



from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	["""vqvae"""]






     def __init__(       self  :  Any, _snake_case  :  AutoencoderKL, _snake_case  :  UNetaDConditionModel, _snake_case  :  Mel, _snake_case  :  Union[DDIMScheduler, DDPMScheduler], )   ->Dict:
            super().__init__()
            self.register_modules(unet=_snake_case, scheduler=_snake_case, mel=_snake_case, vqvae=_snake_case					)






     def    lowercase_   (       self  :  List[str]					)   ->int:
            return 5_0 if isinstance(self.scheduler, _snake_case					) else 1_0_0_0






     @torch.no_grad()
     def __call__(       self  :  Dict, _snake_case  :  int = 1, _snake_case  :  str = None, _snake_case  :  np.ndarray = None, _snake_case  :  int = 0, _snake_case  :  int = 0, _snake_case  :  int = None, _snake_case  :  torch.Generator = None, _snake_case  :  float = 0, _snake_case  :  float = 0, _snake_case  :  torch.Generator = None, _snake_case  :  float = 0, _snake_case  :  torch.Tensor = None, _snake_case  :  torch.Tensor = None, _snake_case  :  Optional[Any]=True, )   ->Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:
            snake_case__							:	Any   	=	steps or self.get_default_steps()
            self.scheduler.set_timesteps(_snake_case					)
            snake_case__							:	Any   	=	step_generator or generator
            # For backwards compatibility
            if type(self.unet.config.sample_size					) == int:
                   snake_case__							:	Optional[Any]   	=	(self.unet.config.sample_size, self.unet.config.sample_size)
            if noise is None:
                   snake_case__							:	str   	=	randn_tensor(
                       (
                           batch_size,
                           self.unet.config.in_channels,
                           self.unet.config.sample_size[0],
                           self.unet.config.sample_size[1],
                       ), generator=_snake_case, device=self.device, )
            snake_case__							:	Optional[int]   	=	noise
            snake_case__							:	str   	=	None

            if audio_file is not None or raw_audio is not None:
                   self.mel.load_audio(_snake_case, _snake_case					)
                   snake_case__							:	Tuple   	=	self.mel.audio_slice_to_image(_snake_case					)
                   snake_case__							:	str   	=	np.frombuffer(input_image.tobytes(), dtype='uint8'					).reshape(
                       (input_image.height, input_image.width)					)
                   snake_case__							:	Union[str, Any]   	=	(input_image / 2_5_5) * 2 - 1
                   snake_case__							:	Optional[Any]   	=	torch.tensor(input_image[np.newaxis, :, :], dtype=torch.float					).to(self.device					)

                   if self.vqvae is not None:
                          snake_case__							:	Any   	=	self.vqvae.encode(torch.unsqueeze(_snake_case, 0					)					).latent_dist.sample(
                              generator=_snake_case					)[0]
                          snake_case__							:	int   	=	self.vqvae.config.scaling_factor * input_images

                   if start_step > 0:
                          snake_case__							:	int   	=	self.scheduler.add_noise(_snake_case, _snake_case, self.scheduler.timesteps[start_step - 1]					)

                   snake_case__							:	Dict   	=	(
                       self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                   )
                   snake_case__							:	List[Any]   	=	int(mask_start_secs * pixels_per_second					)
                   snake_case__							:	int   	=	int(mask_end_secs * pixels_per_second					)
                   snake_case__							:	str   	=	self.scheduler.add_noise(_snake_case, _snake_case, torch.tensor(self.scheduler.timesteps[start_step:]					)					)

            for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
                   if isinstance(self.unet, _snake_case					):
                          snake_case__							:	List[str]   	=	self.unet(_snake_case, _snake_case, _snake_case					)['sample']
                   else:
                          snake_case__							:	List[Any]   	=	self.unet(_snake_case, _snake_case					)['sample']

