

"""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						:







 """simple docstring"""







 def __init__(    self							:    Dict,      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    Dict,      UpperCAmelCase__							:    str			):
    __lowercase			=		cuda
    __lowercase			=		AutoConfig.from_pretrained(UpperCAmelCase__			)
    __lowercase			=		AutoModelForCTC.from_pretrained(UpperCAmelCase__			)
    self.model.eval()
    if self.cuda:
       self.model.to(device="cuda"			)
    __lowercase			=		AutoProcessor.from_pretrained(UpperCAmelCase__			)
    __lowercase			=		torchaudio.transforms.Resample(UpperCAmelCase__,      1_6_0_0_0			)
    __lowercase			=		0
    __lowercase			=		list(self.processor.tokenizer.get_vocab().keys()			)
    for i in range(len(UpperCAmelCase__			)			):
       if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
          __lowercase			=		i
    print("Blank Token id [PAD]/<pad>",      UpperCAmelCase__			)
    __lowercase			=		blank_id


 def       _lowercase				(    self							:    Tuple,      UpperCAmelCase__							:    Dict			):
    __lowercase	,__lowercase			=		torchaudio.load(UpperCAmelCase__			)
    __lowercase			=		self.resampler(UpperCAmelCase__			).squeeze().numpy()
    return speech


 def       _lowercase				(    self							:    Tuple,      UpperCAmelCase__							:    int			):
    __lowercase			=		self.blank_id
    __lowercase			=		"|".join(item["sent"].split(" "			)			)
    if not os.path.isfile(item["wav_path"]			):
       print(item["wav_path"],      "not found in wavs directory"			)

    __lowercase			=		self.speech_file_to_array_fn(item["wav_path"]			)
    __lowercase			=		self.processor(UpperCAmelCase__,      sampling_rate=1_6_0_0_0,      return_tensors="pt",      padding=UpperCAmelCase__			)
    if self.cuda:
       __lowercase			=		inputs.to(device="cuda"			)

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

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

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

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

    def get_trellis(UpperCAmelCase__							:    Tuple,      UpperCAmelCase__							:    List[Any],      UpperCAmelCase__							:    Tuple=0			):
       __lowercase			=		emission.size(0			)
       __lowercase			=		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			=		torch.full((num_frames + 1, num_tokens + 1),      -float("inf"			)			)
       __lowercase			=		0
       for t in range(UpperCAmelCase__			):
          __lowercase			=		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			=		get_trellis(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)




    @dataclass
    class       _lowerCAmelCase						:







     """simple docstring"""







     __UpperCAmelCase			:      int
     __UpperCAmelCase			:      int
     __UpperCAmelCase			:      float

    def backtrack(UpperCAmelCase__							:    Any,      UpperCAmelCase__							:    Union[str, Any],      UpperCAmelCase__							:    List[Any],      UpperCAmelCase__							:    List[str]=0			):
       __lowercase			=		trellis.size(1			) - 1
       __lowercase			=		torch.argmax(trellis[:, j]			).item()

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

          # 2. Store the path with frame-wise probability.
          __lowercase			=		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			=		backtrack(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)




    @dataclass
    class       _lowerCAmelCase						:







     """simple docstring"""







     __UpperCAmelCase			:      str
     __UpperCAmelCase			:      int
     __UpperCAmelCase			:      int
     __UpperCAmelCase			:      float


     def __repr__(    self							:    Optional[Any]			):
        return F"""{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"""



     @property
     def       _lowercase				(    self							:    Optional[int]			):
        return self.end - self.start

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

    __lowercase			=		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							:    str,      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    Tuple			):
    if not os.path.exists(UpperCAmelCase__			):
       os.makedirs(UpperCAmelCase__			)

    # load text file
    __lowercase			=		open(UpperCAmelCase__,      encoding="utf8"			).readlines()

    __lowercase			=		[]
    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			=		line.strip().split("\t"			)
       __lowercase			=		os.path.join(UpperCAmelCase__,      wav_name + ".wav"			)
       __lowercase			=		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						(						)			->		int:
   '''simple docstring'''


