



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		   =				logging.get_logger(__name__)

UpperCAmelCase_		   =				{
    """apple/mobilevit-small""": """https://huggingface.co/apple/mobilevit-small/resolve/main/config.json""",
    """apple/mobilevit-x-small""": """https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json""",
    """apple/mobilevit-xx-small""": """https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json""",
    """apple/deeplabv3-mobilevit-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-x-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-xx-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"""
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class   lowerCamelCase__  (					_A):



 """simple docstring"""

 a__				:   List[Any]   =     "mobilevit"

 def __init__(						self				:				int ,	__lowerCAmelCase				:				Tuple=3 ,	__lowerCAmelCase				:				Optional[Any]=2_56 ,	__lowerCAmelCase				:				Dict=2 ,	__lowerCAmelCase				:				List[Any]=[1_44, 1_92, 2_40] ,	__lowerCAmelCase				:				Union[str, Any]=[16, 32, 64, 96, 1_28, 1_60, 6_40] ,	__lowerCAmelCase				:				Optional[int]=4 ,	__lowerCAmelCase				:				int=2.0 ,	__lowerCAmelCase				:				List[Any]=4.0 ,	__lowerCAmelCase				:				Tuple="silu" ,	__lowerCAmelCase				:				Optional[Any]=3 ,	__lowerCAmelCase				:				Dict=32 ,	__lowerCAmelCase				:				List[Any]=0.1 ,	__lowerCAmelCase				:				Optional[Any]=0.0 ,	__lowerCAmelCase				:				int=0.1 ,	__lowerCAmelCase				:				Union[str, Any]=0.02 ,	__lowerCAmelCase				:				str=1E-5 ,	__lowerCAmelCase				:				str=True ,	__lowerCAmelCase				:				str=2_56 ,	__lowerCAmelCase				:				int=[6, 12, 18] ,	__lowerCAmelCase				:				List[str]=0.1 ,	__lowerCAmelCase				:				Dict=2_55 ,	**__lowerCAmelCase				:				int ,	)      ->  Optional[int]:
    super().__init__(**__lowerCAmelCase			)

    _A						     =					num_channels
    _A						     =					image_size
    _A						     =					patch_size
    _A						     =					hidden_sizes
    _A						     =					neck_hidden_sizes
    _A						     =					num_attention_heads
    _A						     =					mlp_ratio
    _A						     =					expand_ratio
    _A						     =					hidden_act
    _A						     =					conv_kernel_size
    _A						     =					output_stride
    _A						     =					hidden_dropout_prob
    _A						     =					attention_probs_dropout_prob
    _A						     =					classifier_dropout_prob
    _A						     =					initializer_range
    _A						     =					layer_norm_eps
    _A						     =					qkv_bias

    # decode head attributes for semantic segmentation
    _A						     =					aspp_out_channels
    _A						     =					atrous_rates
    _A						     =					aspp_dropout_prob
    _A						     =					semantic_loss_ignore_index


class   lowerCamelCase__  (					_A):



 """simple docstring"""

 a__				:   Dict   =     version.parse("1.11")

 @property
 def 						snake_case_			(						self				:				int			)      ->  Mapping[str, Mapping[int, str]]:
    return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]			)

 @property
 def 						snake_case_			(						self				:				Optional[int]			)      ->  Mapping[str, Mapping[int, str]]:
    if self.task == "image-classification":
       return OrderedDict([('''logits''', {0: '''batch'''})]			)
    else:
       return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]			)

 @property
 def 						snake_case_			(						self				:				Tuple			)      ->  float:
    return 1E-4








import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor


class   lowerCamelCase__  :



 """simple docstring"""

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

 def 						snake_case_			(						self				:				Tuple ,	__lowerCAmelCase				:				List[Any]			)      ->  str:
    _A     ,						_A						     =					torchaudio.load(__lowerCAmelCase			)
    _A						     =					self.resampler(__lowerCAmelCase			).squeeze().numpy()
    return speech

 def 						snake_case_			(						self				:				Optional[int] ,	__lowerCAmelCase				:				Tuple			)      ->  Tuple:
    _A						     =					self.blank_id
    _A						     =					'''|'''.join(item['''sent'''].split(''' '''			)			)
    if not os.path.isfile(item['''wav_path''']			):
       print(item['''wav_path'''] ,	'''not found in wavs directory'''			)

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

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

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

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

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

    def get_trellis(__lowerCAmelCase				:				List[str] ,	__lowerCAmelCase				:				Optional[int] ,	__lowerCAmelCase				:				int=0			):
       _A						     =					emission.size(0			)
       _A						     =					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.
       _A						     =					torch.full((num_frames + 1, num_tokens + 1) ,	-float('''inf'''			)			)
       _A						     =					0
       for t in range(__lowerCAmelCase			):
          _A						     =					torch.maximum(
              # Score for staying at the same token
              trellis[t, 1:] + emission[t, blank_id] ,	trellis[t, :-1] + emission[t, tokens] ,	)
       return trellis

    _A						     =					get_trellis(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase			)


    @dataclass
    class   lowerCamelCase__  :



     """simple docstring"""

     a__				:   int
     a__				:   int
     a__				:   float

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

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

          # 2. Store the path with frame-wise probability.
          _A						     =					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]

    _A						     =					backtrack(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase			)


    @dataclass
    class   lowerCamelCase__  :



     """simple docstring"""

     a__				:   str
     a__				:   int
     a__				:   int
     a__				:   float

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

     @property
     def 						snake_case_			(						self				:				Dict			)      ->  str:
        return self.end - self.start

    def merge_repeats(__lowerCAmelCase				:				Any			):
       _A     ,						_A						     =					0, 0
       _A						     =					[]
       while ia < len(__lowerCAmelCase			):
          while ia < len(__lowerCAmelCase			) and path[ia].token_index == path[ia].token_index:
             ia += 1
          _A						     =					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 ,	)			)
          _A						     =					ia
       return segments

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

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

    # load text file
    _A						     =					open(__lowerCAmelCase ,	encoding='''utf8'''			).readlines()

    _A						     =					[]
    for line in lines:
       if len(line.strip().split('''\t'''			)			) != 2:
          print('''Script must be in format: 00001  this is my sentence'''			)
          exit()

       _A     ,						_A						     =					line.strip().split('''\t'''			)
       _A						     =					os.path.join(__lowerCAmelCase ,	wav_name + '''.wav'''			)
       _A						     =					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   SCREAMING_SNAKE_CASE_       (  )     ->    Dict:
   _A						     =					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=16_000  ,      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'''							)

   _A						     =					parser.parse_args()

   _A						     =					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()



