"""simple docstring"""



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

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


_UpperCamelCase         =		logging.get_logger(__name__)

_UpperCamelCase         =		{
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Any				    =							'blenderbot'
			_UpperCamelCase     :	List[Any]				    =							['past_key_values']
			_UpperCamelCase     :	List[Any]				    =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





			def __init__( self      ,			snake_case_=8_008      ,			snake_case_=128      ,			snake_case_=2      ,			snake_case_=10_240      ,			snake_case_=32      ,			snake_case_=24      ,			snake_case_=10_240      ,			snake_case_=32      ,			snake_case_=0.0      ,			snake_case_=0.0      ,			snake_case_=True      ,			snake_case_=True      ,			snake_case_="gelu"      ,			snake_case_=2_560      ,			snake_case_=0.1      ,			snake_case_=0.0      ,			snake_case_=0.0      ,			snake_case_=0.02      ,			snake_case_=1      ,			snake_case_=False      ,			snake_case_=0      ,			snake_case_=1      ,			snake_case_=2      ,			snake_case_=3      ,			snake_case_=2      ,			**snake_case_      ,			):







										'''simple docstring'''


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

										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_      ,			encoder_no_repeat_ngram_size=snake_case_      ,			forced_eos_token_id=snake_case_      ,			**snake_case_      ,			)





class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''





			@property
			def 		lowerCamelCase				( self       ):







										'''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__					:    Dict														= {0: """batch"""}
																								A__					:    Union[str, Any]														= {0: """batch""", 1: """past_decoder_sequence + sequence"""}
																	else:
																								A__					:    Dict														= {0: """batch""", 1: """decoder_sequence"""}
																								A__					:    List[str]														= {0: """batch""", 1: """decoder_sequence"""}
																	if self.use_past:
																								self.fill_with_past_key_values_(snake_case_      ,			direction="""inputs"""       )
										elif self.task == "causal-lm":
																	A__					:    str														= OrderedDict(
																	    [
																	        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																	        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																	    ]       )
																	if self.use_past:
																								A__							,       A__					:    List[Any]														= self.num_layers
																								for i in range(snake_case_       ):
																															A__					:    Tuple														= {0: """batch""", 2: """past_sequence + sequence"""}
																															A__					:    List[str]														= {0: """batch""", 2: """past_sequence + sequence"""}
										else:
																	A__					:    Optional[Any]														= 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
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_ = -1      ,			snake_case_ = -1      ,			snake_case_ = False      ,			snake_case_ = None      ,			):







										'''simple docstring'''


										A__					:    Optional[Any]														= 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[str]														= seq_length if not self.use_past else 1
										A__					:    Any														= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       )
										A__					:    int														= {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__					:    List[str]														= common_inputs["""input_ids"""].shape
																	A__					:    List[Any]														= common_inputs["""decoder_input_ids"""].shape[1]
																	A__							,       A__					:    Dict														= self.num_attention_heads
																	A__					:    Optional[int]														= (
																	    batch,
																	    num_encoder_attention_heads,
																	    encoder_seq_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	A__					:    List[str]														= decoder_seq_length
																	A__					:    Optional[Any]														= (
																	    batch,
																	    num_decoder_attention_heads,
																	    decoder_past_length,
																	    self._config.hidden_size // num_decoder_attention_heads,
																	)
																	A__					:    str														= torch.cat(
																	    [common_inputs["""decoder_attention_mask"""], torch.ones(snake_case_      ,			snake_case_       )]      ,			dim=1       )
																	A__					:    int														= []
																	A__							,       A__					:    List[str]														= self.num_layers

																	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_       ),
																								    )       )
										return common_inputs





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_ = -1      ,			snake_case_ = -1      ,			snake_case_ = False      ,			snake_case_ = None      ,			):







										'''simple docstring'''


										A__					:    Union[str, Any]														= 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__					:    Dict														= common_inputs["""input_ids"""].shape
																	A__					:    Optional[Any]														= seqlen
																	A__							,       A__					:    Union[str, Any]														= self.num_layers
																	A__							,       A__					:    List[Any]														= self.num_attention_heads
																	A__					:    Optional[Any]														= (
																	    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__					:    Union[str, Any]														= torch.cat(
																	    [common_inputs["""attention_mask"""], torch.ones(snake_case_      ,			snake_case_      ,			dtype=snake_case_       )]      ,			dim=1       )
																	A__					:    Dict														= [
																	    (torch.zeros(snake_case_       ), torch.zeros(snake_case_       )) for _ in range(snake_case_       )
																	]
										return common_inputs





