
'''simple docstring'''



import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    LayoutLMForMaskedLM,
				    LayoutLMForQuestionAnswering,
				    LayoutLMForSequenceClassification,
				    LayoutLMForTokenClassification,
				    LayoutLMModel,
				)





class 			A							:



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=13       ,     lowerCamelCase__=7       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=99       ,     lowerCamelCase__=32       ,     lowerCamelCase__=5       ,     lowerCamelCase__=4       ,     lowerCamelCase__=37       ,     lowerCamelCase__="gelu"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=512       ,     lowerCamelCase__=16       ,     lowerCamelCase__=2       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=3       ,     lowerCamelCase__=4       ,     lowerCamelCase__=None       ,     lowerCamelCase__=1_000       ,     )			->		int:




									'''simple docstring'''



									lowercase__							 =       parent
									lowercase__							 =       batch_size
									lowercase__							 =       seq_length
									lowercase__							 =       is_training
									lowercase__							 =       use_input_mask
									lowercase__							 =       use_token_type_ids
									lowercase__							 =       use_labels
									lowercase__							 =       vocab_size
									lowercase__							 =       hidden_size
									lowercase__							 =       num_hidden_layers
									lowercase__							 =       num_attention_heads
									lowercase__							 =       intermediate_size
									lowercase__							 =       hidden_act
									lowercase__							 =       hidden_dropout_prob
									lowercase__							 =       attention_probs_dropout_prob
									lowercase__							 =       max_position_embeddings
									lowercase__							 =       type_vocab_size
									lowercase__							 =       type_sequence_label_size
									lowercase__							 =       initializer_range
									lowercase__							 =       num_labels
									lowercase__							 =       num_choices
									lowercase__							 =       scope
									lowercase__							 =       range_bbox



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

									lowercase__							 =       ids_tensor([self.batch_size, self.seq_length, 4]       ,     self.range_bbox       )
									# Ensure that bbox is legal
									for i in range(bbox.shape[0]       ):
															for j in range(bbox.shape[1]       ):
																					if bbox[i, j, 3] < bbox[i, j, 1]:
																											lowercase__							 =       bbox[i, j, 3]
																											lowercase__							 =       bbox[i, j, 1]
																											lowercase__							 =       t
																					if bbox[i, j, 2] < bbox[i, j, 0]:
																											lowercase__							 =       bbox[i, j, 2]
																											lowercase__							 =       bbox[i, j, 0]
																											lowercase__							 =       t

									lowercase__							 =       None
									if self.use_input_mask:
															lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     vocab_size=2       )

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

									lowercase__							 =       None
									lowercase__							 =       None
									lowercase__							 =       None
									if self.use_labels:
															lowercase__							 =       ids_tensor([self.batch_size]       ,     self.type_sequence_label_size       )
															lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels       )
															lowercase__							 =       ids_tensor([self.batch_size]       ,     self.num_choices       )

									lowercase__							 =       self.get_config()

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



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									return LayoutLMConfig(
									    vocab_size=self.vocab_size       ,     hidden_size=self.hidden_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       ,     )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       LayoutLMModel(config=lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()
									lowercase__							 =       model(lowerCamelCase__       ,     lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       )
									lowercase__							 =       model(lowerCamelCase__       ,     lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       )
									lowercase__							 =       model(lowerCamelCase__       ,     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 A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[Any]:




									'''simple docstring'''



									lowercase__							 =       LayoutLMForMaskedLM(config=lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()
									lowercase__							 =       model(lowerCamelCase__       ,     lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )
									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       self.num_labels
									lowercase__							 =       LayoutLMForSequenceClassification(lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()
									lowercase__							 =       model(
									    lowerCamelCase__       ,     lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )
									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[Any]:




