





"""simple docstring"""




import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
     from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor


class   a__		:


   def __init__(							self      :							str,     lowerCAmelCase      :							str,     lowerCAmelCase      :							str=13,     lowerCAmelCase      :							Any=32,     lowerCAmelCase      :							Dict=2,     lowerCAmelCase      :							Tuple=3,     lowerCAmelCase      :							Union[str, Any]=16,     lowerCAmelCase      :							List[Any]=[1, 2, 1],     lowerCAmelCase      :							List[str]=[2, 2, 4],     lowerCAmelCase      :							List[Any]=2,     lowerCAmelCase      :							Dict=2.0,     lowerCAmelCase      :							Any=True,     lowerCAmelCase      :							Optional[Any]=0.0,     lowerCAmelCase      :							Optional[int]=0.0,     lowerCAmelCase      :							int=0.1,     lowerCAmelCase      :							List[Any]="gelu",     lowerCAmelCase      :							str=False,     lowerCAmelCase      :							Union[str, Any]=True,     lowerCAmelCase      :							Dict=0.02,     lowerCAmelCase      :							int=1e-5,     lowerCAmelCase      :							Dict=True,     lowerCAmelCase      :							Union[str, Any]=None,     lowerCAmelCase      :							Dict=True,     lowerCAmelCase      :							Union[str, Any]=10,     lowerCAmelCase      :							Union[str, Any]=8,     lowerCAmelCase      :							Any=["stage1", "stage2"],     lowerCAmelCase      :							Optional[int]=[1, 2],     )    ->       str:
         lowercase  :      Any					=   parent
         lowercase  :      int					=   batch_size
         lowercase  :      List[Any]					=   image_size
         lowercase  :      str					=   patch_size
         lowercase  :      Tuple					=   num_channels
         lowercase  :      Tuple					=   embed_dim
         lowercase  :      Tuple					=   depths
         lowercase  :      List[Any]					=   num_heads
         lowercase  :      Optional[Any]					=   window_size
         lowercase  :      str					=   mlp_ratio
         lowercase  :      int					=   qkv_bias
         lowercase  :      Dict					=   hidden_dropout_prob
         lowercase  :      Any					=   attention_probs_dropout_prob
         lowercase  :      str					=   drop_path_rate
         lowercase  :      Union[str, Any]					=   hidden_act
         lowercase  :      Dict					=   use_absolute_embeddings
         lowercase  :      List[str]					=   patch_norm
         lowercase  :      Union[str, Any]					=   layer_norm_eps
         lowercase  :      Tuple					=   initializer_range
         lowercase  :      Tuple					=   is_training
         lowercase  :      List[Any]					=   scope
         lowercase  :      Any					=   use_labels
         lowercase  :      Optional[Any]					=   type_sequence_label_size
         lowercase  :      str					=   encoder_stride
         lowercase  :      Optional[int]					=   out_features
         lowercase  :      List[str]					=   out_indices


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Optional[int]:
         lowercase  :      List[Any]					=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

         lowercase  :      Tuple					=   None
         if self.use_labels:
               lowercase  :      Any					=   ids_tensor([self.batch_size],     self.type_sequence_label_size			)

         lowercase  :      Tuple					=   self.get_config()

         return config, pixel_values, labels


   def        lowercase				(							self      :							Optional[int]			)    ->       List[str]:
         return SwinConfig(
             image_size=self.image_size,     patch_size=self.patch_size,     num_channels=self.num_channels,     embed_dim=self.embed_dim,     depths=self.depths,     num_heads=self.num_heads,     window_size=self.window_size,     mlp_ratio=self.mlp_ratio,     qkv_bias=self.qkv_bias,     hidden_dropout_prob=self.hidden_dropout_prob,     attention_probs_dropout_prob=self.attention_probs_dropout_prob,     drop_path_rate=self.drop_path_rate,     hidden_act=self.hidden_act,     use_absolute_embeddings=self.use_absolute_embeddings,     path_norm=self.patch_norm,     layer_norm_eps=self.layer_norm_eps,     initializer_range=self.initializer_range,     encoder_stride=self.encoder_stride,     out_features=self.out_features,     out_indices=self.out_indices,     )


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							Optional[int],     lowerCAmelCase      :							Optional[int]			)    ->       Tuple:
         lowercase  :      Optional[Any]					=   SwinModel(config=lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()
         lowercase  :      List[str]					=   model(lowerCAmelCase			)

         lowercase  :      Tuple					=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
         lowercase  :      str					=   int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)

         self.parent.assertEqual(result.last_hidden_state.shape,     (self.batch_size, expected_seq_len, expected_dim)			)


