
import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 torch import nn

		from transformers import SwiftFormerForImageClassification, SwiftFormerModel
		from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:str  ,     a	:Optional[Any]=1_3  ,     a	:str=3  ,     a	:Any=True  ,     a	:Union[str, Any]=True  ,     a	:Optional[Any]=0.1  ,     a	:List[str]=0.1  ,     a	:Optional[int]=2_2_4  ,     a	:Optional[Any]=1_0_0_0  ,     a	:Dict=[3, 3, 6, 4]  ,     a	:str=[4_8, 5_6, 1_1_2, 2_2_0]  ,     )	->				List[Any]:
								__UpperCamelCase			:     Dict         =      parent
								__UpperCamelCase			:     Optional[int]         =      batch_size
								__UpperCamelCase			:     List[str]         =      num_channels
								__UpperCamelCase			:     str         =      is_training
								__UpperCamelCase			:     str         =      use_labels
								__UpperCamelCase			:     Dict         =      hidden_dropout_prob
								__UpperCamelCase			:     int         =      attention_probs_dropout_prob
								__UpperCamelCase			:     Optional[int]         =      num_labels
								__UpperCamelCase			:     Optional[int]         =      image_size
								__UpperCamelCase			:     List[Any]         =      layer_depths
								__UpperCamelCase			:     List[Any]         =      embed_dims







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				str:
								__UpperCamelCase			:     Any         =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

								__UpperCamelCase			:     Union[str, Any]         =      None
								if self.use_labels:
															__UpperCamelCase			:     Optional[int]         =      ids_tensor([self.batch_size]  ,     self.num_labels		)

								__UpperCamelCase			:     Any         =      self.get_config()

								return config, pixel_values, labels







	def      _lowerCamelCase       (      self	:List[str]		)	->				Any:
								return SwiftFormerConfig(
								    depths=self.layer_depths  ,     embed_dims=self.embed_dims  ,     mlp_ratio=4  ,     downsamples=[True, True, True, True]  ,     hidden_act="gelu"  ,     num_labels=self.num_labels  ,     down_patch_size=3  ,     down_stride=2  ,     down_pad=1  ,     drop_rate=0.0  ,     drop_path_rate=0.0  ,     use_layer_scale=a  ,     layer_scale_init_value=1E-5  ,     )







	def      _lowerCamelCase       (      self	:int  ,     a	:Optional[Any]  ,     a	:int  ,     a	:Optional[Any]		)	->				Optional[int]:
								__UpperCamelCase			:     Optional[int]         =      SwiftFormerModel(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     Optional[int]         =      model(a		)
								self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.embed_dims[-1], 7, 7)		)







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:Union[str, Any]  ,     a	:Dict  ,     a	:Optional[Any]		)	->				List[str]:
								__UpperCamelCase			:     Any         =      self.num_labels
								__UpperCamelCase			:     Union[str, Any]         =      SwiftFormerForImageClassification(a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     Optional[Any]         =      model(a  ,     labels=a		)
								self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)		)

								__UpperCamelCase			:     List[Any]         =      SwiftFormerForImageClassification(a		)
								model.to(a		)
								model.eval()

								__UpperCamelCase			:     Tuple         =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
								__UpperCamelCase			:     str         =      model(a		)
								self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)		)







	def      _lowerCamelCase       (      self	:Dict		)	->				Any:
								((__UpperCamelCase)     ,		(__UpperCamelCase)     ,		(__UpperCamelCase))			:     Optional[int]         =      self.prepare_config_and_inputs()
								__UpperCamelCase			:     Optional[int]         =      {"pixel_values": pixel_values}
								return config, inputs_dict




@require_torch
class 				lowerCamelCase__							(       __lowercase  ,		__lowercase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	_A			=  (
	    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_A			=  False
	_A			=  False
	_A			=  False
	_A			=  False
	_A			=  False







	def      _lowerCamelCase       (      self	:List[Any]		)	->				int:
								__UpperCamelCase			:     int         =      SwiftFormerModelTester(self		)
								__UpperCamelCase			:     Union[str, Any]         =      ConfigTester(
								    self  ,     config_class=a  ,     has_text_modality=a  ,     hidden_size=3_7  ,     num_attention_heads=1_2  ,     num_hidden_layers=1_2  ,     )







	def      _lowerCamelCase       (      self	:Dict		)	->				Optional[int]:
								self.config_tester.run_common_tests()







