




import collections
import inspect
import unittest

from transformers import SwinvaConfig
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_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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
  from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor
class 	_UpperCamelCase					:
      def __init__(	self      :Dict       ,					lowerCamelCase      :List[Any]       ,					lowerCamelCase      :Optional[Any]=13       ,					lowerCamelCase      :Any=32       ,					lowerCamelCase      :Optional[int]=2       ,					lowerCamelCase      :List[str]=3       ,					lowerCamelCase      :List[str]=16       ,					lowerCamelCase      :Union[str, Any]=[1, 2, 1]       ,					lowerCamelCase      :Dict=[2, 2, 4]       ,					lowerCamelCase      :Tuple=2       ,					lowerCamelCase      :Dict=2.0       ,					lowerCamelCase      :Optional[Any]=True       ,					lowerCamelCase      :List[str]=0.0       ,					lowerCamelCase      :List[str]=0.0       ,					lowerCamelCase      :List[Any]=0.1       ,					lowerCamelCase      :Optional[int]="gelu"       ,					lowerCamelCase      :Optional[int]=False       ,					lowerCamelCase      :List[Any]=True       ,					lowerCamelCase      :List[Any]=0.02       ,					lowerCamelCase      :List[str]=1e-5       ,					lowerCamelCase      :List[Any]=True       ,					lowerCamelCase      :Optional[int]=None       ,					lowerCamelCase      :Tuple=True       ,					lowerCamelCase      :str=10       ,					lowerCamelCase      :Tuple=8       ,					)   ->	List[str]:
       UpperCAmelCase__							       =  parent
       UpperCAmelCase__							       =  batch_size
       UpperCAmelCase__							       =  image_size
       UpperCAmelCase__							       =  patch_size
       UpperCAmelCase__							       =  num_channels
       UpperCAmelCase__							       =  embed_dim
       UpperCAmelCase__							       =  depths
       UpperCAmelCase__							       =  num_heads
       UpperCAmelCase__							       =  window_size
       UpperCAmelCase__							       =  mlp_ratio
       UpperCAmelCase__							       =  qkv_bias
       UpperCAmelCase__							       =  hidden_dropout_prob
       UpperCAmelCase__							       =  attention_probs_dropout_prob
       UpperCAmelCase__							       =  drop_path_rate
       UpperCAmelCase__							       =  hidden_act
       UpperCAmelCase__							       =  use_absolute_embeddings
       UpperCAmelCase__							       =  patch_norm
       UpperCAmelCase__							       =  layer_norm_eps
       UpperCAmelCase__							       =  initializer_range
       UpperCAmelCase__							       =  is_training
       UpperCAmelCase__							       =  scope
       UpperCAmelCase__							       =  use_labels
       UpperCAmelCase__							       =  type_sequence_label_size
       UpperCAmelCase__							       =  encoder_stride
      def 		UpperCAmelCase_  (	self      :List[Any]					)   ->	Any:
       UpperCAmelCase__							       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

       UpperCAmelCase__							       =  self.get_config()

       return config, pixel_values, labels
      def 		UpperCAmelCase_  (	self      :str					)   ->	Optional[int]:
       return SwinvaConfig(
           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       ,					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       ,					)
      def 		UpperCAmelCase_  (	self      :Optional[int]       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :int       ,					lowerCamelCase      :Any					)   ->	List[str]:
       UpperCAmelCase__							       =  SwinvaModel(config=_SCREAMING_SNAKE_CASE					)
       model.to(_SCREAMING_SNAKE_CASE					)
       model.eval()
       UpperCAmelCase__							       =  model(_SCREAMING_SNAKE_CASE					)

       UpperCAmelCase__							       =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
       UpperCAmelCase__							       =  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 		UpperCAmelCase_  (	self      :Union[str, Any]       ,					lowerCamelCase      :Dict       ,					lowerCamelCase      :List[str]       ,					lowerCamelCase      :str					)   ->	str:
       UpperCAmelCase__							       =  SwinvaForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE					)
       model.to(_SCREAMING_SNAKE_CASE					)
       model.eval()
       UpperCAmelCase__							       =  model(_SCREAMING_SNAKE_CASE					)
       self.parent.assertEqual(
           result.logits.shape       ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)					)

       # test greyscale images
       UpperCAmelCase__							       =  1
       UpperCAmelCase__							       =  SwinvaForMaskedImageModeling(_SCREAMING_SNAKE_CASE					)
       model.to(_SCREAMING_SNAKE_CASE					)
       model.eval()

