

'''simple docstring'''







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Any:
       if "model" in orig_key:
              A_						=      orig_key.replace("""model.""",       """"""				)
       if "norm1" in orig_key:
              A_						=      orig_key.replace("""norm1""",       """attention.output.LayerNorm"""				)
       if "norm2" in orig_key:
              A_						=      orig_key.replace("""norm2""",       """output.LayerNorm"""				)
       if "norm" in orig_key:
              A_						=      orig_key.replace("""norm""",       """LayerNorm"""				)
       if "transformer" in orig_key:
              A_						=      orig_key.split("""."""				)[0].split("""_"""				)[-1]
              A_						=      orig_key.replace(F'''transformer_{layer_num}''',       F'''encoder.layer.{layer_num}'''				)
       if "mha.attn" in orig_key:
              A_						=      orig_key.replace("""mha.attn""",       """attention.self"""				)
       if "mha" in orig_key:
              A_						=      orig_key.replace("""mha""",       """attention"""				)
       if "W_q" in orig_key:
              A_						=      orig_key.replace("""W_q""",       """self.query"""				)
       if "W_k" in orig_key:
              A_						=      orig_key.replace("""W_k""",       """self.key"""				)
       if "W_v" in orig_key:
              A_						=      orig_key.replace("""W_v""",       """self.value"""				)
       if "ff1" in orig_key:
              A_						=      orig_key.replace("""ff1""",       """intermediate.dense"""				)
       if "ff2" in orig_key:
              A_						=      orig_key.replace("""ff2""",       """output.dense"""				)
       if "ff" in orig_key:
              A_						=      orig_key.replace("""ff""",       """output.dense"""				)
       if "mlm_class" in orig_key:
              A_						=      orig_key.replace("""mlm.mlm_class""",       """cls.predictions.decoder"""				)
       if "mlm" in orig_key:
              A_						=      orig_key.replace("""mlm""",       """cls.predictions.transform"""				)
       if "cls" not in orig_key:
              A_						=      """yoso.""" + orig_key

       return orig_key
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			int:
       for key in orig_state_dict.copy().keys():
              A_						=      orig_state_dict.pop(UpperCAmelCase__				)

              if ("pooler" in key) or ("sen_class" in key):
                     continue
              else:
                     A_						=      val

       A_						=      orig_state_dict["""cls.predictions.decoder.bias"""]
       A_						=      torch.arange(UpperCAmelCase__				).expand((1, -1)				) + 2

       return orig_state_dict
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			List[str]:
       A_						=      torch.load(UpperCAmelCase__,       map_location="""cpu"""				)["""model_state_dict"""]
       A_						=      YosoConfig.from_json_file(UpperCAmelCase__				)
       A_						=      YosoForMaskedLM(UpperCAmelCase__				)

       A_						=      convert_checkpoint_helper(config.max_position_embeddings,       UpperCAmelCase__				)

       print(model.load_state_dict(UpperCAmelCase__				)				)
       model.eval()
       model.save_pretrained(UpperCAmelCase__				)

       print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
      )
      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help='''The json file for YOSO model config.''',
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      __lowerCamelCase		       =    parser.parse_args()
      convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



'''simple docstring'''







import tensorflow as tf

from ...tf_utils import shape_list


class 					A__      (      tf.keras.layers.Layer			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=1		,				UpperCamelCase__=False		,				**UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__       )

           A_						=      vocab_size
           A_						=      d_embed
           A_						=      d_proj

           A_						=      cutoffs + [vocab_size]
           A_						=      [0] + self.cutoffs
           A_						=      div_val

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

           A_						=      []
           A_						=      []



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           if self.n_clusters > 0:
                  A_						=      self.add_weight(
                      shape=(self.n_clusters, self.d_embed)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name="""cluster_weight"""       )
                  A_						=      self.add_weight(
                      shape=(self.n_clusters,)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name="""cluster_bias"""       )

           if self.div_val == 1:
                  for i in range(len(self.cutoffs       )       ):
                         if self.d_proj != self.d_embed:
                                A_						=      self.add_weight(
                                    shape=(self.d_embed, self.d_proj)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_projs_._{i}'''		,				)
                                self.out_projs.append(UpperCamelCase__       )
                         else:
                                self.out_projs.append(UpperCamelCase__       )
                         A_						=      self.add_weight(
                             shape=(self.vocab_size, self.d_embed)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._weight'''		,				)
                         A_						=      self.add_weight(
                             shape=(self.vocab_size,)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._bias'''		,				)
                         self.out_layers.append((weight, bias)       )
           else:
                  for i in range(len(self.cutoffs       )       ):
                         A_					,			A_						=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                         A_						=      self.d_embed // (self.div_val**i)