									'''simple docstring'''



									lowercase__							 =       self.num_labels
									lowercase__							 =       LayoutLMForTokenClassification(config=lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()
									lowercase__							 =       model(lowerCamelCase__       ,     lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )
									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       LayoutLMForQuestionAnswering(config=lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()
									lowercase__							 =       model(
									    lowerCamelCase__       ,     bbox=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     start_positions=lowerCamelCase__       ,     end_positions=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 A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__							 =       self.prepare_config_and_inputs()
									(
									    (
									    lowercase__						
									) ,						(
									    lowercase__						
									) ,						(
									    lowercase__						
									) ,						(
									    lowercase__						
									) ,						(
									    lowercase__						
									) ,						(
									    lowercase__						
									) ,						(
									    lowercase__						
									) ,						(
									    lowercase__						
									) ,												
									)							 =       config_and_inputs
									lowercase__							 =       {
									    """input_ids""": input_ids,
									    """bbox""": bbox,
									    """token_type_ids""": token_type_ids,
									    """attention_mask""": input_mask,
									}
									return config, inputs_dict





@require_torch
class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
			lowerCamelCase							:							Tuple       		=  (
			    (
			        LayoutLMModel,
			        LayoutLMForMaskedLM,
			        LayoutLMForSequenceClassification,
			        LayoutLMForTokenClassification,
			        LayoutLMForQuestionAnswering,
			    )
			    if is_torch_available()
			    else None
			)
			lowerCamelCase							:							Optional[Any]       		=  (
			    {
			        """document-question-answering""": LayoutLMForQuestionAnswering,
			        """feature-extraction""": LayoutLMModel,
			        """fill-mask""": LayoutLMForMaskedLM,
			        """text-classification""": LayoutLMForSequenceClassification,
			        """token-classification""": LayoutLMForTokenClassification,
			        """zero-shot""": LayoutLMForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase							:							Optional[Any]       		=  True



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       LayoutLMModelTester(self       )
									lowercase__							 =       ConfigTester(self       ,     config_class=lowerCamelCase__       ,     hidden_size=37       )



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									self.config_tester.run_common_tests()



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__       )



			def A__ (					self       )			->		Optional[Any]:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
															lowercase__							 =       type
															self.model_tester.create_and_check_model(*lowerCamelCase__       )



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__       )



			def A__ (					self       )			->		Optional[Any]:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__       )



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__       )



			def A__ (					self       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__       )



			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									pass





def  _A		(							):
						# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
						# fmt: off
						lowercase__							 =       torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]		,		device=lowercase__	)  # noqa: E231
						lowercase__							 =       torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]		,		device=lowercase__	)  # noqa: E231
						lowercase__							 =       torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]		,		device=lowercase__	)  # noqa: E231
						lowercase__							 =       torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]		,		device=lowercase__	)  # noqa: E231
						# these are sequence labels (i.e. at the token level)
						lowercase__							 =       torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]		,		device=lowercase__	)  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 			A							(       unittest.TestCase				):



			@slow
			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""       ).to(lowerCamelCase__       )

									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       prepare_layoutlm_batch_inputs()

									# forward pass
									lowercase__							 =       model(input_ids=lowerCamelCase__       ,     bbox=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       )

									# test the sequence output on [0, :3, :3]
									lowercase__							 =       torch.tensor(
									    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]       ,     device=lowerCamelCase__       ,     )

									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,     lowerCamelCase__       ,     atol=1e-3       )       )

									# test the pooled output on [1, :3]
									lowercase__							 =       torch.tensor([-0.65_80, -0.02_14, 0.85_52]       ,     device=lowerCamelCase__       )

									self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]       ,     lowerCamelCase__       ,     atol=1e-3       )       )



			@slow
			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							 =       LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""       ,     num_labels=2       ).to(
									    lowerCamelCase__       )

									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       prepare_layoutlm_batch_inputs()

									# forward pass
									lowercase__							 =       model(
									    input_ids=lowerCamelCase__       ,     bbox=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     labels=torch.tensor([1, 1]       ,     device=lowerCamelCase__       )       ,     )