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_ = -1      ,			snake_case_ = -1      ,			snake_case_ = False      ,			snake_case_ = None      ,			):







										'''simple docstring'''


										A__					:    int														= 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__					:    Optional[Any]														= tokenizer.num_special_tokens_to_add(snake_case_       )
										A__					:    Tuple														= 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__					:    List[str]														= [""" """.join([tokenizer.unk_token]       ) * seq_length] * batch_size
										A__					:    Any														= dict(tokenizer(snake_case_      ,			return_tensors=snake_case_       )       )
										return common_inputs





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_ = -1      ,			snake_case_ = -1      ,			snake_case_ = False      ,			snake_case_ = 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__					:    Any														= 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__					:    Union[str, 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 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if self.task in ["default", "seq2seq-lm"]:
																	A__					:    Any														= super()._flatten_past_key_values_(snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       )
										else:
																	A__					:    Union[str, Any]														= super(snake_case_      ,			self       )._flatten_past_key_values_(
																	    snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if direction not in ["inputs", "outputs"]:
																	raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given'''       )

										A__					:    str														= """past_key_values""" if direction == """inputs""" else """present"""
										A__							,       A__					:    Tuple														= self.num_layers

										A__					:    Tuple														= """past_encoder_sequence"""
										A__					:    str														= """past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

										for i in range(snake_case_       ):
																	A__					:    Any														= {0: """batch""", 2: decoder_sequence}
																	A__					:    Optional[int]														= {0: """batch""", 2: decoder_sequence}
																	A__					:    Optional[Any]														= {0: """batch""", 2: encoder_sequence}
																	A__					:    List[str]														= {0: """batch""", 2: encoder_sequence}






"""simple docstring"""



# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class 	__UpperCAmelCase						(__A       ,      __A       ,      __A       ,      unittest.TestCase    ):







			'''simple docstring'''
			_UpperCamelCase     :	List[str]				    =							StableDiffusionControlNetInpaintPipeline
			_UpperCamelCase     :	List[Any]				    =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCamelCase     :	Dict				    =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCamelCase     :	Dict				    =							frozenset({'control_image'}    )  # skip `image` and `mask` for now, only test for control_image
			_UpperCamelCase     :	Dict				    =							TEXT_TO_IMAGE_IMAGE_PARAMS





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										torch.manual_seed(0       )
										A__					:    Optional[int]														= UNetaDConditionModel(
										    block_out_channels=(32, 64)      ,			layers_per_block=2      ,			sample_size=32      ,			in_channels=9      ,			out_channels=4      ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,			cross_attention_dim=32      ,			)
										torch.manual_seed(0       )
										A__					:    Any														= ControlNetModel(
										    block_out_channels=(32, 64)      ,			layers_per_block=2      ,			in_channels=4      ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,			cross_attention_dim=32      ,			conditioning_embedding_out_channels=(16, 32)      ,			)
										torch.manual_seed(0       )
										A__					:    List[str]														= DDIMScheduler(
										    beta_start=0.0_00_85      ,			beta_end=0.0_12      ,			beta_schedule="""scaled_linear"""      ,			clip_sample=snake_case_      ,			set_alpha_to_one=snake_case_      ,			)
										torch.manual_seed(0       )
										A__					:    List[Any]														= AutoencoderKL(
										    block_out_channels=[32, 64]      ,			in_channels=3      ,			out_channels=3      ,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,			latent_channels=4      ,			)
										torch.manual_seed(0       )
										A__					:    List[str]														= CLIPTextConfig(
										    bos_token_id=0      ,			eos_token_id=2      ,			hidden_size=32      ,			intermediate_size=37      ,			layer_norm_eps=1E-05      ,			num_attention_heads=4      ,			num_hidden_layers=5      ,			pad_token_id=1      ,			vocab_size=1_000      ,			)
										A__					:    Any														= CLIPTextModel(snake_case_       )
										A__					:    Optional[int]														= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

										A__					:    Union[str, Any]														= {
										    """unet""": unet,
										    """controlnet""": controlnet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_=0       ):







										'''simple docstring'''


										if str(snake_case_       ).startswith("""mps"""       ):
																	A__					:    Tuple														= torch.manual_seed(snake_case_       )
										else:
																	A__					:    Dict														= torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )

										A__					:    Union[str, Any]														= 2
										A__					:    int														= randn_tensor(
										    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,			generator=snake_case_      ,			device=torch.device(snake_case_       )      ,			)
										A__					:    List[Any]														= floats_tensor((1, 3, 32, 32)      ,			rng=random.Random(snake_case_       )       ).to(snake_case_       )
										A__					:    Union[str, Any]														= init_image.cpu().permute(0      ,			2      ,			3      ,			1       )[0]

										A__					:    Dict														= Image.fromarray(np.uinta(snake_case_       )       ).convert("""RGB"""       ).resize((64, 64)       )
										A__					:    Union[str, Any]														= Image.fromarray(np.uinta(init_image + 4       )       ).convert("""RGB"""       ).resize((64, 64)       )

										A__					:    int														= {
										    """prompt""": """A painting of a squirrel eating a burger""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										    """image""": image,
										    """mask_image""": mask_image,
										    """control_image""": control_image,
										}

										return inputs





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return self._test_attention_slicing_forward_pass(expected_max_diff=2E-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       ):







										'''simple docstring'''


										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										self._test_inference_batch_single_identical(expected_max_diff=2E-3       )





class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Tuple				    =							StableDiffusionControlNetInpaintPipeline
			_UpperCamelCase     :	List[Any]				    =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCamelCase     :	List[Any]				    =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCamelCase     :	Tuple				    =							frozenset([]    )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										torch.manual_seed(0       )
										A__					:    List[Any]														= UNetaDConditionModel(
										    block_out_channels=(32, 64)      ,			layers_per_block=2      ,			sample_size=32      ,			in_channels=4      ,			out_channels=4      ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,			cross_attention_dim=32      ,			)
										torch.manual_seed(0       )
										A__					:    Any														= ControlNetModel(
										    block_out_channels=(32, 64)      ,			layers_per_block=2      ,			in_channels=4      ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,			cross_attention_dim=32      ,			conditioning_embedding_out_channels=(16, 32)      ,			)
										torch.manual_seed(0       )
										A__					:    Optional[int]														= DDIMScheduler(
										    beta_start=0.0_00_85      ,			beta_end=0.0_12      ,			beta_schedule="""scaled_linear"""      ,			clip_sample=snake_case_      ,			set_alpha_to_one=snake_case_      ,			)
										torch.manual_seed(0       )
										A__					:    List[str]														= AutoencoderKL(
										    block_out_channels=[32, 64]      ,			in_channels=3      ,			out_channels=3      ,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,			latent_channels=4      ,			)
										torch.manual_seed(0       )
										A__					:    Any														= CLIPTextConfig(
										    bos_token_id=0      ,			eos_token_id=2      ,			hidden_size=32      ,			intermediate_size=37      ,			layer_norm_eps=1E-05      ,			num_attention_heads=4      ,			num_hidden_layers=5      ,			pad_token_id=1      ,			vocab_size=1_000      ,			)
										A__					:    List[Any]														= CLIPTextModel(snake_case_       )
										A__					:    Union[str, Any]														= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

										A__					:    Optional[int]														= {
										    """unet""": unet,
										    """controlnet""": controlnet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components





class 	__UpperCAmelCase						(__A       ,      __A       ,      unittest.TestCase    ):







			'''simple docstring'''
			_UpperCamelCase     :	Dict				    =							StableDiffusionControlNetInpaintPipeline
			_UpperCamelCase     :	int				    =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCamelCase     :	Optional[int]				    =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										torch.manual_seed(0       )
										A__					:    List[Any]														= UNetaDConditionModel(
										    block_out_channels=(32, 64)      ,			layers_per_block=2      ,			sample_size=32      ,			in_channels=9      ,			out_channels=4      ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,			cross_attention_dim=32      ,			)
										torch.manual_seed(0       )

										def init_weights(snake_case_       ):
																	if isinstance(snake_case_      ,			torch.nn.Convad       ):
																								torch.nn.init.normal(m.weight       )
																								m.bias.data.fill_(1.0       )

