






"""simple docstring"""



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


A_     =				logging.get_logger(__name__)

A_     =				{
    '''google/bigbird-pegasus-large-arxiv''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-pubmed''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-bigpatent''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'''
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}






class 				__SCREAMING_SNAKE_CASE    (					UpperCamelCase		):
							snake_case_  =				'bigbird_pegasus'
							snake_case_  =				['past_key_values']
							snake_case_  =				{
							    'num_attention_heads': 'encoder_attention_heads',
							    'hidden_size': 'd_model',
							    'attention_probs_dropout_prob': 'attention_dropout',
							}


							def __init__(      self   :    Any      ,     snake_case   :    Optional[int]=9_6103      ,     snake_case   :    List[Any]=4096      ,     snake_case   :    Union[str, Any]=16      ,     snake_case   :    str=4096      ,     snake_case   :    int=16      ,     snake_case   :    List[str]=16      ,     snake_case   :    List[Any]=4096      ,     snake_case   :    Tuple=16      ,     snake_case   :    str=0.0      ,     snake_case   :    Dict=0.0      ,     snake_case   :    Tuple=True      ,     snake_case   :    List[str]=True      ,     snake_case   :    List[str]="gelu_new"      ,     snake_case   :    Any=1024      ,     snake_case   :    Tuple=0.1      ,     snake_case   :    Optional[int]=0.0      ,     snake_case   :    Tuple=0.0      ,     snake_case   :    Tuple=0.02      ,     snake_case   :    List[Any]=2      ,     snake_case   :    Dict=0.0      ,     snake_case   :    Optional[int]=True      ,     snake_case   :    Dict=0      ,     snake_case   :    List[str]=2      ,     snake_case   :    str=1      ,     snake_case   :    str="block_sparse"      ,     snake_case   :    Optional[Any]=64      ,     snake_case   :    Dict=3      ,     snake_case   :    Tuple=False      ,     **snake_case   :    Any      ,     ):
													'''simple docstring'''





													A__    :	Any			=	vocab_size
													A__    :	Tuple			=	max_position_embeddings
													A__    :	Optional[int]			=	d_model
													A__    :	List[str]			=	encoder_ffn_dim
													A__    :	int			=	encoder_layers
													A__    :	List[Any]			=	encoder_attention_heads
													A__    :	List[str]			=	decoder_ffn_dim
													A__    :	int			=	decoder_layers
													A__    :	List[Any]			=	decoder_attention_heads
													A__    :	Dict			=	dropout
													A__    :	Any			=	attention_dropout
													A__    :	List[str]			=	activation_dropout
													A__    :	List[Any]			=	activation_function
													A__    :	Any			=	init_std
													A__    :	Optional[int]			=	encoder_layerdrop
													A__    :	Any			=	decoder_layerdrop
													A__    :	List[str]			=	classifier_dropout
													A__    :	Any			=	use_cache
													A__    :	List[str]			=	encoder_layers
													A__    :	Any			=	scale_embedding  # scale factor will be sqrt(d_model) if True

													# extra config
													A__    :	List[Any]			=	attention_type
													A__    :	Union[str, Any]			=	block_size
													A__    :	Tuple			=	num_random_blocks
													A__    :	List[str]			=	use_bias

													super().__init__(
													    pad_token_id=snake_case      ,     bos_token_id=snake_case      ,     eos_token_id=snake_case      ,     is_encoder_decoder=snake_case      ,     decoder_start_token_id=snake_case      ,     **snake_case      ,     )






class 				__SCREAMING_SNAKE_CASE    (					UpperCamelCase		):


