




"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      (IPNDMScheduler,)
 lowercase__				      =      (("num_inference_steps", 50),)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   **a_:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						{"""num_train_timesteps""": 1_000}
   config.update(**a_	)
   return config


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	Optional[int]=0,   **a_:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						dict(self.forward_default_kwargs	)
   _snake_case							:	Optional[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[Any]			=						self.dummy_sample
   _snake_case							:	Dict			=						0.1 * sample
   _snake_case							:	Any			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	int			=						self.get_scheduler_config(**a_	)
     _snake_case							:	Dict			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)
     # copy over dummy past residuals
     _snake_case							:	int			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Union[str, Any]			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	Tuple			=						scheduler_class.from_pretrained(a_	)
       new_scheduler.set_timesteps(a_	)
       # copy over dummy past residuals
       _snake_case							:	Optional[Any]			=						dummy_past_residuals[:]

     _snake_case							:	List[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	str			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	Optional[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Optional[int]			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	str,   a_:	Any=0,   **a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)
   _snake_case							:	List[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[int]			=						self.dummy_sample
   _snake_case							:	Tuple			=						0.1 * sample
   _snake_case							:	Optional[int]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Any			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)

     # copy over dummy past residuals (must be after setting timesteps)
     _snake_case							:	Union[str, Any]			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Tuple			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	List[str]			=						scheduler_class.from_pretrained(a_	)
       # copy over dummy past residuals
       new_scheduler.set_timesteps(a_	)

       # copy over dummy past residual (must be after setting timesteps)
       _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	int			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[Any],   **a_:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.scheduler_classes[0]
   _snake_case							:	Any			=						self.get_scheduler_config(**a_	)
   _snake_case							:	List[Any]			=						scheduler_class(**a_	)

   _snake_case							:	Union[str, Any]			=						10
   _snake_case							:	Union[str, Any]			=						self.dummy_model()
   _snake_case							:	List[Any]			=						self.dummy_sample_deter
   scheduler.set_timesteps(a_	)

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Optional[Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Union[str, Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   return sample


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)

   _snake_case							:	int			=						kwargs.pop("""num_inference_steps""",   a_	)

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Union[str, Any]			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)

     _snake_case							:	Dict			=						self.dummy_sample
     _snake_case							:	List[str]			=						0.1 * sample

     if num_inference_steps is not None and hasattr(a_,   """set_timesteps"""	):
       scheduler.set_timesteps(a_	)
     elif num_inference_steps is not None and not hasattr(a_,   """set_timesteps"""	):
       _snake_case							:	Dict			=						num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _snake_case							:	Union[str, Any]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
     _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	Optional[int]			=						scheduler.timesteps[5]
     _snake_case							:	Optional[Any]			=						scheduler.timesteps[6]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	List[str]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)

     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   for timesteps in [100, 1_000]:
     self.check_over_configs(num_train_timesteps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   for t, num_inference_steps in zip([1, 5, 10],   [10, 50, 100]	):
     self.check_over_forward(num_inference_steps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.full_loop()
   _snake_case							:	Optional[int]			=						torch.mean(torch.abs(a_	)	)

   assert abs(result_mean.item() - 2_540_529	) < 10








"""simple docstring"""


import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
     from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor






class       lowercase:







 '''simple docstring'''





 def __init__(       self:	List[str],   a_:	List[Any],   a_:	str=13,   a_:	Dict=32,   a_:	Union[str, Any]=3,   a_:	Union[str, Any]=4,   a_:	Tuple=[10, 20, 30, 40],   a_:	Dict=[2, 2, 3, 2],   a_:	Tuple=True,   a_:	Optional[Any]=True,   a_:	Any=37,   a_:	Any="gelu",   a_:	int=10,   a_:	Tuple=0.02,   a_:	str=["stage2", "stage3", "stage4"],   a_:	List[str]=[2, 3, 4],   a_:	List[str]=None,   ):




   '''simple docstring'''



   _snake_case							:	int			=						parent
   _snake_case							:	int			=						batch_size
   _snake_case							:	List[Any]			=						image_size
   _snake_case							:	List[str]			=						num_channels
   _snake_case							:	Tuple			=						num_stages
   _snake_case							:	Union[str, Any]			=						hidden_sizes
   _snake_case							:	List[Any]			=						depths
   _snake_case							:	Tuple			=						is_training
   _snake_case							:	List[str]			=						use_labels
   _snake_case							:	Tuple			=						intermediate_size
   _snake_case							:	List[str]			=						hidden_act
   _snake_case							:	Optional[Any]			=						num_labels
   _snake_case							:	Tuple			=						initializer_range
   _snake_case							:	Tuple			=						out_features
   _snake_case							:	Tuple			=						out_indices
   _snake_case							:	Dict			=						scope


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

   _snake_case							:	Any			=						None
   if self.use_labels:
     _snake_case							:	Dict			=						ids_tensor([self.batch_size],   self.num_labels	)

   _snake_case							:	Optional[Any]			=						self.get_config()

   return config, pixel_values, labels


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return ConvNextVaConfig(
       num_channels=self.num_channels,   hidden_sizes=self.hidden_sizes,   depths=self.depths,   num_stages=self.num_stages,   hidden_act=self.hidden_act,   is_decoder=a_,   initializer_range=self.initializer_range,   out_features=self.out_features,   out_indices=self.out_indices,   num_labels=self.num_labels,   )


 def 				UpperCamelCase_     (       self:	int,   a_:	Tuple,   a_:	Any,   a_:	Dict	):




