





'''simple docstring'''


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    lowerCAmelCase_		:




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase=13			,     _lowerCAmelCase=32			,     _lowerCAmelCase=2			,     _lowerCAmelCase=3			,     _lowerCAmelCase=16			,     _lowerCAmelCase=[1, 2, 1]			,     _lowerCAmelCase=[2, 2, 4]			,     _lowerCAmelCase=2			,     _lowerCAmelCase=2.0			,     _lowerCAmelCase=True			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=False			,     _lowerCAmelCase=True			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-5			,     _lowerCAmelCase=True			,     _lowerCAmelCase=None			,     _lowerCAmelCase=True			,     _lowerCAmelCase=10			,     _lowerCAmelCase=8			,     )    ->							int:
     _lowerCAmelCase				=      parent
     _lowerCAmelCase				=      batch_size
     _lowerCAmelCase				=      image_size
     _lowerCAmelCase				=      patch_size
     _lowerCAmelCase				=      num_channels
     _lowerCAmelCase				=      embed_dim
     _lowerCAmelCase				=      depths
     _lowerCAmelCase				=      num_heads
     _lowerCAmelCase				=      window_size
     _lowerCAmelCase				=      mlp_ratio
     _lowerCAmelCase				=      qkv_bias
     _lowerCAmelCase				=      hidden_dropout_prob
     _lowerCAmelCase				=      attention_probs_dropout_prob
     _lowerCAmelCase				=      drop_path_rate
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      use_absolute_embeddings
     _lowerCAmelCase				=      patch_norm
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      is_training
     _lowerCAmelCase				=      scope
     _lowerCAmelCase				=      use_labels
     _lowerCAmelCase				=      type_sequence_label_size
     _lowerCAmelCase				=      encoder_stride




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

     _lowerCAmelCase				=      self.get_config()

     return config, pixel_values, labels




 def 							_snake_case   (	self		)    ->							str:
     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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Dict:
     _lowerCAmelCase				=      SwinvaModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase		)

     _lowerCAmelCase				=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
     _lowerCAmelCase				=      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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							int:
     _lowerCAmelCase				=      SwinvaForMaskedImageModeling(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase		)
     self.parent.assertEqual(
         result.logits.shape			,     (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

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

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




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      self.type_sequence_label_size
     _lowerCAmelCase				=      SwinvaForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase			,     labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)		)





 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      config_and_inputs
     _lowerCAmelCase				=      {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
class    lowerCAmelCase_		(		__magic_name__						,__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							List[str]         =					(
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 __lowerCamelCase						:							Tuple         =					(
     {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 __lowerCamelCase						:							Optional[int]         =					False
 __lowerCamelCase						:							Any         =					False
 __lowerCamelCase						:							Any         =					False
 __lowerCamelCase						:							Dict         =					False




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      SwinvaModelTester(self		)
     _lowerCAmelCase				=      ConfigTester(self			,     config_class=_lowerCAmelCase			,     embed_dim=37		)




 def 							_snake_case   (	self		)    ->							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 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							Tuple:
     pass




 @unittest.skip(reason="Swinv2 does not use inputs_embeds"		)
 def 							_snake_case   (	self		)    ->							str:
     pass




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

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




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

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

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




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase				=      True

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

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

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

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

         _lowerCAmelCase				=      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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[Any]:
     _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

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

     _lowerCAmelCase				=      outputs.hidden_states

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

     _lowerCAmelCase				=      (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]			,     )

     _lowerCAmelCase				=      outputs.reshaped_hidden_states
     self.assertEqual(len(_lowerCAmelCase		)			,     _lowerCAmelCase		)

     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      reshaped_hidden_states[0].shape
     _lowerCAmelCase				=      (
         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 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     _lowerCAmelCase				=      (
         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:
         _lowerCAmelCase				=      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"]
         _lowerCAmelCase				=      True

         self.check_hidden_states_output(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase				=      3

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

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

     for model_class in self.all_model_classes:
         _lowerCAmelCase				=      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"]
         _lowerCAmelCase				=      True
         self.check_hidden_states_output(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     (padded_height, padded_width)		)




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)




 @slow
 def 							_snake_case   (	self		)    ->							str:
     for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase				=      SwinvaModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)





 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     _lowerCAmelCase				=      _config_zero_init(_lowerCAmelCase		)
     for model_class in self.all_model_classes:
         _lowerCAmelCase				=      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    lowerCAmelCase_		(		unittest.TestCase		):




 @cached_property
 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     return (
         AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		)
         if is_vision_available()
         else None
     )





 @slow
 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		).to(
         _lowerCAmelCase		)
     _lowerCAmelCase				=      self.default_image_processor

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

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

     # verify the logits
     _lowerCAmelCase				=      torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      torch.tensor([-0.3947, -0.4306, 0.0026]		).to(_lowerCAmelCase		)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     _lowerCAmelCase			,     atol=1E-4		)		)









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE	 =							{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