							@property
							def     _UpperCamelCase							(      self   :    Optional[Any]				):
													'''simple docstring'''





													if self.task in ["default", "seq2seq-lm"]:
																			A__    :	Union[str, Any]			=	OrderedDict(
																			    [
																			        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																			        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																			    ]				)

																			if self.use_past:
																									A__    :	Tuple			=	{0: """batch"""}
																									A__    :	str			=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
																			else:
																									A__    :	Union[str, Any]			=	{0: """batch""", 1: """decoder_sequence"""}
																									A__    :	Any			=	{0: """batch""", 1: """decoder_sequence"""}

																			if self.use_past:
																									self.fill_with_past_key_values_(snake_case      ,     direction="""inputs"""				)
													elif self.task == "causal-lm":
																			# TODO: figure this case out.
																			A__    :	Optional[Any]			=	OrderedDict(
																			    [
																			        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																			        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																			    ]				)
																			if self.use_past:
																									A__	,	A__    :	int			=	self.num_layers
																									for i in range(snake_case				):
																															A__    :	str			=	{0: """batch""", 2: """past_sequence + sequence"""}
																															A__    :	Dict			=	{0: """batch""", 2: """past_sequence + sequence"""}
													else:
																			A__    :	int			=	OrderedDict(
																			    [
																			        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																			        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																			        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
																			        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
																			    ]				)

													return common_inputs


							@property
							def     _UpperCamelCase							(      self   :    str				):
													'''simple docstring'''





													if self.task in ["default", "seq2seq-lm"]:
																			A__    :	str			=	super().outputs
													else:
																			A__    :	int			=	super(snake_case      ,     self				).outputs
																			if self.use_past:
																									A__	,	A__    :	Optional[int]			=	self.num_layers
																									for i in range(snake_case				):
																															A__    :	Optional[int]			=	{0: """batch""", 2: """past_sequence + sequence"""}
																															A__    :	Dict			=	{0: """batch""", 2: """past_sequence + sequence"""}
													return common_outputs


							def     _UpperCamelCase							(      self   :    Dict      ,     snake_case   :    PreTrainedTokenizer      ,     snake_case   :    int = -1      ,     snake_case   :    int = -1      ,     snake_case   :    bool = False      ,     snake_case   :    Optional[TensorType] = None      ,     ):
													'''simple docstring'''





													A__    :	Dict			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    snake_case      ,     snake_case      ,     snake_case      ,     snake_case      ,     snake_case				)

													# Generate decoder inputs
													A__    :	List[Any]			=	seq_length if not self.use_past else 1
													A__    :	int			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    snake_case      ,     snake_case      ,     snake_case      ,     snake_case      ,     snake_case				)
													A__    :	Optional[Any]			=	{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
													A__    :	int			=	dict(**snake_case      ,     **snake_case				)

													if self.use_past:
																			if not is_torch_available():
																									raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
																			else:
																									import torch
																			A__	,	A__    :	Optional[int]			=	common_inputs["""input_ids"""].shape
																			A__    :	str			=	common_inputs["""decoder_input_ids"""].shape[1]
																			A__	,	A__    :	int			=	self.num_attention_heads
																			A__    :	Optional[int]			=	(
																			    batch,
																			    num_encoder_attention_heads,
																			    encoder_seq_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)
																			A__    :	int			=	decoder_seq_length + 3
																			A__    :	str			=	(
																			    batch,
																			    num_decoder_attention_heads,
																			    decoder_past_length,
																			    self._config.hidden_size // num_decoder_attention_heads,
																			)

																			A__    :	Optional[Any]			=	torch.cat(
																			    [common_inputs["""decoder_attention_mask"""], torch.ones(snake_case      ,     snake_case				)]      ,     dim=1				)

																			A__    :	List[Any]			=	[]
																			# If the number of encoder and decoder layers are present in the model configuration, both are considered
																			A__	,	A__    :	Union[str, Any]			=	self.num_layers
																			A__    :	Any			=	min(snake_case      ,     snake_case				)
																			A__    :	str			=	max(snake_case      ,     snake_case				) - min_num_layers
																			A__    :	Optional[Any]			=	"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

																			for _ in range(snake_case				):
																									common_inputs["past_key_values"].append(
																									    (
																									        torch.zeros(snake_case				),
																									        torch.zeros(snake_case				),
																									        torch.zeros(snake_case				),
																									        torch.zeros(snake_case				),
																									    )				)
																			# TODO: test this.
																			A__    :	Any			=	encoder_shape if remaining_side_name == """encoder""" else decoder_shape
																			for _ in range(snake_case      ,     snake_case				):
																									common_inputs["past_key_values"].append((torch.zeros(snake_case				), torch.zeros(snake_case				))				)
													return common_inputs


							def     _UpperCamelCase							(      self   :    Union[str, Any]      ,     snake_case   :    PreTrainedTokenizer      ,     snake_case   :    int = -1      ,     snake_case   :    int = -1      ,     snake_case   :    bool = False      ,     snake_case   :    Optional[TensorType] = None      ,     ):
													'''simple docstring'''