   __lowercase			=		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=16000,    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			=		parser.parse_args()

   __lowercase			=		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
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling




def 						_A						(						UpperCamelCase_  : str)			->		Optional[Any]:
   '''simple docstring'''


   __lowercase			=		"https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"

   with open(UpperCamelCase_,    "wb") as f:
      with requests.get(UpperCamelCase_,    stream=UpperCamelCase_) as req:
         for chunk in req.iter_content(chunk_size=2048):
            f.write(UpperCamelCase_)




def 						_A						(						)			->		VivitConfig:
   '''simple docstring'''


   __lowercase			=		VivitConfig()

   __lowercase			=		400
   __lowercase			=		"huggingface/label-files"
   __lowercase			=		"kinetics400-id2label.json"

   __lowercase			=		json.load(open(hf_hub_download(UpperCamelCase_,    UpperCamelCase_,    repo_type="dataset"),    "r"))
   __lowercase			=		{int(UpperCamelCase_): v for k, v in idalabel.items()}
   __lowercase			=		idalabel
   __lowercase			=		{v: k for k, v in idalabel.items()}
   return config




def 						_A						(						)			->		List[Any]:
   '''simple docstring'''


   __lowercase			=		hf_hub_download(
       repo_id="hf-internal-testing/spaghetti-video",    filename="eating_spaghetti_32_frames.npy",    repo_type="dataset")
   __lowercase			=		np.load(UpperCamelCase_)
   return list(UpperCamelCase_)




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Optional[int]:
   '''simple docstring'''


   if np.ndim(UpperCamelCase_) == 2:
      return transform_attention_bias(UpperCamelCase_)

   elif np.ndim(UpperCamelCase_) == 3:
      return transform_attention_kernel(UpperCamelCase_)

   else:
      raise Exception(F"""Invalid number of dimesions: {np.ndim(UpperCamelCase_)}""")




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Optional[Any]:
   '''simple docstring'''


   return current.flatten()




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Any:
   '''simple docstring'''


   return np.reshape(UpperCamelCase_,    (current.shape[0], current.shape[1] * current.shape[2])).T




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Any:
   '''simple docstring'''


   return np.reshape(UpperCamelCase_,    (current.shape[0] * current.shape[1], current.shape[2])).T




def 						_A						(						UpperCamelCase_  : Tuple,    UpperCamelCase_  : Optional[Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"][F"""encoderblock_{i}"""]

   __lowercase			=		F"""encoder.layer.{i}."""
   __lowercase			=		{
       prefix + "intermediate.dense.bias": state["MlpBlock_0"]["Dense_0"]["bias"],
       prefix + "intermediate.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_0"]["kernel"]),
       prefix + "output.dense.bias": state["MlpBlock_0"]["Dense_1"]["bias"],
       prefix + "output.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_1"]["kernel"]),
       prefix + "layernorm_before.bias": state["LayerNorm_0"]["bias"],
       prefix + "layernorm_before.weight": state["LayerNorm_0"]["scale"],
       prefix + "layernorm_after.bias": state["LayerNorm_1"]["bias"],
       prefix + "layernorm_after.weight": state["LayerNorm_1"]["scale"],
       prefix
       + "attention.attention.query.bias": transform_attention(
           state["MultiHeadDotProductAttention_0"]["query"]["bias"]),
       prefix
       + "attention.attention.query.weight": transform_attention(
           state["MultiHeadDotProductAttention_0"]["query"]["kernel"]),
       prefix
       + "attention.attention.key.bias": transform_attention(state["MultiHeadDotProductAttention_0"]["key"]["bias"]),
       prefix
       + "attention.attention.key.weight": transform_attention(
           state["MultiHeadDotProductAttention_0"]["key"]["kernel"]),
       prefix
       + "attention.attention.value.bias": transform_attention(
           state["MultiHeadDotProductAttention_0"]["value"]["bias"]),
       prefix
       + "attention.attention.value.weight": transform_attention(
           state["MultiHeadDotProductAttention_0"]["value"]["kernel"]),
       prefix + "attention.output.dense.bias": state["MultiHeadDotProductAttention_0"]["out"]["bias"],
       prefix
       + "attention.output.dense.weight": transform_attention_output_weight(
           state["MultiHeadDotProductAttention_0"]["out"]["kernel"]),
   }

