
"""simple docstring"""

import os

import numpy
import onnx




def      lowercase__    (       lowerCAmelCase					:       Any			,					lowerCAmelCase					:       Tuple			)			->   Any:






    """simple docstring"""




    UpperCAmelCase          =       a.name
    UpperCAmelCase          =       b.name

    UpperCAmelCase          =       ''
    UpperCAmelCase          =       ''

    UpperCAmelCase          =       a == b

    UpperCAmelCase          =       name_a
    UpperCAmelCase          =       name_b

    return res




def      lowercase__    (       lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       int			)			->   List[str]:






    """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      lowercase__    (       lowerCAmelCase					:       Any			,					lowerCAmelCase					:       Optional[Any]			,					lowerCAmelCase					:       Any			)			->   int:






    """simple docstring"""




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




def      lowercase__    (       lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Any			)			->   Optional[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			,					lowerCAmelCase			,					lowerCAmelCase			)







def      lowercase__    (       lowerCAmelCase					:       List[Any]			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       os.path.dirname(lowerCAmelCase			)
    UpperCAmelCase          =       os.path.basename(lowerCAmelCase			)

    UpperCAmelCase          =       onnx.load(os.path.join(lowerCAmelCase			,					lowerCAmelCase			)			)

    UpperCAmelCase          =       list(model.graph.initializer			)

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

    UpperCAmelCase          =       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			)

                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: '			,					lowerCAmelCase			)
                total_reduced_size += mem_size

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

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

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

    UpperCAmelCase          =       sorted(lowerCAmelCase			)
    _remove_dup_initializers_from_model(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)

    UpperCAmelCase          =       'optimized_' + model_file_name
    UpperCAmelCase          =       os.path.join(lowerCAmelCase			,					lowerCAmelCase			)
    onnx.save(lowerCAmelCase			,					lowerCAmelCase			)

    return new_model





"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				Tuple				   =						"ibert"




      def __init__(				self ,							lowercase_=3_0_5_2_2 ,							lowercase_=7_6_8 ,							lowercase_=1_2 ,							lowercase_=1_2 ,							lowercase_=3_0_7_2 ,							lowercase_="gelu" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=1E-12 ,							lowercase_=1 ,							lowercase_=0 ,							lowercase_=2 ,							lowercase_="absolute" ,							lowercase_=False ,							lowercase_="none" ,							**lowercase_ ,							)    ->       Optional[int]:
          super().__init__(pad_token_id=lowercase_ ,							bos_token_id=lowercase_ ,							eos_token_id=lowercase_ ,							**lowercase_				)

          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       type_vocab_size
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       layer_norm_eps
          UpperCAmelCase          =       position_embedding_type
          UpperCAmelCase          =       quant_mode
          UpperCAmelCase          =       force_dequant

class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




      @property
      def      a_   (				self				)    ->       Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
              UpperCAmelCase          =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
              UpperCAmelCase          =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ]				)