                   if isinstance(self.scheduler, _snake_case					):
                          snake_case__							:	Any   	=	self.scheduler.step(
                              model_output=_snake_case, timestep=_snake_case, sample=_snake_case, eta=_snake_case, generator=_snake_case, )['prev_sample']
                   else:
                          snake_case__							:	List[str]   	=	self.scheduler.step(
                              model_output=_snake_case, timestep=_snake_case, sample=_snake_case, generator=_snake_case, )['prev_sample']

                   if mask is not None:
                          if mask_start > 0:
                                 snake_case__							:	Optional[int]   	=	mask[:, step, :, :mask_start]
                          if mask_end > 0:
                                 snake_case__							:	Optional[Any]   	=	mask[:, step, :, -mask_end:]

            if self.vqvae is not None:
                   # 0.18215 was scaling factor used in training to ensure unit variance
                   snake_case__							:	List[Any]   	=	1 / self.vqvae.config.scaling_factor * images
                   snake_case__							:	int   	=	self.vqvae.decode(_snake_case					)['sample']

            snake_case__							:	Dict   	=	(images / 2 + 0.5).clamp(0, 1					)
            snake_case__							:	List[Any]   	=	images.cpu().permute(0, 2, 3, 1					).numpy()
            snake_case__							:	Any   	=	(images * 2_5_5).round().astype('uint8'					)
            snake_case__							:	int   	=	list(
                (Image.fromarray(_[:, :, 0]					) for _ in images)
                if images.shape[3] == 1
                else (Image.fromarray(_snake_case, mode='RGB'					).convert('L'					) for _ in images)					)

            snake_case__							:	Any   	=	[self.mel.image_to_audio(_snake_case					) for _ in images]
            if not return_dict:
                   return images, (self.mel.get_sample_rate(), audios)

            return BaseOutput(**AudioPipelineOutput(np.array(_snake_case					)[:, np.newaxis, :]					), **ImagePipelineOutput(_snake_case					)					)






     @torch.no_grad()
     def    lowercase_   (       self  :  List[Any], _snake_case  :  List[Image.Image], _snake_case  :  int = 5_0					)   ->np.ndarray:
            assert isinstance(self.scheduler, _snake_case					)
            self.scheduler.set_timesteps(_snake_case					)
            snake_case__							:	Optional[Any]   	=	np.array(
                [np.frombuffer(image.tobytes(), dtype='uint8'					).reshape((1, image.height, image.width)					) for image in images]					)
            snake_case__							:	Tuple   	=	(sample / 2_5_5) * 2 - 1
            snake_case__							:	Tuple   	=	torch.Tensor(_snake_case					).to(self.device					)

            for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,)					)					):
                   snake_case__							:	Tuple   	=	t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                   snake_case__							:	Optional[Any]   	=	self.scheduler.alphas_cumprod[t]
                   snake_case__							:	Dict   	=	(
                       self.scheduler.alphas_cumprod[prev_timestep]
                       if prev_timestep >= 0
                       else self.scheduler.final_alpha_cumprod
                   )
                   snake_case__							:	int   	=	1 - alpha_prod_t
                   snake_case__							:	Any   	=	self.unet(_snake_case, _snake_case					)['sample']
                   snake_case__							:	List[Any]   	=	(1 - alpha_prod_t_prev) ** 0.5 * model_output
                   snake_case__							:	Dict   	=	(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                   snake_case__							:	List[str]   	=	sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

            return sample






     @staticmethod
     def    lowercase_   (       _snake_case  :  torch.Tensor, _snake_case  :  torch.Tensor, _snake_case  :  float					)   ->torch.Tensor:
            snake_case__							:	str   	=	acos(torch.dot(torch.flatten(_snake_case					), torch.flatten(_snake_case					)					) / torch.norm(_snake_case					) / torch.norm(_snake_case					)					)
            return sin((1 - alpha) * theta					) * xa / sin(_snake_case					) + sin(alpha * theta					) * xa / sin(_snake_case					)











