




'''simple docstring'''





import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import RegNetForImageClassification, RegNetModel
   from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]   ,_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE="relu"   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=None   ,)				->     Any:
          _snake_case				= parent
          _snake_case				= batch_size
          _snake_case				= image_size
          _snake_case				= num_channels
          _snake_case				= embeddings_size
          _snake_case				= hidden_sizes
          _snake_case				= depths
          _snake_case				= is_training
          _snake_case				= use_labels
          _snake_case				= hidden_act
          _snake_case				= num_labels
          _snake_case				= scope
          _snake_case				= len(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Any:
          _snake_case				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

          _snake_case				= None
          if self.use_labels:
               _snake_case				= ids_tensor([self.batch_size]   ,self.num_labels       )

          _snake_case				= self.get_config()

          return config, pixel_values, labels






     def 							_lowercase      (					self       )				->     List[str]:
          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   ,)






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Optional[int]:
          _snake_case				= RegNetModel(config=_SCREAMING_SNAKE_CASE       )
          model.to(_SCREAMING_SNAKE_CASE       )
          model.eval()
          _snake_case				= model(_SCREAMING_SNAKE_CASE       )
          # expected last hidden states: B, C, H // 32, W // 32
          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   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Optional[int]:
          _snake_case				= self.num_labels
          _snake_case				= RegNetForImageClassification(_SCREAMING_SNAKE_CASE       )
          model.to(_SCREAMING_SNAKE_CASE       )
          model.eval()
          _snake_case				= model(_SCREAMING_SNAKE_CASE   ,labels=_SCREAMING_SNAKE_CASE       )
          self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)       )






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







@require_torch
class   _a							(		__lowerCAmelCase   ,   __lowerCAmelCase   ,   unittest.TestCase				):
     SCREAMING_SNAKE_CASE_				:       Dict       							=		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_				:       Tuple       							=		(
         {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE_				:       Tuple       							=		False
     SCREAMING_SNAKE_CASE_				:       Tuple       							=		False
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		False
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		False






     def 							_lowercase      (					self       )				->     Tuple:
          _snake_case				= RegNetModelTester(self       )
          _snake_case				= ConfigTester(self   ,config_class=_SCREAMING_SNAKE_CASE   ,has_text_modality=_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     List[str]:
          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       )				->     int:
          return






     @unittest.skip(reason="RegNet does not use inputs_embeds"       )
     def 							_lowercase      (					self       )				->     Any:
          pass






     @unittest.skip(reason="RegNet does not support input and output embeddings"       )
     def 							_lowercase      (					self       )				->     List[Any]:
          pass






     def 							_lowercase      (					self       )				->     List[str]:
          _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs_for_common()

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

               _snake_case				= ["pixel_values"]
               self.assertListEqual(arg_names[:1]   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Optional[int]:
          _snake_case				= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     List[Any]:
          _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _snake_case				= model_class(config=_SCREAMING_SNAKE_CASE       )
               for name, module in model.named_modules():
                    if isinstance(_SCREAMING_SNAKE_CASE   ,(nn.BatchNormad, nn.GroupNorm)       ):
                         self.assertTrue(
                             torch.all(module.weight == 1       )   ,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   ,)
                         self.assertTrue(
                             torch.all(module.bias == 0       )   ,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   ,)






     def 							_lowercase      (					self       )				->     str:
          def check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= model_class(_SCREAMING_SNAKE_CASE       )
               model.to(_SCREAMING_SNAKE_CASE       )
               model.eval()

               with torch.no_grad():
                    _snake_case				= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )       )

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

               _snake_case				= self.model_tester.num_stages
               self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,expected_num_stages + 1       )

               # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]       )   ,[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,)

          _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case				= ["basic", "bottleneck"]
          for model_class in self.all_model_classes:
               for layer_type in layers_type:
                    _snake_case				= layer_type
                    _snake_case				= True
                    check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )

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

                    check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Tuple:
          _snake_case				= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE       )






     @slow
     def 							_lowercase      (					self       )				->     List[str]:
          for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _snake_case				= RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE       )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )







def       __a      (  )		->					List[str]:

     """simple docstring"""




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





@require_torch
@require_vision
class   _a							(		unittest.TestCase				):






     @cached_property
     def 							_lowercase      (					self       )				->     Any:
          return (
              AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
              if is_vision_available()
              else None
          )






     @slow
     def 							_lowercase      (					self       )				->     List[Any]:
          _snake_case				= RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(_SCREAMING_SNAKE_CASE       )

          _snake_case				= self.default_image_processor
          _snake_case				= prepare_img()
          _snake_case				= image_processor(images=_SCREAMING_SNAKE_CASE   ,return_tensors="pt"       ).to(_SCREAMING_SNAKE_CASE       )

          # forward pass
          with torch.no_grad():
               _snake_case				= model(**_SCREAMING_SNAKE_CASE       )

          # verify the logits
          _snake_case				= torch.Size((1, 1_000)       )
          self.assertEqual(outputs.logits.shape   ,_SCREAMING_SNAKE_CASE       )

          _snake_case				= torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]       ).to(_SCREAMING_SNAKE_CASE       )

          self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,_SCREAMING_SNAKE_CASE   ,atol=1e-4       )       )








'''simple docstring'''





import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
UpperCamelCase_		:     Optional[int]          =		object()

# For specifying empty leaf dict `{}`
UpperCamelCase_		:     Optional[int]          =		object()





def       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Optional[Any]       )		->					Optional[Any]:

     """simple docstring"""




     _snake_case				= tuple((re.compile(x + "$"       ) for x in qs)       )
     for i in range(len(_UpperCamelCase       ) - len(_UpperCamelCase       ) + 1       ):
          _snake_case				= [x.match(_UpperCamelCase       ) for x, y in zip(_UpperCamelCase   ,		ks[i:]       )]
          if matches and all(_UpperCamelCase       ):
               return True
     return False





def       __a      (  _UpperCamelCase:       Optional[Any]       )		->					Union[str, Any]:

     """simple docstring"""




     def replace(_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       List[str]       ):
          for rule, replacement in rules:
               if _match(_UpperCamelCase   ,		_UpperCamelCase       ):
                    return replacement
          return val

     return replace





def       __a      (  )		->					Any:

     """simple docstring"""




     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P("mp"   ,		_UpperCamelCase       )),
         (("transformer", "wte", "embedding"), P("mp"   ,		_UpperCamelCase       )),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(_UpperCamelCase   ,		"mp"       )),
         (("attention", "out_proj", "kernel"), P("mp"   ,		_UpperCamelCase       )),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(_UpperCamelCase   ,		"mp"       )),
         (("mlp", "c_fc", "bias"), P("mp"       )),
         (("mlp", "c_proj", "kernel"), P("mp"   ,		_UpperCamelCase       )),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]





def       __a      (  _UpperCamelCase:       Union[str, Any]       )		->					Any:

     """simple docstring"""




     _snake_case				= _get_partition_rules()
     _snake_case				= _replacement_rules(_UpperCamelCase       )
     _snake_case				= {k: _unmatched for k in flatten_dict(_UpperCamelCase       )}
     _snake_case				= {k: replace(_UpperCamelCase   ,		_UpperCamelCase       ) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(_UpperCamelCase       )       )


