

'''simple docstring'''
import os

import numpy
import onnx







def 					_A		(	snake_case		,							snake_case     )			->	Union[str, Any]:
   _lowercase			:      Optional[int]			     =   a.name
   _lowercase			:      str			     =   b.name

   _lowercase			:      List[Any]			     =   ""
   _lowercase			:      Tuple			     =   ""

   _lowercase			:      str			     =   a == b

   _lowercase			:      Tuple			     =   name_a
   _lowercase			:      int			     =   name_b

   return res







def 					_A		(	snake_case		,							snake_case		,							snake_case     )			->	Optional[Any]:



   for i, input_name in enumerate(node_proto.input     ):
      if input_name == name:
         node_proto.input.insert(A__		,							A__     )
         node_proto.input.pop(i + 1     )

   if node_proto.op_type == "If":
      _graph_replace_input_with(node_proto.attribute[0].g		,							A__		,							A__     )
      _graph_replace_input_with(node_proto.attribute[1].g		,							A__		,							A__     )
   if node_proto.op_type == "Loop":
      _graph_replace_input_with(node_proto.attribute[0].g		,							A__		,							A__     )







def 					_A		(	snake_case		,							snake_case		,							snake_case     )			->	Tuple:



   for n in graph_proto.node:
      _node_replace_input_with(A__		,							A__		,							A__     )







def 					_A		(	snake_case		,							snake_case		,							snake_case     )			->	List[str]:
   _lowercase			:      List[Any]			     =   list(model.graph.initializer     )
   _lowercase			:      str			     =   list(model_without_ext.graph.initializer     )
   for i, ref_i in ind_to_replace:
      assert inits_with_data[i].name == inits[i].name
      assert inits_with_data[ref_i].name == inits[ref_i].name
      assert i > ref_i

      _lowercase			:      Tuple			     =   inits[i].name
      _lowercase			:      str			     =   inits[ref_i].name

      model_without_ext.graph.initializer.remove(inits[i]     )

      # for n in model.graph.node:
      _graph_replace_input_with(model_without_ext.graph		,							A__		,							A__     )







def 					_A		(	snake_case     )			->	List[Any]:
   _lowercase			:      int			     =   os.path.dirname(A__     )
   _lowercase			:      Union[str, Any]			     =   os.path.basename(A__     )

   _lowercase			:      Union[str, Any]			     =   onnx.load(os.path.join(A__		,							A__     )     )

   _lowercase			:      Dict			     =   list(model.graph.initializer     )

   _lowercase			:      str			     =   set()
   _lowercase			:      Union[str, Any]			     =   {}
   _lowercase			:      int			     =   []

   _lowercase			:      Optional[Any]			     =   0

   for i in range(len(A__     )     ):
      if i in dup_set:
         continue

      for j in range(i + 1		,							len(A__     )     ):
         if j in dup_set:
            continue
         if _is_equal_tensor_proto(inits[i]		,							inits[j]     ):
            dup_set.add(A__     )
            dup_set.add(A__     )

            _lowercase			:      Optional[int]			     =   inits[j].data_type
            _lowercase			:      Optional[Any]			     =   numpy.prod(inits[j].dims     )
            if dtype == 1:
               mem_size *= 4
            elif dtype == 6:
               mem_size *= 4
            elif dtype == 7 or dtype == 11:
               mem_size *= 8
            else:
               print("unexpected data type: "		,							A__     )
            total_reduced_size += mem_size

            _lowercase			:      List[str]			     =   inits[i].name
            _lowercase			:      str			     =   inits[j].name

            if name_i in dup_map:
               dup_map[name_i].append(A__     )
            else:
               _lowercase			:      Dict			     =   [name_j]
            ind_to_replace.append((j, i)     )

   print("total reduced size: "		,							total_reduced_size / 10_24 / 10_24 / 10_24		,							"GB"     )

   _lowercase			:      Optional[Any]			     =   sorted(A__     )
   _remove_dup_initializers_from_model(A__		,							A__		,							A__     )

   _lowercase			:      List[Any]			     =   "optimized_" + model_file_name
   _lowercase			:      List[str]			     =   os.path.join(A__		,							A__     )
   onnx.save(A__		,							A__     )

   return new_model









import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE							):




  @register_to_config
  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = False							,   )  -> Tuple:
        '''simple docstring'''






        super().__init__()

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)
        _lowercase	       =False

        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)

        _lowercase	       =TaConfig(
            vocab_size=lowerCAmelCase							,   d_model=lowerCAmelCase							,   num_heads=lowerCAmelCase							,   d_kv=lowerCAmelCase							,   d_ff=lowerCAmelCase							,   dropout_rate=lowerCAmelCase							,   feed_forward_proj=lowerCAmelCase							,   is_decoder=lowerCAmelCase							,   is_encoder_decoder=lowerCAmelCase							,   )

        _lowercase	       =nn.ModuleList()
        for lyr_num in range(lowerCAmelCase					):
              _lowercase	       =TaBlock(lowerCAmelCase					)
              self.encoders.append(lowerCAmelCase					)

        _lowercase	       =TaLayerNorm(lowerCAmelCase					)
        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =self.token_embedder(lowerCAmelCase					)

        _lowercase	       =encoder_input_tokens.shape[1]
        _lowercase	       =torch.arange(lowerCAmelCase							,   device=encoder_input_tokens.device					)
        x += self.position_encoding(lowerCAmelCase					)

        _lowercase	       =self.dropout_pre(lowerCAmelCase					)

        # inverted the attention mask
        _lowercase	       =encoder_input_tokens.size()
        _lowercase	       =self.get_extended_attention_mask(lowerCAmelCase							,   lowerCAmelCase					)

        for lyr in self.encoders:
              _lowercase	       =lyr(lowerCAmelCase							,   lowerCAmelCase					)[0]
        _lowercase	       =self.layer_norm(lowerCAmelCase					)

        return self.dropout_post(lowerCAmelCase					), encoder_inputs_mask