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							str,     lowerCAmelCase      :							str,     lowerCAmelCase      :							int			)    ->       Tuple:
         lowercase  :      Union[str, Any]					=   SwinBackbone(config=lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()
         lowercase  :      List[str]					=   model(lowerCAmelCase			)

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps			),     len(config.out_features			)			)
         self.parent.assertListEqual(list(result.feature_maps[0].shape			),     [self.batch_size, model.channels[0], 16, 16]			)

         # verify channels
         self.parent.assertEqual(len(model.channels			),     len(config.out_features			)			)

         # verify backbone works with out_features=None
         lowercase  :      Dict					=   None
         lowercase  :      str					=   SwinBackbone(config=lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()
         lowercase  :      Optional[int]					=   model(lowerCAmelCase			)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps			),     1			)
         self.parent.assertListEqual(list(result.feature_maps[0].shape			),     [self.batch_size, model.channels[-1], 4, 4]			)

         # verify channels
         self.parent.assertEqual(len(model.channels			),     1			)


   def        lowercase				(							self      :							str,     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							str			)    ->       List[str]:
         lowercase  :      Dict					=   SwinForMaskedImageModeling(config=lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()
         lowercase  :      Any					=   model(lowerCAmelCase			)
         self.parent.assertEqual(
             result.logits.shape,     (self.batch_size, self.num_channels, self.image_size, self.image_size)			)

         # test greyscale images
         lowercase  :      int					=   1
         lowercase  :      int					=   SwinForMaskedImageModeling(lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()

         lowercase  :      Any					=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
         lowercase  :      int					=   model(lowerCAmelCase			)
         self.parent.assertEqual(result.logits.shape,     (self.batch_size, 1, self.image_size, self.image_size)			)


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							int,     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							Dict			)    ->       Any:
         lowercase  :      str					=   self.type_sequence_label_size
         lowercase  :      List[Any]					=   SwinForImageClassification(lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()
         lowercase  :      Any					=   model(lowerCAmelCase,     labels=lowerCAmelCase			)
         self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.type_sequence_label_size)			)

         # test greyscale images
         lowercase  :      List[str]					=   1
         lowercase  :      List[str]					=   SwinForImageClassification(lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()

         lowercase  :      Union[str, Any]					=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
         lowercase  :      int					=   model(lowerCAmelCase			)
         self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.type_sequence_label_size)			)






   def        lowercase				(							self      :							Dict			)    ->       Any:
         lowercase  :      Optional[int]					=   self.prepare_config_and_inputs()
         (
             (
             lowercase		
         )	,    (
             lowercase		
         )	,    (
             lowercase		
         )	,    		
         )  :      str					=   config_and_inputs
         lowercase  :      Dict					=   {'pixel_values': pixel_values}
         return config, inputs_dict




@require_torch
class   a__		(						UpperCAmelCase_,  UpperCAmelCase_,  unittest.TestCase     ):
   _lowerCamelCase												=				(
       (
           SwinModel,
           SwinBackbone,
           SwinForImageClassification,
           SwinForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   _lowerCamelCase												=				(
       {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
       if is_torch_available()
       else {}
   )
   _lowerCamelCase												=				True

   _lowerCamelCase												=				False
   _lowerCamelCase												=				False
   _lowerCamelCase												=				False


   def        lowercase				(							self      :							List[Any]			)    ->       List[str]:
         lowercase  :      Tuple					=   SwinModelTester(self			)
         lowercase  :      Optional[int]					=   ConfigTester(self,     config_class=lowerCAmelCase,     embed_dim=37			)


   def        lowercase				(							self      :							int			)    ->       List[Any]:
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()


   def        lowercase				(							self      :							Dict			)    ->       Optional[Any]:
         return


   def        lowercase				(							self      :							List[str]			)    ->       Optional[Any]:
         lowercase  :      Dict					=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase			)


   @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'			)
   def        lowercase				(							self      :							Any			)    ->       str:
         pass


   def        lowercase				(							self      :							str			)    ->       List[Any]:
         super().test_training_gradient_checkpointing()


   def        lowercase				(							self      :							Any			)    ->       Optional[int]:
         lowercase  :      Optional[int]					=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Optional[int]:
         lowercase  :      str					=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Optional[Any]:
         lowercase  :      Tuple					=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase			)


   @unittest.skip(reason='Swin does not use inputs_embeds'			)
   def        lowercase				(							self      :							Dict			)    ->       Optional[int]:
         pass


