



import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class    _lowercase :
				'''simple docstring'''







				def __init__(			self  ,			snake_case__  ,			snake_case__=13  ,			snake_case__=32  ,			snake_case__=2  ,			snake_case__=3  ,			snake_case__=16  ,			snake_case__=[1, 2, 1]  ,			snake_case__=[2, 2, 4]  ,			snake_case__=2  ,			snake_case__=2.0  ,			snake_case__=True  ,			snake_case__=0.0  ,			snake_case__=0.0  ,			snake_case__=0.1  ,			snake_case__="gelu"  ,			snake_case__=False  ,			snake_case__=True  ,			snake_case__=0.02  ,			snake_case__=1e-5  ,			snake_case__=True  ,			snake_case__=None  ,			snake_case__=True  ,			snake_case__=10  ,			snake_case__=8  ,			snake_case__=["stage1", "stage2"]  ,			snake_case__=[1, 2]  ,			):

									'''simple docstring'''


									UpperCamelCase_			= parent
									UpperCamelCase_			= batch_size
									UpperCamelCase_			= image_size
									UpperCamelCase_			= patch_size
									UpperCamelCase_			= num_channels
									UpperCamelCase_			= embed_dim
									UpperCamelCase_			= depths
									UpperCamelCase_			= num_heads
									UpperCamelCase_			= window_size
									UpperCamelCase_			= mlp_ratio
									UpperCamelCase_			= qkv_bias
									UpperCamelCase_			= hidden_dropout_prob
									UpperCamelCase_			= attention_probs_dropout_prob
									UpperCamelCase_			= drop_path_rate
									UpperCamelCase_			= hidden_act
									UpperCamelCase_			= use_absolute_embeddings
									UpperCamelCase_			= patch_norm
									UpperCamelCase_			= layer_norm_eps
									UpperCamelCase_			= initializer_range
									UpperCamelCase_			= is_training
									UpperCamelCase_			= scope
									UpperCamelCase_			= use_labels
									UpperCamelCase_			= type_sequence_label_size
									UpperCamelCase_			= encoder_stride
									UpperCamelCase_			= out_features
									UpperCamelCase_			= out_indices

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

									UpperCamelCase_			= None
									if self.use_labels:
														UpperCamelCase_			= ids_tensor([self.batch_size]  ,			self.type_sequence_label_size     )

									UpperCamelCase_			= self.get_config()

									return config, pixel_values, labels

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return SwinConfig(
									    image_size=self.image_size  ,			patch_size=self.patch_size  ,			num_channels=self.num_channels  ,			embed_dim=self.embed_dim  ,			depths=self.depths  ,			num_heads=self.num_heads  ,			window_size=self.window_size  ,			mlp_ratio=self.mlp_ratio  ,			qkv_bias=self.qkv_bias  ,			hidden_dropout_prob=self.hidden_dropout_prob  ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,			drop_path_rate=self.drop_path_rate  ,			hidden_act=self.hidden_act  ,			use_absolute_embeddings=self.use_absolute_embeddings  ,			path_norm=self.patch_norm  ,			layer_norm_eps=self.layer_norm_eps  ,			initializer_range=self.initializer_range  ,			encoder_stride=self.encoder_stride  ,			out_features=self.out_features  ,			out_indices=self.out_indices  ,			)

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinModel(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )

									UpperCamelCase_			= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
									UpperCamelCase_			= int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

									self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, expected_seq_len, expected_dim)     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinBackbone(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps     )  ,			len(config.out_features     )     )
									self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,			[self.batch_size, model.channels[0], 16, 16]     )

									# verify channels
									self.parent.assertEqual(len(model.channels     )  ,			len(config.out_features     )     )

									# verify backbone works with out_features=None
									UpperCamelCase_			= None
									UpperCamelCase_			= SwinBackbone(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps     )  ,			1     )
									self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,			[self.batch_size, model.channels[-1], 4, 4]     )

									# verify channels
									self.parent.assertEqual(len(model.channels     )  ,			1     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinForMaskedImageModeling(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )
									self.parent.assertEqual(
									    result.logits.shape  ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

									# test greyscale images
									UpperCamelCase_			= 1
									UpperCamelCase_			= SwinForMaskedImageModeling(snake_case__     )
									model.to(snake_case__     )
									model.eval()

