
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)			:     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			:     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			:     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			:     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			:     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 argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase						:      Any									= argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
lowercase						:      Tuple									= parser.parse_args()


lowercase						:      str									= 'cpu'
lowercase						:      List[Any]									= 'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

lowercase						:      Any									= 'path-to-your-trained-model'
lowercase						:      List[Any]									= StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		lowercase						:      Any									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase						:      Dict									= pipe.to(device)

# to channels last
lowercase						:      int									= pipe.unet.to(memory_format=torch.channels_last)
lowercase						:      int									= pipe.vae.to(memory_format=torch.channels_last)
lowercase						:      Dict									= pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		lowercase						:      List[str]									= pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase						:      Any									= torch.randn(2, 4, 64, 64)
lowercase						:      Union[str, Any]									= torch.rand(1) * 999
lowercase						:      List[Any]									= torch.randn(2, 77, 768)
lowercase						:      List[Any]									= (sample, timestep, encoder_hidden_status)
try:
		lowercase						:      List[Any]									= ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		lowercase						:      str									= ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase						:      Dict									= ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase						:      str									= ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		lowercase						:      Dict									= ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase						:      Any									= 666
lowercase						:      int									= torch.Generator(device).manual_seed(seed)
lowercase						:      List[str]									= {'generator': generator}
if args.steps is not None:
		lowercase						:      List[str]									= args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
		lowercase						:      Dict									= pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')