	@unittest.skip(reason="SwiftFormer does not use inputs_embeds"		)
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				int:
								pass







	def      _lowerCamelCase       (      self	:int		)	->				Optional[Any]:
								__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															__UpperCamelCase			:     int         =      model_class(a		)
															__UpperCamelCase			:     Dict         =      model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(a  ,     nn.Linear		)		)







	def      _lowerCamelCase       (      self	:Dict		)	->				Dict:
								__UpperCamelCase     ,		__UpperCamelCase			:     str         =      self.model_tester.prepare_config_and_inputs_for_common()

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

															__UpperCamelCase			:     Tuple         =      ["pixel_values"]
															self.assertListEqual(arg_names[:1]  ,     a		)







	def      _lowerCamelCase       (      self	:Any		)	->				int:
								__UpperCamelCase			:     Dict         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a		)







	def      _lowerCamelCase       (      self	:Dict		)	->				Any:
								__UpperCamelCase			:     Optional[int]         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a		)







	@slow
	def      _lowerCamelCase       (      self	:Any		)	->				int:
								for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__UpperCamelCase			:     List[str]         =      SwiftFormerModel.from_pretrained(a		)
															self.assertIsNotNone(a		)







	@unittest.skip(reason="SwiftFormer does not output attentions"		)
	def      _lowerCamelCase       (      self	:Dict		)	->				Any:
								pass







	def      _lowerCamelCase       (      self	:str		)	->				int:
								def check_hidden_states_output(a	:Tuple  ,     a	:Any  ,     a	:Dict		):
															__UpperCamelCase			:     int         =      model_class(a		)
															model.to(a		)
															model.eval()

															with torch.no_grad():
																						__UpperCamelCase			:     List[str]         =      model(**self._prepare_for_class(a  ,     a		)		)

															__UpperCamelCase			:     List[Any]         =      outputs.hidden_states

															__UpperCamelCase			:     Union[str, Any]         =      8
															self.assertEqual(len(a		)  ,     a		)  # TODO

															# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
															# with the width and height being successively divided by 2, after every 2 blocks
															for i in range(len(a		)		):
																						self.assertEqual(
																						    hidden_states[i].shape  ,     torch.Size(
																						        [
																						            self.model_tester.batch_size,
																						            self.model_tester.embed_dims[i // 2],
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						        ]		)  ,     )

								__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															__UpperCamelCase			:     Optional[int]         =      True
															check_hidden_states_output(a  ,     a  ,     a		)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__UpperCamelCase			:     Tuple         =      True

															check_hidden_states_output(a  ,     a  ,     a		)







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Optional[int]:
								def _config_zero_init(a	:str		):
															__UpperCamelCase			:     Tuple         =      copy.deepcopy(a		)
															for key in configs_no_init.__dict__.keys():
																						if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																													setattr(a  ,     a  ,     1E-1_0		)
																						if isinstance(getattr(a  ,     a  ,     a		)  ,     a		):
																													__UpperCamelCase			:     List[Any]         =      _config_zero_init(getattr(a  ,     a		)		)
																													setattr(a  ,     a  ,     a		)
															return configs_no_init

								__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      self.model_tester.prepare_config_and_inputs_for_common()

								__UpperCamelCase			:     List[Any]         =      _config_zero_init(a		)
								for model_class in self.all_model_classes:
															__UpperCamelCase			:     List[str]         =      model_class(config=a		)
															for name, param in model.named_parameters():
																						if param.requires_grad:
																													self.assertIn(
																													    ((param.data.mean() * 1E9) / 1E9).round().item()  ,     [0.0, 1.0]  ,     msg=f'Parameter {name} of model {model_class} seems not properly initialized'  ,     )







	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
	def      _lowerCamelCase       (      self	:List[Any]		)	->				int:
								pass




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Tuple:






							'''simple docstring'''





							__UpperCamelCase			:     int         =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
							return image




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




	'''simple docstring'''




	@cached_property
	def      _lowerCamelCase       (      self	:List[Any]		)	->				Tuple:
								return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"		) if is_vision_available() else None







	@slow
	def      _lowerCamelCase       (      self	:List[Any]		)	->				Tuple:
								__UpperCamelCase			:     Union[str, Any]         =      SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"		).to(a		)

								__UpperCamelCase			:     Any         =      self.default_image_processor
								__UpperCamelCase			:     Optional[Any]         =      prepare_img()
								__UpperCamelCase			:     Optional[Any]         =      image_processor(images=a  ,     return_tensors="pt"		).to(a		)

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

								# verify the logits
								__UpperCamelCase			:     int         =      torch.Size((1, 1_0_0_0)		)
								self.assertEqual(outputs.logits.shape  ,     a		)

								__UpperCamelCase			:     Tuple         =      torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]		).to(a		)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,     a  ,     atol=1E-4		)		)

