
'''simple docstring'''





import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
       import torch
       from torch import nn

       from transformers import NatBackbone, NatForImageClassification, NatModel
       from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor






class 						lowercase     :






      def __init__(self ,  a__ ,  a__=1_3 ,  a__=6_4 ,  a__=4 ,  a__=3 ,  a__=1_6 ,  a__=[1, 2, 1] ,  a__=[2, 4, 8] ,  a__=3 ,  a__=2.0 ,  a__=True ,  a__=0.0 ,  a__=0.0 ,  a__=0.1 ,  a__="gelu" ,  a__=True ,  a__=0.02 ,  a__=1E-5 ,  a__=True ,  a__=None ,  a__=True ,  a__=1_0 ,  a__=["stage1", "stage2"] ,  a__=[1, 2] ,  )       ->   str:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= parent
         SCREAMING_SNAKE_CASE__			:				str       						= batch_size
         SCREAMING_SNAKE_CASE__			:				int       						= image_size
         SCREAMING_SNAKE_CASE__			:				Dict       						= patch_size
         SCREAMING_SNAKE_CASE__			:				Tuple       						= num_channels
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= embed_dim
         SCREAMING_SNAKE_CASE__			:				str       						= depths
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= num_heads
         SCREAMING_SNAKE_CASE__			:				str       						= kernel_size
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= mlp_ratio
         SCREAMING_SNAKE_CASE__			:				Tuple       						= qkv_bias
         SCREAMING_SNAKE_CASE__			:				Any       						= hidden_dropout_prob
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE__			:				Any       						= drop_path_rate
         SCREAMING_SNAKE_CASE__			:				Any       						= hidden_act
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= patch_norm
         SCREAMING_SNAKE_CASE__			:				int       						= layer_norm_eps
         SCREAMING_SNAKE_CASE__			:				str       						= initializer_range
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= is_training
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= scope
         SCREAMING_SNAKE_CASE__			:				str       						= use_labels
         SCREAMING_SNAKE_CASE__			:				str       						= num_labels
         SCREAMING_SNAKE_CASE__			:				int       						= out_features
         SCREAMING_SNAKE_CASE__			:				int       						= out_indices






      def    A__(self					)       ->   Optional[Any]:
         SCREAMING_SNAKE_CASE__			:				Dict       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= None
         if self.use_labels:
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= ids_tensor([self.batch_size] ,  self.num_labels					)

         SCREAMING_SNAKE_CASE__			:				str       						= self.get_config()

         return config, pixel_values, labels






      def    A__(self					)       ->   List[Any]:
         return NatConfig(
             num_labels=self.num_labels ,  image_size=self.image_size ,  patch_size=self.patch_size ,  num_channels=self.num_channels ,  embed_dim=self.embed_dim ,  depths=self.depths ,  num_heads=self.num_heads ,  kernel_size=self.kernel_size ,  mlp_ratio=self.mlp_ratio ,  qkv_bias=self.qkv_bias ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  drop_path_rate=self.drop_path_rate ,  hidden_act=self.hidden_act ,  patch_norm=self.patch_norm ,  layer_norm_eps=self.layer_norm_eps ,  initializer_range=self.initializer_range ,  out_features=self.out_features ,  out_indices=self.out_indices ,  )






      def    A__(self ,  a__ ,  a__ ,  a__					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				int       						= NatModel(config=a__					)
         model.to(a__					)
         model.eval()
         SCREAMING_SNAKE_CASE__			:				Dict       						= model(a__					)

         SCREAMING_SNAKE_CASE__			:				str       						= (config.image_size // config.patch_size) // (2 ** (len(config.depths					) - 1))
         SCREAMING_SNAKE_CASE__			:				List[str]       						= int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

         self.parent.assertEqual(
             result.last_hidden_state.shape ,  (self.batch_size, expected_height, expected_width, expected_dim)					)






      def    A__(self ,  a__ ,  a__ ,  a__					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= NatForImageClassification(a__					)
         model.to(a__					)
         model.eval()
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= model(a__ ,  labels=a__					)
         self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)					)