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  :  Tuple, _snake_case  :  Any, _snake_case  :  int=1_3, _snake_case  :  Optional[int]=3_2, _snake_case  :  Tuple=2, _snake_case  :  Any=3, _snake_case  :  Tuple=1_6, _snake_case  :  Tuple=[1, 2, 1], _snake_case  :  Dict=[2, 2, 4], _snake_case  :  str=2, _snake_case  :  Union[str, Any]=2.0, _snake_case  :  Dict=True, _snake_case  :  Dict=0.0, _snake_case  :  str=0.0, _snake_case  :  str=0.1, _snake_case  :  List[str]="gelu", _snake_case  :  int=False, _snake_case  :  Optional[Any]=True, _snake_case  :  List[Any]=0.0_2, _snake_case  :  Union[str, Any]=1e-5, _snake_case  :  Union[str, Any]=True, _snake_case  :  List[Any]=None, _snake_case  :  Any=True, _snake_case  :  List[Any]=1_0, _snake_case  :  str=8, )   ->Union[str, Any]:
            snake_case__							:	Any   	=	parent
            snake_case__							:	Tuple   	=	batch_size
            snake_case__							:	Tuple   	=	image_size
            snake_case__							:	Any   	=	patch_size
            snake_case__							:	Optional[int]   	=	num_channels
            snake_case__							:	Tuple   	=	embed_dim
            snake_case__							:	Any   	=	depths
            snake_case__							:	Any   	=	num_heads
            snake_case__							:	List[str]   	=	window_size
            snake_case__							:	Dict   	=	mlp_ratio
            snake_case__							:	Optional[int]   	=	qkv_bias
            snake_case__							:	Optional[Any]   	=	hidden_dropout_prob
            snake_case__							:	List[str]   	=	attention_probs_dropout_prob
            snake_case__							:	Union[str, Any]   	=	drop_path_rate
            snake_case__							:	str   	=	hidden_act
            snake_case__							:	Union[str, Any]   	=	use_absolute_embeddings
            snake_case__							:	Union[str, Any]   	=	patch_norm
            snake_case__							:	Any   	=	layer_norm_eps
            snake_case__							:	Tuple   	=	initializer_range
            snake_case__							:	Dict   	=	is_training
            snake_case__							:	Any   	=	scope
            snake_case__							:	Optional[Any]   	=	use_labels
            snake_case__							:	str   	=	type_sequence_label_size
            snake_case__							:	List[Any]   	=	encoder_stride






     def    lowercase_   (       self  :  Tuple					)   ->str:
            snake_case__							:	Tuple   	=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

            snake_case__							:	List[Any]   	=	None
            if self.use_labels:
                   snake_case__							:	Optional[Any]   	=	ids_tensor([self.batch_size], self.type_sequence_label_size					)

            snake_case__							:	Any   	=	self.get_config()

            return config, pixel_values, labels






     def    lowercase_   (       self  :  Optional[int]					)   ->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    lowercase_   (       self  :  Optional[int], _snake_case  :  str, _snake_case  :  List[str], _snake_case  :  int					)   ->Dict:
            snake_case__							:	List[Any]   	=	SwinvaModel(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Optional[int]   	=	model(_snake_case					)

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

            # test greyscale images
            snake_case__							:	Optional[Any]   	=	1
            snake_case__							:	Optional[int]   	=	SwinvaForMaskedImageModeling(_snake_case					)
            model.to(_snake_case					)
            model.eval()

            snake_case__							:	Tuple   	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
            snake_case__							:	Any   	=	model(_snake_case					)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.image_size, self.image_size)					)






     def    lowercase_   (       self  :  List[str], _snake_case  :  int, _snake_case  :  List[Any], _snake_case  :  Optional[int]					)   ->Any:
            snake_case__							:	Tuple   	=	self.type_sequence_label_size
            snake_case__							:	int   	=	SwinvaForImageClassification(_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Tuple   	=	model(_snake_case, labels=_snake_case					)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)					)






     def    lowercase_   (       self  :  Any					)   ->Dict:
            snake_case__							:	str   	=	self.prepare_config_and_inputs()
            snake_case__	,				snake_case__	,				snake_case__							:	List[str]   	=	config_and_inputs
            snake_case__							:	Union[str, Any]   	=	{'pixel_values': pixel_values}
            return config, inputs_dict