       UpperCAmelCase__							       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
       UpperCAmelCase__							       =  model(_SCREAMING_SNAKE_CASE					)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, 1, self.image_size, self.image_size)					)
      def 		UpperCAmelCase_  (	self      :Optional[Any]       ,					lowerCamelCase      :List[str]       ,					lowerCamelCase      :Tuple       ,					lowerCamelCase      :Optional[Any]					)   ->	int:
       UpperCAmelCase__							       =  self.type_sequence_label_size
       UpperCAmelCase__							       =  SwinvaForImageClassification(_SCREAMING_SNAKE_CASE					)
       model.to(_SCREAMING_SNAKE_CASE					)
       model.eval()
       UpperCAmelCase__							       =  model(_SCREAMING_SNAKE_CASE       ,					labels=_SCREAMING_SNAKE_CASE					)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.type_sequence_label_size)					)







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



@require_torch
class 	_UpperCamelCase					( UpperCAmelCase__		,				UpperCAmelCase__		,				unittest.TestCase						):
      UpperCAmelCase_						=						(
          (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
      )
      UpperCAmelCase_						=						(
          {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
          if is_torch_available()
          else {}
      )

      UpperCAmelCase_						=						False
      UpperCAmelCase_						=						False
      UpperCAmelCase_						=						False
      UpperCAmelCase_						=						False
      def 		UpperCAmelCase_  (	self      :int					)   ->	Optional[Any]:
       UpperCAmelCase__							       =  SwinvaModelTester(self					)
       UpperCAmelCase__							       =  ConfigTester(self       ,					config_class=_SCREAMING_SNAKE_CASE       ,					embed_dim=37					)
      def 		UpperCAmelCase_  (	self      :Tuple					)   ->	int:
       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      :List[Any]					)   ->	int:
       UpperCAmelCase__							       =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)
      @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."					)
      def 		UpperCAmelCase_  (	self      :Any					)   ->	Union[str, Any]:
       pass
      @unittest.skip(reason="Swinv2 does not use inputs_embeds"					)
      def 		UpperCAmelCase_  (	self      :Dict					)   ->	Optional[int]:
       pass
      def 		UpperCAmelCase_  (	self      :str					)   ->	List[Any]:
       UpperCAmelCase__							       =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCAmelCase__							       =  model_class(_SCREAMING_SNAKE_CASE					)
        self.assertIsInstance(model.get_input_embeddings()       ,					(nn.Module)					)
        UpperCAmelCase__							       =  model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE       ,					nn.Linear					)					)
      def 		UpperCAmelCase_  (	self      :Tuple					)   ->	List[str]:
       UpperCAmelCase__							       =  self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCAmelCase__							       =  ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]       ,					_SCREAMING_SNAKE_CASE					)
      def 		UpperCAmelCase_  (	self      :int					)   ->	Tuple:
       UpperCAmelCase__							       =  self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase__							       =  True

       for model_class in self.all_model_classes:
        UpperCAmelCase__							       =  True
        UpperCAmelCase__							       =  False
        UpperCAmelCase__							       =  True
        UpperCAmelCase__							       =  model_class(_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        with torch.no_grad():
         UpperCAmelCase__							       =  model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					)					)
        UpperCAmelCase__							       =  outputs.attentions
        UpperCAmelCase__							       =  len(self.model_tester.depths					)
        self.assertEqual(len(_SCREAMING_SNAKE_CASE					)       ,					_SCREAMING_SNAKE_CASE					)

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        UpperCAmelCase__							       =  True
        UpperCAmelCase__							       =  config.window_size**2
        UpperCAmelCase__							       =  model_class(_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        with torch.no_grad():
         UpperCAmelCase__							       =  model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					)					)
        UpperCAmelCase__							       =  outputs.attentions
        self.assertEqual(len(_SCREAMING_SNAKE_CASE					)       ,					_SCREAMING_SNAKE_CASE					)

        self.assertListEqual(
            list(attentions[0].shape[-3:]					)       ,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,					)
        UpperCAmelCase__							       =  len(_SCREAMING_SNAKE_CASE					)

        # Check attention is always last and order is fine
        UpperCAmelCase__							       =  True
        UpperCAmelCase__							       =  True
        UpperCAmelCase__							       =  model_class(_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        with torch.no_grad():
         UpperCAmelCase__							       =  model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					)					)

        if hasattr(self.model_tester       ,					"num_hidden_states_types"					):
         UpperCAmelCase__							       =  self.model_tester.num_hidden_states_types
        else:
         # also another +1 for reshaped_hidden_states
         UpperCAmelCase__							       =  2
        self.assertEqual(out_len + added_hidden_states       ,					len(_SCREAMING_SNAKE_CASE					)					)

