


"""simple docstring"""







from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput






@dataclass
class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  None
 __lowerCAmelCase 					=  None
 __lowerCAmelCase 					=  None
 __lowerCAmelCase 					=  None






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 def __init__(		self ,    __A=1 ,    __A=0 ,    __A=2 ,    __A=512 ,    __A="cls" ,    __A=False ,    __A=True ,    **__A ,    )					->     Union[str, Any]:
     super().__init__(pad_token_id=__A ,    bos_token_id=__A ,    eos_token_id=__A ,    **__A						)
     a 		=project_dim
     a 		=pooler_fn
     a 		=learn_encoder
     a 		=use_attention_mask






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  [r"pooler", r"logit_scale"]
 __lowerCAmelCase 					=  [r"position_ids", r"predictions.decoder.bias"]
 __lowerCAmelCase 					=  "roberta"
 __lowerCAmelCase 					=  RobertaSeriesConfig


 def __init__(		self ,    __A						)					->     Dict:
     super().__init__(__A						)
     a 		=XLMRobertaModel(__A						)
     a 		=nn.Linear(config.hidden_size ,    config.project_dim						)
     a 		=getattr(__A ,    '''has_pre_transformation''' ,    __A						)
     if self.has_pre_transformation:
         a 		=nn.Linear(config.hidden_size ,    config.project_dim						)
         a 		=nn.LayerNorm(config.hidden_size ,    eps=config.layer_norm_eps						)
     self.post_init()





 def       SCREAMING_SNAKE_CASE							(		self ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    )					->     Any:
     a 		=return_dict if return_dict is not None else self.config.use_return_dict

     a 		=self.base_model(
         input_ids=__A ,    attention_mask=__A ,    token_type_ids=__A ,    position_ids=__A ,    head_mask=__A ,    inputs_embeds=__A ,    encoder_hidden_states=__A ,    encoder_attention_mask=__A ,    output_attentions=__A ,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states ,    return_dict=__A ,    )

     if self.has_pre_transformation:
         a 		=outputs['''hidden_states'''][-2]
         a 		=self.pre_LN(__A						)
         a 		=self.transformation_pre(__A						)

         return TransformationModelOutput(
             projection_state=__A ,    last_hidden_state=outputs.last_hidden_state ,    hidden_states=outputs.hidden_states ,    attentions=outputs.attentions ,    )
     else:
         a 		=self.transformation(outputs.last_hidden_state						)
         return TransformationModelOutput(
             projection_state=__A ,    last_hidden_state=outputs.last_hidden_state ,    hidden_states=outputs.hidden_states ,    attentions=outputs.attentions ,    )






'''simple docstring'''
import os

import numpy
import onnx
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Union[str, Any]     ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  a.name
       __UpperCamelCase       =  b.name

       __UpperCamelCase       =  ''
       __UpperCamelCase       =  ''

       __UpperCamelCase       =  a == b

       __UpperCamelCase       =  name_a
       __UpperCamelCase       =  name_b

       return res
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  List[Any]     ) ->      Optional[int]:



       """simple docstring"""






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

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g				,  __lowercase				,  __lowercase     )
              _graph_replace_input_with(node_proto.attribute[1].g				,  __lowercase				,  __lowercase     )
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[Any]				,  __lowercase						:  Dict     ) ->      int:



       """simple docstring"""






       for n in graph_proto.node:
              _node_replace_input_with(__lowercase				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  str     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  list(model.graph.initializer     )
       __UpperCamelCase       =  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

              __UpperCamelCase       =  inits[i].name
              __UpperCamelCase       =  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				,  __lowercase				,  __lowercase     )


def 				lowercase__   (   __lowercase						:  Dict     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  os.path.dirname(__lowercase     )
       __UpperCamelCase       =  os.path.basename(__lowercase     )

       __UpperCamelCase       =  onnx.load(os.path.join(__lowercase				,  __lowercase     )     )

       __UpperCamelCase       =  list(model.graph.initializer     )

       __UpperCamelCase       =  set()
       __UpperCamelCase       =  {}
       __UpperCamelCase       =  []

       __UpperCamelCase       =  0

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

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

                            __UpperCamelCase       =  inits[j].data_type
                            __UpperCamelCase       =  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: '				,  __lowercase     )
                            total_reduced_size += mem_size

                            __UpperCamelCase       =  inits[i].name
                            __UpperCamelCase       =  inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(__lowercase     )
                            else:
                                   __UpperCamelCase       =  [name_j]
                            ind_to_replace.append((j, i)     )

       print('total reduced size: '				,  total_reduced_size / 1024 / 1024 / 1024				,  'GB'     )

       __UpperCamelCase       =  sorted(__lowercase     )
       _remove_dup_initializers_from_model(__lowercase				,  __lowercase				,  __lowercase     )

       __UpperCamelCase       =  'optimized_' + model_file_name
       __UpperCamelCase       =  os.path.join(__lowercase				,  __lowercase     )
       onnx.save(__lowercase				,  __lowercase     )

       return new_model
