






"""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 subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


lowerCAmelCase__  =     '1.5.0'

lowerCAmelCase__  =     ['vision-encoder-decoder']



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       if is_optimum_available():
              from optimum.version import __version__ as optimum_version

              snake_case__					:	Dict          =					version.parse(UpperCAmelCase     )
              if parsed_optimum_version < version.parse(UpperCAmelCase     ):
                     raise RuntimeError(
                         f"""transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You """
                         """can upgrade optimum by running: pip install -U optimum[exporters]"""     )
       else:
              raise RuntimeError(
                  """transformers.onnx requires optimum to run, you can install the library by running: pip install """
                  """optimum[exporters]"""     )
       snake_case__					:	Union[str, Any]          =					[
           sys.executable,
           """-m""",
           """optimum.exporters.onnx""",
           f"""--model {args.model}""",
           f"""--task {args.feature}""",
           f"""--framework {args.framework}""" if args.framework is not None else """""",
           f"""{args.output}""",
       ]
       snake_case__					:	Union[str, Any]          =					subprocess.Popen(""" """.join(UpperCAmelCase     )    ,       stdout=subprocess.PIPE    ,       shell=UpperCAmelCase     )
       proc.wait()

       logger.info(
           """The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as """
           """transformers.onnx is deprecated, and will be removed in v5. You can find more information here: """
           """https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""     )



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	str          =					args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""     )
       if not args.output.parent.exists():
              args.output.parent.mkdir(parents=UpperCAmelCase     )

       # Allocate the model
       snake_case__					:	Dict          =					FeaturesManager.get_model_from_feature(
           args.feature    ,       args.model    ,       framework=args.framework    ,       cache_dir=args.cache_dir     )

       snake_case__       ,   snake_case__					:	List[Any]          =					FeaturesManager.check_supported_model_or_raise(UpperCAmelCase    ,       feature=args.feature     )
       snake_case__					:	Any          =					model_onnx_config(model.config     )

       if model_kind in ENCODER_DECODER_MODELS:
              snake_case__					:	Optional[int]          =					model.get_encoder()
              snake_case__					:	Union[str, Any]          =					model.get_decoder()

              snake_case__					:	Optional[Any]          =					onnx_config.get_encoder_config(encoder_model.config     )
              snake_case__					:	Any          =					onnx_config.get_decoder_config(
                  encoder_model.config    ,       decoder_model.config    ,       feature=args.feature     )

              if args.opset is None:
                     snake_case__					:	List[Any]          =					max(encoder_onnx_config.default_onnx_opset    ,       decoder_onnx_config.default_onnx_opset     )

              if args.opset < min(encoder_onnx_config.default_onnx_opset    ,       decoder_onnx_config.default_onnx_opset     ):
                     raise ValueError(
                         f"""Opset {args.opset} is not sufficient to export {model_kind}. At least """
                         f""" {min(encoder_onnx_config.default_onnx_opset    ,       decoder_onnx_config.default_onnx_opset     )} is required."""     )

              snake_case__					:	Dict          =					AutoFeatureExtractor.from_pretrained(args.model     )

              snake_case__       ,   snake_case__					:	List[str]          =					export(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.opset    ,       args.output.parent.joinpath("""encoder_model.onnx"""     )    ,       )

              validate_model_outputs(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.output.parent.joinpath("""encoder_model.onnx"""     )    ,       UpperCAmelCase    ,       args.atol if args.atol else encoder_onnx_config.atol_for_validation    ,       )

              snake_case__					:	List[Any]          =					AutoTokenizer.from_pretrained(args.model     )

              snake_case__       ,   snake_case__					:	Dict          =					export(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.opset    ,       args.output.parent.joinpath("""decoder_model.onnx"""     )    ,       )

              validate_model_outputs(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.output.parent.joinpath("""decoder_model.onnx"""     )    ,       UpperCAmelCase    ,       args.atol if args.atol else decoder_onnx_config.atol_for_validation    ,       )
              logger.info(
                  f"""All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"     ).as_posix()},"""
                  f""" {args.output.parent.joinpath("decoder_model.onnx"     ).as_posix()}"""     )

