'''simple docstring'''





import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor




class 	__A				(			unittest.TestCase     ):



    def __init__(self   :   Optional[int]       ,      __a   :   List[str]       ,      __a   :   Union[str, Any]=3       ,      __a   :   List[str]=32       ,      __a   :   Optional[int]=3       ,      __a   :   Union[str, Any]=10       ,      __a   :   int=[10, 20, 30, 40]       ,      __a   :   Union[str, Any]=[1, 1, 2, 1]       ,      __a   :   Tuple=True       ,      __a   :   List[Any]=True       ,      __a   :   Optional[int]="relu"       ,      __a   :   Union[str, Any]=3       ,      __a   :   Optional[int]=None       ,      ):
         UpperCAmelCase_	   =       parent
         UpperCAmelCase_	   =       batch_size
         UpperCAmelCase_	   =       image_size
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embeddings_size
         UpperCAmelCase_	   =       hidden_sizes
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       is_training
         UpperCAmelCase_	   =       use_labels
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       num_labels
         UpperCAmelCase_	   =       scope
         UpperCAmelCase_	   =       len(__a			)



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

         UpperCAmelCase_	   =       self.get_config()

         return config, pixel_values



    def  _lowercase						(self   :   Union[str, Any]			):
         return RegNetConfig(
             num_channels=self.num_channels       ,      embeddings_size=self.embeddings_size       ,      hidden_sizes=self.hidden_sizes       ,      depths=self.depths       ,      hidden_act=self.hidden_act       ,      num_labels=self.num_labels       ,      image_size=self.image_size       ,      )



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Union[str, Any]       ,      __a   :   Dict			):
         UpperCAmelCase_	   =       FlaxRegNetModel(config=__a			)
         UpperCAmelCase_	   =       model(__a			)

         # Output shape (b, c, h, w)
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,      )



    def  _lowercase						(self   :   Tuple       ,      __a   :   int       ,      __a   :   Any			):
         UpperCAmelCase_	   =       self.num_labels
         UpperCAmelCase_	   =       FlaxRegNetForImageClassification(config=__a			)
         UpperCAmelCase_	   =       model(__a			)
         self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)			)






    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       self.prepare_config_and_inputs()
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       config_and_inputs
         UpperCAmelCase_	   =       {"pixel_values": pixel_values}
         return config, inputs_dict




@require_flax
class 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						str	       =  (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

    a__	:						int	       =  False
    a__	:						int	       =  False
    a__	:						Optional[int]	       =  False



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       FlaxRegNetModelTester(self			)
         UpperCAmelCase_	   =       ConfigTester(self       ,      config_class=__a       ,      has_text_modality=__a			)



    def  _lowercase						(self   :   Dict			):
         self.create_and_test_config_common_properties()
         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  _lowercase						(self   :   Tuple			):
         return



    def  _lowercase						(self   :   int			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a			)



    def  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__a			)



    @unittest.skip(reason="RegNet does not use inputs_embeds"			)
    def  _lowercase						(self   :   Optional[int]			):
         pass



    @unittest.skip(reason="RegNet does not support input and output embeddings"			)
    def  _lowercase						(self   :   int			):
         pass



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_	   =       model_class(__a			)
              UpperCAmelCase_	   =       inspect.signature(model.__call__			)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_	   =       [*signature.parameters.keys()]

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



    def  _lowercase						(self   :   Optional[int]			):
         def check_hidden_states_output(__a   :   Dict       ,      __a   :   str       ,      __a   :   Union[str, Any]			):
              UpperCAmelCase_	   =       model_class(__a			)

              UpperCAmelCase_	   =       model(**self._prepare_for_class(__a       ,      __a			)			)

              UpperCAmelCase_	   =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              UpperCAmelCase_	   =       self.model_tester.num_stages
              self.assertEqual(len(__a			)       ,      expected_num_stages + 1			)

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_	   =       True
              check_hidden_states_output(__a       ,      __a       ,      __a			)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase_	   =       True

              check_hidden_states_output(__a       ,      __a       ,      __a			)






    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__			):
                   UpperCAmelCase_	   =       self._prepare_for_class(__a       ,      __a			)
                   UpperCAmelCase_	   =       model_class(__a			)

                   @jax.jit
                   def model_jitted(__a   :   str       ,      **__a   :   Optional[Any]			):
                        return model(pixel_values=__a       ,      **__a			)

                   with self.subTest("JIT Enabled"			):
                        UpperCAmelCase_	   =       model_jitted(**__a			).to_tuple()

                   with self.subTest("JIT Disabled"			):
                        with jax.disable_jit():
                             UpperCAmelCase_	   =       model_jitted(**__a			).to_tuple()

                   self.assertEqual(len(__a			)       ,      len(__a			)			)
                   for jitted_output, output in zip(__a       ,      __a			):
                        self.assertEqual(jitted_output.shape       ,      output.shape			)





def      lowerCAmelCase_    ( )   ->				List[str]:


     '''simple docstring'''




     UpperCAmelCase_	   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
     return image






@require_flax
class 	__A				(			unittest.TestCase     ):



    @cached_property
    def  _lowercase						(self   :   Union[str, Any]			):
         return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"			) if is_vision_available() else None






    @slow
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"			)

         UpperCAmelCase_	   =       self.default_image_processor
         UpperCAmelCase_	   =       prepare_img()
         UpperCAmelCase_	   =       image_processor(images=__a       ,      return_tensors="np"			)

         UpperCAmelCase_	   =       model(**__a			)

         # verify the logits
         UpperCAmelCase_	   =       (1, 1000)
         self.assertEqual(outputs.logits.shape       ,      __a			)

         UpperCAmelCase_	   =       jnp.array([-0.41_80, -1.50_51, -3.48_36]			)

         self.assertTrue(jnp.allclose(outputs.logits[0, :3]       ,      __a       ,      atol=1E-4			)			)





'''simple docstring'''





import os

import numpy
import onnx





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[int]      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       a.name
     UpperCAmelCase_	   =       b.name

     UpperCAmelCase_	   =       ""
     UpperCAmelCase_	   =       ""

     UpperCAmelCase_	   =       a == b

     UpperCAmelCase_	   =       name_a
     UpperCAmelCase_	   =       name_b

     return res





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any]      )   ->				Any:


     '''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      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			List[str] ,   snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Any ,   snake_case_	:			Optional[Any]      )   ->				Optional[int]:


     '''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 ,   snake_case_ ,   snake_case_      )






def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       os.path.dirname(snake_case_      )
     UpperCAmelCase_	   =       os.path.basename(snake_case_      )

     UpperCAmelCase_	   =       onnx.load(os.path.join(snake_case_ ,   snake_case_      )      )

     UpperCAmelCase_	   =       list(model.graph.initializer      )

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

     UpperCAmelCase_	   =       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_	   =       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: " ,   snake_case_      )
                    total_reduced_size += mem_size

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

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

     print("total reduced size: " ,   total_reduced_size / 10_24 / 10_24 / 10_24 ,   "GB"      )

     UpperCAmelCase_	   =       sorted(snake_case_      )
     _remove_dup_initializers_from_model(snake_case_ ,   snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       "optimized_" + model_file_name
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   snake_case_      )
     onnx.save(snake_case_ ,   snake_case_      )

     return new_model