                         A_						=      self.add_weight(
                             shape=(d_emb_i, self.d_proj)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_projs_._{i}'''       )
                         self.out_projs.append(UpperCamelCase__       )
                         A_						=      self.add_weight(
                             shape=(r_idx - l_idx, d_emb_i)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._weight'''		,				)
                         A_						=      self.add_weight(
                             shape=(r_idx - l_idx,)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._bias'''		,				)
                         self.out_layers.append((weight, bias)       )
           super().build(UpperCamelCase__       )



    @staticmethod
    def 					snake_case_			(					UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      List[Any]:





           '''simple docstring'''
           A_						=      x
           if proj is not None:
                  A_						=      tf.einsum("""ibd,ed->ibe"""		,				UpperCamelCase__		,				UpperCamelCase__       )
           return tf.einsum("""ibd,nd->ibn"""		,				UpperCamelCase__		,				UpperCamelCase__       ) + b



    @staticmethod
    def 					snake_case_			(					UpperCamelCase__		,				UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           A_						=      shape_list(UpperCamelCase__       )
           A_						=      tf.range(lp_size[0]		,				dtype=target.dtype       )
           A_						=      tf.stack([r, target]		,				1       )
           return tf.gather_nd(UpperCamelCase__		,				UpperCamelCase__       )







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=True		,				UpperCamelCase__=False       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      0
           if self.n_clusters == 0:
                  A_						=      self._logit(UpperCamelCase__		,				self.out_layers[0][0]		,				self.out_layers[0][1]		,				self.out_projs[0]       )
                  if target is not None:
                         A_						=      tf.nn.sparse_softmax_cross_entropy_with_logits(labels=UpperCamelCase__		,				logits=UpperCamelCase__       )
                  A_						=      tf.nn.log_softmax(UpperCamelCase__		,				axis=-1       )
           else:
                  A_						=      shape_list(UpperCamelCase__       )
                  A_						=      []
                  A_						=      tf.zeros(hidden_sizes[:2]       )
                  for i in range(len(self.cutoffs       )       ):
                         A_					,			A_						=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                         if target is not None:
                                A_						=      (target >= l_idx) & (target < r_idx)
                                A_						=      tf.where(UpperCamelCase__       )
                                A_						=      tf.boolean_mask(UpperCamelCase__		,				UpperCamelCase__       ) - l_idx

                         if self.div_val == 1:
                                A_						=      self.out_layers[0][0][l_idx:r_idx]
                                A_						=      self.out_layers[0][1][l_idx:r_idx]
                         else:
                                A_						=      self.out_layers[i][0]
                                A_						=      self.out_layers[i][1]

                         if i == 0:
                                A_						=      tf.concat([cur_W, self.cluster_weight]		,				0       )
                                A_						=      tf.concat([cur_b, self.cluster_bias]		,				0       )

                                A_						=      self._logit(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				self.out_projs[0]       )
                                A_						=      tf.nn.log_softmax(UpperCamelCase__       )
                                out.append(head_logprob[..., : self.cutoffs[0]]       )
                                if target is not None:
                                       A_						=      tf.boolean_mask(UpperCamelCase__		,				UpperCamelCase__       )
                                       A_						=      self._gather_logprob(UpperCamelCase__		,				UpperCamelCase__       )
                         else:
                                A_						=      self._logit(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				self.out_projs[i]       )
                                A_						=      tf.nn.log_softmax(UpperCamelCase__       )
                                A_						=      self.cutoffs[0] + i - 1  # No probability for the head cluster
                                A_						=      head_logprob[..., cluster_prob_idx, None] + tail_logprob
                                out.append(UpperCamelCase__       )
                                if target is not None:
                                       A_						=      tf.boolean_mask(UpperCamelCase__		,				UpperCamelCase__       )
                                       A_						=      tf.boolean_mask(UpperCamelCase__		,				UpperCamelCase__       )
                                       A_						=      self._gather_logprob(UpperCamelCase__		,				UpperCamelCase__       )
                                       cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
                         if target is not None:
                                loss += tf.scatter_nd(UpperCamelCase__		,				-cur_logprob		,				shape_list(UpperCamelCase__       )       )
                  A_						=      tf.concat(UpperCamelCase__		,				axis=-1       )

           if target is not None:
                  if return_mean:
                         A_						=      tf.reduce_mean(UpperCamelCase__       )
                  # Add the training-time loss value to the layer using `self.add_loss()`.
                  self.add_loss(UpperCamelCase__       )

                  # Log the loss as a metric (we could log arbitrary metrics,
                  # including different metrics for training and inference.
                  self.add_metric(UpperCamelCase__		,				name=self.name		,				aggregation="""mean""" if return_mean else """"""       )

           return out
