




"""simple docstring"""


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
A_      =    logging.get_logger('''transformers.models.speecht5''')

A_      =    {
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
A_      =    {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
A_      =    {
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
A_      =    {
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
A_      =    {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
A_      =    {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
A_      =    {
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
A_      =    {
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
A_      =    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
A_      =    {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A_      =    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A_      =    []
A_      =    [
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
A_      =    IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
A_      =    IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
A_      =    IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]


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

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

		if weight_type is not None:
				_snake_case							:		Dict								=  getattr(snake_case__		,       snake_case__   ).shape
		else:
				_snake_case							:		Optional[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							:		Any								=  value
		elif weight_type == "weight_g":
				_snake_case							:		str								=  value
		elif weight_type == "weight_v":
				_snake_case							:		Union[str, Any]								=  value
		elif weight_type == "bias":
				_snake_case							:		Dict								=  value
		elif weight_type == "running_mean":
				_snake_case							:		Optional[Any]								=  value
		elif weight_type == "running_var":
				_snake_case							:		Optional[Any]								=  value
		elif weight_type == "num_batches_tracked":
				_snake_case							:		Optional[int]								=  value
		else:
				_snake_case							:		Any								=  value

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


def  UpperCAmelCase__	(snake_case__							: Any		,       snake_case__							: str   ):
		"""simple docstring"""

		for key in ignore_keys:
				if key.endswith(""".*"""   ):
						if name.startswith(key[:-1]   ):
								return True
				elif ".*." in key:
						_snake_case							:		Optional[int]								=  key.split(""".*."""   )
						if prefix in name and suffix in name:
								return True
				elif key in name:
						return True
		return False


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

		_snake_case							:		str								=  []

		if task == "s2t":
				_snake_case							:		Any								=  hf_model.speechta.encoder.prenet.feature_encoder
				_snake_case							:		List[str]								=  MAPPING_S2T
				_snake_case							:		List[str]								=  IGNORE_KEYS_S2T
		elif task == "t2s":
				_snake_case							:		Dict								=  None
				_snake_case							:		Tuple								=  MAPPING_T2S
				_snake_case							:		Tuple								=  IGNORE_KEYS_T2S
		elif task == "s2s":
				_snake_case							:		Any								=  hf_model.speechta.encoder.prenet.feature_encoder
				_snake_case							:		Optional[Any]								=  MAPPING_S2S
				_snake_case							:		int								=  IGNORE_KEYS_S2S
		else:
				raise ValueError(F"Unsupported task: {task}"   )

		for name, value in fairseq_dict.items():
				if should_ignore(snake_case__		,       snake_case__   ):
						logger.info(F"{name} was ignored"   )
						continue

				_snake_case							:		List[str]								=  False
				if "conv_layers" in name:
						load_conv_layer(
						    snake_case__		,       snake_case__		,       snake_case__		,       snake_case__		,       hf_model.config.feat_extract_norm == """group"""		,       )
						_snake_case							:		Tuple								=  True
				else:
						for key, mapped_key in MAPPING.items():
								# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

								if "*" in key:
										_snake_case							:		Dict								=  key.split(""".*."""   )
										if prefix in name and suffix in name:
												_snake_case							:		List[Any]								=  suffix

												    # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
								if key in name:
										_snake_case							:		Tuple								=  True
										if "*" in mapped_key:
												_snake_case							:		str								=  name.split(snake_case__   )[0].split("""."""   )[-2]
												_snake_case							:		Union[str, Any]								=  mapped_key.replace("""*"""		,       snake_case__   )
										if "weight_g" in name:
												_snake_case							:		Dict								=  """weight_g"""
										elif "weight_v" in name:
												_snake_case							:		Any								=  """weight_v"""
										elif "bias" in name:
												_snake_case							:		Union[str, Any]								=  """bias"""
										elif "weight" in name:
												_snake_case							:		Optional[Any]								=  """weight"""
										elif "running_mean" in name:
												_snake_case							:		Optional[int]								=  """running_mean"""
										elif "running_var" in name:
												_snake_case							:		int								=  """running_var"""
										elif "num_batches_tracked" in name:
												_snake_case							:		int								=  """num_batches_tracked"""
										else:
												_snake_case							:		List[Any]								=  None
										set_recursively(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
								continue
				if not is_used:
						unused_weights.append(snake_case__   )

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


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

		_snake_case							:		Optional[int]								=  full_name.split("""conv_layers."""   )[-1]
		_snake_case							:		List[Any]								=  name.split("""."""   )
		_snake_case							:		List[Any]								=  int(items[0]   )
		_snake_case							:		Any								=  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							:		List[Any]								=  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							:		str								=  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							:		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							:		Tuple								=  value
						logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
		else:
				unused_weights.append(snake_case__   )


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

		if config_path is not None:
				_snake_case							:		str								=  SpeechTaConfig.from_pretrained(snake_case__   )
		else:
				_snake_case							:		Optional[int]								=  SpeechTaConfig()

		if task == "s2t":
				_snake_case							:		Union[str, Any]								=  config.max_text_positions
				_snake_case							:		List[str]								=  SpeechTaForSpeechToText(snake_case__   )
		elif task == "t2s":
				_snake_case							:		Optional[Any]								=  18_76
				_snake_case							:		str								=  6_00
				_snake_case							:		Tuple								=  config.max_speech_positions
				_snake_case							:		List[str]								=  SpeechTaForTextToSpeech(snake_case__   )
		elif task == "s2s":
				_snake_case							:		List[str]								=  18_76
				_snake_case							:		Optional[Any]								=  config.max_speech_positions
				_snake_case							:		Optional[int]								=  SpeechTaForSpeechToSpeech(snake_case__   )
		else:
				raise ValueError(F"Unknown task name: {task}"   )

		if vocab_path:
				_snake_case							:		Tuple								=  SpeechTaTokenizer(snake_case__		,       model_max_length=config.max_text_positions   )

				# Mask token behaves like a normal word, i.e. include the space before it
				_snake_case							:		Dict								=  AddedToken("""<mask>"""		,       lstrip=snake_case__		,       rstrip=snake_case__   )
				_snake_case							:		Tuple								=  mask_token
				tokenizer.add_special_tokens({"""mask_token""": mask_token}   )
				tokenizer.add_tokens(["""<ctc_blank>"""]   )

		_snake_case							:		Any								=  SpeechTaFeatureExtractor()
		_snake_case							:		Dict								=  SpeechTaProcessor(tokenizer=snake_case__		,       feature_extractor=snake_case__   )
		processor.save_pretrained(snake_case__   )

		_snake_case							:		Dict								=  torch.load(snake_case__   )
		recursively_load_weights(fairseq_checkpoint["""model"""]		,       snake_case__		,       snake_case__   )

		model.save_pretrained(snake_case__   )

		if repo_id:
				print("""Pushing to the hub..."""   )
				processor.push_to_hub(snake_case__   )
				model.push_to_hub(snake_case__   )


if __name__ == "__main__":
					A_      =    argparse.ArgumentParser()
					parser.add_argument(
					    '''--task''',
					    default='''s2t''',
					    type=str,
					    help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
					)
					parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
					parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
					parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
					)

					A_      =    parser.parse_args()
					convert_speechta_checkpoint(
					    args.task,
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.vocab_path,
					    args.push_to_hub,
					)








"""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


