'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
						from transformers.models.albert.modeling_tf_albert import (
						    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFAlbertForMaskedLM,
						    TFAlbertForMultipleChoice,
						    TFAlbertForPreTraining,
						    TFAlbertForQuestionAnswering,
						    TFAlbertForSequenceClassification,
						    TFAlbertForTokenClassification,
						    TFAlbertModel,
						)




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=13				,			snake_case_=7				,			snake_case_=True				,			snake_case_=True				,			snake_case_=True				,			snake_case_=True				,			snake_case_=99				,			snake_case_=16				,			snake_case_=32				,			snake_case_=2				,			snake_case_=4				,			snake_case_=37				,			snake_case_="gelu"				,			snake_case_=0.1				,			snake_case_=0.1				,			snake_case_=512				,			snake_case_=16				,			snake_case_=2				,			snake_case_=0.02				,			snake_case_=3				,			snake_case_=4				,			snake_case_=None				,			):
									snake_case :       Union[str, Any]				=    parent
									snake_case :       List[Any]				=    13
									snake_case :       Union[str, Any]				=    7
									snake_case :       Optional[int]				=    True
									snake_case :       Union[str, Any]				=    True
									snake_case :       Any				=    True
									snake_case :       int				=    True
									snake_case :       Union[str, Any]				=    99
									snake_case :       Optional[int]				=    16
									snake_case :       Optional[int]				=    32
									snake_case :       List[Any]				=    2
									snake_case :       List[str]				=    4
									snake_case :       Tuple				=    37
									snake_case :       Optional[int]				=    '''gelu'''
									snake_case :       int				=    0.1
									snake_case :       int				=    0.1
									snake_case :       str				=    512
									snake_case :       Tuple				=    16
									snake_case :       Dict				=    2
									snake_case :       str				=    0.02
									snake_case :       str				=    3
									snake_case :       int				=    4
									snake_case :       Any				=    None



			def   lowerCamelCase (							self					):
									snake_case :       Any				=    ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size					)

									snake_case :       List[Any]				=    None
									if self.use_input_mask:
															snake_case :       Optional[Any]				=    random_attention_mask([self.batch_size, self.seq_length]					)

									snake_case :       Tuple				=    None
									if self.use_token_type_ids:
															snake_case :       Dict				=    ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size					)

									snake_case :       List[str]				=    None
									snake_case :       int				=    None
									snake_case :       List[str]				=    None
									if self.use_labels:
															snake_case :       List[Any]				=    ids_tensor([self.batch_size]				,			self.type_sequence_label_size					)
															snake_case :       Union[str, Any]				=    ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels					)
															snake_case :       List[Any]				=    ids_tensor([self.batch_size]				,			self.num_choices					)

									snake_case :       Any				=    AlbertConfig(
									    vocab_size=self.vocab_size				,			hidden_size=self.hidden_size				,			embedding_size=self.embedding_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			max_position_embeddings=self.max_position_embeddings				,			type_vocab_size=self.type_vocab_size				,			initializer_range=self.initializer_range				,			)

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       List[str]				=    TFAlbertModel(config=_lowerCamelCase					)
									# inputs = {'input_ids': input_ids,
									#           'attention_mask': input_mask,
									#           'token_type_ids': token_type_ids}
									# sequence_output, pooled_output = model(**inputs)
									snake_case :       Tuple				=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									snake_case :       int				=    model(_lowerCamelCase					)

									snake_case :       List[Any]				=    [input_ids, input_mask]
									snake_case :       Tuple				=    model(_lowerCamelCase					)

									snake_case :       Tuple				=    model(_lowerCamelCase					)

									self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)					)
									self.parent.assertEqual(result.pooler_output.shape				,			(self.batch_size, self.hidden_size)					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       Dict				=    self.num_labels
									snake_case :       Union[str, Any]				=    TFAlbertForPreTraining(config=_lowerCamelCase					)
									snake_case :       List[Any]				=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									snake_case :       Union[str, Any]				=    model(_lowerCamelCase					)
									self.parent.assertEqual(result.prediction_logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)					)
									self.parent.assertEqual(result.sop_logits.shape				,			(self.batch_size, self.num_labels)					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       str				=    TFAlbertForMaskedLM(config=_lowerCamelCase					)
									snake_case :       List[Any]				=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									snake_case :       Any				=    model(_lowerCamelCase					)
									self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       int				=    self.num_labels
									snake_case :       Optional[int]				=    TFAlbertForSequenceClassification(config=_lowerCamelCase					)
									snake_case :       List[Any]				=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									snake_case :       Dict				=    model(_lowerCamelCase					)
									self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       int				=    TFAlbertForQuestionAnswering(config=_lowerCamelCase					)
									snake_case :       List[Any]				=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									snake_case :       Any				=    model(_lowerCamelCase					)
									self.parent.assertEqual(result.start_logits.shape				,			(self.batch_size, self.seq_length)					)
									self.parent.assertEqual(result.end_logits.shape				,			(self.batch_size, self.seq_length)					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       Tuple				=    self.num_choices
									snake_case :       Any				=    TFAlbertForMultipleChoice(config=_lowerCamelCase					)
									snake_case :       Optional[Any]				=    tf.tile(tf.expand_dims(_lowerCamelCase				,			1					)				,			(1, self.num_choices, 1)					)
									snake_case :       int				=    tf.tile(tf.expand_dims(_lowerCamelCase				,			1					)				,			(1, self.num_choices, 1)					)
									snake_case :       Any				=    tf.tile(tf.expand_dims(_lowerCamelCase				,			1					)				,			(1, self.num_choices, 1)					)
									snake_case :       Dict				=    {
									    '''input_ids''': multiple_choice_inputs_ids,
									    '''attention_mask''': multiple_choice_input_mask,
									    '''token_type_ids''': multiple_choice_token_type_ids,
									}
									snake_case :       Dict				=    model(_lowerCamelCase					)
									self.parent.assertListEqual(list(result["logits"].shape					)				,			[self.batch_size, self.num_choices]					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       Optional[Any]				=    self.num_labels
									snake_case :       Any				=    TFAlbertForTokenClassification(config=_lowerCamelCase					)
									snake_case :       Optional[Any]				=    {
									    '''input_ids''': input_ids,
									    '''attention_mask''': input_mask,
									    '''token_type_ids''': token_type_ids,
									}
									snake_case :       Any				=    model(_lowerCamelCase					)
									self.parent.assertListEqual(list(result["logits"].shape					)				,			[self.batch_size, self.seq_length, self.num_labels]					)






