

import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor





class    __UpperCAmelCase	:



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



   def 			__magic_name__       (				self				:	Optional[int],						__A				:	int		):
     UpperCAmelCase					,		UpperCAmelCase			:				Tuple										=					torchaudio.load(__A		)
     UpperCAmelCase			:				int										=					self.resampler(__A		).squeeze().numpy()
     return speech



   def 			__magic_name__       (				self				:	Optional[Any],						__A				:	Any		):
     UpperCAmelCase			:				Dict										=					self.blank_id
     UpperCAmelCase			:				Any										=					'''|'''.join(item['''sent'''].split(''' '''		)		)
     if not os.path.isfile(item['''wav_path''']		):
       print(item['''wav_path'''],						'''not found in wavs directory'''		)

     UpperCAmelCase			:				List[str]										=					self.speech_file_to_array_fn(item['''wav_path''']		)
     UpperCAmelCase			:				Optional[Any]										=					self.processor(__A,						sampling_rate=1_6_0_0_0,						return_tensors='''pt''',						padding=__A		)
     if self.cuda:
       UpperCAmelCase			:				Union[str, Any]										=					inputs.to(device='''cuda'''		)

     with torch.no_grad():
       UpperCAmelCase			:				Optional[int]										=					self.model(inputs.input_values		).logits

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

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

     UpperCAmelCase			:				List[Any]										=					{c: i for i, c in enumerate(__A		)}
     UpperCAmelCase			:				Dict										=					[]
     for c in transcript:
       if c in dictionary:
         tokens.append(dictionary[c]		)

     def get_trellis(__A				:	List[str],						__A				:	Dict,						__A				:	List[Any]=0		):
       UpperCAmelCase			:				int										=					emission.size(0		)
       UpperCAmelCase			:				str										=					len(__A		)

       # 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			:				Any										=					torch.full((num_frames + 1, num_tokens + 1),						-float('''inf'''		)		)
       UpperCAmelCase			:				Any										=					0
       for t in range(__A		):
         UpperCAmelCase			:				Optional[Any]										=					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(__A,						__A,						__A		)





     @dataclass
     class    __UpperCAmelCase	:
        UpperCamelCase  	=	42
        UpperCamelCase  	=	42
        UpperCamelCase  	=	42

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

       UpperCAmelCase			:				Optional[Any]										=					[]
       for t in range(__A,						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			:				str										=					trellis[t - 1, j] + emission[t - 1, blank_id]
         # Score for token changing from C-1 at T-1 to J at T.
         UpperCAmelCase			:				Dict										=					trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

         # 2. Store the path with frame-wise probability.
         UpperCAmelCase			:				str										=					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,						__A		)		)

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

     UpperCAmelCase			:				Optional[int]										=					backtrack(__A,						__A,						__A,						__A		)





     @dataclass
     class    __UpperCAmelCase	:
        UpperCamelCase  	=	42
        UpperCamelCase  	=	42
        UpperCamelCase  	=	42
        UpperCamelCase  	=	42



        def __repr__(				self				:	Optional[int]		):
          return F'''{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}'''





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

     def merge_repeats(__A				:	Union[str, Any]		):
       UpperCAmelCase					,		UpperCAmelCase			:				Dict										=					0, 0
       UpperCAmelCase			:				Optional[Any]										=					[]
       while ia < len(__A		):
         while ia < len(__A		) and path[ia].token_index == path[ia].token_index:
           ia += 1
         UpperCAmelCase			:				List[str]										=					sum(path[k].score for k in range(__A,						__A		)		) / (ia - ia)
         segments.append(
             Segment(
                 transcript[path[ia].token_index],						path[ia].time_index,						path[ia - 1].time_index + 1,						__A,						)		)
         UpperCAmelCase			:				Optional[Any]										=					ia
       return segments

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





   def 			__magic_name__       (				self				:	str,						__A				:	List[str],						__A				:	Tuple,						__A				:	Union[str, Any]		):
     if not os.path.exists(__A		):
       os.makedirs(__A		)

     # load text file
     UpperCAmelCase			:				str										=					open(__A,						encoding='''utf8'''		).readlines()

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

       UpperCAmelCase					,		UpperCAmelCase			:				Any										=					line.strip().split('''\t'''		)
       UpperCAmelCase			:				Dict										=					os.path.join(__A,						wav_name + '''.wav'''		)
       UpperCAmelCase			:				int										=					os.path.join(__A,						wav_name + '''.txt'''		)

       items.append({'''sent''': sentence, '''wav_path''': wav_path, '''out_path''': out_path}		)
     print('''Number of samples found in script file''',						len(__A		)		)

     for item in tqdm(__A		):
       self.align_single_sample(__A		)






def      a__	(						)    ->		List[str]:
  UpperCAmelCase			:				List[str]										=					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=16_000							,					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'''     )

