




"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A_      =    logging.get_logger(__name__)

A_      =    {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
A_      =    [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def  UpperCAmelCase__	(snake_case__							: List[Any]		,       snake_case__							: Optional[int]		,       snake_case__							: Union[str, Any]		,       snake_case__							: List[str]		,       snake_case__							: List[str]   ):
		"""simple docstring"""

		for attribute in key.split("""."""   ):
				_snake_case							:		List[Any]								=  getattr(lowercase_		,       lowercase_   )

		if weight_type is not None:
				_snake_case							:		Union[str, Any]								=  getattr(lowercase_		,       lowercase_   ).shape
		else:
				_snake_case							:		List[Any]								=  hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
				    F" {value.shape} for {full_name}"   )

		if weight_type == "weight":
				_snake_case							:		List[str]								=  value
		elif weight_type == "weight_g":
				_snake_case							:		List[str]								=  value
		elif weight_type == "weight_v":
				_snake_case							:		Tuple								=  value
		elif weight_type == "bias":
				_snake_case							:		Optional[Any]								=  value
		elif weight_type == "running_mean":
				_snake_case							:		str								=  value
		elif weight_type == "running_var":
				_snake_case							:		Dict								=  value
		elif weight_type == "num_batches_tracked":
				_snake_case							:		Tuple								=  value
		elif weight_type == "inv_freq":
				_snake_case							:		Tuple								=  value
		else:
				_snake_case							:		List[Any]								=  value

		logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."   )


def  UpperCAmelCase__	(snake_case__							: int		,       snake_case__							: Union[str, Any]		,       snake_case__							: Optional[Any]   ):
		"""simple docstring"""

		_snake_case							:		int								=  []
		_snake_case							:		int								=  fairseq_model.state_dict()

		_snake_case							:		Any								=  hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				_snake_case							:		Optional[int]								=  False
				if "conv_layers" in name:
						load_conv_layer(
						    lowercase_		,       lowercase_		,       lowercase_		,       lowercase_		,       hf_model.config.feat_extract_norm == """group"""		,       )
						_snake_case							:		str								=  True
				else:
						for key, mapped_key in MAPPING.items():
								_snake_case							:		Dict								=  """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0]:
										_snake_case							:		int								=  True
										if "*" in mapped_key:
												_snake_case							:		Optional[Any]								=  name.split(lowercase_   )[0].split("""."""   )[-2]
												_snake_case							:		Tuple								=  mapped_key.replace("""*"""		,       lowercase_   )
										if "pos_bias_u" in name:
												_snake_case							:		Optional[int]								=  None
										elif "pos_bias_v" in name:
												_snake_case							:		Dict								=  None
										elif "weight_g" in name:
												_snake_case							:		str								=  """weight_g"""
										elif "weight_v" in name:
												_snake_case							:		List[str]								=  """weight_v"""
										elif "bias" in name:
												_snake_case							:		Union[str, Any]								=  """bias"""
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												_snake_case							:		List[Any]								=  """weight"""
										elif "running_mean" in name:
												_snake_case							:		List[str]								=  """running_mean"""
										elif "inv_freq" in name:
												_snake_case							:		Any								=  """inv_freq"""
										elif "running_var" in name:
												_snake_case							:		str								=  """running_var"""
										elif "num_batches_tracked" in name:
												_snake_case							:		Optional[int]								=  """num_batches_tracked"""
										else:
												_snake_case							:		Optional[int]								=  None
										set_recursively(lowercase_		,       lowercase_		,       lowercase_		,       lowercase_		,       lowercase_   )
								continue
				if not is_used:
						unused_weights.append(lowercase_   )

		logger.warning(F"Unused weights: {unused_weights}"   )


def  UpperCAmelCase__	(snake_case__							: int		,       snake_case__							: Any		,       snake_case__							: List[Any]		,       snake_case__							: Any		,       snake_case__							: Tuple   ):
		"""simple docstring"""

		_snake_case							:		Dict								=  full_name.split("""conv_layers."""   )[-1]
		_snake_case							:		Optional[Any]								=  name.split("""."""   )
		_snake_case							:		Optional[int]								=  int(items[0]   )
		_snake_case							:		str								=  int(items[1]   )

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    F"{full_name} has size {value.shape}, but"
								    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."   )
						_snake_case							:		Optional[int]								=  value
						logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."   )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    F"{full_name} has size {value.shape}, but"
								    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."   )
						_snake_case							:		Optional[int]								=  value
						logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."   )
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    F"{full_name} has size {value.shape}, but"
								    F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."   )
						_snake_case							:		List[Any]								=  value
						logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    F"{full_name} has size {value.shape}, but"
								    F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."   )
						_snake_case							:		str								=  value
						logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
		else:
				unused_weights.append(lowercase_   )


