




import collections
import inspect
import unittest

from transformers import SwinConfig
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
   from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor




class     a__ :







     def __init__(			self						,	A						,	A=13						,	A=32						,	A=2						,	A=3						,	A=16						,	A=[1, 2, 1]						,	A=[2, 2, 4]						,	A=2						,	A=2.0						,	A=True						,	A=0.0						,	A=0.0						,	A=0.1						,	A="gelu"						,	A=False						,	A=True						,	A=0.0_2						,	A=1e-5						,	A=True						,	A=None						,	A=True						,	A=10						,	A=8						,	A=["stage1", "stage2"]						,	A=[1, 2]						,	)						->					Optional[Any]:



          '''simple docstring'''



          a											=   parent
          a											=   batch_size
          a											=   image_size
          a											=   patch_size
          a											=   num_channels
          a											=   embed_dim
          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     lowerCAmelCase_       (			self				)						->					Dict:



          '''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     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



          return SwinConfig(
              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						,	out_features=self.out_features						,	out_indices=self.out_indices						,	)







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A				)						->					Optional[Any]:



          '''simple docstring'''



          a											=   SwinModel(config=snake_case_				)
          model.to(snake_case_				)
          model.eval()
          a											=   model(snake_case_				)

          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     lowerCAmelCase_       (			self						,	A						,	A						,	A				)						->					Any:



          '''simple docstring'''



          a											=   SwinBackbone(config=snake_case_				)
          model.to(snake_case_				)
          model.eval()
          a											=   model(snake_case_				)

          # 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], 16, 16]				)

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

          # verify backbone works with out_features=None
          a											=   None
          a											=   SwinBackbone(config=snake_case_				)
          model.to(snake_case_				)
          model.eval()
          a											=   model(snake_case_				)

          # 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     lowerCAmelCase_       (			self						,	A						,	A						,	A				)						->					Dict:



          '''simple docstring'''



          a											=   SwinForMaskedImageModeling(config=snake_case_				)
          model.to(snake_case_				)
          model.eval()
          a											=   model(snake_case_				)
          self.parent.assertEqual(
              result.logits.shape						,	(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

          # test greyscale images
          a											=   1
          a											=   SwinForMaskedImageModeling(snake_case_				)
          model.to(snake_case_				)
          model.eval()

          a											=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          a											=   model(snake_case_				)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, 1, self.image_size, self.image_size)				)







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A				)						->					Optional[int]:



          '''simple docstring'''



          a											=   self.type_sequence_label_size
          a											=   SwinForImageClassification(snake_case_				)
          model.to(snake_case_				)
          model.eval()
          a											=   model(snake_case_						,	labels=snake_case_				)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)				)

          # test greyscale images
          a											=   1
          a											=   SwinForImageClassification(snake_case_				)
          model.to(snake_case_				)
          model.eval()

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







     def     lowerCAmelCase_       (			self				)						->					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     a__ (      UpperCamelCase__     ,      UpperCamelCase__     ,      unittest.TestCase   ):
     a	:    int	      =			(
         (
             SwinModel,
             SwinBackbone,
             SwinForImageClassification,
             SwinForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     a	:    Dict	      =			(
         {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
         if is_torch_available()
         else {}
     )
     a	:    int	      =			True

     a	:    Any	      =			False
     a	:    Dict	      =			False
     a	:    str	      =			False







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



          '''simple docstring'''



          a											=   SwinModelTester(self				)
          a											=   ConfigTester(self						,	config_class=snake_case_						,	embed_dim=37				)







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''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     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



          return







     def     lowerCAmelCase_       (			self				)						->					Tuple:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_				)







     @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."				)
     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          pass







     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



          super().test_training_gradient_checkpointing()







     def     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*snake_case_				)







     def     lowerCAmelCase_       (			self				)						->					str:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_				)







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_				)







