




"""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 							lowerCamelCase						:







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







   def 					a_							(							self			,					SCREAMING_SNAKE_CASE_       ):
       UpperCamelCase :      str      				=							torchaudio.load(snake_case_       )
       UpperCamelCase :      Optional[int]      				=							self.resampler(snake_case_       ).squeeze().numpy()
       return speech







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

       UpperCamelCase :      Tuple      				=							self.speech_file_to_array_fn(item["""wav_path"""]       )
       UpperCamelCase :      Optional[int]      				=							self.processor(snake_case_			,					sampling_rate=1_6000			,					return_tensors="""pt"""			,					padding=snake_case_       )
       if self.cuda:
           UpperCamelCase :      Dict      				=							inputs.to(device="""cuda"""       )

       with torch.no_grad():
           UpperCamelCase :      Union[str, Any]      				=							self.model(inputs.input_values       ).logits

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

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

       UpperCamelCase :      List[str]      				=							{c: i for i, c in enumerate(snake_case_       )}
       UpperCamelCase :      Any      				=							[]
       for c in transcript:
           if c in dictionary:
               tokens.append(dictionary[c]       )

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

       UpperCamelCase :      int      				=							get_trellis(snake_case_			,					snake_case_			,					snake_case_       )
       @dataclass
       class 							lowerCamelCase						:
          lowercase				:     int
          lowercase				:     int
          lowercase				:     float

       def backtrack(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_=0       ):
           UpperCamelCase :      int      				=							trellis.size(1       ) - 1
           UpperCamelCase :      List[str]      				=							torch.argmax(trellis[:, j]       ).item()

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

               # 2. Store the path with frame-wise probability.
               UpperCamelCase :      List[Any]      				=							emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
               # Return token index and time index in non-trellis coordinate.
               path.append(Point(j - 1			,					t - 1			,					snake_case_       )       )

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

       UpperCamelCase :      Tuple      				=							backtrack(snake_case_			,					snake_case_			,					snake_case_			,					snake_case_       )
       @dataclass
       class 							lowerCamelCase						:
          lowercase				:     str
          lowercase				:     int
          lowercase				:     int
          lowercase				:     float







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







          @property
          def 					a_							(							self       ):
              return self.end - self.start

       def merge_repeats(SCREAMING_SNAKE_CASE_       ):
           UpperCamelCase :      List[str]      				=							0, 0
           UpperCamelCase :      Optional[int]      				=							[]
           while ia < len(snake_case_       ):
               while ia < len(snake_case_       ) and path[ia].token_index == path[ia].token_index:
                   ia += 1
               UpperCamelCase :      List[str]      				=							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_			,					)       )
               UpperCamelCase :      Dict      				=							ia
           return segments

       UpperCamelCase :      List[str]      				=							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 					a_							(							self			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_       ):

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

       # load text file
       UpperCamelCase :      int      				=							open(snake_case_			,					encoding="""utf8"""       ).readlines()

       UpperCamelCase :      Union[str, Any]      				=							[]
       for line in lines:
           if len(line.strip().split("""\t"""       )       ) != 2:
               print("""Script must be in format: 00001  this is my sentence"""       )
               exit()

           UpperCamelCase :      Optional[int]      				=							line.strip().split("""\t"""       )
           UpperCamelCase :      Union[str, Any]      				=							os.path.join(snake_case_			,					wav_name + """.wav"""       )
           UpperCamelCase :      str      				=							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     A_	(						):







    '''simple docstring'''
    UpperCamelCase :      List[str]      				=							argparse.ArgumentParser()

    parser.add_argument(
        """--model_name"""  ,type=lowerCamelCase_  ,default="""arijitx/wav2vec2-xls-r-300m-bengali"""  ,help="""wav2vec model name"""			)
    parser.add_argument("""--wav_dir"""  ,type=lowerCamelCase_  ,default="""./wavs"""  ,help="""directory containing wavs"""			)
    parser.add_argument("""--text_file"""  ,type=lowerCamelCase_  ,default="""script.txt"""  ,help="""file containing text"""			)
    parser.add_argument("""--input_wavs_sr"""  ,type=lowerCamelCase_  ,default=1_6_0_0_0  ,help="""sampling rate of input audios"""			)
    parser.add_argument(
        """--output_dir"""  ,type=lowerCamelCase_  ,default="""./out_alignment"""  ,help="""output directory containing the alignment files"""			)
    parser.add_argument("""--cuda"""  ,action="""store_true"""			)

    UpperCamelCase :      Any      				=							parser.parse_args()

    UpperCamelCase :      Tuple      				=							WavaVecaAligner(args.model_name  ,args.input_wavs_sr  ,args.cuda			)
    aligner.align_data(args.wav_dir  ,args.text_file  ,args.output_dir			)


if __name__ == "__main__":
  main()





'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__     :			Optional[Any]      			=  logging.get_logger(__name__)


def 					_lowerCamelCase    (      lowerCamelCase_					:							List[str]   ):