													A__    :	str			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    snake_case      ,     snake_case      ,     snake_case      ,     snake_case      ,     snake_case				)

													if self.use_past:
																			if not is_torch_available():
																									raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
																			else:
																									import torch
																			A__	,	A__    :	Tuple			=	common_inputs["""input_ids"""].shape
																			# Not using the same length for past_key_values
																			A__    :	Optional[Any]			=	seqlen + 2
																			A__	,	A__    :	List[str]			=	self.num_layers
																			A__	,	A__    :	Tuple			=	self.num_attention_heads
																			A__    :	Dict			=	(
																			    batch,
																			    num_encoder_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)

																			A__    :	Any			=	common_inputs["""attention_mask"""].dtype
																			A__    :	Dict			=	torch.cat(
																			    [common_inputs["""attention_mask"""], torch.ones(snake_case      ,     snake_case      ,     dtype=snake_case				)]      ,     dim=1				)
																			A__    :	int			=	[
																			    (torch.zeros(snake_case				), torch.zeros(snake_case				)) for _ in range(snake_case				)
																			]
													return common_inputs


							def     _UpperCamelCase							(      self   :    Optional[Any]      ,     snake_case   :    PreTrainedTokenizer      ,     snake_case   :    int = -1      ,     snake_case   :    int = -1      ,     snake_case   :    bool = False      ,     snake_case   :    Optional[TensorType] = None      ,     ):
													'''simple docstring'''





													A__    :	str			=	compute_effective_axis_dimension(
													    snake_case      ,     fixed_dimension=OnnxConfig.default_fixed_batch      ,     num_token_to_add=0				)

													# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
													A__    :	str			=	tokenizer.num_special_tokens_to_add(snake_case				)
													A__    :	Optional[Any]			=	compute_effective_axis_dimension(
													    snake_case      ,     fixed_dimension=OnnxConfig.default_fixed_sequence      ,     num_token_to_add=snake_case				)

													# Generate dummy inputs according to compute batch and sequence
													A__    :	Optional[Any]			=	[""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
													A__    :	int			=	dict(tokenizer(snake_case      ,     return_tensors=snake_case				)				)
													return common_inputs


							def     _UpperCamelCase							(      self   :    Optional[int]      ,     snake_case   :    PreTrainedTokenizer      ,     snake_case   :    int = -1      ,     snake_case   :    int = -1      ,     snake_case   :    bool = False      ,     snake_case   :    Optional[TensorType] = None      ,     ):
													'''simple docstring'''





													if self.task in ["default", "seq2seq-lm"]:
																			A__    :	Optional[int]			=	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																			    snake_case      ,     batch_size=snake_case      ,     seq_length=snake_case      ,     is_pair=snake_case      ,     framework=snake_case				)

													elif self.task == "causal-lm":
																			A__    :	Optional[int]			=	self._generate_dummy_inputs_for_causal_lm(
																			    snake_case      ,     batch_size=snake_case      ,     seq_length=snake_case      ,     is_pair=snake_case      ,     framework=snake_case				)
													else:
																			A__    :	Optional[Any]			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																			    snake_case      ,     batch_size=snake_case      ,     seq_length=snake_case      ,     is_pair=snake_case      ,     framework=snake_case				)

													return common_inputs


							def     _UpperCamelCase							(      self   :    str      ,     snake_case   :    List[str]      ,     snake_case   :    Dict      ,     snake_case   :    List[Any]      ,     snake_case   :    Dict				):
													'''simple docstring'''





													if self.task in ["default", "seq2seq-lm"]:
																			A__    :	Tuple			=	super()._flatten_past_key_values_(snake_case      ,     snake_case      ,     snake_case      ,     snake_case				)
													else:
																			A__    :	List[str]			=	super(snake_case      ,     self				)._flatten_past_key_values_(
																			    snake_case      ,     snake_case      ,     snake_case      ,     snake_case				)