									UpperCamelCase_			= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									UpperCamelCase_			= model(snake_case__     )
									self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, 1, self.image_size, self.image_size)     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= self.type_sequence_label_size
									UpperCamelCase_			= SwinForImageClassification(snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__  ,			labels=snake_case__     )
									self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.type_sequence_label_size)     )

									# test greyscale images
									UpperCamelCase_			= 1
									UpperCamelCase_			= SwinForImageClassification(snake_case__     )
									model.to(snake_case__     )
									model.eval()

									UpperCamelCase_			= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									UpperCamelCase_			= model(snake_case__     )
									self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.type_sequence_label_size)     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.prepare_config_and_inputs()
									(
									    (
									    UpperCamelCase_
									)	,     (
									    UpperCamelCase_
									)	,     (
									    UpperCamelCase_
									)	,     
									)			= config_and_inputs
									UpperCamelCase_			= {"pixel_values": pixel_values}
									return config, inputs_dict




@require_torch
class    _lowercase (a_   , a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    (
				    (
				        SwinModel,
				        SwinBackbone,
				        SwinForImageClassification,
				        SwinForMaskedImageModeling,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__     =    (
				    {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
				    if is_torch_available()
				    else {}
				)
				lowercase__     =    True

				lowercase__     =    False
				lowercase__     =    False
				lowercase__     =    False

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinModelTester(self     )
									UpperCamelCase_			= ConfigTester(self  ,			config_class=snake_case__  ,			embed_dim=37     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case__     )

				@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."     )
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									super().test_training_gradient_checkpointing()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case__     )

				@unittest.skip(reason="Swin does not use inputs_embeds"     )
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				@unittest.skip(reason="Swin Transformer does not use feedforward chunking"     )
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														UpperCamelCase_			= model_class(snake_case__     )
														self.assertIsInstance(model.get_input_embeddings()  ,			(nn.Module)     )
														UpperCamelCase_			= model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(snake_case__  ,			nn.Linear     )     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

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

														UpperCamelCase_			= ["pixel_values"]
														self.assertListEqual(arg_names[:1]  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase_			= True

									for model_class in self.all_model_classes:
														UpperCamelCase_			= True
														UpperCamelCase_			= False
														UpperCamelCase_			= True
														UpperCamelCase_			= model_class(snake_case__     )
														model.to(snake_case__     )
														model.eval()
														with torch.no_grad():
																			UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )
														UpperCamelCase_			= outputs.attentions
														UpperCamelCase_			= len(self.model_tester.depths     )
														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														UpperCamelCase_			= True
														UpperCamelCase_			= config.window_size**2
														UpperCamelCase_			= model_class(snake_case__     )
														model.to(snake_case__     )
														model.eval()
														with torch.no_grad():
																			UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )
														UpperCamelCase_			= outputs.attentions
														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

														self.assertListEqual(
														    list(attentions[0].shape[-3:]     )  ,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,			)
														UpperCamelCase_			= len(snake_case__     )

														# Check attention is always last and order is fine
														UpperCamelCase_			= True
														UpperCamelCase_			= True
														UpperCamelCase_			= model_class(snake_case__     )
														model.to(snake_case__     )
														model.eval()
														with torch.no_grad():
																			UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )

														# also another +1 for reshaped_hidden_states
														UpperCamelCase_			= 1 if model_class.__name__ == "SwinBackbone" else 2
														self.assertEqual(out_len + added_hidden_states  ,			len(snake_case__     )     )

														UpperCamelCase_			= outputs.attentions

														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

														self.assertListEqual(
														    list(self_attentions[0].shape[-3:]     )  ,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,			)

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= model_class(snake_case__     )
									model.to(snake_case__     )
									model.eval()

									with torch.no_grad():
														UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )

									UpperCamelCase_			= outputs.hidden_states

									UpperCamelCase_			= getattr(
									    self.model_tester  ,			"expected_num_hidden_layers"  ,			len(self.model_tester.depths     ) + 1     )
									self.assertEqual(len(snake_case__     )  ,			snake_case__     )

									# Swin has a different seq_length
									UpperCamelCase_			= (
									    config.patch_size
									    if isinstance(config.patch_size  ,			collections.abc.Iterable     )
									    else (config.patch_size, config.patch_size)
									)

