
import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import 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 SwinaSRForImageSuperResolution, SwinaSRModel
						from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import SwinaSRImageProcessor







class 							UpperCAmelCase	:

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					Tuple ,      lowerCAmelCase      :					Optional[int]=13 ,      lowerCAmelCase      :					Tuple=32 ,      lowerCAmelCase      :					Union[str, Any]=1 ,      lowerCAmelCase      :					Optional[int]=3 ,      lowerCAmelCase      :					Optional[Any]=16 ,      lowerCAmelCase      :					Union[str, Any]=[1, 2, 1] ,      lowerCAmelCase      :					Any=[2, 2, 4] ,      lowerCAmelCase      :					Optional[int]=2 ,      lowerCAmelCase      :					Tuple=2.0 ,      lowerCAmelCase      :					List[Any]=True ,      lowerCAmelCase      :					List[str]=0.0 ,      lowerCAmelCase      :					int=0.0 ,      lowerCAmelCase      :					int=0.1 ,      lowerCAmelCase      :					int="gelu" ,      lowerCAmelCase      :					Tuple=False ,      lowerCAmelCase      :					int=True ,      lowerCAmelCase      :					Any=0.02 ,      lowerCAmelCase      :					Union[str, Any]=1E-5 ,      lowerCAmelCase      :					Optional[Any]=True ,      lowerCAmelCase      :					Optional[Any]=None ,      lowerCAmelCase      :					List[str]=False ,      lowerCAmelCase      :					List[Any]=2 ,      ):
											lowercase       :    str											=  parent
											lowercase       :    Tuple											=  batch_size
											lowercase       :    Optional[Any]											=  image_size
											lowercase       :    Optional[Any]											=  patch_size
											lowercase       :    List[str]											=  num_channels
											lowercase       :    Optional[int]											=  embed_dim
											lowercase       :    int											=  depths
											lowercase       :    int											=  num_heads
											lowercase       :    List[str]											=  window_size
											lowercase       :    Dict											=  mlp_ratio
											lowercase       :    Union[str, Any]											=  qkv_bias
											lowercase       :    Tuple											=  hidden_dropout_prob
											lowercase       :    int											=  attention_probs_dropout_prob
											lowercase       :    Tuple											=  drop_path_rate
											lowercase       :    Tuple											=  hidden_act
											lowercase       :    Optional[int]											=  use_absolute_embeddings
											lowercase       :    Optional[Any]											=  patch_norm
											lowercase       :    Tuple											=  layer_norm_eps
											lowercase       :    List[str]											=  initializer_range
											lowercase       :    Any											=  is_training
											lowercase       :    List[str]											=  scope
											lowercase       :    Any											=  use_labels
											lowercase       :    int											=  upscale

											# here we set some attributes to make tests pass
											lowercase       :    Optional[int]											=  len(lowercase_					)
											lowercase       :    str											=  embed_dim
											lowercase       :    Optional[int]											=  (image_size // patch_size) ** 2

					def        _lowerCAmelCase	(			self      :					Any					):
											lowercase       :    Tuple											=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

											lowercase       :    Tuple											=  None
											if self.use_labels:
																	lowercase       :    Dict											=  ids_tensor([self.batch_size] ,      self.type_sequence_label_size					)

											lowercase       :    Union[str, Any]											=  self.get_config()

											return config, pixel_values, labels

					def        _lowerCAmelCase	(			self      :					str					):
											return SwinaSRConfig(
											    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 ,      upscale=self.upscale ,      )

					def        _lowerCAmelCase	(			self      :					Any ,      lowerCAmelCase      :					List[str] ,      lowerCAmelCase      :					Any ,      lowerCAmelCase      :					Any					):
											lowercase       :    List[Any]											=  SwinaSRModel(config=lowercase_					)
											model.to(lowercase_					)
											model.eval()
											lowercase       :    List[str]											=  model(lowercase_					)