        UpperCAmelCase__							       =  outputs.attentions

        self.assertEqual(len(_SCREAMING_SNAKE_CASE					)       ,					_SCREAMING_SNAKE_CASE					)

        self.assertListEqual(
            list(self_attentions[0].shape[-3:]					)       ,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,					)
      def 		UpperCAmelCase_  (	self      :int       ,					lowerCamelCase      :Any       ,					lowerCamelCase      :List[str]       ,					lowerCamelCase      :List[Any]       ,					lowerCamelCase      :str					)   ->	List[str]:
       UpperCAmelCase__							       =  model_class(_SCREAMING_SNAKE_CASE					)
       model.to(_SCREAMING_SNAKE_CASE					)
       model.eval()

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

       UpperCAmelCase__							       =  outputs.hidden_states

       UpperCAmelCase__							       =  getattr(
           self.model_tester       ,					"expected_num_hidden_layers"       ,					len(self.model_tester.depths					) + 1					)
       self.assertEqual(len(_SCREAMING_SNAKE_CASE					)       ,					_SCREAMING_SNAKE_CASE					)

       # Swinv2 has a different seq_length
       UpperCAmelCase__							       =  (
           config.patch_size
           if isinstance(config.patch_size       ,					collections.abc.Iterable					)
           else (config.patch_size, config.patch_size)
       )

       UpperCAmelCase__							       =  (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]       ,					)

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

       UpperCAmelCase__							       =  reshaped_hidden_states[0].shape
       UpperCAmelCase__							       =  (
           reshaped_hidden_states[0].view(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					height * width					).permute(0       ,					2       ,					1					)
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]					)       ,					[num_patches, self.model_tester.embed_dim]       ,					)
      def 		UpperCAmelCase_  (	self      :Any					)   ->	Dict:
       UpperCAmelCase__							       =  self.model_tester.prepare_config_and_inputs_for_common()

       UpperCAmelCase__							       =  (
           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:
        UpperCAmelCase__							       =  True
        self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					)

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

        self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					)
      def 		UpperCAmelCase_  (	self      :List[Any]					)   ->	List[str]:
       UpperCAmelCase__							       =  self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase__							       =  3

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

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

       for model_class in self.all_model_classes:
        UpperCAmelCase__							       =  True
        self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					(padded_height, padded_width)					)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        UpperCAmelCase__							       =  True
        self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					(padded_height, padded_width)					)
      def 		UpperCAmelCase_  (	self      :Any					)   ->	Optional[int]:
       UpperCAmelCase__							       =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE					)
      def 		UpperCAmelCase_  (	self      :Union[str, Any]					)   ->	Union[str, Any]:
       UpperCAmelCase__							       =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE					)
      @slow
      def 		UpperCAmelCase_  (	self      :Optional[Any]					)   ->	Optional[Any]:




       for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase__							       =  SwinvaModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
        self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)







      def 		UpperCAmelCase_  (	self      :Optional[int]					)   ->	List[Any]:
       UpperCAmelCase__							       =  self.model_tester.prepare_config_and_inputs_for_common()

       UpperCAmelCase__							       =  _config_zero_init(_SCREAMING_SNAKE_CASE					)
       for model_class in self.all_model_classes:
        UpperCAmelCase__							       =  model_class(config=_SCREAMING_SNAKE_CASE					)
        for name, param in model.named_parameters():
         if "embeddings" not in name and "logit_scale" 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						):
      @cached_property
      def 		UpperCAmelCase_  (	self      :List[Any]					)   ->	Optional[Any]:
       return (
           AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"					)
           if is_vision_available()
           else None
       )







      @slow
      def 		UpperCAmelCase_  (	self      :int					)   ->	str:
       UpperCAmelCase__							       =  SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"					).to(
           _SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  self.default_image_processor

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

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

       # verify the logits
       UpperCAmelCase__							       =  torch.Size((1, 1000)					)
       self.assertEqual(outputs.logits.shape       ,					_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  torch.tensor([-0.39_47, -0.43_06, 0.00_26]					).to(_SCREAMING_SNAKE_CASE					)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,					_SCREAMING_SNAKE_CASE       ,					atol=1e-4					)					)










"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   List[str]								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Tuple								=		["DPTFeatureExtractor"]
      A:   int								=		["DPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[Any]								=		[
          "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPTForDepthEstimation",
          "DPTForSemanticSegmentation",
          "DPTModel",
          "DPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

      A:   str								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