									UpperCamelCase_			= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]     )  ,			[num_patches, self.model_tester.embed_dim]  ,			)

									if not model_class.__name__ == "SwinBackbone":
														UpperCamelCase_			= outputs.reshaped_hidden_states
														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

														UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= reshaped_hidden_states[0].shape
														UpperCamelCase_			= (
														    reshaped_hidden_states[0].view(snake_case__  ,			snake_case__  ,			height * width     ).permute(0  ,			2  ,			1     )
														)
														self.assertListEqual(
														    list(reshaped_hidden_states.shape[-2:]     )  ,			[num_patches, self.model_tester.embed_dim]  ,			)

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_			= (
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size  ,			collections.abc.Iterable     )
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)

									for model_class in self.all_model_classes:
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

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

														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase_			= 3

									UpperCamelCase_			= (
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size  ,			collections.abc.Iterable     )
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)
									UpperCamelCase_			= (
									    config.patch_size
									    if isinstance(config.patch_size  ,			collections.abc.Iterable     )
									    else (config.patch_size, config.patch_size)
									)

									UpperCamelCase_			= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									UpperCamelCase_			= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			(padded_height, padded_width)     )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			(padded_height, padded_width)     )

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCamelCase_			= SwinModel.from_pretrained(snake_case__     )
														self.assertIsNotNone(snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_			= _config_zero_init(snake_case__     )
									for model_class in self.all_model_classes:
														UpperCamelCase_			= model_class(config=snake_case__     )
														for name, param in model.named_parameters():
																			if "embeddings" not in name and param.requires_grad:
																								self.assertIn(
																								    ((param.data.mean() * 1e9).round() / 1e9).item()  ,			[0.0, 1.0]  ,			msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""  ,			)




@require_vision
@require_torch
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@cached_property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return (
									    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"     )
									    if is_vision_available()
									    else None
									)

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"     ).to(snake_case__     )
									UpperCamelCase_			= self.default_image_processor

									UpperCamelCase_			= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
									UpperCamelCase_			= image_processor(images=snake_case__  ,			return_tensors="pt"     ).to(snake_case__     )

									# forward pass
									with torch.no_grad():
														UpperCamelCase_			= model(**snake_case__     )

									# verify the logits
									UpperCamelCase_			= torch.Size((1, 1000)     )
									self.assertEqual(outputs.logits.shape  ,			snake_case__     )
									UpperCamelCase_			= torch.tensor([-0.0_948, -0.6_454, -0.0_921]     ).to(snake_case__     )
									self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,			snake_case__  ,			atol=1e-4     )     )




@require_torch
class    _lowercase (unittest.TestCase   , a_   ):
				'''simple docstring'''







				lowercase__     =    (SwinBackbone,) if is_torch_available() else ()
				lowercase__     =    SwinConfig

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinModelTester(self     )






"""simple docstring"""

import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Any						 =		{}
	for name in unet.attn_processors.keys():
		A_	:					Dict						 =		None if name.endswith('''attn1.processor'''						) else unet.config.cross_attention_dim
		if name.startswith('''mid_block'''						):
			A_	:					List[str]						 =		unet.config.block_out_channels[-1]
		elif name.startswith('''up_blocks'''						):
			A_	:					List[str]						 =		int(name[len('''up_blocks.'''						)]						)
			A_	:					int						 =		list(reversed(unet.config.block_out_channels						)						)[block_id]
		elif name.startswith('''down_blocks'''						):
			A_	:					int						 =		int(name[len('''down_blocks.'''						)]						)
			A_	:					str						 =		unet.config.block_out_channels[block_id]
		A_	:					int						 =		(
		    LoRAAttnProcessor2_0 if hasattr(_UpperCAmelCase	,   '''scaled_dot_product_attention'''						) else LoRAAttnProcessor
		)
		A_	:					Optional[Any]						 =		lora_attn_processor_class(
		    hidden_size=_UpperCAmelCase	,   cross_attention_dim=_UpperCAmelCase						)
	A_	:					Dict						 =		AttnProcsLayers(_UpperCAmelCase						)
	return lora_attn_procs, unet_lora_layers

