



import os

import numpy
import onnx







def    _snake_case (       __snake_case					,  __snake_case    )     ->     str:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		a.name
      UpperCAmelCase_  :	Dict				      =		b.name

      UpperCAmelCase_  :	Dict				      =		""
      UpperCAmelCase_  :	List[Any]				      =		""

      UpperCAmelCase_  :	Tuple				      =		a == b

      UpperCAmelCase_  :	Optional[int]				      =		name_a
      UpperCAmelCase_  :	int				      =		name_b

      return res







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     str:






      '''simple docstring'''







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

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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     int:






      '''simple docstring'''







      UpperCAmelCase_  :	List[Any]				      =		list(model.graph.initializer    )
      UpperCAmelCase_  :	int				      =		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_  :	List[str]				      =		inits[i].name
            UpperCAmelCase_  :	Dict				      =		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					,  __snake_case					,  __snake_case    )







def    _snake_case (       __snake_case    )     ->     int:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		os.path.dirname(__snake_case    )
      UpperCAmelCase_  :	List[Any]				      =		os.path.basename(__snake_case    )

      UpperCAmelCase_  :	Optional[int]				      =		onnx.load(os.path.join(__snake_case					,  __snake_case    )    )

      UpperCAmelCase_  :	str				      =		list(model.graph.initializer    )

      UpperCAmelCase_  :	Dict				      =		set()
      UpperCAmelCase_  :	Optional[int]				      =		{}
      UpperCAmelCase_  :	Optional[int]				      =		[]

      UpperCAmelCase_  :	Optional[int]				      =		0

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

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

                        UpperCAmelCase_  :	Union[str, Any]				      =		inits[j].data_type
                        UpperCAmelCase_  :	Optional[Any]				      =		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: "					,  __snake_case    )
                        total_reduced_size += mem_size

                        UpperCAmelCase_  :	str				      =		inits[i].name
                        UpperCAmelCase_  :	int				      =		inits[j].name

                        if name_i in dup_map:
                              dup_map[name_i].append(__snake_case    )
                        else:
                              UpperCAmelCase_  :	Dict				      =		[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"    )

      UpperCAmelCase_  :	str				      =		sorted(__snake_case    )
      _remove_dup_initializers_from_model(__snake_case					,  __snake_case					,  __snake_case    )

      UpperCAmelCase_  :	int				      =		"optimized_" + model_file_name
      UpperCAmelCase_  :	Optional[Any]				      =		os.path.join(__snake_case					,  __snake_case    )
      onnx.save(__snake_case					,  __snake_case    )

      return new_model





import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
   import torch

   from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,lowercase="resnet50"     ,lowercase=3     ,lowercase=32     ,lowercase=3     ,lowercase=True     ,lowercase=True     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		parent
             UpperCAmelCase_  :	int				      =		out_indices if out_indices is not None else [4]
             UpperCAmelCase_  :	Any				      =		stage_names
             UpperCAmelCase_  :	List[Any]				      =		out_features
             UpperCAmelCase_  :	List[str]				      =		backbone
             UpperCAmelCase_  :	Union[str, Any]				      =		batch_size
             UpperCAmelCase_  :	int				      =		image_size
             UpperCAmelCase_  :	str				      =		num_channels
             UpperCAmelCase_  :	Optional[Any]				      =		use_pretrained_backbone
             UpperCAmelCase_  :	Optional[int]				      =		is_training



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
             UpperCAmelCase_  :	Dict				      =		self.get_config()

             return config, pixel_values



       def  A_   (     self):

             """simple docstring"""





             return TimmBackboneConfig(
                 image_size=self.image_size     ,num_channels=self.num_channels     ,out_features=self.out_features     ,out_indices=self.out_indices     ,stage_names=self.stage_names     ,use_pretrained_backbone=self.use_pretrained_backbone     ,backbone=self.backbone     ,)



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		TimmBackbone(config=lowercase)
             model.to(lowercase)
             model.eval()
             with torch.no_grad():
                   UpperCAmelCase_  :	Union[str, Any]				      =		model(lowercase)
             self.parent.assertEqual(
                 result.feature_map[-1].shape     ,(self.batch_size, model.channels[-1], 14, 14)     ,)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.prepare_config_and_inputs()
             UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		config_and_inputs
             UpperCAmelCase_  :	Union[str, Any]				      =		{"pixel_values": pixel_values}
             return config, inputs_dict


@require_torch
@require_timm
class   snake_case_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				(TimmBackbone,) if is_torch_available() else ()
       _lowerCamelCase			    =				{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		TimmBackboneModelTester(self)
             UpperCAmelCase_  :	Tuple				      =		ConfigTester(self     ,config_class=lowercase     ,has_text_modality=lowercase)



       def  A_   (     self):

