
'''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 			lowerCAmelCase :




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=13					,						UpperCamelCase=3					,						UpperCamelCase=True					,						UpperCamelCase=True					,						UpperCamelCase=0.1					,						UpperCamelCase=0.1					,						UpperCamelCase=224					,						UpperCamelCase=1_000					,						UpperCamelCase=[3, 3, 6, 4]					,						UpperCamelCase=[48, 56, 112, 220]					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= num_channels
								_SCREAMING_SNAKE_CASE				= is_training
								_SCREAMING_SNAKE_CASE				= use_labels
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= num_labels
								_SCREAMING_SNAKE_CASE				= image_size
								_SCREAMING_SNAKE_CASE				= layer_depths
								_SCREAMING_SNAKE_CASE				= embed_dims




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

								_SCREAMING_SNAKE_CASE				= self.get_config()

								return config, pixel_values, labels




		def     lowercase       (				self      ):
								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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= SwiftFormerModel(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      )
								self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.embed_dims[-1], 7, 7)      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.num_labels
								_SCREAMING_SNAKE_CASE				= SwiftFormerForImageClassification(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						labels=UpperCamelCase      )
								self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels)      )

								_SCREAMING_SNAKE_CASE				= SwiftFormerForImageClassification(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()

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




		def     lowercase       (				self      ):
								((_SCREAMING_SNAKE_CASE),							(_SCREAMING_SNAKE_CASE),							(_SCREAMING_SNAKE_CASE))				= self.prepare_config_and_inputs()
								_SCREAMING_SNAKE_CASE				= {"pixel_values": pixel_values}
								return config, inputs_dict


@require_torch
class 			lowerCAmelCase (				__UpperCAmelCase		,     __UpperCAmelCase		,     unittest.TestCase			):
		a							:					Optional[int]       =					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
		a							:					int       =					(
		    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
		    if is_torch_available()
		    else {}
		)

		a							:					str       =					False
		a							:					List[Any]       =					False
		a							:					Any       =					False
		a							:					Optional[Any]       =					False
		a							:					Optional[Any]       =					False




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= SwiftFormerModelTester(self      )
								_SCREAMING_SNAKE_CASE				= ConfigTester(
								    self					,						config_class=UpperCamelCase					,						has_text_modality=UpperCamelCase					,						hidden_size=37					,						num_attention_heads=12					,						num_hidden_layers=12					,						)




		def     lowercase       (				self      ):
								self.config_tester.run_common_tests()




		@unittest.skip(reason="SwiftFormer does not use inputs_embeds"      )
		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()

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

														_SCREAMING_SNAKE_CASE				= ["pixel_values"]
														self.assertListEqual(arg_names[:1]					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase      )




		@slow
		def     lowercase       (				self      ):
								for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE				= SwiftFormerModel.from_pretrained(UpperCamelCase      )
														self.assertIsNotNone(UpperCamelCase      )




		@unittest.skip(reason="SwiftFormer does not output attentions"      )
		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								def check_hidden_states_output(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )
														model.to(UpperCamelCase      )
														model.eval()

														with torch.no_grad():
																				_SCREAMING_SNAKE_CASE				= model(**self._prepare_for_class(UpperCamelCase					,						UpperCamelCase      )      )

														_SCREAMING_SNAKE_CASE				= outputs.hidden_states

														_SCREAMING_SNAKE_CASE				= 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),
																				        ]      )					,						)

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE				= True
														check_hidden_states_output(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )

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

														check_hidden_states_output(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								def _config_zero_init(UpperCamelCase      ):
														_SCREAMING_SNAKE_CASE				= 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      ):
																										_SCREAMING_SNAKE_CASE				= _config_zero_init(getattr(UpperCamelCase					,						UpperCamelCase      )      )
																										setattr(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )
														return configs_no_init

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()

								_SCREAMING_SNAKE_CASE				= _config_zero_init(UpperCamelCase      )
								for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								pass



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
						return image




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




		@cached_property
		def     lowercase       (				self      ):
								return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"      ) if is_vision_available() else None




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"      ).to(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.default_image_processor
								_SCREAMING_SNAKE_CASE				= prepare_img()
								_SCREAMING_SNAKE_CASE				= image_processor(images=UpperCamelCase					,						return_tensors="pt"      ).to(UpperCamelCase      )

								# forward pass
								with torch.no_grad():
														_SCREAMING_SNAKE_CASE				= model(**UpperCamelCase      )

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

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

'''simple docstring'''







import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 			lowerCAmelCase :




