\t''' ) ) != 2:
  										print('''Script must be in format: 00001  this is my sentence''' )
  										exit()
  
  							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   line.strip().split('''\t''' )
  							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.wav''' )
  							SCREAMING_SNAKE_CASE    	=   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			(   )    ->Optional[int]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   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=1_6000		,	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'''  )
  
  			SCREAMING_SNAKE_CASE    	=   parser.parse_args()
  
  			SCREAMING_SNAKE_CASE    	=   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()