






"""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  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   Any:




          '''simple docstring'''




          lowercase__:							Tuple         =     cuda
          lowercase__:							int         =     AutoConfig.from_pretrained(UpperCAmelCase_)
          lowercase__:							Union[str, Any]         =     AutoModelForCTC.from_pretrained(UpperCAmelCase_)
          self.model.eval()
          if self.cuda:
                 self.model.to(device="cuda")
          lowercase__:							int         =     AutoProcessor.from_pretrained(UpperCAmelCase_)
          lowercase__:							Optional[int]         =     torchaudio.transforms.Resample(UpperCAmelCase_					, 16_000)
          lowercase__:							Tuple         =     0
          lowercase__:							List[str]         =     list(self.processor.tokenizer.get_vocab().keys())
          for i in range(len(UpperCAmelCase_)):
                 if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                        lowercase__:							str         =     i
          print("Blank Token id [PAD]/<pad>"					, UpperCAmelCase_)
          lowercase__:							int         =     blank_id





   def __lowercase							(       self					, UpperCAmelCase_)			->   Tuple:




          '''simple docstring'''




          lowercase__    ,      lowercase__:							Any         =     torchaudio.load(UpperCAmelCase_)
          lowercase__:							Any         =     self.resampler(UpperCAmelCase_).squeeze().numpy()
          return speech





   def __lowercase							(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




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

          lowercase__:							int         =     self.speech_file_to_array_fn(item["wav_path"])
          lowercase__:							int         =     self.processor(UpperCAmelCase_					, sampling_rate=16_000					, return_tensors="pt"					, padding=UpperCAmelCase_)
          if self.cuda:
                 lowercase__:							Union[str, Any]         =     inputs.to(device="cuda")

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

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

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

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

          def get_trellis(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_=0):
                 lowercase__:							Any         =     emission.size(0)
                 lowercase__:							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.
                 lowercase__:							str         =     torch.full((num_frames + 1, num_tokens + 1)					, -float("inf"))
                 lowercase__:							str         =     0
                 for t in range(UpperCAmelCase_):
                        lowercase__:							Optional[int]         =     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__:							str         =     get_trellis(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)







          @dataclass
          class 						_a  :





             '''simple docstring'''






             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42

          def backtrack(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_=0):
                 lowercase__:							List[str]         =     trellis.size(1) - 1
                 lowercase__:							Dict         =     torch.argmax(trellis[:, j]).item()

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

                        # 2. Store the path with frame-wise probability.
                        lowercase__:							int         =     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]

          lowercase__:							Optional[Any]         =     backtrack(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)







          @dataclass
          class 						_a  :





             '''simple docstring'''






             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42





             def __repr__(       self)			->   Optional[int]:




                    '''simple docstring'''




                    return F"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""





             @property
             def __lowercase							(       self)			->   Any:




                    '''simple docstring'''




                    return self.end - self.start

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

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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   List[str]:




          '''simple docstring'''




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

          # load text file
          lowercase__:							List[str]         =     open(UpperCAmelCase_					, 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__:							str         =     line.strip().split("\t")
                 lowercase__:							List[Any]         =     os.path.join(UpperCAmelCase_					, wav_name + ".wav")
                 lowercase__:							List[Any]         =     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 							A(    ):
       """simple docstring"""





       lowercase__:							List[Any]         =     argparse.ArgumentParser()

       parser.add_argument(
           "--model_name"		,  type=snake_case_		,  default="arijitx/wav2vec2-xls-r-300m-bengali"		,  help="wav2vec model name"						)
       parser.add_argument("--wav_dir"		,  type=snake_case_		,  default="./wavs"		,  help="directory containing wavs"						)
       parser.add_argument("--text_file"		,  type=snake_case_		,  default="script.txt"		,  help="file containing text"						)
       parser.add_argument("--input_wavs_sr"		,  type=snake_case_		,  default=16000		,  help="sampling rate of input audios"						)
       parser.add_argument(
           "--output_dir"		,  type=snake_case_		,  default="./out_alignment"		,  help="output directory containing the alignment files"						)
       parser.add_argument("--cuda"		,  action="store_true"						)

       lowercase__:							Any         =     parser.parse_args()

       lowercase__:							Any         =     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()