   '''simple docstring'''



   _snake_case							:	int			=						ConvNextVaModel(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Any			=						model(a_	)
   # expected last hidden states: B, C, H // 32, W // 32
   self.parent.assertEqual(
       result.last_hidden_state.shape,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),   )


 def 				UpperCamelCase_     (       self:	Optional[int],   a_:	List[str],   a_:	Tuple,   a_:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						ConvNextVaForImageClassification(a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Optional[int]			=						model(a_,   labels=a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)	)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	Tuple,   a_:	Tuple,   a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						ConvNextVaBackbone(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	int			=						model(a_	)

   # 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, self.hidden_sizes[1], 4, 4]	)

   # 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
   _snake_case							:	Tuple			=						None
   _snake_case							:	Tuple			=						ConvNextVaBackbone(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	List[Any]			=						model(a_	)

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps	),   1	)
   self.parent.assertListEqual(list(result.feature_maps[0].shape	),   [self.batch_size, self.hidden_sizes[-1], 1, 1]	)

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


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.prepare_config_and_inputs()
   _snake_case	,      _snake_case	,      _snake_case							:	Any			=						config_and_inputs
   _snake_case							:	str			=						{"""pixel_values""": pixel_values}
   return config, inputs_dict


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.prepare_config_and_inputs()
   _snake_case	,      _snake_case	,      _snake_case							:	List[str]			=						config_and_inputs
   _snake_case							:	Any			=						{"""pixel_values""": pixel_values, """labels""": labels}
   return config, inputs_dict







@require_torch
class       lowercase(  __a ,    __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      (
     (
         ConvNextVaModel,
         ConvNextVaForImageClassification,
         ConvNextVaBackbone,
     )
     if is_torch_available()
     else ()
 )
 lowercase__				      =      (
     {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
     if is_torch_available()
     else {}
 )

 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						ConvNextVaModelTester(self	)
   _snake_case							:	int			=						ConfigTester(self,   config_class=a_,   has_text_modality=a_,   hidden_size=37	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''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 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return


 @unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""	)
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""	)
 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""	)
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   if not self.model_tester.is_training:
     return

   for model_class in self.all_model_classes:
     _snake_case	,      _snake_case							:	Union[str, Any]			=						self.model_tester.prepare_config_and_inputs_with_labels()
     _snake_case							:	List[Any]			=						True

     if model_class.__name__ in [
         *get_values(a_	),
         *get_values(a_	),
     ]:
       continue

     _snake_case							:	Tuple			=						model_class(a_	)
     model.to(a_	)
     model.train()
     _snake_case							:	Optional[Any]			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	Any			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   if not self.model_tester.is_training:
     return

   for model_class in self.all_model_classes:
     _snake_case	,      _snake_case							:	str			=						self.model_tester.prepare_config_and_inputs_with_labels()
     _snake_case							:	Any			=						False
     _snake_case							:	List[Any]			=						True

     if (
         model_class.__name__
         in [*get_values(a_	), *get_values(a_	)]
         or not model_class.supports_gradient_checkpointing
     ):
       continue

     _snake_case							:	Dict			=						model_class(a_	)
     model.to(a_	)
     model.gradient_checkpointing_enable()
     model.train()
     _snake_case							:	str			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	Optional[int]			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	List[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	List[str]			=						model_class(a_	)
     _snake_case							:	Union[str, Any]			=						inspect.signature(model.forward	)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _snake_case							:	int			=						[*signature.parameters.keys()]

     _snake_case							:	Union[str, Any]			=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1],   a_	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a_	)


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




   '''simple docstring'''



   def check_hidden_states_output(a_:	str,   a_:	Tuple,   a_:	Tuple	):
     _snake_case							:	Optional[Any]			=						model_class(a_	)
     model.to(a_	)
     model.eval()

     with torch.no_grad():
       _snake_case							:	Any			=						model(**self._prepare_for_class(a_,   a_	)	)

     _snake_case							:	Optional[int]			=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     _snake_case							:	Optional[int]			=						self.model_tester.num_stages
     self.assertEqual(len(a_	),   expected_num_stages + 1	)

     # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:]	),   [self.model_tester.image_size // 4, self.model_tester.image_size // 4],   )

   _snake_case	,      _snake_case							:	Optional[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	Optional[Any]			=						True
     check_hidden_states_output(a_,   a_,   a_	)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     _snake_case							:	List[str]			=						True

     check_hidden_states_output(a_,   a_,   a_	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:	str			=						ConvNextVaModel.from_pretrained(a_	)
     self.assertIsNotNone(a_	)







def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	List[Any]			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
  return image






@require_torch
@require_vision
class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 @cached_property
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""	) if is_vision_available() else None


 @slow
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""	).to(a_	)

   _snake_case							:	Union[str, Any]			=						self.default_image_processor
   _snake_case							:	List[Any]			=						prepare_img()
   _snake_case							:	Optional[int]			=						preprocessor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	Optional[int]			=						model(**a_	)

   # verify the logits
   _snake_case							:	Optional[int]			=						torch.Size((1, 1_000)	)
   self.assertEqual(outputs.logits.shape,   a_	)

   _snake_case							:	Optional[int]			=						torch.tensor([0.9_996, 0.1_966, -0.4_386]	).to(a_	)
   self.assertTrue(torch.allclose(outputs.logits[0, :3],   a_,   atol=1E-4	)	)


