




import collections
import inspect
import unittest

from transformers import FocalNetConfig
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, _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 (
      FocalNetBackbone,
      FocalNetForImageClassification,
      FocalNetForMaskedImageModeling,
      FocalNetModel,
  )
  from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     str ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Optional[Any]=13 ,			UpperCAmelCase__				:     Tuple=32 ,			UpperCAmelCase__				:     List[str]=2 ,			UpperCAmelCase__				:     Dict=3 ,			UpperCAmelCase__				:     int=16 ,			UpperCAmelCase__				:     Optional[Any]=[32, 64, 128] ,			UpperCAmelCase__				:     int=[1, 2, 1] ,			UpperCAmelCase__				:     List[str]=[2, 2, 4] ,			UpperCAmelCase__				:     Any=2 ,			UpperCAmelCase__				:     List[str]=2.0 ,			UpperCAmelCase__				:     str=True ,			UpperCAmelCase__				:     Tuple=0.0 ,			UpperCAmelCase__				:     List[Any]=0.0 ,			UpperCAmelCase__				:     Optional[Any]=0.1 ,			UpperCAmelCase__				:     Dict="gelu" ,			UpperCAmelCase__				:     Any=False ,			UpperCAmelCase__				:     List[Any]=True ,			UpperCAmelCase__				:     List[str]=0.02 ,			UpperCAmelCase__				:     str=1e-5 ,			UpperCAmelCase__				:     List[str]=True ,			UpperCAmelCase__				:     Union[str, Any]=None ,			UpperCAmelCase__				:     List[Any]=True ,			UpperCAmelCase__				:     Any=10 ,			UpperCAmelCase__				:     Any=8 ,			UpperCAmelCase__				:     str=["stage1", "stage2"] ,			UpperCAmelCase__				:     Optional[Any]=[1, 2] ,			)						->Any:





        '''simple docstring'''

        A__							=   parent
        A__							=   batch_size
        A__							=   image_size
        A__							=   patch_size
        A__							=   num_channels
        A__							=   embed_dim
        A__							=   hidden_sizes
        A__							=   depths
        A__							=   num_heads
        A__							=   window_size
        A__							=   mlp_ratio
        A__							=   qkv_bias
        A__							=   hidden_dropout_prob
        A__							=   attention_probs_dropout_prob
        A__							=   drop_path_rate
        A__							=   hidden_act
        A__							=   use_absolute_embeddings
        A__							=   patch_norm
        A__							=   layer_norm_eps
        A__							=   initializer_range
        A__							=   is_training
        A__							=   scope
        A__							=   use_labels
        A__							=   type_sequence_label_size
        A__							=   encoder_stride
        A__							=   out_features
        A__							=   out_indices


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->List[Any]:





        '''simple docstring'''

        A__							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        A__							=   None
        if self.use_labels:
            A__							=   ids_tensor([self.batch_size] ,			self.type_sequence_label_size)

        A__							=   self.get_config()

        return config, pixel_values, labels


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Union[str, Any]:





        '''simple docstring'''

        return FocalNetConfig(
            image_size=self.image_size ,			patch_size=self.patch_size ,			num_channels=self.num_channels ,			embed_dim=self.embed_dim ,			hidden_sizes=self.hidden_sizes ,			depths=self.depths ,			num_heads=self.num_heads ,			window_size=self.window_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 ,			use_absolute_embeddings=self.use_absolute_embeddings ,			path_norm=self.patch_norm ,			layer_norm_eps=self.layer_norm_eps ,			initializer_range=self.initializer_range ,			encoder_stride=self.encoder_stride ,			out_features=self.out_features ,			out_indices=self.out_indices ,			)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int)						->int:





        '''simple docstring'''

        A__							=   FocalNetModel(config=UpperCAmelCase__)
        model.to(UpperCAmelCase__)
        model.eval()
        A__							=   model(UpperCAmelCase__)

        A__							=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
        A__							=   int(config.embed_dim * 2 ** (len(config.depths) - 1))

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     List[Any])						->Tuple:





        '''simple docstring'''

        A__							=   FocalNetBackbone(config=UpperCAmelCase__)
        model.to(UpperCAmelCase__)
        model.eval()
        A__							=   model(UpperCAmelCase__)