  UpperCAmelCase			:				Tuple										=					parser.parse_args()

  UpperCAmelCase			:				str										=					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()








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				:     Dict     )    ->		Any:
  UpperCAmelCase			:				List[str]										=					'''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=2_048     ):
        f.write(UpperCAmelCase     )

def      a__	(						)    ->		VivitConfig:
  UpperCAmelCase			:				Any										=					VivitConfig()

  UpperCAmelCase			:				Any										=					400
  UpperCAmelCase			:				int										=					'''huggingface/label-files'''
  UpperCAmelCase			:				int										=					'''kinetics400-id2label.json'''

  UpperCAmelCase			:				Tuple										=					json.load(open(hf_hub_download(UpperCAmelCase							,					UpperCAmelCase							,					repo_type='''dataset'''     )							,					'''r'''     )     )
  UpperCAmelCase			:				Dict										=					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
  UpperCAmelCase			:				Optional[Any]										=					idalabel
  UpperCAmelCase			:				Tuple										=					{v: k for k, v in idalabel.items()}
  return config

def      a__	(						)    ->		List[Any]:
  UpperCAmelCase			:				int										=					hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video'''							,					filename='''eating_spaghetti_32_frames.npy'''							,					repo_type='''dataset'''     )
  UpperCAmelCase			:				Tuple										=					np.load(UpperCAmelCase     )
  return list(UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Any:
  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     )    ->		List[Any]:
  return current.flatten()

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Optional[Any]:
  return np.reshape(UpperCAmelCase							,					(current.shape[0], current.shape[1] * current.shape[2])     ).T

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Any:
  return np.reshape(UpperCAmelCase							,					(current.shape[0] * current.shape[1], current.shape[2])     ).T

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Union[str, Any]     )    ->		Dict:
  UpperCAmelCase			:				Any										=					state_dict['''optimizer''']['''target''']['''Transformer'''][f'''encoderblock_{i}''']

  UpperCAmelCase			:				Dict										=					f'''encoder.layer.{i}.'''
  UpperCAmelCase			:				Optional[int]										=					{
      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				:     Any     )    ->		List[str]:
  return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]     )

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict=False     )    ->		Dict:
  UpperCAmelCase			:				int										=					get_n_layers(UpperCAmelCase     )

  UpperCAmelCase			:				List[str]										=					OrderedDict()

  UpperCAmelCase			:				int										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
  UpperCAmelCase			:				Tuple										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

  UpperCAmelCase			:				Union[str, Any]										=					np.transpose(
      state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']							,					(4, 3, 0, 1, 2)     )
  UpperCAmelCase			:				List[str]										=					state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

  UpperCAmelCase			:				Optional[int]										=					state_dict['''optimizer''']['''target''']['''cls''']
  UpperCAmelCase			:				str										=					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:
    UpperCAmelCase			:				List[Any]										=					{'''vivit.''' + k: v for k, v in new_state.items()}
    UpperCAmelCase			:				str										=					np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']     )
    UpperCAmelCase			:				Dict										=					np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']     )

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

def      a__	(						)    ->		VivitImageProcessor:
  UpperCAmelCase			:				Optional[int]										=					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     )    ->		Tuple:
  UpperCAmelCase			:				Union[str, Any]										=					'''checkpoint'''

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

  UpperCAmelCase			:				Tuple										=					restore_checkpoint(UpperCAmelCase							,					UpperCAmelCase     )
  UpperCAmelCase			:				Any										=					transform_state(UpperCAmelCase							,					classification_head=UpperCAmelCase     )

  UpperCAmelCase			:				Union[str, Any]										=					get_vivit_config()

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

  UpperCAmelCase			:				Optional[int]										=					VivitForVideoClassification(UpperCAmelCase     )
  model.load_state_dict(UpperCAmelCase     )
  model.eval()

  UpperCAmelCase			:				List[Any]										=					get_processor()

  UpperCAmelCase			:				str										=					prepare_video()
  UpperCAmelCase			:				Union[str, Any]										=					extractor(UpperCAmelCase							,					return_tensors='''pt'''     )

  UpperCAmelCase			:				Optional[Any]										=					model(**UpperCAmelCase     )

  UpperCAmelCase			:				str										=					torch.Size([1, 400]     )
  UpperCAmelCase			:				int										=					torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]     )

  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__":
     _lowerCamelCase							:  Optional[Any]     =				argparse.ArgumentParser()

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

     _lowerCamelCase							:  str     =				parser.parse_args()
     convert(args.output_model_name)





