






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()

__UpperCamelCase   :						List[str]							=  logging.get_logger(__name__)
def        a_	( _A ,				_A = True					)     ->     str:







				"""simple docstring"""
				snake_case__													=       {}
				for name in model.attn_processors.keys():
								snake_case__													=       name.endswith('attn2.processor'					) and not (
								    name.startswith('transformer_in'					) or 'temp_attentions' in name.split('.'					)
								)
								snake_case__													=       model.config.cross_attention_dim if has_cross_attention else None
								if name.startswith('mid_block'					):
												snake_case__													=       model.config.block_out_channels[-1]
								elif name.startswith('up_blocks'					):
												snake_case__													=       int(name[len('up_blocks.'					)]					)
												snake_case__													=       list(reversed(model.config.block_out_channels					)					)[block_id]
								elif name.startswith('down_blocks'					):
												snake_case__													=       int(name[len('down_blocks.'					)]					)
												snake_case__													=       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
												snake_case__													=       8 * model.config.attention_head_dim

								snake_case__													=       LoRAAttnProcessor(hidden_size=_A ,				cross_attention_dim=_A					)
								snake_case__													=       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 					__SCREAMING_SNAKE_CASE(		a_      ,					a_      ,					unittest.TestCase		):
							_UpperCAmelCase	      =    UNetaDConditionModel
							_UpperCAmelCase	      =    "sample"
							@property
							def lowerCAmelCase_	(       self: Optional[Any]  )						->						Tuple:
											snake_case__													=       4
											snake_case__													=       4
											snake_case__													=       4
											snake_case__													=       (32, 32)

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

											return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
							@property
							def lowerCAmelCase_	(       self: Any  )						->						int:
											return (4, 4, 32, 32)
							@property
							def lowerCAmelCase_	(       self: Union[str, Any]  )						->						Any:
											return (4, 4, 32, 32)
							def lowerCAmelCase_	(       self: int  )						->						int:
											snake_case__													=       {
											    '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,
											}
											snake_case__													=       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 lowerCAmelCase_	(       self: str  )						->						List[Any]:
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()
											snake_case__													=       self.model_class(**UpperCamelCase  )

											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 lowerCAmelCase_	(       self: Tuple  )						->						int:
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()

											snake_case__													=       32

											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )
											model.eval()

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  )

															if isinstance(UpperCamelCase    ,		UpperCamelCase  ):
																			snake_case__													=       output.sample

											self.assertIsNotNone(UpperCamelCase  )
											snake_case__													=       inputs_dict['sample'].shape
											self.assertEqual(output.shape    ,		UpperCamelCase    ,		'Input and output shapes do not match'  )
							def lowerCAmelCase_	(       self: Optional[Any]  )						->						Dict:
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()
											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )
											model.eval()

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

															snake_case__													=       model(**UpperCamelCase  )
															if isinstance(UpperCamelCase    ,		UpperCamelCase  ):
																			snake_case__													=       first.sample

															snake_case__													=       model(**UpperCamelCase  )
															if isinstance(UpperCamelCase    ,		UpperCamelCase  ):
																			snake_case__													=       second.sample

											snake_case__													=       first.cpu().numpy()
											snake_case__													=       second.cpu().numpy()
											snake_case__													=       out_a[~np.isnan(UpperCamelCase  )]
											snake_case__													=       out_a[~np.isnan(UpperCamelCase  )]
											snake_case__													=       np.amax(np.abs(out_a - out_a  )  )
											self.assertLessEqual(UpperCamelCase    ,		1e-5  )
							def lowerCAmelCase_	(       self: Optional[Any]  )						->						List[str]:
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()

											snake_case__													=       8

											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )
											model.eval()

											model.set_attention_slice('auto'  )
											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  )
											assert output is not None

											model.set_attention_slice('max'  )
											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  )
											assert output is not None

											model.set_attention_slice(2  )
											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  )
											assert output is not None
							def lowerCAmelCase_	(       self: List[Any]  )						->						List[Any]:
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()

											snake_case__													=       8

											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  ).sample

											snake_case__													=       create_lora_layers(UpperCamelCase  )