											self.parent.assertEqual(
											    result.last_hidden_state.shape ,      (self.batch_size, self.embed_dim, self.image_size, self.image_size)					)

					def        _lowerCAmelCase	(			self      :					Any ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					List[Any]					):
											lowercase       :    Optional[int]											=  SwinaSRForImageSuperResolution(lowercase_					)
											model.to(lowercase_					)
											model.eval()
											lowercase       :    Union[str, Any]											=  model(lowercase_					)

											lowercase       :    Optional[Any]											=  self.image_size * self.upscale
											self.parent.assertEqual(
											    result.reconstruction.shape ,      (self.batch_size, self.num_channels, expected_image_size, expected_image_size)					)




					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											lowercase       :    int											=  self.prepare_config_and_inputs()
											lowercase				,				lowercase				,				lowercase       :    List[str]											=  config_and_inputs
											lowercase       :    Optional[Any]											=  {'''pixel_values''': pixel_values}
											return config, inputs_dict







@require_torch
class 							UpperCAmelCase	(   lowerCamelCase__						, lowerCamelCase__						, unittest.TestCase					):
					a__: Dict			  =				(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
					a__: Optional[int]			  =				{'feature-extraction': SwinaSRModel} if is_torch_available() else {}

					a__: str			  =				False
					a__: Any			  =				False
					a__: Dict			  =				False
					a__: int			  =				False
					a__: Any			  =				False

					def        _lowerCAmelCase	(			self      :					List[Any]					):
											lowercase       :    int											=  SwinaSRModelTester(self					)
											lowercase       :    int											=  ConfigTester(self ,      config_class=lowercase_ ,      embed_dim=37					)

					def        _lowerCAmelCase	(			self      :					str					):
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()

					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											lowercase       :    Tuple											=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_					)

					def        _lowerCAmelCase	(			self      :					List[Any]					):
											lowercase       :    str											=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_super_resolution(*lowercase_					)

					@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''					)
					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											pass

					@unittest.skip(reason='''Swin2SR does not use inputs_embeds'''					)
					def        _lowerCAmelCase	(			self      :					str					):
											pass

					@unittest.skip(reason='''Swin2SR does not support training yet'''					)
					def        _lowerCAmelCase	(			self      :					Optional[int]					):
											pass

					@unittest.skip(reason='''Swin2SR does not support training yet'''					)
					def        _lowerCAmelCase	(			self      :					List[str]					):
											pass

					def        _lowerCAmelCase	(			self      :					int					):
											lowercase				,				lowercase       :    List[Any]											=  self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowercase       :    List[Any]											=  model_class(lowercase_					)
																	self.assertIsInstance(model.get_input_embeddings() ,      (nn.Module)					)
																	lowercase       :    Optional[int]											=  model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(lowercase_ ,      nn.Linear					)					)

					def        _lowerCAmelCase	(			self      :					str					):
											lowercase				,				lowercase       :    Tuple											=  self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase       :    Tuple											=  ['''pixel_values''']
																	self.assertListEqual(arg_names[:1] ,      lowercase_					)

					@slow
					def        _lowerCAmelCase	(			self      :					Any					):






											for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase       :    List[str]											=  SwinaSRModel.from_pretrained(lowercase_					)
																	self.assertIsNotNone(lowercase_					)

					def        _lowerCAmelCase	(			self      :					int					):
											lowercase				,				lowercase       :    Any											=  self.model_tester.prepare_config_and_inputs_for_common()

											lowercase       :    Any											=  _config_zero_init(lowercase_					)
											for model_class in self.all_model_classes:
																	lowercase       :    str											=  model_class(config=lowercase_					)
																	for name, param in model.named_parameters():
																							if "logit_scale" in name:
																													continue
																							if 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''' ,      )