"""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   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       bool = True   )		->Tuple:
						A__    :	List[str]			=	{}
						for name in model.attn_processors.keys():
												A__    :	Optional[Any]			=	name.endswith("""attn2.processor"""   ) and not (
												    name.startswith("""transformer_in"""   ) or """temp_attentions""" in name.split("""."""   )
												)
												A__    :	Tuple			=	model.config.cross_attention_dim if has_cross_attention else None
												if name.startswith("""mid_block"""   ):
																		A__    :	Tuple			=	model.config.block_out_channels[-1]
												elif name.startswith("""up_blocks"""   ):
																		A__    :	str			=	int(name[len("""up_blocks."""   )]   )
																		A__    :	Tuple			=	list(reversed(model.config.block_out_channels   )   )[block_id]
												elif name.startswith("""down_blocks"""   ):
																		A__    :	List[str]			=	int(name[len("""down_blocks."""   )]   )
																		A__    :	Tuple			=	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
																		A__    :	List[str]			=	8 * model.config.attention_head_dim

												A__    :	Optional[Any]			=	LoRAAttnProcessor(hidden_size=UpperCAmelCase__,    cross_attention_dim=UpperCAmelCase__   )
												A__    :	Optional[int]			=	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    (					UpperCamelCase							,		UpperCamelCase							,		unittest.TestCase		):
							snake_case_  =				UNetaDConditionModel
							snake_case_  =				'sample'


							@property
							def     _UpperCamelCase							(      self   :    str				):
													'''simple docstring'''





													A__    :	Dict			=	4
													A__    :	str			=	4
													A__    :	str			=	4
													A__    :	Union[str, Any]			=	(32, 32)

													A__    :	List[Any]			=	floats_tensor((batch_size, num_channels, num_frames) + sizes				).to(snake_case				)
													A__    :	Any			=	torch.tensor([10]				).to(snake_case				)
													A__    :	Optional[Any]			=	floats_tensor((batch_size, 4, 32)				).to(snake_case				)

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


							@property
							def     _UpperCamelCase							(      self   :    Union[str, Any]				):
													'''simple docstring'''





													return (4, 4, 32, 32)


							@property
							def     _UpperCamelCase							(      self   :    Optional[int]				):
													'''simple docstring'''





													return (4, 4, 32, 32)


							def     _UpperCamelCase							(      self   :    int				):
													'''simple docstring'''





													A__    :	Tuple			=	{
													    """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,
													}
													A__    :	Optional[Any]			=	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     _UpperCamelCase							(      self   :    int				):
													'''simple docstring'''





													A__	,	A__    :	Optional[Any]			=	self.prepare_init_args_and_inputs_for_common()
													A__    :	Dict			=	self.model_class(**snake_case				)

													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     _UpperCamelCase							(      self   :    List[str]				):
													'''simple docstring'''





													A__	,	A__    :	Optional[int]			=	self.prepare_init_args_and_inputs_for_common()

													A__    :	Tuple			=	32

													A__    :	List[Any]			=	self.model_class(**snake_case				)
													model.to(snake_case				)
													model.eval()

													with torch.no_grad():
																			A__    :	Dict			=	model(**snake_case				)

																			if isinstance(snake_case      ,     snake_case				):
																									A__    :	int			=	output.sample

													self.assertIsNotNone(snake_case				)
													A__    :	Any			=	inputs_dict["""sample"""].shape
													self.assertEqual(output.shape      ,     snake_case      ,     """Input and output shapes do not match"""				)


							def     _UpperCamelCase							(      self   :    int				):
													'''simple docstring'''





													A__	,	A__    :	Optional[Any]			=	self.prepare_init_args_and_inputs_for_common()
													A__    :	Any			=	self.model_class(**snake_case				)
													model.to(snake_case				)
													model.eval()