@torch.no_grad()
def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: int		,       snake_case__							: Any=None		,       snake_case__							: Dict=None		,       snake_case__							: Optional[int]=True   ):
		"""simple docstring"""

		if config_path is not None:
				_snake_case							:		List[str]								=  WavaVecaConformerConfig.from_pretrained(lowercase_		,       hidden_act="""swish"""   )
		else:
				_snake_case							:		Union[str, Any]								=  WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				_snake_case							:		List[str]								=  """rotary"""

		if is_finetuned:
				if dict_path:
						_snake_case							:		int								=  Dictionary.load(lowercase_   )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						_snake_case							:		List[Any]								=  target_dict.pad_index
						_snake_case							:		Optional[Any]								=  target_dict.bos_index
						_snake_case							:		Tuple								=  target_dict.eos_index
						_snake_case							:		Optional[int]								=  len(target_dict.symbols   )
						_snake_case							:		Union[str, Any]								=  os.path.join(lowercase_		,       """vocab.json"""   )
						if not os.path.isdir(lowercase_   ):
								logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase_   )   )
								return
						os.makedirs(lowercase_		,       exist_ok=lowercase_   )
						_snake_case							:		Tuple								=  target_dict.indices

						# fairseq has the <pad> and <s> switched
						_snake_case							:		List[str]								=  0
						_snake_case							:		Optional[int]								=  1
						with open(lowercase_		,       """w"""		,       encoding="""utf-8"""   ) as vocab_handle:
								json.dump(lowercase_		,       lowercase_   )
						_snake_case							:		Any								=  WavaVecaCTCTokenizer(
						    lowercase_		,       unk_token=target_dict.unk_word		,       pad_token=target_dict.pad_word		,       bos_token=target_dict.bos_word		,       eos_token=target_dict.eos_word		,       word_delimiter_token="""|"""		,       do_lower_case=lowercase_		,       )
						_snake_case							:		Union[str, Any]								=  True if config.feat_extract_norm == """layer""" else False
						_snake_case							:		Optional[int]								=  WavaVecaFeatureExtractor(
						    feature_size=1		,       sampling_rate=1_60_00		,       padding_value=0		,       do_normalize=lowercase_		,       return_attention_mask=lowercase_		,       )
						_snake_case							:		Optional[Any]								=  WavaVecaProcessor(feature_extractor=lowercase_		,       tokenizer=lowercase_   )
						processor.save_pretrained(lowercase_   )

				_snake_case							:		Tuple								=  WavaVecaConformerForCTC(lowercase_   )
		else:
				_snake_case							:		Tuple								=  WavaVecaConformerForPreTraining(lowercase_   )

		if is_finetuned:
				_snake_case		, _snake_case		, _snake_case							:		Any								=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]		,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
		else:
				_snake_case							:		List[Any]								=  argparse.Namespace(task="""audio_pretraining"""   )
				_snake_case							:		int								=  fairseq.tasks.setup_task(lowercase_   )

				_snake_case		, _snake_case		, _snake_case							:		Optional[int]								=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,       task=lowercase_   )

		_snake_case							:		List[Any]								=  model[0].eval()

		recursively_load_weights(lowercase_		,       lowercase_		,       not is_finetuned   )

		hf_wavavec.save_pretrained(lowercase_   )


if __name__ == "__main__":
					A_      =    argparse.ArgumentParser()
					parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
					parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
					parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					parser.add_argument(
					    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
					)
					A_      =    parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)








"""simple docstring"""


import torch
from torch import nn






class  lowercase(					nn.Module			):







 '''simple docstring'''





 def __init__(    self:		Any,				a_:		List[str],				a_:		Union[str, Any],				a_:		int,				a_:		int,				a_:		List[Any]=1,				a_:		Union[str, Any]=False		):




   '''simple docstring'''



   super().__init__()

   _snake_case							:		int								=  n_token
   _snake_case							:		Tuple								=  d_embed
   _snake_case							:		List[str]								=  d_proj

   _snake_case							:		Optional[int]								=  cutoffs + [n_token]
   _snake_case							:		Any								=  [0] + self.cutoffs
   _snake_case							:		Tuple								=  div_val

   _snake_case							:		Optional[int]								=  self.cutoffs[0]
   _snake_case							:		Union[str, Any]								=  len(self.cutoffs		) - 1
   _snake_case							:		Union[str, Any]								=  self.shortlist_size + self.n_clusters

   if self.n_clusters > 0:
     _snake_case							:		List[Any]								=  nn.Parameter(torch.zeros(self.n_clusters,				self.d_embed		)		)
     _snake_case							:		Tuple								=  nn.Parameter(torch.zeros(self.n_clusters		)		)