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Union[str, Any]						 =		{}
	A_	:					List[str]						 =		(
	    LoRAAttnProcessor2_0 if hasattr(_UpperCAmelCase	,   '''scaled_dot_product_attention'''						) else LoRAAttnProcessor
	)
	for name, module in text_encoder.named_modules():
		if name.endswith(_UpperCAmelCase						):
			A_	:					Optional[int]						 =		lora_attn_processor_class(
			    hidden_size=module.out_proj.out_features	,   cross_attention_dim=_UpperCAmelCase						)
	return text_lora_attn_procs

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Optional[Any]						 =		create_text_encoder_lora_attn_procs(_UpperCAmelCase						)
	A_	:					List[str]						 =		AttnProcsLayers(_UpperCAmelCase						)
	return text_encoder_lora_layers

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase=False						):




	"""simple docstring"""






	for _, attn_proc in text_lora_attn_procs.items():
		# set up.weights
		for layer_name, layer_module in attn_proc.named_modules():
			if layer_name.endswith('''_lora'''						):
				A_	:					List[Any]						 =		(
				    torch.randn_like(layer_module.up.weight						)
				    if randn_weight
				    else torch.zeros_like(layer_module.up.weight						)
				)
				A_	:					Tuple						 =		torch.nn.Parameter(_UpperCAmelCase						)

class 							lowercase   (     unittest.TestCase):







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					torch.manual_seed(0							)
					A_	:					List[str]						 =		UNetaDConditionModel(
					    block_out_channels=(32, 64)   ,  layers_per_block=2   ,  sample_size=32   ,  in_channels=4   ,  out_channels=4   ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,  cross_attention_dim=32   ,  )
					A_	:					Optional[int]						 =		DDIMScheduler(
					    beta_start=0.00085   ,  beta_end=0.012   ,  beta_schedule='''scaled_linear'''   ,  clip_sample=_lowerCamelCase   ,  set_alpha_to_one=_lowerCamelCase   ,  steps_offset=1   ,  )
					torch.manual_seed(0							)
					A_	:					Any						 =		AutoencoderKL(
					    block_out_channels=[32, 64]   ,  in_channels=3   ,  out_channels=3   ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,  latent_channels=4   ,  )
					A_	:					Union[str, Any]						 =		CLIPTextConfig(
					    bos_token_id=0   ,  eos_token_id=2   ,  hidden_size=32   ,  intermediate_size=37   ,  layer_norm_eps=1E-05   ,  num_attention_heads=4   ,  num_hidden_layers=5   ,  pad_token_id=1   ,  vocab_size=10_00   ,  )
					A_	:					List[Any]						 =		CLIPTextModel(_lowerCamelCase							)
					A_	:					str						 =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

					A_ ,			A_	:					Any						 =		create_unet_lora_layers(_lowerCamelCase							)
					A_	:					Tuple						 =		create_text_encoder_lora_layers(_lowerCamelCase							)

					A_	:					Any						 =		{
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''vae''': vae,
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''safety_checker''': None,
					    '''feature_extractor''': None,
					}
					A_	:					Union[str, Any]						 =		{
					    '''unet_lora_layers''': unet_lora_layers,
					    '''text_encoder_lora_layers''': text_encoder_lora_layers,
					    '''unet_lora_attn_procs''': unet_lora_attn_procs,
					}
					return pipeline_components, lora_components







				def        a_	(				self :       str							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		1
					A_	:					Tuple						 =		10
					A_	:					int						 =		4
					A_	:					List[Any]						 =		(32, 32)

					A_	:					str						 =		torch.manual_seed(0							)
					A_	:					Dict						 =		floats_tensor((batch_size, num_channels) + sizes							)
					A_	:					List[str]						 =		torch.randint(1   ,  _lowerCamelCase   ,  size=(batch_size, sequence_length)   ,  generator=_lowerCamelCase							)

					A_	:					int						 =		{
					    '''prompt''': '''A painting of a squirrel eating a burger''',
					    '''generator''': generator,
					    '''num_inference_steps''': 2,
					    '''guidance_scale''': 6.0,
					    '''output_type''': '''numpy''',
					}

					return noise, input_ids, pipeline_inputs

					# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb







				def        a_	(				self :       str							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		77