					def        _lowerCAmelCase	(			self      :					Optional[int]					):
											lowercase				,				lowercase       :    List[str]											=  self.model_tester.prepare_config_and_inputs_for_common()
											lowercase       :    Dict											=  True

											for model_class in self.all_model_classes:
																	lowercase       :    Tuple											=  True
																	lowercase       :    Optional[Any]											=  False
																	lowercase       :    List[Any]											=  True
																	lowercase       :    Tuple											=  model_class(lowercase_					)
																	model.to(lowercase_					)
																	model.eval()
																	with torch.no_grad():
																							lowercase       :    List[Any]											=  model(**self._prepare_for_class(lowercase_ ,      lowercase_					)					)
																	lowercase       :    Optional[Any]											=  outputs.attentions
																	lowercase       :    Dict											=  len(self.model_tester.depths					)
																	self.assertEqual(len(lowercase_					) ,      lowercase_					)

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

																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]					) ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,      )
																	lowercase       :    Union[str, Any]											=  len(lowercase_					)

																	# Check attention is always last and order is fine
																	lowercase       :    Tuple											=  True
																	lowercase       :    str											=  True
																	lowercase       :    Any											=  model_class(lowercase_					)
																	model.to(lowercase_					)
																	model.eval()
																	with torch.no_grad():
																							lowercase       :    List[Any]											=  model(**self._prepare_for_class(lowercase_ ,      lowercase_					)					)

																	self.assertEqual(out_len + 1 ,      len(lowercase_					)					)

																	lowercase       :    List[str]											=  outputs.attentions

																	self.assertEqual(len(lowercase_					) ,      lowercase_					)

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







@require_vision
@require_torch
@slow
class 							UpperCAmelCase	(   unittest.TestCase					):

					def        _lowerCAmelCase	(			self      :					Dict					):
											lowercase       :    Optional[int]											=  SwinaSRImageProcessor()
											lowercase       :    Optional[Any]											=  SwinaSRForImageSuperResolution.from_pretrained('''caidas/swin2SR-classical-sr-x2-64'''					).to(lowercase_					)

											lowercase       :    Optional[int]											=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
											lowercase       :    Optional[Any]											=  processor(images=lowercase_ ,      return_tensors='''pt'''					).to(lowercase_					)

											# forward pass
											with torch.no_grad():
																	lowercase       :    Union[str, Any]											=  model(**lowercase_					)

											# verify the logits
											lowercase       :    int											=  torch.Size([1, 3, 976, 1296]					)
											self.assertEqual(outputs.reconstruction.shape ,      lowercase_					)
											lowercase       :    Tuple											=  torch.tensor(
											    [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]					).to(lowercase_					)
											self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3] ,      lowercase_ ,      atol=1E-4					)					)




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: UNetaDModel
					a__: PNDMScheduler

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					UNetaDModel ,      lowerCAmelCase      :					PNDMScheduler					):
											super().__init__()

											lowercase       :    int											=  PNDMScheduler.from_config(scheduler.config					)

											self.register_modules(unet=lowerCAmelCase ,      scheduler=lowerCAmelCase					)




					@torch.no_grad()
					def __call__(			self      :					List[str] ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					int = 50 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Tuple											=  randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,      generator=lowerCAmelCase ,      device=self.device ,      )

											self.scheduler.set_timesteps(lowerCAmelCase					)
											for t in self.progress_bar(self.scheduler.timesteps					):
																	lowercase       :    Dict											=  self.unet(lowerCAmelCase ,      lowerCAmelCase					).sample

																	lowercase       :    Tuple											=  self.scheduler.step(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					).prev_sample

											lowercase       :    Optional[Any]											=  (image / 2 + 0.5).clamp(0 ,      1					)
											lowercase       :    Optional[int]											=  image.cpu().permute(0 ,      2 ,      3 ,      1					).numpy()
											if output_type == "pil":
																	lowercase       :    Union[str, Any]											=  self.numpy_to_pil(lowerCAmelCase					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)


