import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Tuple    			=		'''sew-d'''



 def __init__(					self		,				lowerCAmelCase_=32		,				lowerCAmelCase_=7_68		,				lowerCAmelCase_=12		,				lowerCAmelCase_=12		,				lowerCAmelCase_=30_72		,				lowerCAmelCase_=2		,				lowerCAmelCase_=5_12		,				lowerCAmelCase_=2_56		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=("p2c", "c2p")		,				lowerCAmelCase_="layer_norm"		,				lowerCAmelCase_="gelu_python"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=1E-7		,				lowerCAmelCase_=1E-5		,				lowerCAmelCase_="group"		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)		,				lowerCAmelCase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,				lowerCAmelCase_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,				lowerCAmelCase_=False		,				lowerCAmelCase_=1_28		,				lowerCAmelCase_=16		,				lowerCAmelCase_=True		,				lowerCAmelCase_=0.05		,				lowerCAmelCase_=10		,				lowerCAmelCase_=2		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=10		,				lowerCAmelCase_=0		,				lowerCAmelCase_="mean"		,				lowerCAmelCase_=False		,				lowerCAmelCase_=False		,				lowerCAmelCase_=2_56		,				lowerCAmelCase_=0		,				lowerCAmelCase_=1		,				lowerCAmelCase_=2		,				**lowerCAmelCase_		,				)  ->					List[Any]:
        super().__init__(**lowerCAmelCase_		,				pad_token_id=lowerCAmelCase_		,				bos_token_id=lowerCAmelCase_		,				eos_token_id=lowerCAmelCase_							)
        _A        =  hidden_size
        _A        =  feat_extract_norm
        _A        =  feat_extract_activation
        _A        =  list(lowerCAmelCase_							)
        _A        =  list(lowerCAmelCase_							)
        _A        =  list(lowerCAmelCase_							)
        _A        =  conv_bias
        _A        =  num_conv_pos_embeddings
        _A        =  num_conv_pos_embedding_groups
        _A        =  len(self.conv_dim							)
        _A        =  num_hidden_layers
        _A        =  intermediate_size
        _A        =  squeeze_factor
        _A        =  max_position_embeddings
        _A        =  position_buckets
        _A        =  share_att_key
        _A        =  relative_attention
        _A        =  norm_rel_ebd
        _A        =  list(lowerCAmelCase_							)
        _A        =  hidden_act
        _A        =  num_attention_heads
        _A        =  hidden_dropout
        _A        =  attention_dropout
        _A        =  activation_dropout
        _A        =  feat_proj_dropout
        _A        =  final_dropout
        _A        =  layer_norm_eps
        _A        =  feature_layer_norm_eps
        _A        =  initializer_range
        _A        =  vocab_size

        if (
            (len(self.conv_stride							) != self.num_feat_extract_layers)
            or (len(self.conv_kernel							) != self.num_feat_extract_layers)
            or (len(self.conv_dim							) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   """Configuration for convolutional layers is incorrect."""
                   """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
                   F'''but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)'''
                   F'''= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        _A        =  apply_spec_augment
        _A        =  mask_time_prob
        _A        =  mask_time_length
        _A        =  mask_time_min_masks
        _A        =  mask_feature_prob
        _A        =  mask_feature_length
        _A        =  mask_feature_min_masks

        # ctc loss
        _A        =  ctc_loss_reduction
        _A        =  ctc_zero_infinity

        # sequence classification
        _A        =  use_weighted_layer_sum
        _A        =  classifier_proj_size



 @property
 def      UpperCAmelCase							(					self							)  ->					str:
        return functools.reduce(operator.mul		,				self.conv_stride		,				1							)





import torch
from torch import nn



class 				a							(			nn.Module       ):







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_=1		,				lowerCAmelCase_=False							)  ->					Any:
        super().__init__()

        _A        =  n_token
        _A        =  d_embed
        _A        =  d_proj

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

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

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

        _A        =  nn.ModuleList()
        _A        =  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(lowerCAmelCase_		,				lowerCAmelCase_							)							)							)
                      else:
                             self.out_projs.append(lowerCAmelCase_							)

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

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

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

        _A        =  keep_order



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

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

        if self.n_clusters == 0:
               _A        =  self._compute_logit(lowerCAmelCase_		,				self.out_layers[0].weight		,				self.out_layers[0].bias		,				self.out_projs[0]							)
               if labels is not None:
                      _A        =  labels != -1_00
                      _A        =  torch.zeros_like(lowerCAmelCase_		,				dtype=hidden.dtype		,				device=hidden.device							)
                      _A        =  (
                          -nn.functional.log_softmax(lowerCAmelCase_		,				dim=-1							)[mask].gather(1		,				labels[mask].unsqueeze(1							)							).squeeze(1							)
                      )
               else:
                      _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=-1							)
        else:
               # construct weights and biases
               _A   ,  _A        =  [], []
               for i in range(len(self.cutoffs							)							):
                      if self.div_val == 1:
                             _A   ,  _A        =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
                             _A        =  self.out_layers[0].weight[l_idx:r_idx]
                             _A        =  self.out_layers[0].bias[l_idx:r_idx]
                      else:
                             _A        =  self.out_layers[i].weight
                             _A        =  self.out_layers[i].bias

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

                      weights.append(lowerCAmelCase_							)
                      biases.append(lowerCAmelCase_							)

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

               _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
               _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)

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

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

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

                             if indices_i.numel() == 0:
                                    continue

                             _A        =  labels.index_select(0		,				lowerCAmelCase_							) - l_idx
                             _A        =  head_logprob.index_select(0		,				lowerCAmelCase_							)
                             _A        =  hidden.index_select(0		,				lowerCAmelCase_							)
                      else:
                             _A        =  hidden

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

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

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

        return out



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

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

                      weights.append(lowerCAmelCase_							)
                      biases.append(lowerCAmelCase_							)

               _A   ,  _A   ,  _A        =  weights[0], biases[0], self.out_projs[0]
               _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

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

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

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

                             _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
                             _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)

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

               return out