        # 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.image_size, 8, 8])

        # 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
        A__							=   None
        A__							=   FocalNetBackbone(config=UpperCAmelCase__)
        model.to(UpperCAmelCase__)
        model.eval()
        A__							=   model(UpperCAmelCase__)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps) ,			1)
        self.parent.assertListEqual(list(result.feature_maps[0].shape) ,			[self.batch_size, self.image_size * 2, 4, 4])

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int)						->int:





        '''simple docstring'''

        A__							=   FocalNetForMaskedImageModeling(config=UpperCAmelCase__)
        model.to(UpperCAmelCase__)
        model.eval()
        A__							=   model(UpperCAmelCase__)
        self.parent.assertEqual(
            result.reconstruction.shape ,			(self.batch_size, self.num_channels, self.image_size, self.image_size))

        # test greyscale images
        A__							=   1
        A__							=   FocalNetForMaskedImageModeling(UpperCAmelCase__)
        model.to(UpperCAmelCase__)
        model.eval()

        A__							=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        A__							=   model(UpperCAmelCase__)
        self.parent.assertEqual(result.reconstruction.shape ,			(self.batch_size, 1, self.image_size, self.image_size))


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Optional[Any])						->Any:





        '''simple docstring'''

        A__							=   self.type_sequence_label_size
        A__							=   FocalNetForImageClassification(UpperCAmelCase__)
        model.to(UpperCAmelCase__)
        model.eval()
        A__							=   model(UpperCAmelCase__ ,			labels=UpperCAmelCase__)
        self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.type_sequence_label_size))

        # test greyscale images
        A__							=   1
        A__							=   FocalNetForImageClassification(UpperCAmelCase__)
        model.to(UpperCAmelCase__)
        model.eval()

        A__							=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        A__							=   model(UpperCAmelCase__)
        self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.type_sequence_label_size))





    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->List[str]:





        '''simple docstring'''

        A__							=   self.prepare_config_and_inputs()

        A__							=   config_and_inputs
        A__							=   {'pixel_values': pixel_values}
        return config, inputs_dict


@require_torch
class     UpperCamelCase_   (			snake_case_		, snake_case_		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(
        (
            FocalNetModel,
            FocalNetForImageClassification,
            FocalNetForMaskedImageModeling,
            FocalNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__							  =				(
        {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
        if is_torch_available()
        else {}
    )
    UpperCAmelCase__							  =				False

    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Union[str, Any]:





        '''simple docstring'''

        A__							=   FocalNetModelTester(self)
        A__							=   ConfigTester(self ,			config_class=UpperCAmelCase__ ,			embed_dim=37 ,			has_text_modality=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Any:





        '''simple docstring'''

        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 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Union[str, Any]:





        '''simple docstring'''

        return


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->List[Any]:





        '''simple docstring'''

        A__							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->str:





        '''simple docstring'''

        A__							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Optional[int]:





        '''simple docstring'''

        A__							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Union[str, Any]:





        '''simple docstring'''

        A__							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__)


    @unittest.skip(reason='''FocalNet does not use inputs_embeds''')
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->str:





        '''simple docstring'''

        pass


    @unittest.skip(reason='''FocalNet does not use feedforward chunking''')
    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Tuple:





        '''simple docstring'''

        pass


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Dict:





        '''simple docstring'''

        A__							=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:-1]:
            A__							=   model_class(UpperCAmelCase__)
            self.assertIsInstance(model.get_input_embeddings() ,			(nn.Module))
            A__							=   model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(UpperCAmelCase__ ,			nn.Linear))


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Union[str, Any]:





        '''simple docstring'''

        A__							=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:-1]:
            A__							=   model_class(UpperCAmelCase__)
            A__							=   inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            A__							=   [*signature.parameters.keys()]

            A__							=   ['pixel_values']
            self.assertListEqual(arg_names[:1] ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Optional[int])						->Any:





        '''simple docstring'''

        A__							=   model_class(UpperCAmelCase__)
        model.to(UpperCAmelCase__)
        model.eval()

        with torch.no_grad():
            A__							=   model(**self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__))