   @unittest.skip(reason='Swin Transformer does not use feedforward chunking'			)
   def        lowercase				(							self      :							Optional[Any]			)    ->       Tuple:
         pass


   def        lowercase				(							self      :							List[str]			)    ->       Optional[int]:
         lowercase	,    lowercase  :      Optional[Any]					=   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase  :      Any					=   model_class(lowerCAmelCase			)
               self.assertIsInstance(model.get_input_embeddings(),     (nn.Module)			)
               lowercase  :      Optional[int]					=   model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase,     nn.Linear			)			)


   def        lowercase				(							self      :							Tuple			)    ->       Tuple:
         lowercase	,    lowercase  :      Dict					=   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase  :      Union[str, Any]					=   model_class(lowerCAmelCase			)
               lowercase  :      Dict					=   inspect.signature(model.forward			)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase  :      int					=   [*signature.parameters.keys()]

               lowercase  :      str					=   ['pixel_values']
               self.assertListEqual(arg_names[:1],     lowerCAmelCase			)


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Tuple:
         lowercase	,    lowercase  :      Any					=   self.model_tester.prepare_config_and_inputs_for_common()
         lowercase  :      Optional[int]					=   True

         for model_class in self.all_model_classes:
               lowercase  :      str					=   True
               lowercase  :      List[str]					=   False
               lowercase  :      List[str]					=   True
               lowercase  :      Dict					=   model_class(lowerCAmelCase			)
               model.to(lowerCAmelCase			)
               model.eval()
               with torch.no_grad():
                     lowercase  :      Optional[Any]					=   model(**self._prepare_for_class(lowerCAmelCase,     lowerCAmelCase			)			)
               lowercase  :      Optional[Any]					=   outputs.attentions
               lowercase  :      int					=   len(self.model_tester.depths			)
               self.assertEqual(len(lowerCAmelCase			),     lowerCAmelCase			)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               lowercase  :      Union[str, Any]					=   True
               lowercase  :      Union[str, Any]					=   config.window_size**2
               lowercase  :      int					=   model_class(lowerCAmelCase			)
               model.to(lowerCAmelCase			)
               model.eval()
               with torch.no_grad():
                     lowercase  :      Union[str, Any]					=   model(**self._prepare_for_class(lowerCAmelCase,     lowerCAmelCase			)			)
               lowercase  :      Optional[Any]					=   outputs.attentions
               self.assertEqual(len(lowerCAmelCase			),     lowerCAmelCase			)

               self.assertListEqual(
                   list(attentions[0].shape[-3:]			),     [self.model_tester.num_heads[0], window_size_squared, window_size_squared],     )
               lowercase  :      List[str]					=   len(lowerCAmelCase			)

               # Check attention is always last and order is fine
               lowercase  :      List[str]					=   True
               lowercase  :      List[Any]					=   True
               lowercase  :      Tuple					=   model_class(lowerCAmelCase			)
               model.to(lowerCAmelCase			)
               model.eval()
               with torch.no_grad():
                     lowercase  :      Any					=   model(**self._prepare_for_class(lowerCAmelCase,     lowerCAmelCase			)			)

               # also another +1 for reshaped_hidden_states
               lowercase  :      Tuple					=   1 if model_class.__name__ == 'SwinBackbone' else 2
               self.assertEqual(out_len + added_hidden_states,     len(lowerCAmelCase			)			)

               lowercase  :      Any					=   outputs.attentions

               self.assertEqual(len(lowerCAmelCase			),     lowerCAmelCase			)

               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]			),     [self.model_tester.num_heads[0], window_size_squared, window_size_squared],     )


   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							str			)    ->       Union[str, Any]:
         lowercase  :      int					=   model_class(lowerCAmelCase			)
         model.to(lowerCAmelCase			)
         model.eval()

         with torch.no_grad():
               lowercase  :      int					=   model(**self._prepare_for_class(lowerCAmelCase,     lowerCAmelCase			)			)

         lowercase  :      List[str]					=   outputs.hidden_states

         lowercase  :      Tuple					=   getattr(
             self.model_tester,     'expected_num_hidden_layers',     len(self.model_tester.depths			) + 1			)
         self.assertEqual(len(lowerCAmelCase			),     lowerCAmelCase			)

