

'''simple docstring'''
import os

import numpy
import onnx




def 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    Union[str, Any]	)		->	List[Any]:




  '''simple docstring'''



  lowercase									=a.name
  lowercase									=b.name

  lowercase									=''''''
  lowercase									=''''''

  lowercase									=a == b

  lowercase									=name_a
  lowercase									=name_b

  return res




def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    Tuple     ,					lowercase_						:    int	)		->	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 	UpperCamelCase   (       lowercase_						:    Optional[Any]     ,					lowercase_						:    str     ,					lowercase_						:    Optional[Any]	)		->	Union[str, Any]:




  '''simple docstring'''



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




def 	UpperCamelCase   (       lowercase_						:    Union[str, Any]     ,					lowercase_						:    Optional[int]     ,					lowercase_						:    int	)		->	Tuple:




  '''simple docstring'''



  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     ,					lowercase_     ,					lowercase_	)




def 	UpperCamelCase   (       lowercase_						:    Optional[int]	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=os.path.dirname(lowercase_	)
  lowercase									=os.path.basename(lowercase_	)

  lowercase									=onnx.load(os.path.join(lowercase_     ,					lowercase_	)	)

  lowercase									=list(model.graph.initializer	)

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

  lowercase									=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_	)

        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 == 1_1:
          mem_size *= 8
        else:
          print('''unexpected data type: '''     ,					lowercase_	)
        total_reduced_size += mem_size

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

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

  print('''total reduced size: '''     ,					total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4     ,					'''GB'''	)

  lowercase									=sorted(lowercase_	)
  _remove_dup_initializers_from_model(lowercase_     ,					lowercase_     ,					lowercase_	)

  lowercase									='''optimized_''' + model_file_name
  lowercase									=os.path.join(lowercase_     ,					lowercase_	)
  onnx.save(lowercase_     ,					lowercase_	)

  return new_model






from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Dict	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      SMALL_MODEL_IDENTIFIER
						__UpperCamelCase					:	List[str]             =      """pt"""
						__UpperCamelCase					:	List[Any]             =      """tf"""






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(lowerCAmelCase	)






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







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      TFAutoModel.from_pretrained(self.test_model					,     from_pt=lowerCAmelCase	)
						model_tf.save_pretrained(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						str	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      """mock_framework"""

						# Framework provided - return whatever the user provides
						__UpperCamelCase					:	List[Any]             =      FeaturesManager.determine_framework(self.test_model					,     lowerCAmelCase	)
						self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      FeaturesManager.determine_framework(lowerCAmelCase					,     lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(lowerCAmelCase					,     lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       str:







						"""simple docstring"""







						with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Optional[Any]             =      FeaturesManager.determine_framework(lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      FeaturesManager.determine_framework(lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     self.framework_tf	)

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
											with self.assertRaises(lowerCAmelCase	):
																__UpperCamelCase					:	int             =      FeaturesManager.determine_framework(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Dict	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						__UpperCamelCase					:	Optional[int]             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_tf	)

						# Both in environment -> use PyTorch
						__UpperCamelCase					:	Dict             =      MagicMock(return_value=lowerCAmelCase	)
						__UpperCamelCase					:	Any             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	), patch(
						    """transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	int             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# Both not in environment -> raise error
						__UpperCamelCase					:	Union[str, Any]             =      MagicMock(return_value=lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	), patch(
						    """transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											with self.assertRaises(lowerCAmelCase	):
																__UpperCamelCase					:	str             =      FeaturesManager.determine_framework(self.test_model	)






