



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__					:


				def __init__(							self      :  List[str]						, __UpperCamelCase      :  int						, __UpperCamelCase      :  List[Any]=13						, __UpperCamelCase      :  Optional[Any]=3						, __UpperCamelCase      :  List[Any]=True						, __UpperCamelCase      :  List[Any]=True						, __UpperCamelCase      :  List[Any]=0.1						, __UpperCamelCase      :  str=0.1						, __UpperCamelCase      :  Any=224						, __UpperCamelCase      :  List[str]=1_000						, __UpperCamelCase      :  Optional[Any]=[3, 3, 6, 4]						, __UpperCamelCase      :  Union[str, Any]=[48, 56, 112, 220]						, )		->     Optional[int]:
											A      						=    parent
											A      						=    batch_size
											A      						=    num_channels
											A      						=    is_training
											A      						=    use_labels
											A      						=    hidden_dropout_prob
											A      						=    attention_probs_dropout_prob
											A      						=    num_labels
											A      						=    image_size
											A      						=    layer_depths
											A      						=    embed_dims


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

											A      						=    None
											if self.use_labels:
																		A      						=    ids_tensor([self.batch_size]						, self.num_labels       )

											A      						=    self.get_config()

											return config, pixel_values, labels


				def __UpperCamelCase						(							self      :  Dict       )		->     str:
											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=__UpperCamelCase						, layer_scale_init_value=1e-5						, )


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


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

											A      						=    SwiftFormerForImageClassification(__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()

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


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Tuple:
											((A)						,   (A)						,   (A))      						=    self.prepare_config_and_inputs()
											A      						=    {'pixel_values': pixel_values}
											return config, inputs_dict





@require_torch
class        lowerCAmelCase__					(  _lowerCamelCase ,				_lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Union[str, Any]								=     (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
				A_   :     List[Any]								=     (
				    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

				A_   :     List[str]								=     False
				A_   :     List[Any]								=     False
				A_   :     int								=     False
				A_   :     List[str]								=     False
				A_   :     Any								=     False


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Optional[int]:
											A      						=    SwiftFormerModelTester(self       )
											A      						=    ConfigTester(
											    self						, config_class=__UpperCamelCase						, has_text_modality=__UpperCamelCase						, hidden_size=37						, num_attention_heads=12						, num_hidden_layers=12						, )


				def __UpperCamelCase						(							self      :  Tuple       )		->     Optional[Any]:
											self.config_tester.run_common_tests()


				@unittest.skip(reason='SwiftFormer does not use inputs_embeds'       )
				def __UpperCamelCase						(							self      :  str       )		->     int:
											pass


				def __UpperCamelCase						(							self      :  Dict       )		->     Any:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

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


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

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

																		A      						=    ['pixel_values']
																		self.assertListEqual(arg_names[:1]						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     str:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     int:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase       )


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


				@unittest.skip(reason='SwiftFormer does not output attentions'       )
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Tuple:
											pass


				def __UpperCamelCase						(							self      :  str       )		->     Optional[Any]:
											def check_hidden_states_output(__UpperCamelCase      :  Tuple						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  Tuple       ):
																		A      						=    model_class(__UpperCamelCase       )
																		model.to(__UpperCamelCase       )
																		model.eval()

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

																		A      						=    outputs.hidden_states

																		A      						=    8
																		self.assertEqual(len(__UpperCamelCase       )						, __UpperCamelCase       )  # 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(__UpperCamelCase       )       ):
																									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),
																									        ]       )						, )

											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		A      						=    True
																		check_hidden_states_output(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		A      						=    True

																		check_hidden_states_output(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Any       )		->     List[str]:
											def _config_zero_init(__UpperCamelCase      :  int       ):
																		A      						=    copy.deepcopy(__UpperCamelCase       )
																		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(__UpperCamelCase						, __UpperCamelCase						, 1e-10       )
																									if isinstance(getattr(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )						, __UpperCamelCase       ):
																																A      						=    _config_zero_init(getattr(__UpperCamelCase						, __UpperCamelCase       )       )
																																setattr(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )
																		return configs_no_init

											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

											A      						=    _config_zero_init(__UpperCamelCase       )
											for model_class in self.all_model_classes:
																		A      						=    model_class(config=__UpperCamelCase       )
																		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 __UpperCamelCase						(							self      :  List[str]       )		->     List[str]:
											pass






def  lowerCamelCase_	(      )       ->							Optional[Any]:

							'''simple docstring'''






							A      						=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
							return image



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


				@cached_property
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Optional[Any]:
											return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'       ) if is_vision_available() else None


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[Any]:
											A      						=    SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'       ).to(__UpperCamelCase       )

											A      						=    self.default_image_processor
											A      						=    prepare_img()
											A      						=    image_processor(images=__UpperCamelCase						, return_tensors='pt'       ).to(__UpperCamelCase       )

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

											# verify the logits
											A      						=    torch.Size((1, 1_000)       )
											self.assertEqual(outputs.logits.shape						, __UpperCamelCase       )

											A      						=    torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]       ).to(__UpperCamelCase       )
											self.assertTrue(torch.allclose(outputs.logits[0, :3]						, __UpperCamelCase						, atol=1e-4       )       )







"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :Optional[Any]				)      ->Dict:
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase    :      Optional[Any]       = DDIMScheduler.from_config(scheduler.config				)

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






						@torch.no_grad()
						def __call__(		self  :Optional[int]			, __magic_name__  :int = 1			, __magic_name__  :Optional[Union[torch.Generator, List[torch.Generator]]] = None			, __magic_name__  :float = 0.0			, __magic_name__  :int = 50			, __magic_name__  :Optional[bool] = None			, __magic_name__  :Optional[str] = "pil"			, __magic_name__  :bool = True			, )      ->Union[ImagePipelineOutput, Tuple]:

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size			, __magic_name__				):
														lowercase    :      int       = (
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
										else:
														lowercase    :      Tuple       = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(__magic_name__			, __magic_name__				) and len(__magic_name__				) != batch_size:
														raise ValueError(
														    f"""You have passed a list of generators of length {len(__magic_name__				)}, but requested an effective batch"""
														    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

										lowercase    :      Dict       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=self.device			, dtype=self.unet.dtype				)

										# set step values
										self.scheduler.set_timesteps(__magic_name__				)

										for t in self.progress_bar(self.scheduler.timesteps				):
														# 1. predict noise model_output
														lowercase    :      Union[str, Any]       = self.unet(__magic_name__			, __magic_name__				).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														lowercase    :      int       = self.scheduler.step(
														    __magic_name__			, __magic_name__			, __magic_name__			, eta=__magic_name__			, use_clipped_model_output=__magic_name__			, generator=__magic_name__				).prev_sample

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

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__magic_name__				)
