





'''simple docstring'''

import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       NezhaForMaskedLM,
       NezhaForMultipleChoice,
       NezhaForNextSentencePrediction,
       NezhaForPreTraining,
       NezhaForQuestionAnswering,
       NezhaForSequenceClassification,
       NezhaForTokenClassification,
       NezhaModel,
   )
   from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST





class lowerCamelCase						:







  def __init__(		self     ,     lowercase__     ,     lowercase__=13     ,     lowercase__=7     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=99     ,     lowercase__=32     ,     lowercase__=5     ,     lowercase__=4     ,     lowercase__=37     ,     lowercase__="gelu"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=128     ,     lowercase__=32     ,     lowercase__=16     ,     lowercase__=2     ,     lowercase__=0.02     ,     lowercase__=3     ,     lowercase__=4     ,     lowercase__=None     ,     )		->						str:






        """simple docstring"""





        _snake_case		:	Tuple					     =     parent
        _snake_case		:	Union[str, Any]					     =     batch_size
        _snake_case		:	List[Any]					     =     seq_length
        _snake_case		:	str					     =     is_training
        _snake_case		:	Union[str, Any]					     =     use_input_mask
        _snake_case		:	Optional[int]					     =     use_token_type_ids
        _snake_case		:	List[Any]					     =     use_labels
        _snake_case		:	str					     =     vocab_size
        _snake_case		:	Union[str, Any]					     =     hidden_size
        _snake_case		:	Dict					     =     num_hidden_layers
        _snake_case		:	List[str]					     =     num_attention_heads
        _snake_case		:	Dict					     =     intermediate_size
        _snake_case		:	Union[str, Any]					     =     hidden_act
        _snake_case		:	List[str]					     =     hidden_dropout_prob
        _snake_case		:	str					     =     attention_probs_dropout_prob
        _snake_case		:	Any					     =     max_position_embeddings
        _snake_case		:	List[str]					     =     type_vocab_size
        _snake_case		:	Dict					     =     type_sequence_label_size
        _snake_case		:	List[str]					     =     initializer_range
        _snake_case		:	int					     =     num_labels
        _snake_case		:	Optional[int]					     =     num_choices
        _snake_case		:	Dict					     =     scope







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     ids_tensor([self.batch_size, self.seq_length]     ,     self.vocab_size		)

        _snake_case		:	Optional[int]					     =     None
        if self.use_input_mask:
              _snake_case		:	int					     =     random_attention_mask([self.batch_size, self.seq_length]		)

        _snake_case		:	int					     =     None
        if self.use_token_type_ids:
              _snake_case		:	Tuple					     =     ids_tensor([self.batch_size, self.seq_length]     ,     self.type_vocab_size		)

        _snake_case		:	Optional[Any]					     =     None
        _snake_case		:	Any					     =     None
        _snake_case		:	List[str]					     =     None
        if self.use_labels:
              _snake_case		:	Union[str, Any]					     =     ids_tensor([self.batch_size]     ,     self.type_sequence_label_size		)
              _snake_case		:	Optional[int]					     =     ids_tensor([self.batch_size, self.seq_length]     ,     self.num_labels		)
              _snake_case		:	str					     =     ids_tensor([self.batch_size]     ,     self.num_choices		)

        _snake_case		:	List[str]					     =     self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        return NezhaConfig(
            vocab_size=self.vocab_size     ,     hidden_size=self.hidden_size     ,     num_hidden_layers=self.num_hidden_layers     ,     num_attention_heads=self.num_attention_heads     ,     intermediate_size=self.intermediate_size     ,     hidden_act=self.hidden_act     ,     hidden_dropout_prob=self.hidden_dropout_prob     ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,     max_position_embeddings=self.max_position_embeddings     ,     type_vocab_size=self.type_vocab_size     ,     is_decoder=lowercase__     ,     initializer_range=self.initializer_range     ,     )







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        (
            (
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							
        )		:	List[Any]					     =     self.prepare_config_and_inputs()

