





import os

import numpy
import onnx




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Optional[Any]:
  lowercase__           =  a.name
  lowercase__           =  b.name

  lowercase__           =  """"""
  lowercase__           =  """"""

  lowercase__           =  a == b

  lowercase__           =  name_a
  lowercase__           =  name_b

  return res




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Dict:
  for i, input_name in enumerate(node_proto.input		):
    if input_name == name:
      node_proto.input.insert(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		)
      node_proto.input.pop(i + 1		)

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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple:
  for n in graph_proto.node:
    _node_replace_input_with(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  lowercase__           =  list(model.graph.initializer		)
  lowercase__           =  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__           =  inits[i].name
    lowercase__           =  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		,						__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       str:
  lowercase__           =  os.path.dirname(__SCREAMING_SNAKE_CASE		)
  lowercase__           =  os.path.basename(__SCREAMING_SNAKE_CASE		)

  lowercase__           =  onnx.load(os.path.join(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		)		)

  lowercase__           =  list(model.graph.initializer		)

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

  lowercase__           =  0

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

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

        lowercase__           =  inits[j].data_type
        lowercase__           =  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: '		,						__SCREAMING_SNAKE_CASE		)
        total_reduced_size += mem_size

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

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

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

  lowercase__           =  sorted(__SCREAMING_SNAKE_CASE		)
  _remove_dup_initializers_from_model(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		)

  lowercase__           =  """optimized_""" + model_file_name
  lowercase__           =  os.path.join(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		)
  onnx.save(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		)

  return new_model









import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.distilbert.modeling_flax_distilbert import (
          FlaxDistilBertForMaskedLM,
          FlaxDistilBertForMultipleChoice,
          FlaxDistilBertForQuestionAnswering,
          FlaxDistilBertForSequenceClassification,
          FlaxDistilBertForTokenClassification,
          FlaxDistilBertModel,
      )
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   def __init__(   self      :    List[str]      ,					a      :    Dict      ,					a      :    Optional[int]=13      ,					a      :    int=7      ,					a      :    List[str]=True      ,					a      :    Any=True      ,					a      :    Dict=True      ,					a      :    List[Any]=True      ,					a      :    List[str]=99      ,					a      :    Dict=32      ,					a      :    List[str]=5      ,					a      :    Tuple=4      ,					a      :    Optional[int]=37      ,					a      :    Union[str, Any]="gelu"      ,					a      :    Optional[Any]=0.1      ,					a      :    Optional[int]=0.1      ,					a      :    Optional[Any]=512      ,					a      :    Dict=16      ,					a      :    Any=2      ,					a      :    Tuple=0.02      ,					a      :    Optional[Any]=4      ,					)->       int:





     """simple docstring"""







     lowercase__           =  parent
     lowercase__           =  batch_size
     lowercase__           =  seq_length
     lowercase__           =  is_training
     lowercase__           =  use_attention_mask
     lowercase__           =  use_token_type_ids
     lowercase__           =  use_labels
     lowercase__           =  vocab_size
     lowercase__           =  hidden_size
     lowercase__           =  num_hidden_layers
     lowercase__           =  num_attention_heads
     lowercase__           =  intermediate_size
     lowercase__           =  hidden_act
     lowercase__           =  hidden_dropout_prob
     lowercase__           =  attention_probs_dropout_prob
     lowercase__           =  max_position_embeddings
     lowercase__           =  type_vocab_size
     lowercase__           =  type_sequence_label_size
     lowercase__           =  initializer_range
     lowercase__           =  num_choices


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





     """simple docstring"""







     lowercase__           =  ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size	)

     lowercase__           =  None
     if self.use_attention_mask:
       lowercase__           =  random_attention_mask([self.batch_size, self.seq_length]	)

     lowercase__           =  DistilBertConfig(
         vocab_size=self.vocab_size      ,					dim=self.hidden_size      ,					n_layers=self.num_hidden_layers      ,					n_heads=self.num_attention_heads      ,					hidden_dim=self.intermediate_size      ,					hidden_act=self.hidden_act      ,					dropout=self.hidden_dropout_prob      ,					attention_dropout=self.attention_probs_dropout_prob      ,					max_position_embeddings=self.max_position_embeddings      ,					initializer_range=self.initializer_range      ,					tie_weights_=a      ,					)

     return config, input_ids, attention_mask


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





     """simple docstring"""







     lowercase__           =  self.prepare_config_and_inputs()
     lowercase__      ,   lowercase__      ,   lowercase__           =  config_and_inputs
     lowercase__           =  {'input_ids': input_ids, 'attention_mask': attention_mask}
     return config, inputs_dict






@require_flax
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		List[Any]			 =	(
       (
           FlaxDistilBertModel,
           FlaxDistilBertForMaskedLM,
           FlaxDistilBertForMultipleChoice,
           FlaxDistilBertForQuestionAnswering,
           FlaxDistilBertForSequenceClassification,
           FlaxDistilBertForTokenClassification,
           FlaxDistilBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       List[str]:





     """simple docstring"""







     lowercase__           =  FlaxDistilBertModelTester(self	)


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





     """simple docstring"""







     for model_class_name in self.all_model_classes:
       lowercase__           =  model_class_name.from_pretrained('distilbert-base-uncased'	)
       lowercase__           =  model(np.ones((1, 1)	)	)
       self.assertIsNotNone(a	)






@require_flax
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


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





     """simple docstring"""







     lowercase__           =  FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'	)
     lowercase__           =  np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]	)
     lowercase__           =  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
     lowercase__           =  model(a      ,					attention_mask=a	)[0]
     lowercase__           =  (1, 11, 768)
     self.assertEqual(output.shape      ,					a	)
     lowercase__           =  np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]	)

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


