code: 
  
  
  
  
  
  '''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 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Optional[int]      ,      _lowerCAmelCase						:      Optional[Any]      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      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_6000      )
  					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        A     (self						:      Any      ,      _lowerCAmelCase						:      int      ):
  					A  ,    A							  =      torchaudio.load(_lowerCAmelCase      )
  					A							  =      self.resampler(_lowerCAmelCase      ).squeeze().numpy()
  					return speech
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      Optional[Any]      ):
  					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_6000      ,      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[Any]      ,      _lowerCAmelCase						:      Any      ,      _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 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							__lowerCAmelCase 				=    42
  							__lowerCAmelCase 				=    42
  							__lowerCAmelCase 				=    42
  
  					def backtrack(_lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      Union[str, Any]      ,      _lowerCAmelCase						:      Optional[Any]=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 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							__lowerCAmelCase 				=    42
  							__lowerCAmelCase 				=    42
  							__lowerCAmelCase 				=    42
  							__lowerCAmelCase 				=    42
  
  
  
  
  
  
  
  							def __repr__(self						:      Optional[Any]      ):
  										return F"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""
  
  
  
  
  
  
  
  							@property
  							def        A     (self						:      List[str]      ):
  										return self.end - self.start
  
  					def merge_repeats(_lowerCAmelCase						:      List[str]      ):
  								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        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      str      ):
  
  
  
  
  
  
  					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    __a (     )      ->Optional[int]:
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			A							  =      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"""    )
  
  			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()
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->List[Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      TaConfig.from_pretrained(UpperCAmelCase    )
  	A							  =      FlaxTaForConditionalGeneration(config=UpperCAmelCase    )
  	A							  =      checkpoints.load_tax_checkpoint(UpperCAmelCase    )
  
  	A							  =      """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
  	# Encoder
  	for layer_index in range(config.num_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_attention_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  		A							  =      tax_mlp_layer_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_encoder_rel_embedding
  
  	# Assigning
  	A							  =      tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  	A							  =      tax_encoder_norm
  
  	# Decoder
  	for layer_index in range(config.num_decoder_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
  		    """scale"""
  		]
  
  		# Encoder-Decoder-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
  		    """kernel"""
  		]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
  		# MLP
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_pre_attention_layer_norm
  
  		A							  =      tax_enc_dec_attention_key
  		A							  =      tax_enc_dec_attention_out
  		A							  =      tax_enc_dec_attention_query
  		A							  =      tax_enc_dec_attention_value
  
  		A							  =      tax_cross_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  
  		A							  =      txa_mlp_layer_norm
  
  	# Decoder Normalization
  	A							  =      tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  	A							  =      txa_decoder_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_decoder_rel_embedding
  
  	# Token Embeddings
  	A							  =      tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  	A							  =      txa_token_embeddings
  
  	# LM Head (only in v1.1 checkpoints)
  	if "logits_dense" in tax_model["target"]["decoder"]:
  		A							  =      tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
  	flax_model.save_pretrained(UpperCAmelCase    )
  	print("""T5X Model was sucessfully converted!"""    )
  
  
  if __name__ == "__main__":
  				_lowerCamelCase				:	Tuple				    =       argparse.ArgumentParser()
  				# Required parameters
  				parser.add_argument(
  				    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
  				)
  				parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
  				parser.add_argument(
  				    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
  				)
  				_lowerCamelCase				:	Union[str, Any]				    =       parser.parse_args()
  				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  