         # Swin has a different seq_length
         lowercase  :      Tuple					=   (
             config.patch_size
             if isinstance(config.patch_size,     collections.abc.Iterable			)
             else (config.patch_size, config.patch_size)
         )

         lowercase  :      int					=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]			),     [num_patches, self.model_tester.embed_dim],     )

         if not model_class.__name__ == "SwinBackbone":
               lowercase  :      Tuple					=   outputs.reshaped_hidden_states
               self.assertEqual(len(lowerCAmelCase			),     lowerCAmelCase			)

               lowercase	,    lowercase	,    lowercase	,    lowercase  :      str					=   reshaped_hidden_states[0].shape
               lowercase  :      List[str]					=   (
                   reshaped_hidden_states[0].view(lowerCAmelCase,     lowerCAmelCase,     height * width			).permute(0,     2,     1			)
               )
               self.assertListEqual(
                   list(reshaped_hidden_states.shape[-2:]			),     [num_patches, self.model_tester.embed_dim],     )


   def        lowercase				(							self      :							List[str]			)    ->       Optional[int]:
         lowercase	,    lowercase  :      str					=   self.model_tester.prepare_config_and_inputs_for_common()

         lowercase  :      Optional[int]					=   (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size,     collections.abc.Iterable			)
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
               lowercase  :      int					=   True
               self.check_hidden_states_output(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               lowercase  :      List[str]					=   True

               self.check_hidden_states_output(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[Any]			)    ->       Union[str, Any]:
         lowercase	,    lowercase  :      List[str]					=   self.model_tester.prepare_config_and_inputs_for_common()
         lowercase  :      Tuple					=   3

         lowercase  :      int					=   (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size,     collections.abc.Iterable			)
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         lowercase  :      List[Any]					=   (
             config.patch_size
             if isinstance(config.patch_size,     collections.abc.Iterable			)
             else (config.patch_size, config.patch_size)
         )

         lowercase  :      Union[str, Any]					=   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         lowercase  :      Union[str, Any]					=   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
               lowercase  :      Tuple					=   True
               self.check_hidden_states_output(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     (padded_height, padded_width)			)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               lowercase  :      str					=   True
               self.check_hidden_states_output(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     (padded_height, padded_width)			)


   @slow
   def        lowercase				(							self      :							Any			)    ->       Optional[Any]:
         for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase  :      str					=   SwinModel.from_pretrained(lowerCAmelCase			)
               self.assertIsNotNone(lowerCAmelCase			)






   def        lowercase				(							self      :							Dict			)    ->       Any:
         lowercase	,    lowercase  :      int					=   self.model_tester.prepare_config_and_inputs_for_common()

         lowercase  :      List[Any]					=   _config_zero_init(lowerCAmelCase			)
         for model_class in self.all_model_classes:
               lowercase  :      List[str]					=   model_class(config=lowerCAmelCase			)
               for name, param in model.named_parameters():
                     if "embeddings" not in name and param.requires_grad:
                           self.assertIn(
                               ((param.data.mean() * 1e9).round() / 1e9).item(),     [0.0, 1.0],     msg=f'''Parameter {name} of model {model_class} seems not properly initialized''',     )




@require_vision
@require_torch
class   a__		(						unittest.TestCase     ):


   @cached_property
   def        lowercase				(							self      :							Union[str, Any]			)    ->       int:
         return (
             AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'			)
             if is_vision_available()
             else None
         )






   @slow
   def        lowercase				(							self      :							Union[str, Any]			)    ->       Dict:
         lowercase  :      str					=   SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'			).to(lowerCAmelCase			)
         lowercase  :      Any					=   self.default_image_processor

         lowercase  :      str					=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
         lowercase  :      Optional[Any]					=   image_processor(images=lowerCAmelCase,     return_tensors='pt'			).to(lowerCAmelCase			)

         # forward pass
         with torch.no_grad():
               lowercase  :      Union[str, Any]					=   model(**lowerCAmelCase			)

         # verify the logits
         lowercase  :      List[str]					=   torch.Size((1, 1000)			)
         self.assertEqual(outputs.logits.shape,     lowerCAmelCase			)
         lowercase  :      Any					=   torch.tensor([-0.0948, -0.6454, -0.0921]			).to(lowerCAmelCase			)
         self.assertTrue(torch.allclose(outputs.logits[0, :3],     lowerCAmelCase,     atol=1e-4			)			)




@require_torch
class   a__		(						unittest.TestCase,  UpperCAmelCase_     ):
   _lowerCamelCase												=				(SwinBackbone,) if is_torch_available() else ()
   _lowerCamelCase												=				SwinConfig


   def        lowercase				(							self      :							List[Any]			)    ->       str:
         lowercase  :      Optional[int]					=   SwinModelTester(self			)