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

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

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase    ,		cross_attention_kwargs={'scale': 0.0}  ).sample
															snake_case__													=       model(**UpperCamelCase    ,		cross_attention_kwargs={'scale': 0.5}  ).sample
															snake_case__													=       model(**UpperCamelCase    ,		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 lowerCAmelCase_	(       self: List[str]  )						->						List[str]:
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()

											snake_case__													=       8

											torch.manual_seed(0  )
											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  ).sample

											snake_case__													=       create_lora_layers(UpperCamelCase  )
											model.set_attn_processor(UpperCamelCase  )

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase    ,		cross_attention_kwargs={'scale': 0.5}  ).sample

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

											with torch.no_grad():
															snake_case__													=       new_model(**UpperCamelCase    ,		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 lowerCAmelCase_	(       self: Any  )						->						Union[str, Any]:
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()

											snake_case__													=       8

											torch.manual_seed(0  )
											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  ).sample

											snake_case__													=       create_lora_layers(UpperCamelCase  )
											model.set_attn_processor(UpperCamelCase  )

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase    ,		cross_attention_kwargs={'scale': 0.5}  ).sample

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

											with torch.no_grad():
															snake_case__													=       new_model(**UpperCamelCase    ,		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 lowerCAmelCase_	(       self: Any  )						->						Union[str, Any]:
											# enable deterministic behavior for gradient checkpointing
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()

											snake_case__													=       8

											torch.manual_seed(0  )
											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )

											snake_case__													=       create_lora_layers(UpperCamelCase    ,		mock_weights=UpperCamelCase  )
											model.set_attn_processor(UpperCamelCase  )
											# Saving as torch, properly reloads with directly filename
											with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(UpperCamelCase  )
															self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase    ,		'pytorch_lora_weights.bin'  )  )  )
															torch.manual_seed(0  )
															snake_case__													=       self.model_class(**UpperCamelCase  )
															new_model.to(UpperCamelCase  )
															new_model.load_attn_procs(UpperCamelCase    ,		weight_name='pytorch_lora_weights.bin'  )
							def lowerCAmelCase_	(       self: Optional[int]  )						->						List[Any]:
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()

											snake_case__													=       8

											torch.manual_seed(0  )
											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )

											snake_case__													=       create_lora_layers(UpperCamelCase    ,		mock_weights=UpperCamelCase  )
											model.set_attn_processor(UpperCamelCase  )
											# Saving as torch, properly reloads with directly filename
											with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(UpperCamelCase  )
															self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase    ,		'pytorch_lora_weights.bin'  )  )  )
															torch.manual_seed(0  )
															snake_case__													=       self.model_class(**UpperCamelCase  )
															new_model.to(UpperCamelCase  )
															with self.assertRaises(UpperCamelCase  ) as e:
																			new_model.load_attn_procs(UpperCamelCase    ,		use_safetensors=UpperCamelCase  )
															self.assertIn('Error no file named pytorch_lora_weights.safetensors'    ,		str(e.exception  )  )
							def lowerCAmelCase_	(       self: Optional[int]  )						->						List[str]:
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()

											snake_case__													=       8

											torch.manual_seed(0  )
											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  ).sample

											snake_case__													=       create_lora_layers(UpperCamelCase  )
											model.set_attn_processor(UpperCamelCase  )

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase    ,		cross_attention_kwargs={'scale': 0.0}  ).sample

											model.set_attn_processor(AttnProcessor()  )

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  ).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 lowerCAmelCase_	(       self: Tuple  )						->						Union[str, Any]:
											# enable deterministic behavior for gradient checkpointing
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()

											snake_case__													=       4

											torch.manual_seed(0  )
											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )
											snake_case__													=       create_lora_layers(UpperCamelCase  )
											model.set_attn_processor(UpperCamelCase  )

											# default
											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  ).sample

															model.enable_xformers_memory_efficient_attention()
															snake_case__													=       model(**UpperCamelCase  ).sample

															model.disable_xformers_memory_efficient_attention()
															snake_case__													=       model(**UpperCamelCase  ).sample

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


							def lowerCAmelCase_	(       self: Any  )						->						List[Any]:
											snake_case__							,  snake_case__													=       self.prepare_init_args_and_inputs_for_common()
											snake_case__													=       32

											snake_case__													=       self.model_class(**UpperCamelCase  )
											model.to(UpperCamelCase  )
											model.eval()

											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  )[0]

											model.enable_forward_chunking()
											with torch.no_grad():
															snake_case__													=       model(**UpperCamelCase  )[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.