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

																			A__    :	List[str]			=	model(**snake_case				)
																			if isinstance(snake_case      ,     snake_case				):
																									A__    :	List[Any]			=	first.sample

																			A__    :	List[str]			=	model(**snake_case				)
																			if isinstance(snake_case      ,     snake_case				):
																									A__    :	str			=	second.sample

													A__    :	Optional[Any]			=	first.cpu().numpy()
													A__    :	Tuple			=	second.cpu().numpy()
													A__    :	List[Any]			=	out_a[~np.isnan(snake_case				)]
													A__    :	Dict			=	out_a[~np.isnan(snake_case				)]
													A__    :	str			=	np.amax(np.abs(out_a - out_a				)				)
													self.assertLessEqual(snake_case      ,     1e-5				)


							def     _UpperCamelCase							(      self   :    Any				):
													'''simple docstring'''





													A__	,	A__    :	List[Any]			=	self.prepare_init_args_and_inputs_for_common()

													A__    :	List[str]			=	8

													A__    :	Optional[Any]			=	self.model_class(**snake_case				)
													model.to(snake_case				)
													model.eval()

													model.set_attention_slice("""auto"""				)
													with torch.no_grad():
																			A__    :	List[str]			=	model(**snake_case				)
													assert output is not None

													model.set_attention_slice("""max"""				)
													with torch.no_grad():
																			A__    :	Union[str, Any]			=	model(**snake_case				)
													assert output is not None

													model.set_attention_slice(2				)
													with torch.no_grad():
																			A__    :	Dict			=	model(**snake_case				)
													assert output is not None


							def     _UpperCamelCase							(      self   :    str				):
													'''simple docstring'''





													A__	,	A__    :	Dict			=	self.prepare_init_args_and_inputs_for_common()

													A__    :	List[Any]			=	8

													A__    :	Union[str, Any]			=	self.model_class(**snake_case				)
													model.to(snake_case				)

													with torch.no_grad():
																			A__    :	Optional[int]			=	model(**snake_case				).sample

													A__    :	Dict			=	create_lora_layers(snake_case				)

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

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

													with torch.no_grad():
																			A__    :	int			=	model(**snake_case      ,     cross_attention_kwargs={"""scale""": 0.0}				).sample
																			A__    :	Optional[Any]			=	model(**snake_case      ,     cross_attention_kwargs={"""scale""": 0.5}				).sample
																			A__    :	List[Any]			=	model(**snake_case      ,     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     _UpperCamelCase							(      self   :    str				):
													'''simple docstring'''





													A__	,	A__    :	Dict			=	self.prepare_init_args_and_inputs_for_common()

													A__    :	Optional[Any]			=	8

													torch.manual_seed(0				)
													A__    :	str			=	self.model_class(**snake_case				)
													model.to(snake_case				)

													with torch.no_grad():
																			A__    :	Any			=	model(**snake_case				).sample

													A__    :	str			=	create_lora_layers(snake_case				)
													model.set_attn_processor(snake_case				)

													with torch.no_grad():
																			A__    :	Union[str, Any]			=	model(**snake_case      ,     cross_attention_kwargs={"""scale""": 0.5}				).sample

													with tempfile.TemporaryDirectory() as tmpdirname:
																			model.save_attn_procs(snake_case				)
																			self.assertTrue(os.path.isfile(os.path.join(snake_case      ,     """pytorch_lora_weights.bin"""				)				)				)
																			torch.manual_seed(0				)
																			A__    :	Union[str, Any]			=	self.model_class(**snake_case				)
																			new_model.to(snake_case				)
																			new_model.load_attn_procs(snake_case				)

													with torch.no_grad():
																			A__    :	List[Any]			=	new_model(**snake_case      ,     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     _UpperCamelCase							(      self   :    List[Any]				):
													'''simple docstring'''





													A__	,	A__    :	Optional[Any]			=	self.prepare_init_args_and_inputs_for_common()