   return new_state




def 						_A						(						UpperCamelCase_  : str)			->		List[Any]:
   '''simple docstring'''


   return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()])




def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : Any=False)			->		Any:
   '''simple docstring'''


   __lowercase			=		get_n_layers(UpperCamelCase_)

   __lowercase			=		OrderedDict()

   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

   __lowercase			=		np.transpose(
       state_dict["optimizer"]["target"]["embedding"]["kernel"],    (4, 3, 0, 1, 2))
   __lowercase			=		state_dict["optimizer"]["target"]["embedding"]["bias"]

   __lowercase			=		state_dict["optimizer"]["target"]["cls"]
   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
       "pos_embedding"
   ]

   for i in range(UpperCamelCase_):
      new_state.update(transform_state_encoder_block(UpperCamelCase_,    UpperCamelCase_))

   if classification_head:
      __lowercase			=		{"vivit." + k: v for k, v in new_state.items()}
      __lowercase			=		np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"])
      __lowercase			=		np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"])

   return {k: torch.tensor(UpperCamelCase_) for k, v in new_state.items()}




def 						_A						(						)			->		VivitImageProcessor:
   '''simple docstring'''


   __lowercase			=		VivitImageProcessor()

   assert extractor.do_resize is True
   assert extractor.size == {"shortest_edge": 256}
   assert extractor.do_center_crop is True
   assert extractor.crop_size == {"width": 224, "height": 224}
   assert extractor.resample == PILImageResampling.BILINEAR

   # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
   # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
   # which effectively means no normalization (and ViViT does not overwrite those when calling this func)
   assert extractor.do_normalize is False
   assert extractor.do_rescale is True
   assert extractor.rescale_factor == 1 / 255

   # zero-centering = True in original implementation
   assert extractor.do_zero_centering is True

   return extractor




def 						_A						(						UpperCamelCase_  : str)			->		List[str]:
   '''simple docstring'''


   __lowercase			=		"checkpoint"

   if not os.path.exists(UpperCamelCase_):
      download_checkpoint(UpperCamelCase_)

   __lowercase			=		restore_checkpoint(UpperCamelCase_,    UpperCamelCase_)
   __lowercase			=		transform_state(UpperCamelCase_,    classification_head=UpperCamelCase_)

   __lowercase			=		get_vivit_config()

   assert config.image_size == 224
   assert config.num_frames == 32

   __lowercase			=		VivitForVideoClassification(UpperCamelCase_)
   model.load_state_dict(UpperCamelCase_)
   model.eval()

   __lowercase			=		get_processor()

   __lowercase			=		prepare_video()
   __lowercase			=		extractor(UpperCamelCase_,    return_tensors="pt")

   __lowercase			=		model(**UpperCamelCase_)

   __lowercase			=		torch.Size([1, 400])
   __lowercase			=		torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658])

   assert outputs.logits.shape == expected_shape
   assert torch.allclose(outputs.logits[0, :5],    UpperCamelCase_,    atol=1E-4), outputs.logits[0, :5]

   model.save_pretrained(UpperCamelCase_)
   extractor.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
   _a				   =							argparse.ArgumentParser()

   parser.add_argument('--output_model_name', '-o', type=str, help='Output path for the converted HuggingFace model')

   _a				   =							parser.parse_args()
   convert(args.output_model_name)