@require_torch
class 					snake_case__						(       lowerCAmelCase_				,     lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


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

     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Dict:
            snake_case__							:	Optional[int]   	=	SwinvaModelTester(self					)
            snake_case__							:	int   	=	ConfigTester(self, config_class=_snake_case, embed_dim=3_7					)






     def    lowercase_   (       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    lowercase_   (       self  :  Any					)   ->str:
            snake_case__							:	int   	=	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    lowercase_   (       self  :  Any					)   ->Union[str, Any]:
            pass






     @unittest.skip(reason='Swinv2 does not use inputs_embeds'					)
     def    lowercase_   (       self  :  str					)   ->Union[str, Any]:
            pass






     def    lowercase_   (       self  :  Optional[Any]					)   ->Union[str, Any]:
            snake_case__	,				snake_case__							:	Optional[int]   	=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   snake_case__							:	Union[str, Any]   	=	model_class(_snake_case					)
                   self.assertIsInstance(model.get_input_embeddings(), (nn.Module)					)
                   snake_case__							:	Union[str, Any]   	=	model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(_snake_case, nn.Linear					)					)






     def    lowercase_   (       self  :  List[str]					)   ->Optional[int]:
            snake_case__	,				snake_case__							:	Any   	=	self.model_tester.prepare_config_and_inputs_for_common()

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

                   snake_case__							:	List[Any]   	=	['pixel_values']
                   self.assertListEqual(arg_names[:1], _snake_case					)






     def    lowercase_   (       self  :  str					)   ->Union[str, Any]:
            snake_case__	,				snake_case__							:	Any   	=	self.model_tester.prepare_config_and_inputs_for_common()
            snake_case__							:	int   	=	True

            for model_class in self.all_model_classes:
                   snake_case__							:	str   	=	True
                   snake_case__							:	Union[str, Any]   	=	False
                   snake_case__							:	Tuple   	=	True
                   snake_case__							:	int   	=	model_class(_snake_case					)
                   model.to(_snake_case					)
                   model.eval()
                   with torch.no_grad():
                          snake_case__							:	Optional[int]   	=	model(**self._prepare_for_class(_snake_case, _snake_case					)					)
                   snake_case__							:	List[str]   	=	outputs.attentions
                   snake_case__							:	List[Any]   	=	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"]
                   snake_case__							:	str   	=	True
                   snake_case__							:	Tuple   	=	config.window_size**2
                   snake_case__							:	Optional[int]   	=	model_class(_snake_case					)
                   model.to(_snake_case					)
                   model.eval()
                   with torch.no_grad():
                          snake_case__							:	str   	=	model(**self._prepare_for_class(_snake_case, _snake_case					)					)
                   snake_case__							:	Tuple   	=	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], )
                   snake_case__							:	Optional[Any]   	=	len(_snake_case					)

                   # Check attention is always last and order is fine
                   snake_case__							:	Optional[int]   	=	True
                   snake_case__							:	Dict   	=	True
                   snake_case__							:	List[Any]   	=	model_class(_snake_case					)
                   model.to(_snake_case					)
                   model.eval()
                   with torch.no_grad():
                          snake_case__							:	Optional[int]   	=	model(**self._prepare_for_class(_snake_case, _snake_case					)					)

                   if hasattr(self.model_tester, 'num_hidden_states_types'					):
                          snake_case__							:	str   	=	self.model_tester.num_hidden_states_types
                   else:
                          # also another +1 for reshaped_hidden_states
                          snake_case__							:	Dict   	=	2
                   self.assertEqual(out_len + added_hidden_states, len(_snake_case					)					)