import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.electra.modeling_flax_electra import (
							    FlaxElectraForCausalLM,
							    FlaxElectraForMaskedLM,
							    FlaxElectraForMultipleChoice,
							    FlaxElectraForPreTraining,
							    FlaxElectraForQuestionAnswering,
							    FlaxElectraForSequenceClassification,
							    FlaxElectraForTokenClassification,
							    FlaxElectraModel,
							)


class 					__SCREAMING_SNAKE_CASE(		unittest.TestCase		):
							def __init__(       self: int    ,		UpperCamelCase: List[str]    ,		UpperCamelCase: Dict=13    ,		UpperCamelCase: Dict=7    ,		UpperCamelCase: Union[str, Any]=True    ,		UpperCamelCase: List[Any]=True    ,		UpperCamelCase: Optional[int]=True    ,		UpperCamelCase: Union[str, Any]=True    ,		UpperCamelCase: Optional[int]=99    ,		UpperCamelCase: Optional[int]=24    ,		UpperCamelCase: Any=32    ,		UpperCamelCase: Any=5    ,		UpperCamelCase: Any=4    ,		UpperCamelCase: int=37    ,		UpperCamelCase: Union[str, Any]="gelu"    ,		UpperCamelCase: str=0.1    ,		UpperCamelCase: str=0.1    ,		UpperCamelCase: int=5_12    ,		UpperCamelCase: Tuple=16    ,		UpperCamelCase: Dict=2    ,		UpperCamelCase: Any=0.02    ,		UpperCamelCase: List[Any]=4    ,		)						->						int:
											snake_case__													=       parent
											snake_case__													=       batch_size
											snake_case__													=       seq_length
											snake_case__													=       is_training
											snake_case__													=       use_attention_mask
											snake_case__													=       use_token_type_ids
											snake_case__													=       use_labels
											snake_case__													=       vocab_size
											snake_case__													=       hidden_size
											snake_case__													=       embedding_size
											snake_case__													=       num_hidden_layers
											snake_case__													=       num_attention_heads
											snake_case__													=       intermediate_size
											snake_case__													=       hidden_act
											snake_case__													=       hidden_dropout_prob
											snake_case__													=       attention_probs_dropout_prob
											snake_case__													=       max_position_embeddings
											snake_case__													=       type_vocab_size
											snake_case__													=       type_sequence_label_size
											snake_case__													=       initializer_range
											snake_case__													=       num_choices
							def lowerCAmelCase_	(       self: List[str]  )						->						List[str]:
											snake_case__													=       ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size  )

											snake_case__													=       None
											if self.use_attention_mask:
															snake_case__													=       random_attention_mask([self.batch_size, self.seq_length]  )

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

											snake_case__													=       ElectraConfig(
											    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, attention_mask


							def lowerCAmelCase_	(       self: Tuple  )						->						Any:
											snake_case__													=       self.prepare_config_and_inputs()
											snake_case__							,  snake_case__							,  snake_case__							,  snake_case__													=       config_and_inputs
											snake_case__													=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


@require_flax
class 					__SCREAMING_SNAKE_CASE(		a_      ,					unittest.TestCase		):
							_UpperCAmelCase	      =    True

							_UpperCAmelCase	      =    (
							    (
							        FlaxElectraModel,
							        FlaxElectraForCausalLM,
							        FlaxElectraForMaskedLM,
							        FlaxElectraForPreTraining,
							        FlaxElectraForTokenClassification,
							        FlaxElectraForQuestionAnswering,
							        FlaxElectraForMultipleChoice,
							        FlaxElectraForSequenceClassification,
							    )
							    if is_flax_available()
							    else ()
							)
							def lowerCAmelCase_	(       self: List[Any]  )						->						Dict:
											snake_case__													=       FlaxElectraModelTester(self  )


							@slow
							def lowerCAmelCase_	(       self: int  )						->						Optional[int]:
											for model_class_name in self.all_model_classes:
															if model_class_name == FlaxElectraForMaskedLM:
																			snake_case__													=       model_class_name.from_pretrained('google/electra-small-generator'  )
															else:
																			snake_case__													=       model_class_name.from_pretrained('google/electra-small-discriminator'  )
															snake_case__													=       model(np.ones((1, 1)  )  )
															self.assertIsNotNone(UpperCamelCase  )