   _snake_case							:		Any								=  nn.ModuleList()
   _snake_case							:		Optional[Any]								=  nn.ParameterList()

   if div_val == 1:
     for i in range(len(self.cutoffs		)		):
       if d_proj != d_embed:
         self.out_projs.append(nn.Parameter(torch.FloatTensor(a_,				a_		)		)		)
       else:
         self.out_projs.append(a_		)

     self.out_layers.append(nn.Linear(a_,				a_		)		)
   else:
     for i in range(len(self.cutoffs		)		):
       _snake_case		, _snake_case							:		List[Any]								=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
       _snake_case							:		Union[str, Any]								=  d_embed // (div_val**i)

       self.out_projs.append(nn.Parameter(torch.FloatTensor(a_,				a_		)		)		)

       self.out_layers.append(nn.Linear(a_,				r_idx - l_idx		)		)

   _snake_case							:		Optional[int]								=  keep_order


 def 			UpperCamelCase_       (    self:		str,				a_:		Union[str, Any],				a_:		Dict,				a_:		int,				a_:		Tuple		):




   '''simple docstring'''



   if proj is None:
     _snake_case							:		List[Any]								=  nn.functional.linear(a_,				a_,				bias=a_		)
   else:
     # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
     _snake_case							:		List[Any]								=  nn.functional.linear(a_,				proj.t().contiguous()		)
     _snake_case							:		Tuple								=  nn.functional.linear(a_,				a_,				bias=a_		)
     # else:
     #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
     #     if bias is not None:
     #         logit = logit + bias

   return logit


 def 			UpperCamelCase_       (    self:		Dict,				a_:		Dict,				a_:		str=None,				a_:		Union[str, Any]=False		):




   '''simple docstring'''



   if labels is not None:
     # Shift so that tokens < n predict n
     _snake_case							:		int								=  hidden[..., :-1, :].contiguous()
     _snake_case							:		List[Any]								=  labels[..., 1:].contiguous()
     _snake_case							:		str								=  hidden.view(-1,				hidden.size(-1		)		)
     _snake_case							:		Dict								=  labels.view(-1		)
     if hidden.size(0		) != labels.size(0		):
       raise RuntimeError("""Input and labels should have the same size in the batch dimension."""		)
   else:
     _snake_case							:		int								=  hidden.view(-1,				hidden.size(-1		)		)

   if self.n_clusters == 0:
     _snake_case							:		Tuple								=  self._compute_logit(a_,				self.out_layers[0].weight,				self.out_layers[0].bias,				self.out_projs[0]		)
     if labels is not None:
       _snake_case							:		Dict								=  labels != -100
       _snake_case							:		str								=  torch.zeros_like(a_,				dtype=hidden.dtype,				device=hidden.device		)
       _snake_case							:		str								=  (
           -nn.functional.log_softmax(a_,				dim=-1		)[mask].gather(1,				labels[mask].unsqueeze(1		)		).squeeze(1		)
       )
     else:
       _snake_case							:		Optional[int]								=  nn.functional.log_softmax(a_,				dim=-1		)
   else:
     # construct weights and biases
     _snake_case		, _snake_case							:		Optional[int]								=  [], []
     for i in range(len(self.cutoffs		)		):
       if self.div_val == 1:
         _snake_case		, _snake_case							:		Optional[int]								=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
         _snake_case							:		List[Any]								=  self.out_layers[0].weight[l_idx:r_idx]
         _snake_case							:		Tuple								=  self.out_layers[0].bias[l_idx:r_idx]
       else:
         _snake_case							:		Optional[int]								=  self.out_layers[i].weight
         _snake_case							:		int								=  self.out_layers[i].bias

       if i == 0:
         _snake_case							:		List[str]								=  torch.cat([weight_i, self.cluster_weight],				dim=0		)
         _snake_case							:		int								=  torch.cat([bias_i, self.cluster_bias],				dim=0		)

       weights.append(a_		)
       biases.append(a_		)

     _snake_case		, _snake_case		, _snake_case							:		Any								=  weights[0], biases[0], self.out_projs[0]

     _snake_case							:		List[str]								=  self._compute_logit(a_,				a_,				a_,				a_		)
     _snake_case							:		Union[str, Any]								=  nn.functional.log_softmax(a_,				dim=1		)

     if labels is None:
       _snake_case							:		Tuple								=  hidden.new_empty((head_logit.size(0		), self.n_token)		)
     else:
       _snake_case							:		Dict								=  torch.zeros_like(a_,				dtype=hidden.dtype,				device=hidden.device		)