					A_	:					str						 =		torch.randint(2   ,  56   ,  size=(1, max_seq_length)   ,  generator=torch.manual_seed(0							)							)

					A_	:					Optional[Any]						 =		{}
					A_	:					int						 =		inputs
					return prepared_inputs







				def        a_	(				self :       Optional[int]   ,  _lowerCamelCase :       List[Any]							):



					"""simple docstring"""

					A_ ,			A_	:					List[str]						 =		self.get_dummy_components()
					LoraLoaderMixin.save_lora_weights(
					    save_directory=_lowerCamelCase   ,  unet_lora_layers=lora_components['''unet_lora_layers''']   ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']   ,  )
					self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase   ,  '''pytorch_lora_weights.bin'''							)							)							)







				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_ ,			A_	:					Optional[Any]						 =		self.get_dummy_components()
					A_	:					List[str]						 =		StableDiffusionPipeline(**_lowerCamelCase							)
					A_	:					List[str]						 =		sd_pipe.to(_lowerCamelCase							)
					sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

					A_ ,			A_ ,			A_	:					str						 =		self.get_dummy_inputs()

					A_	:					Optional[int]						 =		sd_pipe(**_lowerCamelCase							).images
					A_	:					str						 =		original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
						LoraLoaderMixin.save_lora_weights(
						    save_directory=_lowerCamelCase   ,  unet_lora_layers=lora_components['''unet_lora_layers''']   ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']   ,  )
						self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase   ,  '''pytorch_lora_weights.bin'''							)							)							)
						sd_pipe.load_lora_weights(_lowerCamelCase							)

					A_	:					List[Any]						 =		sd_pipe(**_lowerCamelCase							).images
					A_	:					str						 =		lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(_lowerCamelCase							)   ,  torch.from_numpy(_lowerCamelCase							)							)							)







				def        a_	(				self :       Optional[int]							):



					"""simple docstring"""

					A_ ,			A_	:					List[Any]						 =		self.get_dummy_components()
					A_	:					str						 =		StableDiffusionPipeline(**_lowerCamelCase							)
					A_	:					Any						 =		sd_pipe.to(_lowerCamelCase							)
					sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

					A_ ,			A_ ,			A_	:					List[str]						 =		self.get_dummy_inputs()

					A_	:					Union[str, Any]						 =		sd_pipe(**_lowerCamelCase							).images
					A_	:					int						 =		original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
						LoraLoaderMixin.save_lora_weights(
						    save_directory=_lowerCamelCase   ,  unet_lora_layers=lora_components['''unet_lora_layers''']   ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']   ,  safe_serialization=_lowerCamelCase   ,  )
						self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase   ,  '''pytorch_lora_weights.safetensors'''							)							)							)
						sd_pipe.load_lora_weights(_lowerCamelCase							)

					A_	:					Union[str, Any]						 =		sd_pipe(**_lowerCamelCase							).images
					A_	:					Dict						 =		lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(_lowerCamelCase							)   ,  torch.from_numpy(_lowerCamelCase							)							)							)







				def        a_	(				self :       Optional[int]							):



					"""simple docstring"""

					A_ ,			A_	:					List[Any]						 =		self.get_dummy_components()
					A_	:					Any						 =		lora_components['''unet_lora_attn_procs''']
					A_	:					Any						 =		StableDiffusionPipeline(**_lowerCamelCase							)
					A_	:					Tuple						 =		sd_pipe.to(_lowerCamelCase							)
					sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

					A_ ,			A_ ,			A_	:					Tuple						 =		self.get_dummy_inputs()

					A_	:					Tuple						 =		sd_pipe(**_lowerCamelCase							).images
					A_	:					Optional[Any]						 =		original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
						A_	:					Tuple						 =		sd_pipe.unet
						unet.set_attn_processor(_lowerCamelCase							)
						unet.save_attn_procs(_lowerCamelCase							)
						self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase   ,  '''pytorch_lora_weights.bin'''							)							)							)
						sd_pipe.load_lora_weights(_lowerCamelCase							)

					A_	:					Any						 =		sd_pipe(**_lowerCamelCase							).images
					A_	:					Optional[int]						 =		lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(_lowerCamelCase							)   ,  torch.from_numpy(_lowerCamelCase							)							)							)