		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(
								    sample_size=32					,						layers_per_block=1					,						block_out_channels=[32, 64]					,						down_block_types=[
								        "ResnetDownsampleBlock2D",
								        "SimpleCrossAttnDownBlock2D",
								    ]					,						mid_block_type="UNetMidBlock2DSimpleCrossAttn"					,						up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]					,						in_channels=3					,						out_channels=6					,						cross_attention_dim=32					,						encoder_hid_dim=32					,						attention_head_dim=8					,						addition_embed_type="text"					,						addition_embed_type_num_heads=2					,						cross_attention_norm="group_norm"					,						resnet_time_scale_shift="scale_shift"					,						act_fn="gelu"					,						)
								unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						beta_end=0.02					,						thresholding=UpperCamelCase					,						dynamic_thresholding_ratio=0.95					,						sample_max_value=1.0					,						prediction_type="epsilon"					,						variance_type="learned_range"					,						)

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}




		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(
								    sample_size=32					,						layers_per_block=[1, 2]					,						block_out_channels=[32, 64]					,						down_block_types=[
								        "ResnetDownsampleBlock2D",
								        "SimpleCrossAttnDownBlock2D",
								    ]					,						mid_block_type="UNetMidBlock2DSimpleCrossAttn"					,						up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]					,						in_channels=6					,						out_channels=6					,						cross_attention_dim=32					,						encoder_hid_dim=32					,						attention_head_dim=8					,						addition_embed_type="text"					,						addition_embed_type_num_heads=2					,						cross_attention_norm="group_norm"					,						resnet_time_scale_shift="scale_shift"					,						act_fn="gelu"					,						class_embed_type="timestep"					,						mid_block_scale_factor=1.4_14					,						time_embedding_act_fn="gelu"					,						time_embedding_dim=32					,						)
								unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						beta_end=0.02					,						thresholding=UpperCamelCase					,						dynamic_thresholding_ratio=0.95					,						sample_max_value=1.0					,						prediction_type="epsilon"					,						variance_type="learned_range"					,						)

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						beta_end=0.02					,						)

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= inputs["prompt"]
								_SCREAMING_SNAKE_CASE				= inputs["generator"]
								_SCREAMING_SNAKE_CASE				= inputs["num_inference_steps"]
								_SCREAMING_SNAKE_CASE				= inputs["output_type"]

								if "image" in inputs:
														_SCREAMING_SNAKE_CASE				= inputs["image"]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if "mask_image" in inputs:
														_SCREAMING_SNAKE_CASE				= inputs["mask_image"]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if "original_image" in inputs:
														_SCREAMING_SNAKE_CASE				= inputs["original_image"]
								else:
														_SCREAMING_SNAKE_CASE				= None

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= pipe.encode_prompt(UpperCamelCase      )

								# inputs with prompt converted to embeddings
								_SCREAMING_SNAKE_CASE				= {
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
														_SCREAMING_SNAKE_CASE				= image

								if mask_image is not None:
														_SCREAMING_SNAKE_CASE				= mask_image

								if original_image is not None:
														_SCREAMING_SNAKE_CASE				= original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
														setattr(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.pipeline_class.from_pretrained(UpperCamelCase      )
														pipe_loaded.to(UpperCamelCase      )
														pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								for optional_component in pipe._optional_components:
														self.assertTrue(
														    getattr(UpperCamelCase					,						UpperCamelCase      ) is None					,						F'`{optional_component}` did not stay set to None after loading.'					,						)

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= inputs["generator"]
								_SCREAMING_SNAKE_CASE				= inputs["num_inference_steps"]
								_SCREAMING_SNAKE_CASE				= inputs["output_type"]

								# inputs with prompt converted to embeddings
								_SCREAMING_SNAKE_CASE				= {
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
														_SCREAMING_SNAKE_CASE				= image

								if mask_image is not None:
														_SCREAMING_SNAKE_CASE				= mask_image

								if original_image is not None:
														_SCREAMING_SNAKE_CASE				= original_image

								_SCREAMING_SNAKE_CASE				= pipe_loaded(**UpperCamelCase      )[0]

								_SCREAMING_SNAKE_CASE				= np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
								self.assertLess(UpperCamelCase					,						1e-4      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.pipeline_class.from_pretrained(UpperCamelCase      )
														pipe_loaded.to(UpperCamelCase      )
														pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe_loaded(**UpperCamelCase      )[0]

								_SCREAMING_SNAKE_CASE				= np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
								self.assertLess(UpperCamelCase					,						1e-4      )