             """simple docstring"""





             self.config_tester.create_and_test_config_to_json_string()
             self.config_tester.create_and_test_config_to_json_file()
             self.config_tester.create_and_test_config_from_and_save_pretrained()
             self.config_tester.create_and_test_config_with_num_labels()
             self.config_tester.check_config_can_be_init_without_params()
             self.config_tester.check_config_arguments_init()



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		"resnet18"
             UpperCAmelCase_  :	Tuple				      =		"microsoft/resnet-18"

             UpperCAmelCase_  :	int				      =		AutoBackbone.from_pretrained(lowercase     ,use_timm_backbone=lowercase)
             UpperCAmelCase_  :	Dict				      =		AutoBackbone.from_pretrained(lowercase)

             self.assertEqual(len(timm_model.out_features)     ,len(transformers_model.out_features))
             self.assertEqual(len(timm_model.stage_names)     ,len(transformers_model.stage_names))
             self.assertEqual(timm_model.channels     ,transformers_model.channels)
             # Out indices are set to the last layer by default. For timm models, we don't know
             # the number of layers in advance, so we set it to (-1,), whereas for transformers
             # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
             self.assertEqual(timm_model.out_indices     ,(-1,))
             self.assertEqual(transformers_model.out_indices     ,[len(timm_model.stage_names) - 1])

             UpperCAmelCase_  :	Dict				      =		AutoBackbone.from_pretrained(lowercase     ,use_timm_backbone=lowercase     ,out_indices=[1, 2, 3])
             UpperCAmelCase_  :	List[str]				      =		AutoBackbone.from_pretrained(lowercase     ,out_indices=[1, 2, 3])

             self.assertEqual(timm_model.out_indices     ,transformers_model.out_indices)
             self.assertEqual(len(timm_model.out_features)     ,len(transformers_model.out_features))
             self.assertEqual(timm_model.channels     ,transformers_model.channels)



       @unittest.skip("TimmBackbone doesn't support feed forward chunking")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone initialization is managed on the timm side")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("model weights aren't tied in TimmBackbone.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("model weights aren't tied in TimmBackbone.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't support output_attentions.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Safetensors is not supported by timm.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
       def  A_   (     self):

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	int				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	Union[str, Any]				      =		model_class(lowercase)
                   UpperCAmelCase_  :	Optional[Any]				      =		inspect.signature(model.forward)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   UpperCAmelCase_  :	str				      =		[*signature.parameters.keys()]

                   UpperCAmelCase_  :	Any				      =		["pixel_values"]
                   self.assertListEqual(arg_names[:1]     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Tuple				      =		self.model_tester.prepare_config_and_inputs_for_common()
             UpperCAmelCase_  :	Dict				      =		True
             UpperCAmelCase_  :	str				      =		self.has_attentions

             # no need to test all models as different heads yield the same functionality
             UpperCAmelCase_  :	str				      =		self.all_model_classes[0]
             UpperCAmelCase_  :	List[Any]				      =		model_class(lowercase)
             model.to(lowercase)

             UpperCAmelCase_  :	Any				      =		self._prepare_for_class(lowercase     ,lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		model(**lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		outputs[0][-1]

             # Encoder-/Decoder-only models
             UpperCAmelCase_  :	List[Any]				      =		outputs.hidden_states[0]
             hidden_states.retain_grad()

             if self.has_attentions:
                   UpperCAmelCase_  :	int				      =		outputs.attentions[0]
                   attentions.retain_grad()

             output.flatten()[0].backward(retain_graph=lowercase)

             self.assertIsNotNone(hidden_states.grad)

             if self.has_attentions:
                   self.assertIsNotNone(attentions.grad)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	Any				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Tuple				      =		model(**lowercase)

                   self.assertEqual(len(result.feature_maps)     ,len(config.out_indices))
                   self.assertEqual(len(model.channels)     ,len(config.out_indices))

                   # Check output of last stage is taken if out_features=None, out_indices=None
                   UpperCAmelCase_  :	Tuple				      =		copy.deepcopy(lowercase)
                   UpperCAmelCase_  :	List[Any]				      =		None
                   UpperCAmelCase_  :	Optional[Any]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Tuple				      =		model(**lowercase)

                   self.assertEqual(len(result.feature_maps)     ,1)
                   self.assertEqual(len(model.channels)     ,1)

                   # Check backbone can be initialized with fresh weights
                   UpperCAmelCase_  :	str				      =		copy.deepcopy(lowercase)
                   UpperCAmelCase_  :	Optional[Any]				      =		False
                   UpperCAmelCase_  :	List[str]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Union[str, Any]				      =		model(**lowercase)
