

import torch
from torch import nn






class 						__magic_name__					(							nn.Module				):






     def __init__(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase=1		,						_lowercase=False	)->     str:
         super().__init__()

         UpperCamelCase_			  =						n_token
         UpperCamelCase_			  =						d_embed
         UpperCamelCase_			  =						d_proj

         UpperCamelCase_			  =						cutoffs + [n_token]
         UpperCamelCase_			  =						[0] + self.cutoffs
         UpperCamelCase_			  =						div_val

         UpperCamelCase_			  =						self.cutoffs[0]
         UpperCamelCase_			  =						len(self.cutoffs	) - 1
         UpperCamelCase_			  =						self.shortlist_size + self.n_clusters

         if self.n_clusters > 0:
             UpperCamelCase_			  =						nn.Parameter(torch.zeros(self.n_clusters		,						self.d_embed	)	)
             UpperCamelCase_			  =						nn.Parameter(torch.zeros(self.n_clusters	)	)

         UpperCamelCase_			  =						nn.ModuleList()
         UpperCamelCase_			  =						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(_lowercase		,						_lowercase	)	)	)
                 else:
                     self.out_projs.append(_lowercase	)

             self.out_layers.append(nn.Linear(_lowercase		,						_lowercase	)	)
         else:
             for i in range(len(self.cutoffs	)	):
                 UpperCamelCase_  ,       UpperCamelCase_			  =						self.cutoff_ends[i], self.cutoff_ends[i + 1]
                 UpperCamelCase_			  =						d_embed // (div_val**i)

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

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

         UpperCamelCase_			  =						keep_order






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)->     Dict:
         if proj is None:
             UpperCamelCase_			  =						nn.functional.linear(_lowercase		,						_lowercase		,						bias=_lowercase	)
         else:
             # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
             UpperCamelCase_			  =						nn.functional.linear(_lowercase		,						proj.t().contiguous()	)
             UpperCamelCase_			  =						nn.functional.linear(_lowercase		,						_lowercase		,						bias=_lowercase	)
             # 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		,						_lowercase		,						_lowercase=None		,						_lowercase=False	)->     Optional[Any]:

         if labels is not None:
             # Shift so that tokens < n predict n
             UpperCamelCase_			  =						hidden[..., :-1, :].contiguous()
             UpperCamelCase_			  =						labels[..., 1:].contiguous()
             UpperCamelCase_			  =						hidden.view(-1		,						hidden.size(-1	)	)
             UpperCamelCase_			  =						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:
             UpperCamelCase_			  =						hidden.view(-1		,						hidden.size(-1	)	)

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

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

                 weights.append(_lowercase	)
                 biases.append(_lowercase	)

             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						weights[0], biases[0], self.out_projs[0]

             UpperCamelCase_			  =						self._compute_logit(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						nn.functional.log_softmax(_lowercase		,						dim=1	)

             if labels is None:
                 UpperCamelCase_			  =						hidden.new_empty((head_logit.size(0	), self.n_token)	)
             else:
                 UpperCamelCase_			  =						torch.zeros_like(_lowercase		,						dtype=hidden.dtype		,						device=hidden.device	)

             UpperCamelCase_			  =						0
             UpperCamelCase_			  =						[0] + self.cutoffs
             for i in range(len(_lowercase	) - 1	):
                 UpperCamelCase_  ,       UpperCamelCase_			  =						cutoff_values[i], cutoff_values[i + 1]

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

                     if indices_i.numel() == 0:
                         continue

                     UpperCamelCase_			  =						labels.index_select(0		,						_lowercase	) - l_idx
                     UpperCamelCase_			  =						head_logprob.index_select(0		,						_lowercase	)
                     UpperCamelCase_			  =						hidden.index_select(0		,						_lowercase	)
                 else:
                     UpperCamelCase_			  =						hidden

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

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

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

         return out






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Optional[Any]:
         if self.n_clusters == 0:
             UpperCamelCase_			  =						self._compute_logit(_lowercase		,						self.out_layers[0].weight		,						self.out_layers[0].bias		,						self.out_projs[0]	)
             return nn.functional.log_softmax(_lowercase		,						dim=-1	)
         else:
             # construct weights and biases
             UpperCamelCase_  ,       UpperCamelCase_			  =						[], []
             for i in range(len(self.cutoffs	)	):
                 if self.div_val == 1:
                     UpperCamelCase_  ,       UpperCamelCase_			  =						self.cutoff_ends[i], self.cutoff_ends[i + 1]
                     UpperCamelCase_			  =						self.out_layers[0].weight[l_idx:r_idx]
                     UpperCamelCase_			  =						self.out_layers[0].bias[l_idx:r_idx]
                 else:
                     UpperCamelCase_			  =						self.out_layers[i].weight
                     UpperCamelCase_			  =						self.out_layers[i].bias

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

                 weights.append(_lowercase	)
                 biases.append(_lowercase	)

             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						weights[0], biases[0], self.out_projs[0]
             UpperCamelCase_			  =						self._compute_logit(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						hidden.new_empty((head_logit.size(0	), self.n_token)	)
             UpperCamelCase_			  =						nn.functional.log_softmax(_lowercase		,						dim=1	)

             UpperCamelCase_			  =						[0] + self.cutoffs
             for i in range(len(_lowercase	) - 1	):
                 UpperCamelCase_  ,       UpperCamelCase_			  =						cutoff_values[i], cutoff_values[i + 1]

                 if i == 0:
                     UpperCamelCase_			  =						head_logprob[:, : self.cutoffs[0]]
                 else:
                     UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						weights[i], biases[i], self.out_projs[i]

                     UpperCamelCase_			  =						self._compute_logit(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
                     UpperCamelCase_			  =						nn.functional.log_softmax(_lowercase		,						dim=1	)

                     UpperCamelCase_			  =						head_logprob[:, -i] + tail_logprob_i
                     UpperCamelCase_			  =						logprob_i

             return out



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Any						=     [
     """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SpeechT5ForSpeechToText""",
     """SpeechT5ForSpeechToSpeech""",
     """SpeechT5ForTextToSpeech""",
     """SpeechT5Model""",
     """SpeechT5PreTrainedModel""",
     """SpeechT5HifiGan""",
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