     _snake_case							:		Union[str, Any]								=  0
     _snake_case							:		Optional[Any]								=  [0] + self.cutoffs
     for i in range(len(a_		) - 1		):
       _snake_case		, _snake_case							:		Dict								=  cutoff_values[i], cutoff_values[i + 1]

       if labels is not None:
         _snake_case							:		Dict								=  (labels >= l_idx) & (labels < r_idx)
         _snake_case							:		int								=  mask_i.nonzero().squeeze()

         if indices_i.numel() == 0:
           continue

         _snake_case							:		List[str]								=  labels.index_select(0,				a_		) - l_idx
         _snake_case							:		List[str]								=  head_logprob.index_select(0,				a_		)
         _snake_case							:		List[str]								=  hidden.index_select(0,				a_		)
       else:
         _snake_case							:		List[str]								=  hidden

       if i == 0:
         if labels is not None:
           _snake_case							:		Dict								=  head_logprob_i.gather(1,				target_i[:, None]		).squeeze(1		)
         else:
           _snake_case							:		Optional[Any]								=  head_logprob[:, : self.cutoffs[0]]
       else:
         _snake_case		, _snake_case		, _snake_case							:		Dict								=  weights[i], biases[i], self.out_projs[i]

         _snake_case							:		int								=  self._compute_logit(a_,				a_,				a_,				a_		)
         _snake_case							:		int								=  nn.functional.log_softmax(a_,				dim=1		)
         _snake_case							:		Dict								=  self.cutoffs[0] + i - 1  # No probability for the head cluster
         if labels is not None:
           _snake_case							:		Dict								=  head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
               1,				target_i[:, None]		).squeeze(1		)
         else:
           _snake_case							:		Optional[Any]								=  head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
           _snake_case							:		Any								=  logprob_i

       if labels is not None:
         if (hasattr(self,				"""keep_order"""		) and self.keep_order) or keep_order:
           out.index_copy_(0,				a_,				-logprob_i		)
         else:
           out[offset : offset + logprob_i.size(0		)].copy_(-logprob_i		)
         offset += logprob_i.size(0		)

   return out


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Optional[int]		):




   '''simple docstring'''



   if self.n_clusters == 0:
     _snake_case							:		Optional[int]								=  self._compute_logit(a_,				self.out_layers[0].weight,				self.out_layers[0].bias,				self.out_projs[0]		)
     return nn.functional.log_softmax(a_,				dim=-1		)
   else:
     # construct weights and biases
     _snake_case		, _snake_case							:		List[Any]								=  [], []
     for i in range(len(self.cutoffs		)		):
       if self.div_val == 1:
         _snake_case		, _snake_case							:		Optional[Any]								=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
         _snake_case							:		Optional[int]								=  self.out_layers[0].weight[l_idx:r_idx]
         _snake_case							:		str								=  self.out_layers[0].bias[l_idx:r_idx]
       else:
         _snake_case							:		List[Any]								=  self.out_layers[i].weight
         _snake_case							:		Union[str, Any]								=  self.out_layers[i].bias

       if i == 0:
         _snake_case							:		int								=  torch.cat([weight_i, self.cluster_weight],				dim=0		)
         _snake_case							:		Dict								=  torch.cat([bias_i, self.cluster_bias],				dim=0		)

       weights.append(a_		)
       biases.append(a_		)

     _snake_case		, _snake_case		, _snake_case							:		int								=  weights[0], biases[0], self.out_projs[0]
     _snake_case							:		List[Any]								=  self._compute_logit(a_,				a_,				a_,				a_		)

     _snake_case							:		List[Any]								=  hidden.new_empty((head_logit.size(0		), self.n_token)		)
     _snake_case							:		int								=  nn.functional.log_softmax(a_,				dim=1		)

     _snake_case							:		List[Any]								=  [0] + self.cutoffs
     for i in range(len(a_		) - 1		):
       _snake_case		, _snake_case							:		List[str]								=  cutoff_values[i], cutoff_values[i + 1]

       if i == 0:
         _snake_case							:		List[str]								=  head_logprob[:, : self.cutoffs[0]]
       else:
         _snake_case		, _snake_case		, _snake_case							:		List[Any]								=  weights[i], biases[i], self.out_projs[i]

         _snake_case							:		Optional[int]								=  self._compute_logit(a_,				a_,				a_,				a_		)
         _snake_case							:		Any								=  nn.functional.log_softmax(a_,				dim=1		)

         _snake_case							:		Dict								=  head_logprob[:, -i] + tail_logprob_i
         _snake_case							:		Any								=  logprob_i

     return out