"""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      :							Any,     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							Dict,     lowerCAmelCase      :							Optional[Any]			)    ->       Optional[Any]:
         lowercase  :      Optional[Any]					=   cuda
         lowercase  :      Optional[Any]					=   AutoConfig.from_pretrained(lowerCAmelCase			)
         lowercase  :      str					=   AutoModelForCTC.from_pretrained(lowerCAmelCase			)
         self.model.eval()
         if self.cuda:
               self.model.to(device='cuda'			)
         lowercase  :      Tuple					=   AutoProcessor.from_pretrained(lowerCAmelCase			)
         lowercase  :      Tuple					=   torchaudio.transforms.Resample(lowerCAmelCase,     16000			)
         lowercase  :      Any					=   0
         lowercase  :      Tuple					=   list(self.processor.tokenizer.get_vocab().keys()			)
         for i in range(len(lowerCAmelCase			)			):
               if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                     lowercase  :      Dict					=   i
         print('Blank Token id [PAD]/<pad>',     lowerCAmelCase			)
         lowercase  :      List[str]					=   blank_id


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							Optional[int]			)    ->       Optional[Any]:
         lowercase	,    lowercase  :      Any					=   torchaudio.load(lowerCAmelCase			)
         lowercase  :      List[Any]					=   self.resampler(lowerCAmelCase			).squeeze().numpy()
         return speech


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

         lowercase  :      Dict					=   self.speech_file_to_array_fn(item['wav_path']			)
         lowercase  :      List[str]					=   self.processor(lowerCAmelCase,     sampling_rate=16000,     return_tensors='pt',     padding=lowerCAmelCase			)
         if self.cuda:
               lowercase  :      Union[str, Any]					=   inputs.to(device='cuda'			)

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

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

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

         lowercase  :      Optional[int]					=   {c: i for i, c in enumerate(lowerCAmelCase			)}
         lowercase  :      Union[str, Any]					=   []
         for c in transcript:
               if c in dictionary:
                     tokens.append(dictionary[c]			)

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

         lowercase  :      Any					=   get_trellis(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)


         @dataclass
         class   a__		:
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42

         def backtrack(lowerCAmelCase      :							Any,     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							str,     lowerCAmelCase      :							Any=0			):
               lowercase  :      Tuple					=   trellis.size(1			) - 1
               lowercase  :      Optional[int]					=   torch.argmax(trellis[:, j]			).item()

               lowercase  :      Any					=   []
               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  :      Optional[Any]					=   trellis[t - 1, j] + emission[t - 1, blank_id]
                     # Score for token changing from C-1 at T-1 to J at T.
                     lowercase  :      List[str]					=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                     # 2. Store the path with frame-wise probability.
                     lowercase  :      str					=   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  :      int					=   backtrack(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)


         @dataclass
         class   a__		:
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42


            def __repr__(							self      :							Union[str, Any]			)    ->       Optional[Any]:
                  return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''






            @property
            def        lowercase				(							self      :							Tuple			)    ->       int:
                  return self.end - self.start

         def merge_repeats(lowerCAmelCase      :							Optional[Any]			):
               lowercase	,    lowercase  :      Tuple					=   0, 0
               lowercase  :      Union[str, Any]					=   []
               while ia < len(lowerCAmelCase			):
                     while ia < len(lowerCAmelCase			) and path[ia].token_index == path[ia].token_index:
                           ia += 1
                     lowercase  :      List[str]					=   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  :      List[Any]					=   ia
               return segments

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






   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							int			)    ->       Optional[Any]:
         if not os.path.exists(lowerCAmelCase			):
               os.makedirs(lowerCAmelCase			)

         # load text file
         lowercase  :      Dict					=   open(lowerCAmelCase,     encoding='utf8'			).readlines()

         lowercase  :      Optional[Any]					=   []
         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  :      int					=   line.strip().split('\t'			)
               lowercase  :      List[Any]					=   os.path.join(lowerCAmelCase,     wav_name + '.wav'			)
               lowercase  :      List[Any]					=   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   lowercase__     (			)     ->			Dict:






      '''simple docstring'''


      lowercase  :      Union[str, Any]					=   argparse.ArgumentParser()

      parser.add_argument(
          '--model_name'	,      type=_UpperCAmelCase	,      default='arijitx/wav2vec2-xls-r-300m-bengali'	,      help='wav2vec model name'				)
      parser.add_argument('--wav_dir'	,      type=_UpperCAmelCase	,      default='./wavs'	,      help='directory containing wavs'				)
      parser.add_argument('--text_file'	,      type=_UpperCAmelCase	,      default='script.txt'	,      help='file containing text'				)
      parser.add_argument('--input_wavs_sr'	,      type=_UpperCAmelCase	,      default=1_60_00	,      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'				)

      lowercase  :      List[Any]					=   parser.parse_args()

      lowercase  :      Dict					=   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()