     """simple docstring"""





     UpperCAmelCase_							:   Dict   =      ConvNextConfig()

     if "tiny" in checkpoint_url:
          UpperCAmelCase_							:   Any   =      [3, 3, 9, 3]
          UpperCAmelCase_							:   Any   =      [96, 192, 384, 768]
     if "small" in checkpoint_url:
          UpperCAmelCase_							:   int   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Optional[int]   =      [96, 192, 384, 768]
     if "base" in checkpoint_url:
          UpperCAmelCase_							:   str   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Dict   =      [128, 256, 512, 1024]
     if "large" in checkpoint_url:
          UpperCAmelCase_							:   Optional[Any]   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   List[Any]   =      [192, 384, 768, 1536]
     if "xlarge" in checkpoint_url:
          UpperCAmelCase_							:   str   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Any   =      [256, 512, 1024, 2048]

     if "1k" in checkpoint_url:
          UpperCAmelCase_							:   int   =      1000
          UpperCAmelCase_							:   Any   =      'imagenet-1k-id2label.json'
          UpperCAmelCase_							:   str   =      (1, 1000)
     else:
          UpperCAmelCase_							:   Dict   =      21841
          UpperCAmelCase_							:   Optional[Any]   =      'imagenet-22k-id2label.json'
          UpperCAmelCase_							:   Optional[Any]   =      (1, 21841)

     UpperCAmelCase_							:   int   =      'huggingface/label-files'
     UpperCAmelCase_							:   Any   =      num_labels
     UpperCAmelCase_							:   Dict   =      json.load(open(hf_hub_download(lowerCamelCase_	,     lowerCamelCase_	,     repo_type='dataset'   )	,     'r'   )   )
     UpperCAmelCase_							:   List[Any]   =      {int(lowerCamelCase_   ): v for k, v in idalabel.items()}
     if "1k" not in checkpoint_url:
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9205]
          del idalabel[15027]
     UpperCAmelCase_							:   Optional[Any]   =      idalabel
     UpperCAmelCase_							:   Dict   =      {v: k for k, v in idalabel.items()}
     UpperCAmelCase_							:   Tuple   =      hidden_sizes
     UpperCAmelCase_							:   List[str]   =      depths

     return config, expected_shape


def 					_lowerCamelCase    (      lowerCamelCase_					:							int   ):



     """simple docstring"""





     if "downsample_layers.0.0" in name:
          UpperCAmelCase_							:   List[Any]   =      name.replace('downsample_layers.0.0'	,     'embeddings.patch_embeddings'   )
     if "downsample_layers.0.1" in name:
          UpperCAmelCase_							:   List[Any]   =      name.replace('downsample_layers.0.1'	,     'embeddings.norm'   )  # we rename to layernorm later on
     if "downsample_layers.1.0" in name:
          UpperCAmelCase_							:   int   =      name.replace('downsample_layers.1.0'	,     'stages.1.downsampling_layer.0'   )
     if "downsample_layers.1.1" in name:
          UpperCAmelCase_							:   List[str]   =      name.replace('downsample_layers.1.1'	,     'stages.1.downsampling_layer.1'   )
     if "downsample_layers.2.0" in name:
          UpperCAmelCase_							:   str   =      name.replace('downsample_layers.2.0'	,     'stages.2.downsampling_layer.0'   )
     if "downsample_layers.2.1" in name:
          UpperCAmelCase_							:   List[str]   =      name.replace('downsample_layers.2.1'	,     'stages.2.downsampling_layer.1'   )
     if "downsample_layers.3.0" in name:
          UpperCAmelCase_							:   Optional[int]   =      name.replace('downsample_layers.3.0'	,     'stages.3.downsampling_layer.0'   )
     if "downsample_layers.3.1" in name:
          UpperCAmelCase_							:   Dict   =      name.replace('downsample_layers.3.1'	,     'stages.3.downsampling_layer.1'   )
     if "stages" in name and "downsampling_layer" not in name:
          # stages.0.0. for instance should be renamed to stages.0.layers.0.
          UpperCAmelCase_							:   str   =      name[: len('stages.0'   )] + '.layers' + name[len('stages.0'   ) :]
     if "stages" in name:
          UpperCAmelCase_							:   int   =      name.replace('stages'	,     'encoder.stages'   )
     if "norm" in name:
          UpperCAmelCase_							:   List[str]   =      name.replace('norm'	,     'layernorm'   )
     if "gamma" in name:
          UpperCAmelCase_							:   Any   =      name.replace('gamma'	,     'layer_scale_parameter'   )
     if "head" in name:
          UpperCAmelCase_							:   List[str]   =      name.replace('head'	,     'classifier'   )

     return name


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   List[str]   =      'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCAmelCase_							:   Optional[int]   =      Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_   ).raw   )
     return im


@torch.no_grad()
def 					_lowerCamelCase    (      lowerCamelCase_					:							int	,     lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





     UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[Any]   =      get_convnext_config(lowerCamelCase_   )
     # load original state_dict from URL
     UpperCAmelCase_							:   Dict   =      torch.hub.load_state_dict_from_url(lowerCamelCase_   )['model']
     # rename keys
     for key in state_dict.copy().keys():
          UpperCAmelCase_							:   Optional[int]   =      state_dict.pop(lowerCamelCase_   )
          UpperCAmelCase_							:   Tuple   =      val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          UpperCAmelCase_							:   int   =      state_dict.pop(lowerCamelCase_   )
          if not key.startswith('classifier'   ):
               UpperCAmelCase_							:   int   =      'convnext.' + key
          UpperCAmelCase_							:   Tuple   =      val

     # load HuggingFace model
     UpperCAmelCase_							:   Tuple   =      ConvNextForImageClassification(lowerCamelCase_   )
     model.load_state_dict(lowerCamelCase_   )
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     UpperCAmelCase_							:   Tuple   =      224 if '224' in checkpoint_url else 384
     UpperCAmelCase_							:   Optional[Any]   =      ConvNextImageProcessor(size=lowerCamelCase_   )
     UpperCAmelCase_							:   Tuple   =      image_processor(images=prepare_img()	,     return_tensors='pt'   ).pixel_values

     UpperCAmelCase_							:   Dict   =      model(lowerCamelCase_   ).logits

     # note: the logits below were obtained without center cropping
     if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
          UpperCAmelCase_							:   Tuple   =      torch.tensor([-0.1210, -0.6605, 0.1918]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
          UpperCAmelCase_							:   str   =      torch.tensor([-0.4473, -0.1847, -0.6365]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
          UpperCAmelCase_							:   Tuple   =      torch.tensor([0.4525, 0.7539, 0.0308]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          UpperCAmelCase_							:   List[Any]   =      torch.tensor([0.3561, 0.6350, -0.0384]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
          UpperCAmelCase_							:   List[str]   =      torch.tensor([0.4174, -0.0989, 0.1489]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          UpperCAmelCase_							:   Any   =      torch.tensor([0.2513, -0.1349, -0.1613]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
          UpperCAmelCase_							:   Dict   =      torch.tensor([1.2980, 0.3631, -0.1198]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
          UpperCAmelCase_							:   Union[str, Any]   =      torch.tensor([1.2963, 0.1227, 0.1723]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          UpperCAmelCase_							:   Optional[Any]   =      torch.tensor([1.7956, 0.8390, 0.2820]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
          UpperCAmelCase_							:   Tuple   =      torch.tensor([-0.2822, -0.0502, -0.0878]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
          UpperCAmelCase_							:   Union[str, Any]   =      torch.tensor([-0.5672, -0.0730, -0.4348]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
          UpperCAmelCase_							:   Optional[Any]   =      torch.tensor([0.2681, 0.2365, 0.6246]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
          UpperCAmelCase_							:   Optional[int]   =      torch.tensor([-0.2642, 0.3931, 0.5116]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
          UpperCAmelCase_							:   Union[str, Any]   =      torch.tensor([-0.6677, -0.1873, -0.8379]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
          UpperCAmelCase_							:   str   =      torch.tensor([-0.7749, -0.2967, -0.6444]   )
     else:
          raise ValueError(F'''Unknown URL: {checkpoint_url}'''   )

     assert torch.allclose(logits[0, :3]	,     lowerCamelCase_	,     atol=1e-3   )
     assert logits.shape == expected_shape

     Path(lowerCamelCase_   ).mkdir(exist_ok=lowerCamelCase_   )
     print(F'''Saving model to {pytorch_dump_folder_path}'''   )
     model.save_pretrained(lowerCamelCase_   )
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
     image_processor.save_pretrained(lowerCamelCase_   )

     print('Pushing model to the hub...'   )
     UpperCAmelCase_							:   Union[str, Any]   =      'convnext'
     if "tiny" in checkpoint_url:
          model_name += "-tiny"
     elif "small" in checkpoint_url:
          model_name += "-small"
     elif "base" in checkpoint_url:
          model_name += "-base"
     elif "xlarge" in checkpoint_url:
          model_name += "-xlarge"
     elif "large" in checkpoint_url:
          model_name += "-large"
     if "224" in checkpoint_url:
          model_name += "-224"
     elif "384" in checkpoint_url:
          model_name += "-384"
     if "22k" in checkpoint_url and "1k" not in checkpoint_url:
          model_name += "-22k"
     if "22k" in checkpoint_url and "1k" in checkpoint_url:
          model_name += "-22k-1k"

     model.push_to_hub(
         repo_path_or_name=Path(lowerCamelCase_	,     lowerCamelCase_   )	,     organization='nielsr'	,     commit_message='Add model'	,     )


if __name__ == "__main__":
 snake_case__     :			List[Any]      			=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
     type=str,
     help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the output PyTorch model directory.''',
 )

 snake_case__     :			int      			=  parser.parse_args()
 convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






