'''simple docstring'''
import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
 from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class _lowerCAmelCase			:







      def __init__(  self  ,    lowercase  ,    lowercase=3  ,    lowercase=32  ,    lowercase=3  ,    lowercase=10  ,    lowercase=[10, 20, 30, 40]  ,    lowercase=[1, 1, 2, 1]  ,    lowercase=True  ,    lowercase=True  ,    lowercase="relu"  ,    lowercase=3  ,    lowercase=None  ,    lowercase=["stage2", "stage3", "stage4"]  ,    lowercase=[2, 3, 4]  ,    ):
        lowercase				:							str									=   parent
        lowercase				:							List[str]									=   batch_size
        lowercase				:							List[str]									=   image_size
        lowercase				:							str									=   num_channels
        lowercase				:							int									=   embeddings_size
        lowercase				:							Optional[int]									=   hidden_sizes
        lowercase				:							int									=   depths
        lowercase				:							Tuple									=   is_training
        lowercase				:							List[Any]									=   use_labels
        lowercase				:							Tuple									=   hidden_act
        lowercase				:							Any									=   num_labels
        lowercase				:							Optional[int]									=   scope
        lowercase				:							Optional[int]									=   len(lowercase     )
        lowercase				:							List[str]									=   out_features
        lowercase				:							Union[str, Any]									=   out_indices







      def      lowerCamelCase__      (  self     ):
        lowercase				:							str									=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

        lowercase				:							Optional[Any]									=   self.get_config()

        return config, pixel_values, labels







      def      lowerCamelCase__      (  self     ):
        return ResNetConfig(
            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  ,    out_features=self.out_features  ,    out_indices=self.out_indices  ,    )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Tuple									=   ResNetModel(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							Union[str, Any]									=   model(lowercase     )
        # 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      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Any									=   self.num_labels
        lowercase				:							Dict									=   ResNetForImageClassification(lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							Union[str, Any]									=   model(lowercase  ,    labels=lowercase     )
        self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_labels)     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Dict									=   ResNetBackbone(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							Any									=   model(lowercase     )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps     )  ,    len(config.out_features     )     )
        self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,    [self.batch_size, self.hidden_sizes[1], 4, 4]     )

        # verify channels
        self.parent.assertEqual(len(model.channels     )  ,    len(config.out_features     )     )
        self.parent.assertListEqual(model.channels  ,    config.hidden_sizes[1:]     )

        # verify backbone works with out_features=None
        lowercase				:							Union[str, Any]									=   None
        lowercase				:							str									=   ResNetBackbone(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							int									=   model(lowercase     )

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

        # verify channels
        self.parent.assertEqual(len(model.channels     )  ,    1     )
        self.parent.assertListEqual(model.channels  ,    [config.hidden_sizes[-1]]     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   self.prepare_config_and_inputs()
        lowercase				,      lowercase				,      lowercase				:							Optional[Any]									=   config_and_inputs
        lowercase				:							Optional[Any]									=   {'''pixel_values''': pixel_values}
        return config, inputs_dict





@require_torch
class _lowerCAmelCase			(	snake_case							,   snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						Optional[Any]	 =       (
          (
              ResNetModel,
              ResNetForImageClassification,
              ResNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_	:						Union[str, Any]	 =       (
          {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
          if is_torch_available()
          else {}
      )

      UpperCAmelCase_	:						Union[str, Any]	 =       True
      UpperCAmelCase_	:						Optional[int]	 =       False
      UpperCAmelCase_	:						Dict	 =       False
      UpperCAmelCase_	:						List[Any]	 =       False
      UpperCAmelCase_	:						List[str]	 =       False







      def      lowerCamelCase__      (  self     ):
        lowercase				:							List[Any]									=   ResNetModelTester(self     )
        lowercase				:							Union[str, Any]									=   ConfigTester(self  ,    config_class=lowercase  ,    has_text_modality=lowercase     )







      def      lowerCamelCase__      (  self     ):
        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      lowerCamelCase__      (  self     ):
        return







      @unittest.skip(reason='''ResNet does not use inputs_embeds'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      @unittest.skip(reason='''ResNet does not support input and output embeddings'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							str									=   self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase				:							Optional[int]									=   ['''pixel_values''']
          self.assertListEqual(arg_names[:1]  ,    lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							str									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							List[Any]									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							List[Any]									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							Tuple									=   model_class(config=lowercase     )
          for name, module in model.named_modules():
            if isinstance(lowercase  ,    (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      lowerCamelCase__      (  self     ):
        def check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase     ):
          lowercase				:							Dict									=   model_class(lowercase     )
          model.to(lowercase     )
          model.eval()

          with torch.no_grad():
            lowercase				:							Tuple									=   model(**self._prepare_for_class(lowercase  ,    lowercase     )     )

          lowercase				:							Any									=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          lowercase				:							Union[str, Any]									=   self.model_tester.num_stages
          self.assertEqual(len(lowercase     )  ,    expected_num_stages + 1     )

          # ResNet'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 // 4, self.model_tester.image_size // 4]  ,    )

        lowercase				,      lowercase				:							Tuple									=   self.model_tester.prepare_config_and_inputs_for_common()
        lowercase				:							Dict									=   ['''basic''', '''bottleneck''']
        for model_class in self.all_model_classes:
          for layer_type in layers_type:
            lowercase				:							Optional[Any]									=   layer_type
            lowercase				:							Optional[int]									=   True
            check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase     )

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

            check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase     )







      @unittest.skip(reason='''ResNet does not use feedforward chunking'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Dict									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase     )







      @slow
      def      lowerCamelCase__      (  self     ):
        for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase				:							List[str]									=   ResNetModel.from_pretrained(lowercase     )
          self.assertIsNotNone(lowercase     )







def 				__UpperCamelCase (					)							->	Optional[int]:
  lowercase				:							Any									=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
  return image




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







      @cached_property
      def      lowerCamelCase__      (  self     ):
        return (
            AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
            if is_vision_available()
            else None
        )







      @slow
      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[int]									=   ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(lowercase     )

        lowercase				:							int									=   self.default_image_processor
        lowercase				:							Any									=   prepare_img()
        lowercase				:							Union[str, Any]									=   image_processor(images=lowercase  ,    return_tensors='''pt'''     ).to(lowercase     )

        # forward pass
        with torch.no_grad():
          lowercase				:							Any									=   model(**lowercase     )

        # verify the logits
        lowercase				:							str									=   torch.Size((1, 1000)     )
        self.assertEqual(outputs.logits.shape  ,    lowercase     )

        lowercase				:							List[Any]									=   torch.tensor([-11.1069, -9.7877, -8.3777]     ).to(lowercase     )

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





@require_torch
class _lowerCAmelCase			(	snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						str	 =       (ResNetBackbone,) if is_torch_available() else ()
      UpperCAmelCase_	:						int	 =       False
      UpperCAmelCase_	:						Any	 =       ResNetConfig







      def      lowerCamelCase__      (  self     ):
        lowercase				:							str									=   ResNetModelTester(self     )
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_A		:      Optional[Any]						   ={
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:      Union[str, Any]						   =[
     '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileViTV2ForImageClassification''',
     '''MobileViTV2ForSemanticSegmentation''',
     '''MobileViTV2Model''',
     '''MobileViTV2PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 _A		:      Tuple						   =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)