			def   lowerCamelCase (							self					):
									snake_case :       Dict				=    self.prepare_config_and_inputs()
									(
									    snake_case			
									) :       Optional[int]				=    config_and_inputs
									snake_case :       List[Any]				=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
									return config, inputs_dict







@require_tf
class 		SCREAMING_SNAKE_CASE_					(__UpperCAmelCase							,				__UpperCAmelCase							,				unittest.TestCase      ):



			"""simple docstring"""






			__magic_name__ :				Union[str, Any]   							=       (
			    (
			        TFAlbertModel,
			        TFAlbertForPreTraining,
			        TFAlbertForMaskedLM,
			        TFAlbertForSequenceClassification,
			        TFAlbertForQuestionAnswering,
			        TFAlbertForTokenClassification,
			        TFAlbertForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			__magic_name__ :				Optional[Any]   							=       (
			    {
			        """feature-extraction""": TFAlbertModel,
			        """fill-mask""": TFAlbertForMaskedLM,
			        """question-answering""": TFAlbertForQuestionAnswering,
			        """text-classification""": TFAlbertForSequenceClassification,
			        """token-classification""": TFAlbertForTokenClassification,
			        """zero-shot""": TFAlbertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			__magic_name__ :				Tuple   							=       False
			__magic_name__ :				Union[str, Any]   							=       False



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_=False					):
									snake_case :       Union[str, Any]				=    super()._prepare_for_class(_lowerCamelCase				,			_lowerCamelCase				,			return_labels=_lowerCamelCase					)

									if return_labels:
															if model_class in get_values(_lowerCamelCase					):
																					snake_case :       Dict				=    tf.zeros(self.model_tester.batch_size				,			dtype=tf.intaa					)

									return inputs_dict



			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    TFAlbertModelTester(self					)
									snake_case :       Dict				=    ConfigTester(self				,			config_class=_lowerCamelCase				,			hidden_size=37					)



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



			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_model(*_lowerCamelCase					)



			def   lowerCamelCase (							self					):
									snake_case :       int				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_for_pretraining(*_lowerCamelCase					)



			def   lowerCamelCase (							self					):
									snake_case :       Union[str, Any]				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_for_masked_lm(*_lowerCamelCase					)



			def   lowerCamelCase (							self					):
									snake_case :       Any				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_for_multiple_choice(*_lowerCamelCase					)



			def   lowerCamelCase (							self					):
									snake_case :       Optional[int]				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_for_sequence_classification(*_lowerCamelCase					)



			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_albert_for_question_answering(*_lowerCamelCase					)






			@slow
			def   lowerCamelCase (							self					):

									for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case :       Tuple				=    TFAlbertModel.from_pretrained(_lowerCamelCase					)
															self.assertIsNotNone(_lowerCamelCase					)







@require_tf
class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			@slow
			def   lowerCamelCase (							self					):
									snake_case :       Union[str, Any]				=    TFAlbertForPreTraining.from_pretrained("albert-base-v2"					)
									snake_case :       Optional[int]				=    tf.constant([[0, 1, 2, 3, 4, 5]]					)
									snake_case :       str				=    model(_lowerCamelCase					)[0]

									snake_case :       Optional[int]				=    [1, 6, 30_000]
									self.assertEqual(output.shape				,			_lowerCamelCase					)

									snake_case :       List[str]				=    tf.constant(
									    [
									        [
									            [4.595_668, 0.74_462_754, -1.818_147],
									            [4.5_954_347, 0.7_454_184, -1.8_188_258],
									            [4.5_954_905, 0.7_448_235, -1.8_182_316],
									        ]
									    ]					)
									tf.debugging.assert_near(output[:, :3, :3]				,			_lowerCamelCase				,			atol=1e-4					)











"""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							)							)							)