                   snake_case__							:	Any   	=	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    lowercase_   (       self  :  Dict, _snake_case  :  Tuple, _snake_case  :  Any, _snake_case  :  int, _snake_case  :  Optional[int]					)   ->str:
            snake_case__							:	Dict   	=	model_class(_snake_case					)
            model.to(_snake_case					)
            model.eval()

            with torch.no_grad():
                   snake_case__							:	List[Any]   	=	model(**self._prepare_for_class(_snake_case, _snake_case					)					)

            snake_case__							:	Dict   	=	outputs.hidden_states

            snake_case__							:	int   	=	getattr(
                self.model_tester, 'expected_num_hidden_layers', len(self.model_tester.depths					) + 1					)
            self.assertEqual(len(_snake_case					), _snake_case					)

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

            snake_case__							:	Optional[Any]   	=	(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], )

            snake_case__							:	Union[str, Any]   	=	outputs.reshaped_hidden_states
            self.assertEqual(len(_snake_case					), _snake_case					)

            snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	str   	=	reshaped_hidden_states[0].shape
            snake_case__							:	Any   	=	(
                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    lowercase_   (       self  :  str					)   ->List[Any]:
            snake_case__	,				snake_case__							:	Any   	=	self.model_tester.prepare_config_and_inputs_for_common()

            snake_case__							:	List[Any]   	=	(
                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:
                   snake_case__							:	Optional[int]   	=	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"]
                   snake_case__							:	Dict   	=	True

                   self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, _snake_case					)






     def    lowercase_   (       self  :  List[str]					)   ->str:
            snake_case__	,				snake_case__							:	Optional[Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()
            snake_case__							:	List[str]   	=	3

            snake_case__							:	Union[str, Any]   	=	(
                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)
            )
            snake_case__							:	str   	=	(
                config.patch_size
                if isinstance(config.patch_size, collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            snake_case__							:	Tuple   	=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            snake_case__							:	Optional[Any]   	=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                   snake_case__							:	int   	=	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"]
                   snake_case__							:	List[str]   	=	True
                   self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, (padded_height, padded_width)					)






     def    lowercase_   (       self  :  List[str]					)   ->Optional[int]:
            snake_case__							:	Any   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*_snake_case					)






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






     @slow
     def    lowercase_   (       self  :  str					)   ->Union[str, Any]:
            for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   snake_case__							:	Dict   	=	SwinvaModel.from_pretrained(_snake_case					)
                   self.assertIsNotNone(_snake_case					)






     def    lowercase_   (       self  :  Optional[int]					)   ->List[str]:
            snake_case__	,				snake_case__							:	Any   	=	self.model_tester.prepare_config_and_inputs_for_common()

            snake_case__							:	List[Any]   	=	_config_zero_init(_snake_case					)
            for model_class in self.all_model_classes:
                   snake_case__							:	List[str]   	=	model_class(config=_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 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


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






     @slow
     def    lowercase_   (       self  :  int					)   ->List[Any]:
            snake_case__							:	Any   	=	SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'					).to(
                _snake_case					)
            snake_case__							:	int   	=	self.default_image_processor

            snake_case__							:	Union[str, Any]   	=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
            snake_case__							:	Optional[Any]   	=	image_processor(images=_snake_case, return_tensors='pt'					).to(_snake_case					)

            # forward pass
            with torch.no_grad():
                   snake_case__							:	List[str]   	=	model(**_snake_case					)

            # verify the logits
            snake_case__							:	int   	=	torch.Size((1, 1_0_0_0)					)
            self.assertEqual(outputs.logits.shape, _snake_case					)
            snake_case__							:	Optional[int]   	=	torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]					).to(_snake_case					)
            self.assertTrue(torch.allclose(outputs.logits[0, :3], _snake_case, atol=1e-4					)					)