									# test whether we get a loss as a scalar
									lowercase__							 =       outputs.loss
									lowercase__							 =       torch.Size([]       )
									self.assertEqual(loss.shape       ,     lowerCamelCase__       )

									# test the shape of the logits
									lowercase__							 =       outputs.logits
									lowercase__							 =       torch.Size((2, 2)       )
									self.assertEqual(logits.shape       ,     lowerCamelCase__       )



			@slow
			def A__ (					self       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""       ,     num_labels=13       ).to(
									    lowerCamelCase__       )

									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       prepare_layoutlm_batch_inputs()

									# forward pass
									lowercase__							 =       model(
									    input_ids=lowerCamelCase__       ,     bbox=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )

									# test the loss calculation to be around 2.65
									# expected_loss = torch.tensor(2.65, device=torch_device)

									# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
									# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

									# test the shape of the logits
									lowercase__							 =       outputs.logits
									lowercase__							 =       torch.Size((2, 25, 13)       )
									self.assertEqual(logits.shape       ,     lowerCamelCase__       )



			@slow
			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""       ).to(lowerCamelCase__       )

									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       prepare_layoutlm_batch_inputs()

									# forward pass
									lowercase__							 =       model(input_ids=lowerCamelCase__       ,     bbox=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       )

									# test the shape of the logits
									lowercase__							 =       torch.Size((2, 25)       )
									self.assertEqual(outputs.start_logits.shape       ,     lowerCamelCase__       )
									self.assertEqual(outputs.end_logits.shape       ,     lowerCamelCase__       )








'''simple docstring'''



import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

__A          =    logging.get_logger(__name__)





def  _A		(							lowercase__		,		lowercase__ = True	):
						lowercase__							 =       {}
						for name in model.attn_processors.keys():
												lowercase__							 =       name.endswith("""attn2.processor"""	) and not (
												    name.startswith("""transformer_in"""	) or """temp_attentions""" in name.split("""."""	)
												)
												lowercase__							 =       model.config.cross_attention_dim if has_cross_attention else None
												if name.startswith("""mid_block"""	):
																		lowercase__							 =       model.config.block_out_channels[-1]
												elif name.startswith("""up_blocks"""	):
																		lowercase__							 =       int(name[len("""up_blocks."""	)]	)
																		lowercase__							 =       list(reversed(model.config.block_out_channels	)	)[block_id]
												elif name.startswith("""down_blocks"""	):
																		lowercase__							 =       int(name[len("""down_blocks."""	)]	)
																		lowercase__							 =       model.config.block_out_channels[block_id]
												elif name.startswith("""transformer_in"""	):
																		# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
																		lowercase__							 =       8 * model.config.attention_head_dim

												lowercase__							 =       LoRAAttnProcessor(hidden_size=lowercase__		,		cross_attention_dim=lowercase__	)
												lowercase__							 =       lora_attn_procs[name].to(model.device	)

												if mock_weights:
																		# add 1 to weights to mock trained weights
																		with torch.no_grad():
																								lora_attn_procs[name].to_q_lora.up.weight += 1
																								lora_attn_procs[name].to_k_lora.up.weight += 1
																								lora_attn_procs[name].to_v_lora.up.weight += 1
																								lora_attn_procs[name].to_out_lora.up.weight += 1

						return lora_attn_procs







@skip_mps
class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
			lowerCamelCase							:							List[Any]       		=  UNetaDConditionModel
			lowerCamelCase							:							int       		=  """sample"""



			@property
			def A__ (					self       )			->		Optional[Any]:




									'''simple docstring'''



									lowercase__							 =       4
									lowercase__							 =       4
									lowercase__							 =       4
									lowercase__							 =       (32, 32)

									lowercase__							 =       floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(lowerCamelCase__       )
									lowercase__							 =       torch.tensor([10]       ).to(lowerCamelCase__       )
									lowercase__							 =       floats_tensor((batch_size, 4, 32)       ).to(lowerCamelCase__       )

									return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}



			@property
			def A__ (					self       )			->		Any:




									'''simple docstring'''



									return (4, 4, 32, 32)



			@property
			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									return (4, 4, 32, 32)



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       {
									    """block_out_channels""": (32, 64),
									    """down_block_types""": (
									        """CrossAttnDownBlock3D""",
									        """DownBlock3D""",
									    ),
									    """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
									    """cross_attention_dim""": 32,
									    """attention_head_dim""": 8,
									    """out_channels""": 4,
									    """in_channels""": 4,
									    """layers_per_block""": 1,
									    """sample_size""": 32,
									}
									lowercase__							 =       self.dummy_input
									return init_dict, inputs_dict



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
			def A__ (					self       )			->		str:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       self.model_class(**lowerCamelCase__       )

									model.enable_xformers_memory_efficient_attention()

									assert (
									    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
									    == "XFormersAttnProcessor"
									), "xformers is not enabled"



			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       32

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )

															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       output.sample

									self.assertIsNotNone(lowerCamelCase__       )
									lowercase__							 =       inputs_dict["""sample"""].shape
									self.assertEqual(output.shape       ,     lowerCamelCase__       ,     """Input and output shapes do not match"""       )



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															# Warmup pass when using mps (see #372)
															if torch_device == "mps" and isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					model(**self.dummy_input       )

															lowercase__							 =       model(**lowerCamelCase__       )
															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       first.sample

															lowercase__							 =       model(**lowerCamelCase__       )
															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       second.sample

									lowercase__							 =       first.cpu().numpy()
									lowercase__							 =       second.cpu().numpy()
									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
									lowercase__							 =       np.amax(np.abs(out_a - out_a       )       )
									self.assertLessEqual(lowerCamelCase__       ,     1e-5       )



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									model.set_attention_slice("""auto"""       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None

									model.set_attention_slice("""max"""       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None

									model.set_attention_slice(2       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None



			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )

									# make sure we can set a list of attention processors
									model.set_attn_processor(lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									# test that attn processors can be set to itself
									model.set_attn_processor(model.attn_processors       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									assert (samplea - samplea).abs().max() < 3e-3
									assert (samplea - samplea).abs().max() < 3e-3

									# sample 2 and sample 3 should be different
									assert (samplea - samplea).abs().max() > 3e-3



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									assert (sample - new_sample).abs().max() < 1e-3

									# LoRA and no LoRA should NOT be the same
									assert (sample - old_sample).abs().max() > 1e-4



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       ,     safe_serialization=lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.safetensors"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									assert (sample - new_sample).abs().max() < 3e-3

									# LoRA and no LoRA should NOT be the same
									assert (sample - old_sample).abs().max() > 1e-4



			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       ,     weight_name="""pytorch_lora_weights.bin"""       )



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															with self.assertRaises(lowerCamelCase__       ) as e:
																					new_model.load_attn_procs(lowerCamelCase__       ,     use_safetensors=lowerCamelCase__       )
															self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""       ,     str(e.exception       )       )



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample

									model.set_attn_processor(AttnProcessor()       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									assert (sample - new_sample).abs().max() < 1e-4
									assert (sample - old_sample).abs().max() < 3e-3



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       4

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									# default
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

															model.enable_xformers_memory_efficient_attention()
															lowercase__							 =       model(**lowerCamelCase__       ).sample

															model.disable_xformers_memory_efficient_attention()
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									assert (sample - on_sample).abs().max() < 1e-4
									assert (sample - off_sample).abs().max() < 1e-4



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       32

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )[0]

									model.enable_forward_chunking()
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )[0]

									self.assertEqual(output.shape       ,     output_a.shape       ,     """Shape doesn't match"""       )
									assert np.abs(output.cpu() - output_a.cpu()       ).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.