        A__							=   outputs.hidden_states

        A__							=   getattr(
            self.model_tester ,			'''expected_num_hidden_layers''' ,			len(self.model_tester.depths) + 1)
        self.assertEqual(len(UpperCAmelCase__) ,			UpperCAmelCase__)

        # FocalNet has a different seq_length
        A__							=   (
            config.patch_size
            if isinstance(config.patch_size ,			collections.abc.Iterable)
            else (config.patch_size, config.patch_size)
        )

        A__							=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]) ,			[num_patches, self.model_tester.embed_dim] ,			)

        A__							=   outputs.reshaped_hidden_states
        self.assertEqual(len(UpperCAmelCase__) ,			UpperCAmelCase__)

        A__							=   reshaped_hidden_states[0].shape
        A__							=   (
            reshaped_hidden_states[0].view(UpperCAmelCase__ ,			UpperCAmelCase__ ,			height * width).permute(0 ,			2 ,			1)
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]) ,			[num_patches, self.model_tester.embed_dim] ,			)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Tuple:





        '''simple docstring'''

        A__							=   self.model_tester.prepare_config_and_inputs_for_common()

        A__							=   (
            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[:-1]:
            A__							=   True
            self.check_hidden_states_output(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)

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

            self.check_hidden_states_output(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Dict:





        '''simple docstring'''

        A__							=   self.model_tester.prepare_config_and_inputs_for_common()
        A__							=   3

        A__							=   (
            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)
        )
        A__							=   (
            config.patch_size
            if isinstance(config.patch_size ,			collections.abc.Iterable)
            else (config.patch_size, config.patch_size)
        )

        A__							=   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        A__							=   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes[:-1]:
            A__							=   True
            self.check_hidden_states_output(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			(padded_height, padded_width))

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            A__							=   True
            self.check_hidden_states_output(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			(padded_height, padded_width))


    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[str]:





        '''simple docstring'''

        for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A__							=   FocalNetModel.from_pretrained(UpperCAmelCase__)
            self.assertIsNotNone(UpperCAmelCase__)





    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Optional[Any]:





        '''simple docstring'''

        A__							=   self.model_tester.prepare_config_and_inputs_for_common()

        A__							=   _config_zero_init(UpperCAmelCase__)
        for model_class in self.all_model_classes:
            A__							=   model_class(config=UpperCAmelCase__)
            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_vision
@require_torch
class     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






    @cached_property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[int]:





        '''simple docstring'''

        return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''') if is_vision_available() else None





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Optional[Any]:





        '''simple docstring'''

        A__							=   FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''').to(UpperCAmelCase__)
        A__							=   self.default_image_processor

        A__							=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        A__							=   image_processor(images=UpperCAmelCase__ ,			return_tensors='''pt''').to(UpperCAmelCase__)

        # forward pass
        with torch.no_grad():
            A__							=   model(**UpperCAmelCase__)

        # verify the logits
        A__							=   torch.Size((1, 1_000))
        self.assertEqual(outputs.logits.shape ,			UpperCAmelCase__)
        A__							=   torch.tensor([0.2166, -0.4368, 0.2191]).to(UpperCAmelCase__)
        self.assertTrue(torch.allclose(outputs.logits[0, :3] ,			UpperCAmelCase__ ,			atol=1e-4))
        self.assertTrue(outputs.logits.argmax(dim=-1).item() ,			281)


@require_torch
class     UpperCamelCase_   (			snake_case_		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(FocalNetBackbone,) if is_torch_available() else ()
    UpperCAmelCase__							  =				FocalNetConfig

    UpperCAmelCase__							  =				False


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->List[Any]:





        '''simple docstring'''

        A__							=   FocalNetModelTester(self)








from typing import TYPE_CHECKING

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


_lowerCamelCase     :		str								=       {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Dict								=       [
      """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FocalNetForImageClassification""",
      """FocalNetForMaskedImageModeling""",
      """FocalNetBackbone""",
      """FocalNetModel""",
      """FocalNetPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_focalnet import (
        FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FocalNetBackbone,
        FocalNetForImageClassification,
        FocalNetForMaskedImageModeling,
        FocalNetModel,
        FocalNetPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		Tuple								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