'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 						lowerCAmelCase_	(       UpperCAmelCase_       ,					unittest.TestCase							):







  '''simple docstring'''

  UpperCamelCase_			:      Any										=				RePaintPipeline
  UpperCamelCase_			:      int										=				IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
  UpperCamelCase_			:      List[str]										=				PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  UpperCamelCase_			:      List[str]										=				IMAGE_INPAINTING_BATCH_PARAMS
  UpperCamelCase_			:      str										=				False



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







       '''simple docstring'''



       torch.manual_seed(0	)
       torch.manual_seed(0	)
       A:		Optional[Any]          =		UNetaDModel(
           block_out_channels=(32, 64)					,  layers_per_block=2					,  sample_size=32					,  in_channels=3					,  out_channels=3					,  down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,  up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,  )
       A:		Optional[int]          =		RePaintScheduler()
       A:		Optional[int]          =		{'''unet''': unet, '''scheduler''': scheduler}
       return components



  def 					_snake_case     ( self				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							Tuple=0	)			->		Optional[int]:







       '''simple docstring'''



       if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
            A:		str          =		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
       else:
            A:		Any          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		str          =		np.random.RandomState(SCREAMING_SNAKE_CASE_	).standard_normal((1, 3, 32, 32)	)
       A:		Tuple          =		torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       A:		List[Any]          =		(image > 0).to(device=SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       A:		Tuple          =		{
           '''image''': image,
           '''mask_image''': mask,
           '''generator''': generator,
           '''num_inference_steps''': 5,
           '''eta''': 0.0,
           '''jump_length''': 2,
           '''jump_n_sample''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs



  def 					_snake_case     ( self				:							int	)			->		Tuple:







       '''simple docstring'''



       A:		int          =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       A:		List[str]          =		self.get_dummy_components()
       A:		int          =		RePaintPipeline(**SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
       sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Dict          =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
       A:		List[str]          =		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
       A:		Optional[Any]          =		image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       A:		List[Any]          =		np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3



  @skip_mps
  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Optional[Any]:







       '''simple docstring'''



       return super().test_save_load_local()



  @unittest.skip('''non-deterministic pipeline'''	)
  def 					_snake_case     ( self				:							Tuple	)			->		Optional[Any]:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()



  @skip_mps
  def 					_snake_case     ( self				:							List[Any]	)			->		str:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()



  @skip_mps
  def 					_snake_case     ( self				:							List[Any]	)			->		str:







       '''simple docstring'''



       return super().test_save_load_optional_components()




  @skip_mps
  def 					_snake_case     ( self				:							Any	)			->		Optional[Any]:







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class 						lowerCAmelCase_	(       unittest.TestCase							):







  '''simple docstring'''

  def 					_snake_case     ( self				:							Optional[Any]	)			->		Optional[Any]:







       '''simple docstring'''



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




  def 					_snake_case     ( self				:							Optional[int]	)			->		List[Any]:







       '''simple docstring'''



       A:		str          =		load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256.png'''	)
       A:		Union[str, Any]          =		load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''	)
       A:		Optional[Any]          =		load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256_result.npy'''	)

       A:		Dict          =		'''google/ddpm-ema-celebahq-256'''
       A:		Tuple          =		UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE_	)

       A:		Optional[Any]          =		RePaintPipeline(unet=SCREAMING_SNAKE_CASE_					,  scheduler=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)
       repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
       repaint.enable_attention_slicing()

       A:		str          =		torch.manual_seed(0	)
       A:		str          =		repaint(
           SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  num_inference_steps=2_50					,  eta=0.0					,  jump_length=10					,  jump_n_sample=10					,  generator=SCREAMING_SNAKE_CASE_					,  output_type='''np'''					,  )
       A:		Optional[Any]          =		output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image	).mean() < 1E-2