										A__					:    Dict														= ControlNetModel(
										    block_out_channels=(32, 64)      ,			layers_per_block=2      ,			in_channels=4      ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,			cross_attention_dim=32      ,			conditioning_embedding_out_channels=(16, 32)      ,			)
										controlneta.controlnet_down_blocks.apply(snake_case_       )

										torch.manual_seed(0       )
										A__					:    Union[str, Any]														= ControlNetModel(
										    block_out_channels=(32, 64)      ,			layers_per_block=2      ,			in_channels=4      ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,			cross_attention_dim=32      ,			conditioning_embedding_out_channels=(16, 32)      ,			)
										controlneta.controlnet_down_blocks.apply(snake_case_       )

										torch.manual_seed(0       )
										A__					:    Dict														= DDIMScheduler(
										    beta_start=0.0_00_85      ,			beta_end=0.0_12      ,			beta_schedule="""scaled_linear"""      ,			clip_sample=snake_case_      ,			set_alpha_to_one=snake_case_      ,			)
										torch.manual_seed(0       )
										A__					:    Union[str, Any]														= AutoencoderKL(
										    block_out_channels=[32, 64]      ,			in_channels=3      ,			out_channels=3      ,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,			latent_channels=4      ,			)
										torch.manual_seed(0       )
										A__					:    str														= CLIPTextConfig(
										    bos_token_id=0      ,			eos_token_id=2      ,			hidden_size=32      ,			intermediate_size=37      ,			layer_norm_eps=1E-05      ,			num_attention_heads=4      ,			num_hidden_layers=5      ,			pad_token_id=1      ,			vocab_size=1_000      ,			)
										A__					:    List[Any]														= CLIPTextModel(snake_case_       )
										A__					:    int														= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

										A__					:    Optional[Any]														= MultiControlNetModel([controlneta, controlneta]       )

										A__					:    str														= {
										    """unet""": unet,
										    """controlnet""": controlnet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_=0       ):







										'''simple docstring'''


										if str(snake_case_       ).startswith("""mps"""       ):
																	A__					:    Optional[Any]														= torch.manual_seed(snake_case_       )
										else:
																	A__					:    Optional[Any]														= torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )

										A__					:    Any														= 2

										A__					:    List[str]														= [
										    randn_tensor(
										        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,			generator=snake_case_      ,			device=torch.device(snake_case_       )      ,			),
										    randn_tensor(
										        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,			generator=snake_case_      ,			device=torch.device(snake_case_       )      ,			),
										]
										A__					:    Optional[int]														= floats_tensor((1, 3, 32, 32)      ,			rng=random.Random(snake_case_       )       ).to(snake_case_       )
										A__					:    Optional[Any]														= init_image.cpu().permute(0      ,			2      ,			3      ,			1       )[0]

										A__					:    Tuple														= Image.fromarray(np.uinta(snake_case_       )       ).convert("""RGB"""       ).resize((64, 64)       )
										A__					:    Union[str, Any]														= Image.fromarray(np.uinta(init_image + 4       )       ).convert("""RGB"""       ).resize((64, 64)       )

										A__					:    Any														= {
										    """prompt""": """A painting of a squirrel eating a burger""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										    """image""": image,
										    """mask_image""": mask_image,
										    """control_image""": control_image,
										}

										return inputs





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    int														= self.get_dummy_components()
										A__					:    str														= self.pipeline_class(**snake_case_       )
										pipe.to(snake_case_       )

										A__					:    Union[str, Any]														= 10.0
										A__					:    Union[str, Any]														= 4

										A__					:    List[Any]														= self.get_dummy_inputs(snake_case_       )
										A__					:    Optional[int]														= steps
										A__					:    List[Any]														= scale
										A__					:    List[Any]														= pipe(**snake_case_       )[0]

										A__					:    Union[str, Any]														= self.get_dummy_inputs(snake_case_       )
										A__					:    Dict														= steps
										A__					:    List[str]														= scale
										A__					:    str														= pipe(**snake_case_      ,			control_guidance_start=0.1      ,			control_guidance_end=0.2       )[0]

										A__					:    Union[str, Any]														= self.get_dummy_inputs(snake_case_       )
										A__					:    Union[str, Any]														= steps
										A__					:    Tuple														= scale
										A__					:    int														= pipe(**snake_case_      ,			control_guidance_start=[0.1, 0.3]      ,			control_guidance_end=[0.2, 0.7]       )[0]

										A__					:    Any														= self.get_dummy_inputs(snake_case_       )
										A__					:    Union[str, Any]														= steps
										A__					:    str														= scale
										A__					:    Optional[Any]														= pipe(**snake_case_      ,			control_guidance_start=0.4      ,			control_guidance_end=[0.5, 0.8]       )[0]