         # test greyscale images
         SCREAMING_SNAKE_CASE__			:				int       						= 1
         SCREAMING_SNAKE_CASE__			:				Tuple       						= NatForImageClassification(a__					)
         model.to(a__					)
         model.eval()

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model(a__					)
         self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)					)






      def    A__(self ,  a__ ,  a__ ,  a__					)       ->   Tuple:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= NatBackbone(config=a__					)
         model.to(a__					)
         model.eval()
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= model(a__					)

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps					) ,  len(config.out_features					)					)
         self.parent.assertListEqual(list(result.feature_maps[0].shape					) ,  [self.batch_size, model.channels[0], 1_6, 1_6]					)

         # verify channels
         self.parent.assertEqual(len(model.channels					) ,  len(config.out_features					)					)

         # verify backbone works with out_features=None
         SCREAMING_SNAKE_CASE__			:				Tuple       						= None
         SCREAMING_SNAKE_CASE__			:				Tuple       						= NatBackbone(config=a__					)
         model.to(a__					)
         model.eval()
         SCREAMING_SNAKE_CASE__			:				Tuple       						= model(a__					)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps					) ,  1					)
         self.parent.assertListEqual(list(result.feature_maps[0].shape					) ,  [self.batch_size, model.channels[-1], 4, 4]					)

         # verify channels
         self.parent.assertEqual(len(model.channels					) ,  1					)






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






@require_natten
@require_torch
class 						lowercase     (   UpperCAmelCase_				,			UpperCAmelCase_				,			unittest.TestCase):
      __lowercase 		=  (
          (
              NatModel,
              NatForImageClassification,
              NatBackbone,
          )
          if is_torch_available()
          else ()
      )
      __lowercase 		=  (
          {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
          if is_torch_available()
          else {}
      )
      __lowercase 		=  False

      __lowercase 		=  False
      __lowercase 		=  False
      __lowercase 		=  False
      __lowercase 		=  False






      def    A__(self					)       ->   Dict:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= NatModelTester(self					)
         SCREAMING_SNAKE_CASE__			:				str       						= ConfigTester(self ,  config_class=a__ ,  embed_dim=3_7					)






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






      def    A__(self					)       ->   Tuple:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__					)






      def    A__(self					)       ->   str:
         SCREAMING_SNAKE_CASE__			:				Dict       						= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a__					)






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*a__					)






      @unittest.skip(reason='''Nat does not use inputs_embeds'''					)
      def    A__(self					)       ->   Tuple:
         pass






      @unittest.skip(reason='''Nat does not use feedforward chunking'''					)
      def    A__(self					)       ->   str:
         pass






      def    A__(self					)       ->   Any:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				int       						= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__			:				List[str]       						= model_class(a__					)
            self.assertIsInstance(model.get_input_embeddings() ,  (nn.Module)					)
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(a__ ,  nn.Linear					)					)






      def    A__(self					)       ->   Optional[int]:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Any       						= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= model_class(a__					)
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= inspect.signature(model.forward					)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            SCREAMING_SNAKE_CASE__			:				Tuple       						= [*signature.parameters.keys()]

            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= ['''pixel_values''']
            self.assertListEqual(arg_names[:1] ,  a__					)






      def    A__(self					)       ->   Tuple:
         self.skipTest('''Nat\'s attention operation is handled entirely by NATTEN.'''					)






      def    A__(self ,  a__ ,  a__ ,  a__ ,  a__					)       ->   Dict:
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= model_class(a__					)
         model.to(a__					)
         model.eval()

         with torch.no_grad():
            SCREAMING_SNAKE_CASE__			:				Dict       						= model(**self._prepare_for_class(a__ ,  a__					)					)

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= outputs.hidden_states

         SCREAMING_SNAKE_CASE__			:				Tuple       						= getattr(
             self.model_tester ,  '''expected_num_hidden_layers''' ,  len(self.model_tester.depths					) + 1					)
         self.assertEqual(len(a__					) ,  a__					)