     @unittest.skip(reason="Swin does not use inputs_embeds"				)
     def     lowerCAmelCase_       (			self				)						->					Dict:



          '''simple docstring'''



          pass







     @unittest.skip(reason="Swin Transformer does not use feedforward chunking"				)
     def     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



          pass







     def     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               a											=   model_class(snake_case_				)
               self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)				)
               a											=   model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(snake_case_						,	nn.Linear				)				)







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               a											=   model_class(snake_case_				)
               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]						,	snake_case_				)







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs_for_common()
          a											=   True

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

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

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

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

               # also another +1 for reshaped_hidden_states
               a											=   1 if model_class.__name__ == '''SwinBackbone''' else 2
               self.assertEqual(out_len + added_hidden_states						,	len(snake_case_				)				)

               a											=   outputs.attentions

               self.assertEqual(len(snake_case_				)						,	snake_case_				)

               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]				)						,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,	)







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A						,	A				)						->					List[str]:



          '''simple docstring'''



          a											=   model_class(snake_case_				)
          model.to(snake_case_				)
          model.eval()

          with torch.no_grad():
               a											=   model(**self._prepare_for_class(snake_case_						,	snake_case_				)				)

          a											=   outputs.hidden_states

          a											=   getattr(
              self.model_tester						,	"expected_num_hidden_layers"						,	len(self.model_tester.depths				) + 1				)
          self.assertEqual(len(snake_case_				)						,	snake_case_				)

          # Swin 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]						,	)

          if not model_class.__name__ == "SwinBackbone":
               a											=   outputs.reshaped_hidden_states
               self.assertEqual(len(snake_case_				)						,	snake_case_				)

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







     def     lowerCAmelCase_       (			self				)						->					Optional[Any]:



          '''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:
               a											=   True
               self.check_hidden_states_output(snake_case_						,	snake_case_						,	snake_case_						,	snake_case_				)

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

               self.check_hidden_states_output(snake_case_						,	snake_case_						,	snake_case_						,	snake_case_				)







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



          '''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:
               a											=   True
               self.check_hidden_states_output(snake_case_						,	snake_case_						,	snake_case_						,	(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(snake_case_						,	snake_case_						,	snake_case_						,	(padded_height, padded_width)				)







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



          '''simple docstring'''



          for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               a											=   SwinModel.from_pretrained(snake_case_				)
               self.assertIsNotNone(snake_case_				)







     def     lowerCAmelCase_       (			self				)						->					str:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs_for_common()

          a											=   _config_zero_init(snake_case_				)
          for model_class in self.all_model_classes:
               a											=   model_class(config=snake_case_				)
               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     a__ (      unittest.TestCase   ):







     @cached_property
     def     lowerCAmelCase_       (			self				)						->					List[Any]:



          '''simple docstring'''



          return (
              AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"				)
              if is_vision_available()
              else None
          )







     @slow
     def     lowerCAmelCase_       (			self				)						->					str:



          '''simple docstring'''



          a											=   SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"				).to(snake_case_				)
          a											=   self.default_image_processor

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

          # forward pass
          with torch.no_grad():
               a											=   model(**snake_case_				)

          # verify the logits
          a											=   torch.Size((1, 1000)				)
          self.assertEqual(outputs.logits.shape						,	snake_case_				)
          a											=   torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]				).to(snake_case_				)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]						,	snake_case_						,	atol=1e-4				)				)







@require_torch
class     a__ (      unittest.TestCase     ,      UpperCamelCase__   ):
     a	:    Union[str, Any]	      =			(SwinBackbone,) if is_torch_available() else ()
     a	:    Union[str, Any]	      =			SwinConfig







     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



          a											=   SwinModelTester(self				)







from typing import TYPE_CHECKING

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


lowercase__   :    str 					=      {
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    int 					=      [
       "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
       "RwkvForCausalLM",
       "RwkvModel",
       "RwkvPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

   lowercase__   :    List[Any] 					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

