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 __snake_case    :






   """simple docstring"""




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



     """simple docstring"""






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




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__      							=				self.get_config()

     return config, pixel_values, labels




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     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      __a						(   self      :    Tuple	,     _lowercase      :    List[str]	,     _lowercase      :    Optional[int]	,     _lowercase      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				SwinvaModel(config=lowerCamelCase__				)
     model.to(lowerCamelCase__				)
     model.eval()
     SCREAMING_SNAKE_CASE__      							=				model(lowerCamelCase__				)

     SCREAMING_SNAKE_CASE__      							=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
     SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    List[Any]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Tuple	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				SwinvaForMaskedImageModeling(config=lowerCamelCase__				)
     model.to(lowerCamelCase__				)
     model.eval()
     SCREAMING_SNAKE_CASE__      							=				model(lowerCamelCase__				)
     self.parent.assertEqual(
         result.logits.shape	,     (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

     # test greyscale images
     SCREAMING_SNAKE_CASE__      							=				1
     SCREAMING_SNAKE_CASE__      							=				SwinvaForMaskedImageModeling(lowerCamelCase__				)
     model.to(lowerCamelCase__				)
     model.eval()

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




   def      __a						(   self      :    str	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    str	,     _lowercase      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.type_sequence_label_size
     SCREAMING_SNAKE_CASE__      							=				SwinvaForImageClassification(lowerCamelCase__				)
     model.to(lowerCamelCase__				)
     model.eval()
     SCREAMING_SNAKE_CASE__      							=				model(lowerCamelCase__	,     labels=lowerCamelCase__				)
     self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.type_sequence_label_size)				)




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				config_and_inputs
     SCREAMING_SNAKE_CASE__      							=				{"""pixel_values""": pixel_values}
     return config, inputs_dict







@require_torch
class __snake_case    (						A__       ,		A__       ,		unittest.TestCase		):






   """simple docstring"""




   lowerCAmelCase_														=							(
       (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
   )
   lowerCAmelCase_														=							(
       {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
       if is_torch_available()
       else {}
   )

   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				SwinvaModelTester(self				)
     SCREAMING_SNAKE_CASE__      							=				ConfigTester(self	,     config_class=lowerCamelCase__	,     embed_dim=37				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






     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      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCamelCase__				)




   @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""				)
   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     pass




   @unittest.skip(reason="""Swinv2 does not use inputs_embeds"""				)
   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     pass




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()

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




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()

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

       SCREAMING_SNAKE_CASE__      							=				["""pixel_values"""]
       self.assertListEqual(arg_names[:1]	,     lowerCamelCase__				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE__      							=				True

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

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

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

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

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

       SCREAMING_SNAKE_CASE__      							=				outputs.attentions

       self.assertEqual(len(lowerCamelCase__				)	,     lowerCamelCase__				)

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




   def      __a						(   self      :    Tuple	,     _lowercase      :    str	,     _lowercase      :    Dict	,     _lowercase      :    int	,     _lowercase      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				model_class(lowerCamelCase__				)
     model.to(lowerCamelCase__				)
     model.eval()

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

     SCREAMING_SNAKE_CASE__      							=				outputs.hidden_states

     SCREAMING_SNAKE_CASE__      							=				getattr(
         self.model_tester	,     """expected_num_hidden_layers"""	,     len(self.model_tester.depths				) + 1				)
     self.assertEqual(len(lowerCamelCase__				)	,     lowerCamelCase__				)

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

     SCREAMING_SNAKE_CASE__      							=				(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]	,     )

     SCREAMING_SNAKE_CASE__      							=				outputs.reshaped_hidden_states
     self.assertEqual(len(lowerCamelCase__				)	,     lowerCamelCase__				)

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




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__      							=				(
         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__      							=				True
       self.check_hidden_states_output(lowerCamelCase__	,     lowerCamelCase__	,     lowerCamelCase__	,     lowerCamelCase__				)

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

       self.check_hidden_states_output(lowerCamelCase__	,     lowerCamelCase__	,     lowerCamelCase__	,     lowerCamelCase__				)




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE__      							=				3

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

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

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__      							=				True
       self.check_hidden_states_output(lowerCamelCase__	,     lowerCamelCase__	,     lowerCamelCase__	,     (padded_height, padded_width)				)

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




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__				)




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__				)




   @slow
   def      __a						(   self      :    Tuple				):



     """simple docstring"""






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




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__      							=				_config_zero_init(lowerCamelCase__				)
     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__      							=				model_class(config=lowerCamelCase__				)
       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 __snake_case    (						unittest.TestCase		):






   """simple docstring"""




   @cached_property
   def      __a						(   self      :    str				):



     """simple docstring"""






     return (
         AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""				)
         if is_vision_available()
         else None
     )




   @slow
   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""				).to(
         lowerCamelCase__				)
     SCREAMING_SNAKE_CASE__      							=				self.default_image_processor

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

     # forward pass
     with torch.no_grad():
       SCREAMING_SNAKE_CASE__      							=				model(**lowerCamelCase__				)

     # verify the logits
     SCREAMING_SNAKE_CASE__      							=				torch.Size((1, 10_00)				)
     self.assertEqual(outputs.logits.shape	,     lowerCamelCase__				)
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([-0.39_47, -0.43_06, 0.00_26]				).to(lowerCamelCase__				)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]	,     lowerCamelCase__	,     atol=1E-4				)				)

from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