        _snake_case		:	Optional[int]					     =     True
        _snake_case		:	Union[str, Any]					     =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
        _snake_case		:	Optional[Any]					     =     ids_tensor([self.batch_size, self.seq_length]     ,     vocab_size=2		)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	List[str]					     =     NezhaModel(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Dict					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__		)
        _snake_case		:	str					     =     model(lowercase__     ,     token_type_ids=lowercase__		)
        _snake_case		:	Union[str, Any]					     =     model(lowercase__		)
        self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)		)
        self.parent.assertEqual(result.pooler_output.shape     ,     (self.batch_size, self.hidden_size)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     True
        _snake_case		:	Optional[Any]					     =     NezhaModel(lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Union[str, Any]					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__     ,     encoder_hidden_states=lowercase__     ,     encoder_attention_mask=lowercase__     ,     )
        _snake_case		:	Any					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__     ,     encoder_hidden_states=lowercase__     ,     )
        _snake_case		:	int					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__		)
        self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)		)
        self.parent.assertEqual(result.pooler_output.shape     ,     (self.batch_size, self.hidden_size)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	Dict					     =     NezhaForMaskedLM(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	int					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__     ,     labels=lowercase__		)
        self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.seq_length, self.vocab_size)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     NezhaForNextSentencePrediction(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Union[str, Any]					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__     ,     labels=lowercase__     ,     )
        self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, 2)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	str					     =     NezhaForPreTraining(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Dict					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__     ,     labels=lowercase__     ,     next_sentence_label=lowercase__     ,     )
        self.parent.assertEqual(result.prediction_logits.shape     ,     (self.batch_size, self.seq_length, self.vocab_size)		)
        self.parent.assertEqual(result.seq_relationship_logits.shape     ,     (self.batch_size, 2)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	str					     =     NezhaForQuestionAnswering(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Optional[int]					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__     ,     start_positions=lowercase__     ,     end_positions=lowercase__     ,     )
        self.parent.assertEqual(result.start_logits.shape     ,     (self.batch_size, self.seq_length)		)
        self.parent.assertEqual(result.end_logits.shape     ,     (self.batch_size, self.seq_length)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.num_labels
        _snake_case		:	str					     =     NezhaForSequenceClassification(lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	int					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__     ,     labels=lowercase__		)
        self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_labels)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Any:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     self.num_labels
        _snake_case		:	Any					     =     NezhaForTokenClassification(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Optional[int]					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__     ,     labels=lowercase__		)
        self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.seq_length, self.num_labels)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Dict					     =     self.num_choices
        _snake_case		:	Tuple					     =     NezhaForMultipleChoice(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Optional[Any]					     =     input_ids.unsqueeze(1		).expand(-1     ,     self.num_choices     ,     -1		).contiguous()
        _snake_case		:	Dict					     =     token_type_ids.unsqueeze(1		).expand(-1     ,     self.num_choices     ,     -1		).contiguous()
        _snake_case		:	List[str]					     =     input_mask.unsqueeze(1		).expand(-1     ,     self.num_choices     ,     -1		).contiguous()
        _snake_case		:	Dict					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     token_type_ids=lowercase__     ,     labels=lowercase__     ,     )
        self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_choices)		)







  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.prepare_config_and_inputs()
        (
            (
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							
        )		:	int					     =     config_and_inputs
        _snake_case		:	str					     =     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict





@require_torch
class lowerCamelCase						(a__						,      a__						,      a__						,      unittest.TestCase					):
  _lowercase  :	Optional[int]       					=				(
      (
          NezhaModel,
          NezhaForMaskedLM,
          NezhaForMultipleChoice,
          NezhaForNextSentencePrediction,
          NezhaForPreTraining,
          NezhaForQuestionAnswering,
          NezhaForSequenceClassification,
          NezhaForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  _lowercase  :	Dict       					=				(
      {
          """feature-extraction""": NezhaModel,
          """fill-mask""": NezhaForMaskedLM,
          """question-answering""": NezhaForQuestionAnswering,
          """text-classification""": NezhaForSequenceClassification,
          """token-classification""": NezhaForTokenClassification,
          """zero-shot""": NezhaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  _lowercase  :	Any       					=				True