       else:
              # Instantiate the appropriate preprocessor
              if args.preprocessor == "auto":
                     snake_case__					:	int          =					get_preprocessor(args.model     )
              elif args.preprocessor == "tokenizer":
                     snake_case__					:	List[str]          =					AutoTokenizer.from_pretrained(args.model     )
              elif args.preprocessor == "image_processor":
                     snake_case__					:	Optional[int]          =					AutoImageProcessor.from_pretrained(args.model     )
              elif args.preprocessor == "feature_extractor":
                     snake_case__					:	Tuple          =					AutoFeatureExtractor.from_pretrained(args.model     )
              elif args.preprocessor == "processor":
                     snake_case__					:	int          =					AutoProcessor.from_pretrained(args.model     )
              else:
                     raise ValueError(f"""Unknown preprocessor type '{args.preprocessor}'"""     )

              # Ensure the requested opset is sufficient
              if args.opset is None:
                     snake_case__					:	Dict          =					onnx_config.default_onnx_opset

              if args.opset < onnx_config.default_onnx_opset:
                     raise ValueError(
                         f"""Opset {args.opset} is not sufficient to export {model_kind}. """
                         f"""At least  {onnx_config.default_onnx_opset} is required."""     )

              snake_case__       ,   snake_case__					:	Dict          =					export(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.opset    ,       args.output    ,       )

              if args.atol is None:
                     snake_case__					:	int          =					onnx_config.atol_for_validation

              validate_model_outputs(UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.output    ,       UpperCAmelCase    ,       args.atol     )
              logger.info(f"""All good, model saved at: {args.output.as_posix()}"""     )
              warnings.warn(
                  """The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"""
                  """ using optimum.exporters.onnx in future. You can find more information here:"""
                  """ https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""    ,       UpperCAmelCase    ,       )



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	List[str]          =					ArgumentParser("""Hugging Face Transformers ONNX exporter"""     )
       parser.add_argument(
           """-m"""    ,       """--model"""    ,       type=UpperCAmelCase    ,       required=UpperCAmelCase    ,       help="""Model ID on huggingface.co or path on disk to load model from."""     )
       parser.add_argument(
           """--feature"""    ,       default="""default"""    ,       help="""The type of features to export the model with."""    ,       )
       parser.add_argument("""--opset"""    ,       type=UpperCAmelCase    ,       default=UpperCAmelCase    ,       help="""ONNX opset version to export the model with."""     )
       parser.add_argument(
           """--atol"""    ,       type=UpperCAmelCase    ,       default=UpperCAmelCase    ,       help="""Absolute difference tolerance when validating the model."""     )
       parser.add_argument(
           """--framework"""    ,       type=UpperCAmelCase    ,       choices=["""pt""", """tf"""]    ,       default=UpperCAmelCase    ,       help=(
               """The framework to use for the ONNX export."""
               """ If not provided, will attempt to use the local checkpoint's original framework"""
               """ or what is available in the environment."""
           )    ,       )
       parser.add_argument("""output"""    ,       type=UpperCAmelCase    ,       help="""Path indicating where to store generated ONNX model."""     )
       parser.add_argument("""--cache_dir"""    ,       type=UpperCAmelCase    ,       default=UpperCAmelCase    ,       help="""Path indicating where to store cache."""     )
       parser.add_argument(
           """--preprocessor"""    ,       type=UpperCAmelCase    ,       choices=["""auto""", """tokenizer""", """feature_extractor""", """image_processor""", """processor"""]    ,       default="""auto"""    ,       help="""Which type of preprocessor to use. 'auto' tries to automatically detect it."""    ,       )
       parser.add_argument(
           """--export_with_transformers"""    ,       action="""store_true"""    ,       help=(
               """Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be """
               """useful when exporting a model supported in transformers but not in optimum, otherwise it is not """
               """recommended."""
           )    ,       )

       snake_case__					:	str          =					parser.parse_args()
       if args.export_with_transformers or not is_optimum_available():
              export_with_transformers(UpperCAmelCase     )
       else:
              export_with_optimum(UpperCAmelCase     )


if __name__ == "__main__":
     lowerCAmelCase__  =     logging.get_logger('transformers.onnx')  # pylint: disable=invalid-name
     logger.setLevel(logging.INFO)
     main()




