





"""simple docstring"""
import unittest

from transformers import LayoutLMConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           LayoutLMForMaskedLM,
           LayoutLMForQuestionAnswering,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMModel,
       )







class 			a       :



 def __init__(    self							,					_snake_case							,					_snake_case=13							,					_snake_case=7							,					_snake_case=True							,					_snake_case=True							,					_snake_case=True							,					_snake_case=True							,					_snake_case=99							,					_snake_case=32							,					_snake_case=5							,					_snake_case=4							,					_snake_case=37							,					_snake_case="gelu"							,					_snake_case=0.1							,					_snake_case=0.1							,					_snake_case=5_12							,					_snake_case=16							,					_snake_case=2							,					_snake_case=0.02							,					_snake_case=3							,					_snake_case=4							,					_snake_case=None							,					_snake_case=10_00							,					):







  """simple docstring"""






  lowerCAmelCase													=   parent
  lowerCAmelCase													=   batch_size
  lowerCAmelCase													=   seq_length
  lowerCAmelCase													=   is_training
  lowerCAmelCase													=   use_input_mask
  lowerCAmelCase													=   use_token_type_ids
  lowerCAmelCase													=   use_labels
  lowerCAmelCase													=   vocab_size
  lowerCAmelCase													=   hidden_size
  lowerCAmelCase													=   num_hidden_layers
  lowerCAmelCase													=   num_attention_heads
  lowerCAmelCase													=   intermediate_size
  lowerCAmelCase													=   hidden_act
  lowerCAmelCase													=   hidden_dropout_prob
  lowerCAmelCase													=   attention_probs_dropout_prob
  lowerCAmelCase													=   max_position_embeddings
  lowerCAmelCase													=   type_vocab_size
  lowerCAmelCase													=   type_sequence_label_size
  lowerCAmelCase													=   initializer_range
  lowerCAmelCase													=   num_labels
  lowerCAmelCase													=   num_choices
  lowerCAmelCase													=   scope
  lowerCAmelCase													=   range_bbox



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

  lowerCAmelCase													=   ids_tensor([self.batch_size, self.seq_length, 4]							,					self.range_bbox    )
  # Ensure that bbox is legal
  for i in range(bbox.shape[0]    ):
   for j in range(bbox.shape[1]    ):
    if bbox[i, j, 3] < bbox[i, j, 1]:
     lowerCAmelCase													=   bbox[i, j, 3]
     lowerCAmelCase													=   bbox[i, j, 1]
     lowerCAmelCase													=   t
    if bbox[i, j, 2] < bbox[i, j, 0]:
     lowerCAmelCase													=   bbox[i, j, 2]
     lowerCAmelCase													=   bbox[i, j, 0]
     lowerCAmelCase													=   t

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

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

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

  lowerCAmelCase													=   self.get_config()

  return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return LayoutLMConfig(
      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       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMModel(config=_snake_case    )
  model.to(_snake_case    )
  model.eval()
  lowerCAmelCase													=   model(_snake_case							,					_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case    )
  lowerCAmelCase													=   model(_snake_case							,					_snake_case							,					token_type_ids=_snake_case    )
  lowerCAmelCase													=   model(_snake_case							,					_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       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMForMaskedLM(config=_snake_case    )
  model.to(_snake_case    )
  model.eval()
  lowerCAmelCase													=   model(_snake_case							,					_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					labels=_snake_case    )
  self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.num_labels
  lowerCAmelCase													=   LayoutLMForSequenceClassification(_snake_case    )
  model.to(_snake_case    )
  model.eval()
  lowerCAmelCase													=   model(
      _snake_case							,					_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					labels=_snake_case    )
  self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.num_labels
  lowerCAmelCase													=   LayoutLMForTokenClassification(config=_snake_case    )
  model.to(_snake_case    )
  model.eval()
  lowerCAmelCase													=   model(_snake_case							,					_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					labels=_snake_case    )
  self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMForQuestionAnswering(config=_snake_case    )
  model.to(_snake_case    )
  model.eval()
  lowerCAmelCase													=   model(
      _snake_case							,					bbox=_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					start_positions=_snake_case							,					end_positions=_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       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,
  )													=   config_and_inputs
  lowerCAmelCase													=   {
      'input_ids': input_ids,
      'bbox': bbox,
      'token_type_ids': token_type_ids,
      'attention_mask': input_mask,
  }
  return config, inputs_dict







@require_torch
class 			a       (      a__      ,   a__      ,   unittest.TestCase     ):
 snake_case__								=					(
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 snake_case__								=					(
     {
         '''document-question-answering''': LayoutLMForQuestionAnswering,
         '''feature-extraction''': LayoutLMModel,
         '''fill-mask''': LayoutLMForMaskedLM,
         '''text-classification''': LayoutLMForSequenceClassification,
         '''token-classification''': LayoutLMForTokenClassification,
         '''zero-shot''': LayoutLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 snake_case__								=					True



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMModelTester(self    )
  lowerCAmelCase													=   ConfigTester(self							,					config_class=_snake_case							,					hidden_size=37    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  for type in ["absolute", "relative_key", "relative_key_query"]:
   lowerCAmelCase													=   type
   self.model_tester.create_and_check_model(*_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*_snake_case    )