         # Nat has a different seq_length
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= (
             config.patch_size
             if isinstance(config.patch_size ,  collections.abc.Iterable					)
             else (config.patch_size, config.patch_size)
         )

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= image_size[0] // patch_size[0]
         SCREAMING_SNAKE_CASE__			:				str       						= image_size[1] // patch_size[1]

         self.assertListEqual(
             list(hidden_states[0].shape[-3:]					) ,  [height, width, self.model_tester.embed_dim] ,  )

         if model_class.__name__ != "NatBackbone":
            SCREAMING_SNAKE_CASE__			:				Tuple       						= outputs.reshaped_hidden_states
            self.assertEqual(len(a__					) ,  a__					)

            SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				List[Any]       						= reshaped_hidden_states[0].shape
            SCREAMING_SNAKE_CASE__			:				str       						= (
                reshaped_hidden_states[0].view(a__ ,  a__ ,  a__ ,  a__					).permute(0 ,  2 ,  3 ,  1					)
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-3:]					) ,  [height, width, self.model_tester.embed_dim] ,  )






      def    A__(self					)       ->   str:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size ,  collections.abc.Iterable					)
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__			:				str       						= True
            self.check_hidden_states_output(a__ ,  a__ ,  a__ ,  a__					)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= True

            self.check_hidden_states_output(a__ ,  a__ ,  a__ ,  a__					)






      @slow
      def    A__(self					)       ->   Union[str, Any]:
         for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= NatModel.from_pretrained(a__					)
            self.assertIsNotNone(a__					)






      def    A__(self					)       ->   Optional[Any]:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= _config_zero_init(a__					)
         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= model_class(config=a__					)
            for name, param in model.named_parameters():
               if "embeddings" not in name and param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1E9).round() / 1E9).item() ,  [0.0, 1.0] ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,  )






@require_natten
@require_vision
@require_torch
class 						lowercase     (   unittest.TestCase):






      @cached_property
      def    A__(self					)       ->   Tuple:
         return AutoImageProcessor.from_pretrained('''shi-labs/nat-mini-in1k-224'''					) if is_vision_available() else None






      @slow
      def    A__(self					)       ->   Optional[int]:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= NatForImageClassification.from_pretrained('''shi-labs/nat-mini-in1k-224'''					).to(a__					)
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.default_image_processor

         SCREAMING_SNAKE_CASE__			:				str       						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
         SCREAMING_SNAKE_CASE__			:				str       						= image_processor(images=a__ ,  return_tensors='''pt'''					).to(a__					)

         # forward pass
         with torch.no_grad():
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= model(**a__					)

         # verify the logits
         SCREAMING_SNAKE_CASE__			:				Any       						= torch.Size((1, 1_0_0_0)					)
         self.assertEqual(outputs.logits.shape ,  a__					)
         SCREAMING_SNAKE_CASE__			:				int       						= torch.tensor([0.38_05, -0.86_76, -0.39_12]					).to(a__					)
         self.assertTrue(torch.allclose(outputs.logits[0, :3] ,  a__ ,  atol=1E-4					)					)






@require_torch
@require_natten
class 						lowercase     (   unittest.TestCase				,			UpperCAmelCase_):
      __lowercase 		=  (NatBackbone,) if is_torch_available() else ()
      __lowercase 		=  NatConfig






      def    A__(self					)       ->   int:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= NatModelTester(self					)







'''simple docstring'''





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__			=	{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase__			=	[
           '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ConvNextV2ForImageClassification''',
           '''ConvNextV2Model''',
           '''ConvNextV2PreTrainedModel''',
           '''ConvNextV2Backbone''',
       ]


if TYPE_CHECKING:
       from .configuration_convnextva import (
           CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           ConvNextVaConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_convnextva import (
                  CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ConvNextVaBackbone,
                  ConvNextVaForImageClassification,
                  ConvNextVaModel,
                  ConvNextVaPreTrainedModel,
              )

else:
       import sys

       lowercase__			=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)

