




'''simple docstring'''

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 						a				:


				def __init__(		self    :					int       ,				lowercase_    :					List[str]       ,				lowercase_    :					str=13       ,				lowercase_    :					List[Any]=3       ,				lowercase_    :					Tuple=True       ,				lowercase_    :					Dict=True       ,				lowercase_    :					List[Any]=0.1       ,				lowercase_    :					str=0.1       ,				lowercase_    :					List[str]=224       ,				lowercase_    :					int=1000       ,				lowercase_    :					Any=[3, 3, 6, 4]       ,				lowercase_    :					Dict=[48, 56, 112, 220]       ,				):
									snake_case_									=				parent
									snake_case_									=				batch_size
									snake_case_									=				num_channels
									snake_case_									=				is_training
									snake_case_									=				use_labels
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				num_labels
									snake_case_									=				image_size
									snake_case_									=				layer_depths
									snake_case_									=				embed_dims


				def   A_ (		self    :					Tuple					):
									snake_case_									=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

									snake_case_									=				self.get_config()

									return config, pixel_values, labels


				def   A_ (		self    :					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=lowercase_       ,				layer_scale_init_value=1e-5       ,				)


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


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					Any       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Union[str, Any]					):
									snake_case_									=				self.num_labels
									snake_case_									=				SwiftFormerForImageClassification(lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				model(lowercase_       ,				labels=lowercase_					)
									self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)					)

									snake_case_									=				SwiftFormerForImageClassification(lowercase_					)
									model.to(lowercase_					)
									model.eval()

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


				def   A_ (		self    :					List[str]					):
									(snake_case_)									=				self.prepare_config_and_inputs()
									snake_case_									=				{'pixel_values': pixel_values}
									return config, inputs_dict







@require_torch
class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
				snake_case_      =		(
				    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False


				def   A_ (		self    :					str					):
									snake_case_									=				SwiftFormerModelTester(self					)
									snake_case_									=				ConfigTester(
									    self       ,				config_class=lowercase_       ,				has_text_modality=lowercase_       ,				hidden_size=37       ,				num_attention_heads=12       ,				num_hidden_layers=12       ,				)


				def   A_ (		self    :					Union[str, Any]					):
									self.config_tester.run_common_tests()


				@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''					)
				def   A_ (		self    :					Tuple					):
									pass


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

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


				def   A_ (		self    :					List[str]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

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

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


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowercase_					)


				@slow
				def   A_ (		self    :					Optional[Any]					):
									for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_									=				SwiftFormerModel.from_pretrained(lowercase_					)
														self.assertIsNotNone(lowercase_					)


				@unittest.skip(reason='''SwiftFormer does not output attentions'''					)
				def   A_ (		self    :					Union[str, Any]					):
									pass


				def   A_ (		self    :					List[str]					):
									def check_hidden_states_output(lowercase_    :					int       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Optional[int]					):
														snake_case_									=				model_class(lowercase_					)
														model.to(lowercase_					)
														model.eval()

														with torch.no_grad():
																			snake_case_									=				model(**self._prepare_for_class(lowercase_       ,				lowercase_					)					)

														snake_case_									=				outputs.hidden_states

														snake_case_									=				8
														self.assertEqual(len(lowercase_					)       ,				lowercase_					)  # 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(lowercase_					)					):
																			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),
																			        ]					)       ,				)

									snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														snake_case_									=				True
														check_hidden_states_output(lowercase_       ,				lowercase_       ,				lowercase_					)

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

														check_hidden_states_output(lowercase_       ,				lowercase_       ,				lowercase_					)


				def   A_ (		self    :					str					):
									def _config_zero_init(lowercase_    :					str					):
														snake_case_									=				copy.deepcopy(lowercase_					)
														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(lowercase_       ,				lowercase_       ,				1e-10					)
																			if isinstance(getattr(lowercase_       ,				lowercase_       ,				lowercase_					)       ,				lowercase_					):
																								snake_case_									=				_config_zero_init(getattr(lowercase_       ,				lowercase_					)					)
																								setattr(lowercase_       ,				lowercase_       ,				lowercase_					)
														return configs_no_init

									snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

									snake_case_									=				_config_zero_init(lowercase_					)
									for model_class in self.all_model_classes:
														snake_case_									=				model_class(config=lowercase_					)
														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   A_ (		self    :					int					):
									pass







def 		__magic_name__    (	)		->					List[Any]:

					'''simple docstring'''

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






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


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


				@slow
				def   A_ (		self    :					List[Any]					):
									snake_case_									=				SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''					).to(lowercase_					)

									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(images=lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)

									# forward pass
									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)

									# verify the logits
									snake_case_									=				torch.Size((1, 1000)					)
									self.assertEqual(outputs.logits.shape       ,				lowercase_					)

									snake_case_									=				torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]					).to(lowercase_					)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				lowercase_       ,				atol=1e-4					)					)







"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			DDIMPipeline
			lowerCamelCase__      =			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			lowerCamelCase__      =			PipelineTesterMixin.required_optional_params - {
			    """num_images_per_prompt""",
			    """latents""",
			    """callback""",
			    """callback_steps""",
			}
			lowerCamelCase__      =			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			lowerCamelCase__      =			False
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			List[Any]    =    UNetaDModel(
							    block_out_channels=(32, 64)  ,      layers_per_block=2  ,      sample_size=32  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,      )
							_lowerCamelCase     :			List[str]    =    DDIMScheduler()
							_lowerCamelCase     :			Optional[int]    =    {'unet': unet, 'scheduler': scheduler}
							return components
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ):
							if str(lowercase  ).startswith('mps'  ):
											_lowerCamelCase     :			Dict    =    torch.manual_seed(lowercase  )
							else:
											_lowerCamelCase     :			List[str]    =    torch.Generator(device=lowercase  ).manual_seed(lowercase  )
							_lowerCamelCase     :			Tuple    =    {
							    'batch_size': 1,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    'cpu'

							_lowerCamelCase     :			Tuple    =    self.get_dummy_components()
							_lowerCamelCase     :			Optional[Any]    =    self.pipeline_class(**lowercase  )
							pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			str    =    self.get_dummy_inputs(lowercase  )
							_lowerCamelCase     :			int    =    pipe(**lowercase  ).images
							_lowerCamelCase     :			Any    =    image[0, -3:, -3:, -1]

							self.assertEqual(image.shape  ,      (1, 32, 32, 3)  )
							_lowerCamelCase     :			Tuple    =    np.array(
							    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]  )
							_lowerCamelCase     :			str    =    np.abs(image_slice.flatten() - expected_slice  ).max()
							self.assertLessEqual(lowercase  ,      1E-3  )
			def 					A_    (  self  ):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_save_load_local(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_save_load_optional_components(expected_max_difference=3E-3  )






			def 					A_    (  self  ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3  )






@slow
@require_torch_gpu
class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    'google/ddpm-cifar10-32'

							_lowerCamelCase     :			Optional[Any]    =    UNetaDModel.from_pretrained(lowercase  )
							_lowerCamelCase     :			Dict    =    DDIMScheduler()

							_lowerCamelCase     :			Dict    =    DDIMPipeline(unet=lowercase  ,      scheduler=lowercase  )
							ddim.to(lowercase  )
							ddim.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			List[str]    =    torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    ddim(generator=lowercase  ,      eta=0.0  ,      output_type='numpy'  ).images

							_lowerCamelCase     :			Dict    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_lowerCamelCase     :			List[Any]    =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2






			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    'google/ddpm-ema-bedroom-256'

							_lowerCamelCase     :			str    =    UNetaDModel.from_pretrained(lowercase  )
							_lowerCamelCase     :			str    =    DDIMScheduler.from_pretrained(lowercase  )

							_lowerCamelCase     :			Optional[int]    =    DDIMPipeline(unet=lowercase  ,      scheduler=lowercase  )
							ddpm.to(lowercase  )
							ddpm.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			Tuple    =    torch.manual_seed(0  )
							_lowerCamelCase     :			int    =    ddpm(generator=lowercase  ,      output_type='numpy'  ).images

							_lowerCamelCase     :			Dict    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							_lowerCamelCase     :			str    =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2