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase						:      Any									= logging.get_logger(__name__)  # pylint: disable=invalid-name




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Tuple:






							'''simple docstring'''





							warnings.warn(
							    "The preprocess method is deprecated and will be removed in a future version. Please"
							    " use VaeImageProcessor.preprocess instead"		,						_lowerCamelCase		,						)
							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return image
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     int         =      [image]

							if isinstance(image[0]		,						PIL.Image.Image):
														__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      image[0].size
														__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														__UpperCamelCase			:     Any         =      [np.array(i.resize((w, h)		,						resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
														__UpperCamelCase			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
														__UpperCamelCase			:     Any         =      np.array(_lowerCamelCase).astype(np.floataa) / 2_5_5.0
														__UpperCamelCase			:     Union[str, Any]         =      image.transpose(0		,						3		,						1		,						2)
														__UpperCamelCase			:     Optional[int]         =      2.0 * image - 1.0
														__UpperCamelCase			:     List[str]         =      torch.from_numpy(_lowerCamelCase)
							elif isinstance(image[0]		,						torch.Tensor):
														__UpperCamelCase			:     Dict         =      torch.cat(_lowerCamelCase		,						dim=0)
							return image




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Optional[int]:






							'''simple docstring'''





							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return mask
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     Any         =      [mask]

							if isinstance(mask[0]		,						PIL.Image.Image):
														__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      mask[0].size
														__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														__UpperCamelCase			:     List[str]         =      [np.array(m.convert("L").resize((w, h)		,						resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask]
														__UpperCamelCase			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
														__UpperCamelCase			:     Any         =      mask.astype(np.floataa) / 2_5_5.0
														__UpperCamelCase			:     List[str]         =      0
														__UpperCamelCase			:     Dict         =      1
														__UpperCamelCase			:     List[Any]         =      torch.from_numpy(_lowerCamelCase)
							elif isinstance(mask[0]		,						torch.Tensor):
														__UpperCamelCase			:     Optional[Any]         =      torch.cat(_lowerCamelCase		,						dim=0)
							return mask




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  42
	_A			=  42







	def __init__(      self	:Optional[Any]  ,     a	:str  ,     a	:List[str]		)	->				Union[str, Any]:
								super().__init__()
								self.register_modules(unet=a  ,     scheduler=a		)







	@torch.no_grad()
	def __call__(      self	:Union[str, Any]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:int = 2_5_0  ,     a	:float = 0.0  ,     a	:int = 1_0  ,     a	:int = 1_0  ,     a	:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     a	:Optional[str] = "pil"  ,     a	:bool = True  ,     )	->				Union[ImagePipelineOutput, Tuple]:
								__UpperCamelCase			:     Tuple         =      image

								__UpperCamelCase			:     Optional[Any]         =      _preprocess_image(a		)
								__UpperCamelCase			:     Optional[int]         =      original_image.to(device=self.device  ,     dtype=self.unet.dtype		)
								__UpperCamelCase			:     Dict         =      _preprocess_mask(a		)
								__UpperCamelCase			:     Any         =      mask_image.to(device=self.device  ,     dtype=self.unet.dtype		)

								__UpperCamelCase			:     str         =      original_image.shape[0]

								# sample gaussian noise to begin the loop
								if isinstance(a  ,     a		) and len(a		) != batch_size:
															raise ValueError(
															    f'You have passed a list of generators of length {len(a		)}, but requested an effective batch'
															    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

								__UpperCamelCase			:     List[str]         =      original_image.shape
								__UpperCamelCase			:     List[Any]         =      randn_tensor(a  ,     generator=a  ,     device=self.device  ,     dtype=self.unet.dtype		)

								# set step values
								self.scheduler.set_timesteps(a  ,     a  ,     a  ,     self.device		)
								__UpperCamelCase			:     Tuple         =      eta

								__UpperCamelCase			:     List[str]         =      self.scheduler.timesteps[0] + 1
								__UpperCamelCase			:     str         =      generator[0] if isinstance(a  ,     a		) else generator
								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															if t < t_last:
																						# predict the noise residual
																						__UpperCamelCase			:     Union[str, Any]         =      self.unet(a  ,     a		).sample
																						# compute previous image: x_t -> x_t-1
																						__UpperCamelCase			:     Tuple         =      self.scheduler.step(a  ,     a  ,     a  ,     a  ,     a  ,     a		).prev_sample

															else:
																						# compute the reverse: x_t-1 -> x_t
																						__UpperCamelCase			:     List[str]         =      self.scheduler.undo_step(a  ,     a  ,     a		)
															__UpperCamelCase			:     Dict         =      t

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a		)