





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase					:		Union[str, Any]		=							logging.get_logger(__name__)

UpperCAmelCase					:		Union[str, Any]		=							{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class lowerCamelCase						(a__					):
  _lowercase  :	List[str]       					=				"""xlm-roberta-xl"""







  def __init__(		self     ,     lowercase__=250_880     ,     lowercase__=2_560     ,     lowercase__=36     ,     lowercase__=32     ,     lowercase__=10_240     ,     lowercase__="gelu"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=514     ,     lowercase__=1     ,     lowercase__=0.02     ,     lowercase__=1E-0_5     ,     lowercase__=1     ,     lowercase__=0     ,     lowercase__=2     ,     lowercase__="absolute"     ,     lowercase__=True     ,     lowercase__=None     ,     **lowercase__     ,     )		->						List[str]:






        """simple docstring"""





        super().__init__(pad_token_id=lowercase__     ,     bos_token_id=lowercase__     ,     eos_token_id=lowercase__     ,     **lowercase__		)
        _snake_case		:	Dict					     =     vocab_size
        _snake_case		:	Optional[Any]					     =     hidden_size
        _snake_case		:	Union[str, Any]					     =     num_hidden_layers
        _snake_case		:	Optional[int]					     =     num_attention_heads
        _snake_case		:	List[str]					     =     hidden_act
        _snake_case		:	Optional[Any]					     =     intermediate_size
        _snake_case		:	Optional[int]					     =     hidden_dropout_prob
        _snake_case		:	Optional[Any]					     =     attention_probs_dropout_prob
        _snake_case		:	Union[str, Any]					     =     max_position_embeddings
        _snake_case		:	Union[str, Any]					     =     type_vocab_size
        _snake_case		:	Union[str, Any]					     =     initializer_range
        _snake_case		:	int					     =     layer_norm_eps
        _snake_case		:	Any					     =     position_embedding_type
        _snake_case		:	int					     =     use_cache
        _snake_case		:	Tuple					     =     classifier_dropout





class lowerCamelCase						(a__					):







  @property
  def 				UpperCAmelCase_				(		self		)		->						Mapping[str, Mapping[int, str]]:






        """simple docstring"""





        if self.task == "multiple-choice":
              _snake_case		:	int					     =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
              _snake_case		:	int					     =     {0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ]		)









'''simple docstring'''

import os

import numpy
import onnx




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     a.name
      _snake_case		:	List[Any]					     =     b.name

      _snake_case		:	Tuple					     =     ''''''
      _snake_case		:	Tuple					     =     ''''''

      _snake_case		:	Optional[Any]					     =     a == b

      _snake_case		:	List[Any]					     =     name_a
      _snake_case		:	str					     =     name_b

      return res




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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

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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     list(model.graph.initializer	)
      _snake_case		:	List[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

            _snake_case		:	List[Any]					     =     inits[i].name
            _snake_case		:	List[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			,      lowerCAmelCase_			,      lowerCAmelCase_	)




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Tuple					     =     os.path.dirname(lowerCAmelCase_	)
      _snake_case		:	str					     =     os.path.basename(lowerCAmelCase_	)

      _snake_case		:	Tuple					     =     onnx.load(os.path.join(lowerCAmelCase_			,      lowerCAmelCase_	)	)

      _snake_case		:	Union[str, Any]					     =     list(model.graph.initializer	)

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

      _snake_case		:	Union[str, Any]					     =     0

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

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

                        _snake_case		:	List[Any]					     =     inits[j].data_type
                        _snake_case		:	Dict					     =     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: '''			,      lowerCAmelCase_	)
                        total_reduced_size += mem_size

                        _snake_case		:	Union[str, Any]					     =     inits[i].name
                        _snake_case		:	Any					     =     inits[j].name

                        if name_i in dup_map:
                              dup_map[name_i].append(lowerCAmelCase_	)
                        else:
                              _snake_case		:	Union[str, Any]					     =     [name_j]
                        ind_to_replace.append((j, i)	)

      print('''total reduced size: '''			,      total_reduced_size / 1_024 / 1_024 / 1_024			,      '''GB'''	)

      _snake_case		:	List[str]					     =     sorted(lowerCAmelCase_	)
      _remove_dup_initializers_from_model(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)

      _snake_case		:	List[str]					     =     '''optimized_''' + model_file_name
      _snake_case		:	List[Any]					     =     os.path.join(lowerCAmelCase_			,      lowerCAmelCase_	)
      onnx.save(lowerCAmelCase_			,      lowerCAmelCase_	)

      return new_model