													A__    :	Optional[Any]			=	8

													torch.manual_seed(0				)
													A__    :	Any			=	self.model_class(**snake_case				)
													model.to(snake_case				)

													with torch.no_grad():
																			A__    :	Tuple			=	model(**snake_case				).sample

													A__    :	Union[str, Any]			=	create_lora_layers(snake_case				)
													model.set_attn_processor(snake_case				)

													with torch.no_grad():
																			A__    :	Tuple			=	model(**snake_case      ,     cross_attention_kwargs={"""scale""": 0.5}				).sample

													with tempfile.TemporaryDirectory() as tmpdirname:
																			model.save_attn_procs(snake_case      ,     safe_serialization=snake_case				)
																			self.assertTrue(os.path.isfile(os.path.join(snake_case      ,     """pytorch_lora_weights.safetensors"""				)				)				)
																			torch.manual_seed(0				)
																			A__    :	Optional[Any]			=	self.model_class(**snake_case				)
																			new_model.to(snake_case				)
																			new_model.load_attn_procs(snake_case				)

													with torch.no_grad():
																			A__    :	Any			=	new_model(**snake_case      ,     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     _UpperCamelCase							(      self   :    List[Any]				):
													'''simple docstring'''





													A__	,	A__    :	List[Any]			=	self.prepare_init_args_and_inputs_for_common()

													A__    :	Any			=	8

													torch.manual_seed(0				)
													A__    :	str			=	self.model_class(**snake_case				)
													model.to(snake_case				)

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


							def     _UpperCamelCase							(      self   :    Union[str, Any]				):
													'''simple docstring'''





													A__	,	A__    :	Union[str, Any]			=	self.prepare_init_args_and_inputs_for_common()

													A__    :	Optional[Any]			=	8

													torch.manual_seed(0				)
													A__    :	Any			=	self.model_class(**snake_case				)
													model.to(snake_case				)

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


							def     _UpperCamelCase							(      self   :    List[str]				):
													'''simple docstring'''





													A__	,	A__    :	Dict			=	self.prepare_init_args_and_inputs_for_common()

													A__    :	Tuple			=	8

													torch.manual_seed(0				)
													A__    :	Optional[int]			=	self.model_class(**snake_case				)
													model.to(snake_case				)

													with torch.no_grad():
																			A__    :	Dict			=	model(**snake_case				).sample

													A__    :	Union[str, Any]			=	create_lora_layers(snake_case				)
													model.set_attn_processor(snake_case				)

													with torch.no_grad():
																			A__    :	Any			=	model(**snake_case      ,     cross_attention_kwargs={"""scale""": 0.0}				).sample

													model.set_attn_processor(AttnProcessor()				)

													with torch.no_grad():
																			A__    :	str			=	model(**snake_case				).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     _UpperCamelCase							(      self   :    List[str]				):
													'''simple docstring'''





													A__	,	A__    :	List[str]			=	self.prepare_init_args_and_inputs_for_common()

													A__    :	List[Any]			=	4

													torch.manual_seed(0				)
													A__    :	Tuple			=	self.model_class(**snake_case				)
													model.to(snake_case				)
													A__    :	List[str]			=	create_lora_layers(snake_case				)
													model.set_attn_processor(snake_case				)

													# default
													with torch.no_grad():
																			A__    :	Optional[Any]			=	model(**snake_case				).sample

																			model.enable_xformers_memory_efficient_attention()
																			A__    :	List[str]			=	model(**snake_case				).sample

																			model.disable_xformers_memory_efficient_attention()
																			A__    :	Union[str, Any]			=	model(**snake_case				).sample

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


							def     _UpperCamelCase							(      self   :    Any				):
													'''simple docstring'''





													A__	,	A__    :	Optional[Any]			=	self.prepare_init_args_and_inputs_for_common()
													A__    :	List[Any]			=	32

													A__    :	Any			=	self.model_class(**snake_case				)
													model.to(snake_case				)
													model.eval()

													with torch.no_grad():
																			A__    :	List[Any]			=	model(**snake_case				)[0]

													model.enable_forward_chunking()
													with torch.no_grad():
																			A__    :	Union[str, Any]			=	model(**snake_case				)[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.






