
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 							UpperCamelCase__						:






       """simple docstring"""


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



       def   _UpperCamelCase     (				self     )							->							Optional[Any]:
          SCREAMING_SNAKE_CASE_     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

          SCREAMING_SNAKE_CASE_     =	self.get_config()

          return config, pixel_values, labels



       def   _UpperCamelCase     (				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      ,			)



       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A     )							->							Union[str, Any]:
          SCREAMING_SNAKE_CASE_     =	RegNetModel(config=_SCREAMING_SNAKE_CASE     )
          model.to(_SCREAMING_SNAKE_CASE     )
          model.eval()
          SCREAMING_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   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A     )							->							Union[str, Any]:
          SCREAMING_SNAKE_CASE_     =	self.num_labels
          SCREAMING_SNAKE_CASE_     =	RegNetForImageClassification(_SCREAMING_SNAKE_CASE     )
          model.to(_SCREAMING_SNAKE_CASE     )
          model.eval()
          SCREAMING_SNAKE_CASE_     =	model(_SCREAMING_SNAKE_CASE      ,			labels=_SCREAMING_SNAKE_CASE     )
          self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.num_labels)     )




       def   _UpperCamelCase     (				self     )							->							Union[str, Any]:
          SCREAMING_SNAKE_CASE_     =	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_     =	config_and_inputs
          SCREAMING_SNAKE_CASE_     =	{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class 							UpperCamelCase__						(     __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  unittest.TestCase     ):






       """simple docstring"""


       UpperCAmelCase_							    =(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
       UpperCAmelCase_							    =(
           {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCAmelCase_							    =False
       UpperCAmelCase_							    =False
       UpperCAmelCase_							    =False
       UpperCAmelCase_							    =False



       def   _UpperCamelCase     (				self     )							->							Union[str, Any]:
          SCREAMING_SNAKE_CASE_     =	RegNetModelTester(self     )
          SCREAMING_SNAKE_CASE_     =	ConfigTester(self      ,			config_class=_SCREAMING_SNAKE_CASE      ,			has_text_modality=_SCREAMING_SNAKE_CASE     )



       def   _UpperCamelCase     (				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   _UpperCamelCase     (				self     )							->							Tuple:
          return



       @unittest.skip(reason='''RegNet does not use inputs_embeds'''     )
       def   _UpperCamelCase     (				self     )							->							Dict:
          pass



       @unittest.skip(reason='''RegNet does not support input and output embeddings'''     )
       def   _UpperCamelCase     (				self     )							->							str:
          pass



       def   _UpperCamelCase     (				self     )							->							List[Any]:
          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_     =	['''pixel_values''']
             self.assertListEqual(arg_names[:1]      ,			_SCREAMING_SNAKE_CASE     )



       def   _UpperCamelCase     (				self     )							->							Any:
          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE     )



       def   _UpperCamelCase     (				self     )							->							Optional[Any]:
          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             SCREAMING_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   _UpperCamelCase     (				self     )							->							List[Any]:





          def check_hidden_states_output(_A      ,			_A      ,			_A     ):
             SCREAMING_SNAKE_CASE_     =	model_class(_SCREAMING_SNAKE_CASE     )
             model.to(_SCREAMING_SNAKE_CASE     )
             model.eval()

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

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

             SCREAMING_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]      ,			)

          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_     =	['''basic''', '''bottleneck''']
          for model_class in self.all_model_classes:
             for layer_type in layers_type:
                SCREAMING_SNAKE_CASE_     =	layer_type
                SCREAMING_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"]
                SCREAMING_SNAKE_CASE_     =	True

                check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE     )



       def   _UpperCamelCase     (				self     )							->							Dict:
          SCREAMING_SNAKE_CASE_     =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE     )




       @slow
       def   _UpperCamelCase     (				self     )							->							str:





          for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_     =	RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE     )
             self.assertIsNotNone(_SCREAMING_SNAKE_CASE     )







def       A__      (    ):
   SCREAMING_SNAKE_CASE_     =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
   return image





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






       """simple docstring"""


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




       @slow
       def   _UpperCamelCase     (				self     )							->							Tuple:
          SCREAMING_SNAKE_CASE_     =	RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(_SCREAMING_SNAKE_CASE     )

          SCREAMING_SNAKE_CASE_     =	self.default_image_processor
          SCREAMING_SNAKE_CASE_     =	prepare_img()
          SCREAMING_SNAKE_CASE_     =	image_processor(images=_SCREAMING_SNAKE_CASE      ,			return_tensors='''pt'''     ).to(_SCREAMING_SNAKE_CASE     )

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

          # verify the logits
          SCREAMING_SNAKE_CASE_     =	torch.Size((1, 1000)     )
          self.assertEqual(outputs.logits.shape      ,			_SCREAMING_SNAKE_CASE     )

          SCREAMING_SNAKE_CASE_     =	torch.tensor([-0.4180, -1.5051, -3.4836]     ).to(_SCREAMING_SNAKE_CASE     )

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase								=	{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """NezhaForNextSentencePrediction""",
         """NezhaForMaskedLM""",
         """NezhaForPreTraining""",
         """NezhaForMultipleChoice""",
         """NezhaForQuestionAnswering""",
         """NezhaForSequenceClassification""",
         """NezhaForTokenClassification""",
         """NezhaModel""",
         """NezhaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nezha import (
              NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
              NezhaModel,
              NezhaPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