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_ ,			A_	:					List[str]						 =		self.get_dummy_components()
					A_	:					Dict						 =		StableDiffusionPipeline(**_lowerCamelCase							)

					A_	:					Any						 =		self.get_dummy_tokens()

					# inference without lora
					A_	:					str						 =		pipe.text_encoder(**_lowerCamelCase							)[0]
					assert outputs_without_lora.shape == (1, 77, 32)

					# create lora_attn_procs with zeroed out up.weights
					A_	:					str						 =		create_text_encoder_lora_attn_procs(pipe.text_encoder							)
					set_lora_up_weights(_lowerCamelCase   ,  randn_weight=_lowerCamelCase							)

					# monkey patch
					pipe._modify_text_encoder(_lowerCamelCase							)

					# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
					del text_attn_procs
					gc.collect()

					# inference with lora
					A_	:					List[str]						 =		pipe.text_encoder(**_lowerCamelCase							)[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert torch.allclose(
					    _lowerCamelCase   ,  _lowerCamelCase							), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

					# create lora_attn_procs with randn up.weights
					A_	:					Tuple						 =		create_text_encoder_lora_attn_procs(pipe.text_encoder							)
					set_lora_up_weights(_lowerCamelCase   ,  randn_weight=_lowerCamelCase							)

					# monkey patch
					pipe._modify_text_encoder(_lowerCamelCase							)

					# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
					del text_attn_procs
					gc.collect()

					# inference with lora
					A_	:					str						 =		pipe.text_encoder(**_lowerCamelCase							)[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert not torch.allclose(
					    _lowerCamelCase   ,  _lowerCamelCase							), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"







				def        a_	(				self :       Optional[int]							):



					"""simple docstring"""

					A_ ,			A_	:					List[Any]						 =		self.get_dummy_components()
					A_	:					Optional[int]						 =		StableDiffusionPipeline(**_lowerCamelCase							)

					A_	:					List[Any]						 =		self.get_dummy_tokens()

					# inference without lora
					A_	:					Tuple						 =		pipe.text_encoder(**_lowerCamelCase							)[0]
					assert outputs_without_lora.shape == (1, 77, 32)

					# create lora_attn_procs with randn up.weights
					A_	:					List[Any]						 =		create_text_encoder_lora_attn_procs(pipe.text_encoder							)
					set_lora_up_weights(_lowerCamelCase   ,  randn_weight=_lowerCamelCase							)

					# monkey patch
					pipe._modify_text_encoder(_lowerCamelCase							)

					# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
					del text_attn_procs
					gc.collect()

					# inference with lora
					A_	:					List[Any]						 =		pipe.text_encoder(**_lowerCamelCase							)[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert not torch.allclose(
					    _lowerCamelCase   ,  _lowerCamelCase							), "lora outputs should be different to without lora outputs"

					# remove monkey patch
					pipe._remove_text_encoder_monkey_patch()

					# inference with removed lora
					A_	:					Optional[int]						 =		pipe.text_encoder(**_lowerCamelCase							)[0]
					assert outputs_without_lora_removed.shape == (1, 77, 32)

					assert torch.allclose(
					    _lowerCamelCase   ,  _lowerCamelCase							), "remove lora monkey patch should restore the original outputs"







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_ ,			A_	:					Dict						 =		self.get_dummy_components()
					A_	:					str						 =		StableDiffusionPipeline(**_lowerCamelCase							)
					A_	:					Dict						 =		sd_pipe.to(_lowerCamelCase							)
					sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

					A_ ,			A_ ,			A_	:					Tuple						 =		self.get_dummy_inputs()

					with tempfile.TemporaryDirectory() as tmpdirname:
						LoraLoaderMixin.save_lora_weights(
						    save_directory=_lowerCamelCase   ,  unet_lora_layers=lora_components['''unet_lora_layers''']   ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']   ,  )
						self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase   ,  '''pytorch_lora_weights.bin'''							)							)							)
						sd_pipe.load_lora_weights(_lowerCamelCase							)

					A_	:					Dict						 =		sd_pipe(**_lowerCamelCase							).images
					A_	:					Union[str, Any]						 =		lora_images[0, -3:, -3:, -1]