 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  pass


def 		_SCREAMING_SNAKE_CASE ():
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 lowerCAmelCase													=   torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]     ,    device=_UpperCAmelCase )  # noqa: E231
 lowerCAmelCase													=   torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]     ,    device=_UpperCAmelCase )  # noqa: E231
 lowerCAmelCase													=   torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]     ,    device=_UpperCAmelCase )  # noqa: E231
 lowerCAmelCase													=   torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]     ,    device=_UpperCAmelCase )  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 lowerCAmelCase													=   torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]     ,    device=_UpperCAmelCase )  # noqa: E231
 # fmt: on

 return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 			a       (      unittest.TestCase     ):



 @slow
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(_snake_case    )

  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase													=   prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase													=   model(input_ids=_snake_case							,					bbox=_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case    )

  # test the sequence output on [0, :3, :3]
  lowerCAmelCase													=   torch.tensor(
      [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]							,					device=_snake_case							,					)

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

  # test the pooled output on [1, :3]
  lowerCAmelCase													=   torch.tensor([-0.6_580, -0.0_214, 0.8_552]							,					device=_snake_case    )

  self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]							,					_snake_case							,					atol=1E-3    )    )



 @slow
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'							,					num_labels=2    ).to(
      _snake_case    )

  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase													=   prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase													=   model(
      input_ids=_snake_case							,					bbox=_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					labels=torch.tensor([1, 1]							,					device=_snake_case    )							,					)

  # test whether we get a loss as a scalar
  lowerCAmelCase													=   outputs.loss
  lowerCAmelCase													=   torch.Size([]    )
  self.assertEqual(loss.shape							,					_snake_case    )

  # test the shape of the logits
  lowerCAmelCase													=   outputs.logits
  lowerCAmelCase													=   torch.Size((2, 2)    )
  self.assertEqual(logits.shape							,					_snake_case    )



 @slow
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'							,					num_labels=13    ).to(
      _snake_case    )

  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase													=   prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase													=   model(
      input_ids=_snake_case							,					bbox=_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					labels=_snake_case    )

  # test the loss calculation to be around 2.65
  # expected_loss = torch.tensor(2.65, device=torch_device)

  # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
  # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

  # test the shape of the logits
  lowerCAmelCase													=   outputs.logits
  lowerCAmelCase													=   torch.Size((2, 25, 13)    )
  self.assertEqual(logits.shape							,					_snake_case    )



 @slow
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(_snake_case    )

  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase													=   prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase													=   model(input_ids=_snake_case							,					bbox=_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case    )

  # test the shape of the logits
  lowerCAmelCase													=   torch.Size((2, 25)    )
  self.assertEqual(outputs.start_logits.shape							,					_snake_case    )
  self.assertEqual(outputs.end_logits.shape							,					_snake_case    )












"""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							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   cuda
  lowerCAmelCase													=   AutoConfig.from_pretrained(_snake_case    )
  lowerCAmelCase													=   AutoModelForCTC.from_pretrained(_snake_case    )
  self.model.eval()
  if self.cuda:
   self.model.to(device='cuda'    )
  lowerCAmelCase													=   AutoProcessor.from_pretrained(_snake_case    )
  lowerCAmelCase													=   torchaudio.transforms.Resample(_snake_case							,					1_60_00    )
  lowerCAmelCase													=   0
  lowerCAmelCase													=   list(self.processor.tokenizer.get_vocab().keys()    )
  for i in range(len(_snake_case    )    ):
   if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
    lowerCAmelCase													=   i
  print('Blank Token id [PAD]/<pad>'							,					_snake_case    )
  lowerCAmelCase													=   blank_id



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase													=   torchaudio.load(_snake_case    )
  lowerCAmelCase													=   self.resampler(_snake_case    ).squeeze().numpy()
  return speech



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






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

  lowerCAmelCase													=   self.speech_file_to_array_fn(item['wav_path']    )
  lowerCAmelCase													=   self.processor(_snake_case							,					sampling_rate=1_60_00							,					return_tensors='pt'							,					padding=_snake_case    )
  if self.cuda:
   lowerCAmelCase													=   inputs.to(device='cuda'    )

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

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

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

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

  def get_trellis(_snake_case							,					_snake_case							,					_snake_case=0    ):
   lowerCAmelCase													=   emission.size(0    )
   lowerCAmelCase													=   len(_snake_case    )

   # 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													=   torch.full((num_frames + 1, num_tokens + 1)							,					-float('inf'    )    )
   lowerCAmelCase													=   0
   for t in range(_snake_case    ):
    lowerCAmelCase													=   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													=   get_trellis(_snake_case							,					_snake_case							,					_snake_case    )







  @dataclass
  class 			a       :
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42

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

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

    # 2. Store the path with frame-wise probability.
    lowerCAmelCase													=   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							,					_snake_case    )    )

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

  lowerCAmelCase													=   backtrack(_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    )







  @dataclass
  class 			a       :
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42



   def __repr__(    self    ):







    """simple docstring"""






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



   @property
   def       UpperCamelCase__     (    self    ):







    """simple docstring"""






    return self.end - self.start

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

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



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






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

  # load text file
  lowerCAmelCase													=   open(_snake_case							,					encoding='utf8'    ).readlines()

  lowerCAmelCase													=   []
  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													=   line.strip().split('\t'    )
   lowerCAmelCase													=   os.path.join(_snake_case							,					wav_name + '.wav'    )
   lowerCAmelCase													=   os.path.join(_snake_case							,					wav_name + '.txt'    )

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

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


def 		_SCREAMING_SNAKE_CASE ():
 lowerCAmelCase													=   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=1_6000     ,    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													=   parser.parse_args()

 lowerCAmelCase													=   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()