										# make sure that all outputs are different
										assert np.sum(np.abs(output_a - output_a       )       ) > 1E-3
										assert np.sum(np.abs(output_a - output_a       )       ) > 1E-3
										assert np.sum(np.abs(output_a - output_a       )       ) > 1E-3





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return self._test_attention_slicing_forward_pass(expected_max_diff=2E-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       ):







										'''simple docstring'''


										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										self._test_inference_batch_single_identical(expected_max_diff=2E-3       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Any														= self.get_dummy_components()
										A__					:    int														= self.pipeline_class(**snake_case_       )
										pipe.to(snake_case_       )
										pipe.set_progress_bar_config(disable=snake_case_       )
										with tempfile.TemporaryDirectory() as tmpdir:
																	try:
																								# save_pretrained is not implemented for Multi-ControlNet
																								pipe.save_pretrained(snake_case_       )
																	except NotImplementedError:
																								pass





@slow
@require_torch_gpu
class 	__UpperCAmelCase						(unittest.TestCase    ):







			'''simple docstring'''





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""       )

										A__					:    Optional[int]														= StableDiffusionControlNetInpaintPipeline.from_pretrained(
										    """runwayml/stable-diffusion-inpainting"""      ,			safety_checker=snake_case_      ,			controlnet=snake_case_       )
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=snake_case_       )

										A__					:    List[str]														= torch.Generator(device="""cpu"""       ).manual_seed(0       )
										A__					:    int														= load_image(
										    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""       ).resize((512, 512)       )

										A__					:    str														= load_image(
										    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
										    """/stable_diffusion_inpaint/input_bench_mask.png"""       ).resize((512, 512)       )

										A__					:    Optional[int]														= """pitch black hole"""

										A__					:    Optional[int]														= load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""       ).resize((512, 512)       )

										A__					:    str														= pipe(
										    snake_case_      ,			image=snake_case_      ,			mask_image=snake_case_      ,			control_image=snake_case_      ,			generator=snake_case_      ,			output_type="""np"""      ,			num_inference_steps=3      ,			)

										A__					:    List[Any]														= output.images[0]

										assert image.shape == (512, 512, 3)

										A__					:    List[str]														= load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"""       )

										assert np.abs(expected_image - image       ).max() < 9E-2





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[int]														= ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""       )

										A__					:    List[str]														= StableDiffusionControlNetInpaintPipeline.from_pretrained(
										    """runwayml/stable-diffusion-v1-5"""      ,			safety_checker=snake_case_      ,			controlnet=snake_case_       )
										A__					:    str														= DDIMScheduler.from_config(pipe.scheduler.config       )
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=snake_case_       )

										A__					:    List[Any]														= torch.Generator(device="""cpu"""       ).manual_seed(33       )

										A__					:    Any														= load_image(
										    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""       )
										A__					:    Tuple														= init_image.resize((512, 512)       )

										A__					:    Union[str, Any]														= load_image(
										    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""       )
										A__					:    int														= mask_image.resize((512, 512)       )

										A__					:    int														= """a handsome man with ray-ban sunglasses"""

										def make_inpaint_condition(snake_case_      ,			snake_case_       ):
																	A__					:    str														= np.array(image.convert("""RGB"""       )       ).astype(np.floataa       ) / 2_55.0
																	A__					:    str														= np.array(image_mask.convert("""L"""       )       ).astype(np.floataa       ) / 2_55.0

																	assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
																	A__					:    Tuple														= -1.0  # set as masked pixel
																	A__					:    Any														= np.expand_dims(snake_case_      ,			0       ).transpose(0      ,			3      ,			1      ,			2       )
																	A__					:    Tuple														= torch.from_numpy(snake_case_       )
																	return image

										A__					:    int														= make_inpaint_condition(snake_case_      ,			snake_case_       )

										A__					:    Optional[Any]														= pipe(
										    snake_case_      ,			image=snake_case_      ,			mask_image=snake_case_      ,			control_image=snake_case_      ,			guidance_scale=9.0      ,			eta=1.0      ,			generator=snake_case_      ,			num_inference_steps=20      ,			output_type="""np"""      ,			)
										A__					:    Tuple														= output.images[0]

										assert image.shape == (512, 512, 3)

										A__					:    Optional[int]														= load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""       )

										assert np.abs(expected_image - image       ).max() < 9E-2