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__=False		)		->						Dict:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     super()._prepare_for_class(lowercase__     ,     lowercase__     ,     return_labels=lowercase__		)

        if return_labels:
              if model_class in get_values(lowercase__		):
                    _snake_case		:	Optional[Any]					     =     torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.seq_length)     ,     dtype=torch.long     ,     device=lowercase__		)
                    _snake_case		:	Union[str, Any]					     =     torch.zeros(
                        self.model_tester.batch_size     ,     dtype=torch.long     ,     device=lowercase__		)
        return inputs_dict







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	str					     =     NezhaModelTester(self		)
        _snake_case		:	List[str]					     =     ConfigTester(self     ,     config_class=lowercase__     ,     hidden_size=37		)







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        self.config_tester.run_common_tests()







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__		)







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






        """simple docstring"""





        _snake_case		:	str					     =     self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        (
            (
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							(
            _snake_case
        )     ,							
        )		:	Optional[Any]					     =     self.model_tester.prepare_config_and_inputs_for_decoder()

        _snake_case		:	Optional[Any]					     =     None

        self.model_tester.create_and_check_model_as_decoder(
            lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Any					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_next_sequence_prediction(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	int					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*lowercase__		)







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






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase__		)







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _snake_case		:	Any					     =     NezhaModel.from_pretrained(lowercase__		)
              self.assertIsNotNone(lowercase__		)







  @slow
  @require_torch_gpu
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case     ,							_snake_case		:	Optional[int]					     =     self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
              # NezhaForMultipleChoice behaves incorrectly in JIT environments.
              if model_class == NezhaForMultipleChoice:
                    return

              _snake_case		:	Dict					     =     True
              _snake_case		:	Tuple					     =     model_class(config=lowercase__		)

              _snake_case		:	Dict					     =     self._prepare_for_class(lowercase__     ,     lowercase__		)
              _snake_case		:	Dict					     =     torch.jit.trace(
                  lowercase__     ,     (inputs_dict['''input_ids'''].to('''cpu'''		), inputs_dict['''attention_mask'''].to('''cpu'''		))		)

              with tempfile.TemporaryDirectory() as tmp:
                    torch.jit.save(lowercase__     ,     os.path.join(lowercase__     ,     '''bert.pt'''		)		)
                    _snake_case		:	Optional[int]					     =     torch.jit.load(os.path.join(lowercase__     ,     '''bert.pt'''		)     ,     map_location=lowercase__		)
                    loaded(inputs_dict['''input_ids'''].to(lowercase__		)     ,     inputs_dict['''attention_mask'''].to(lowercase__		)		)





@require_torch
class lowerCamelCase						(unittest.TestCase					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     NezhaModel.from_pretrained('''sijunhe/nezha-cn-base'''		)
        _snake_case		:	str					     =     torch.tensor([[0, 1, 2, 3, 4, 5]]		)
        _snake_case		:	List[str]					     =     torch.tensor([[0, 1, 1, 1, 1, 1]]		)
        with torch.no_grad():
              _snake_case		:	Any					     =     model(lowercase__     ,     attention_mask=lowercase__		)[0]
        _snake_case		:	Optional[int]					     =     torch.Size((1, 6, 768)		)
        self.assertEqual(output.shape     ,     lowercase__		)
        _snake_case		:	Optional[Any]					     =     torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]]		)

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,     lowercase__     ,     atol=1E-4		)		)







  @slow
  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	str					     =     NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base'''		)
        _snake_case		:	str					     =     torch.tensor([[0, 1, 2, 3, 4, 5]]		)
        _snake_case		:	List[Any]					     =     torch.tensor([[1, 1, 1, 1, 1, 1]]		)
        with torch.no_grad():
              _snake_case		:	Any					     =     model(lowercase__     ,     attention_mask=lowercase__		)[0]
        _snake_case		:	int					     =     torch.Size((1, 6, 21_128)		)
        self.assertEqual(output.shape     ,     lowercase__		)
        _snake_case		:	Tuple					     =     torch.tensor(
            [[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]]		)

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,     lowercase__     ,     atol=1E-4		)		)









'''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