					A_	:					int						 =		sd_pipe(**_lowerCamelCase   ,  cross_attention_kwargs={'''scale''': 0.5}							).images
					A_	:					int						 =		lora_images_with_scale[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(
					    torch.allclose(torch.from_numpy(_lowerCamelCase							)   ,  torch.from_numpy(_lowerCamelCase							)							)							)







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					with tempfile.TemporaryDirectory() as tmpdirname:
						self.create_lora_weight_file(_lowerCamelCase							)

						A_ ,			A_	:					str						 =		self.get_dummy_components()
						A_	:					Optional[Any]						 =		StableDiffusionPipeline(**_lowerCamelCase							)
						A_	:					List[Any]						 =		sd_pipe.to(_lowerCamelCase							)
						sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

						# check if vanilla attention processors are used
						for _, module in sd_pipe.unet.named_modules():
							if isinstance(_lowerCamelCase   ,  _lowerCamelCase							):
								self.assertIsInstance(module.processor   ,  (AttnProcessor, AttnProcessor2_0)							)

            # load LoRA weight file
						sd_pipe.load_lora_weights(_lowerCamelCase							)

						# check if lora attention processors are used
						for _, module in sd_pipe.unet.named_modules():
							if isinstance(_lowerCamelCase   ,  _lowerCamelCase							):
								A_	:					Any						 =		(
								    LoRAAttnProcessor2_0 if hasattr(_lowerCamelCase   ,  '''scaled_dot_product_attention'''							) else LoRAAttnProcessor
								)
								self.assertIsInstance(module.processor   ,  _lowerCamelCase							)







				@unittest.skipIf(torch_device != '''cuda'''   ,  '''This test is supposed to run on GPU'''							)
				def        a_	(				self :       Optional[Any]							):



					"""simple docstring"""

					with tempfile.TemporaryDirectory() as tmpdirname:
						self.create_lora_weight_file(_lowerCamelCase							)

						A_ ,			A_	:					Tuple						 =		self.get_dummy_components()
						A_	:					Optional[int]						 =		StableDiffusionPipeline(**_lowerCamelCase							)
						A_	:					Any						 =		sd_pipe.to(_lowerCamelCase							)
						sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

						# enable XFormers
						sd_pipe.enable_xformers_memory_efficient_attention()

						# check if xFormers attention processors are used
						for _, module in sd_pipe.unet.named_modules():
							if isinstance(_lowerCamelCase   ,  _lowerCamelCase							):
								self.assertIsInstance(module.processor   ,  _lowerCamelCase							)

            # load LoRA weight file
						sd_pipe.load_lora_weights(_lowerCamelCase							)

						# check if lora attention processors are used
						for _, module in sd_pipe.unet.named_modules():
							if isinstance(_lowerCamelCase   ,  _lowerCamelCase							):
								self.assertIsInstance(module.processor   ,  _lowerCamelCase							)







				@unittest.skipIf(torch_device != '''cuda'''   ,  '''This test is supposed to run on GPU'''							)
				def        a_	(				self :       Optional[Any]							):



					"""simple docstring"""

					A_ ,			A_	:					Any						 =		self.get_dummy_components()
					A_	:					Union[str, Any]						 =		StableDiffusionPipeline(**_lowerCamelCase							)
					A_	:					Optional[int]						 =		sd_pipe.to(_lowerCamelCase							)
					sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

					A_ ,			A_ ,			A_	:					int						 =		self.get_dummy_inputs()

					# enable XFormers
					sd_pipe.enable_xformers_memory_efficient_attention()

					A_	:					str						 =		sd_pipe(**_lowerCamelCase							).images
					A_	:					Tuple						 =		original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
						LoraLoaderMixin.save_lora_weights(
						    save_directory=_lowerCamelCase   ,  unet_lora_layers=lora_components['''unet_lora_layers''']   ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']   ,  )
						self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase   ,  '''pytorch_lora_weights.bin'''							)							)							)
						sd_pipe.load_lora_weights(_lowerCamelCase							)

					A_	:					List[Any]						 =		sd_pipe(**_lowerCamelCase							).images
					A_	:					int						 =		lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(_lowerCamelCase							)   ,  torch.from_numpy(_lowerCamelCase							)							)							)





