"""simple docstring"""



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				_UpperCamelCase         =		"platform"

				import jax
				import jax.numpy as jnp

				from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase=None ,     lowerCAmelCase=None       ):
							if attention_mask is None:
														A__					:    Tuple														= np.where(input_ids != config.pad_token_id ,     1 ,     0       )
							return {
							    "input_ids": input_ids,
							    "attention_mask": attention_mask,
							}
@require_flax
class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_      ,			snake_case_=13      ,			snake_case_=7      ,			snake_case_=True      ,			snake_case_=False      ,			snake_case_=99      ,			snake_case_=16      ,			snake_case_=2      ,			snake_case_=4      ,			snake_case_=4      ,			snake_case_="gelu"      ,			snake_case_=0.1      ,			snake_case_=0.1      ,			snake_case_=20      ,			snake_case_=2      ,			snake_case_=1      ,			snake_case_=0      ,			snake_case_=16      ,			snake_case_=16      ,			snake_case_=0.02      ,			):







										'''simple docstring'''


										A__					:    Dict														= parent
										A__					:    Dict														= batch_size
										A__					:    str														= seq_length
										A__					:    Union[str, Any]														= is_training
										A__					:    Optional[Any]														= use_labels
										A__					:    List[Any]														= vocab_size
										A__					:    List[Any]														= hidden_size
										A__					:    int														= num_hidden_layers
										A__					:    Optional[Any]														= num_attention_heads
										A__					:    List[str]														= intermediate_size
										A__					:    Any														= hidden_act
										A__					:    str														= hidden_dropout_prob
										A__					:    Optional[Any]														= attention_probs_dropout_prob
										A__					:    Tuple														= max_position_embeddings
										A__					:    Dict														= eos_token_id
										A__					:    str														= pad_token_id
										A__					:    List[Any]														= bos_token_id
										A__					:    List[Any]														= embed_dim
										A__					:    List[str]														= word_embed_proj_dim
										A__					:    Optional[int]														= initializer_range
										A__					:    str														= False





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,			self.vocab_size       )      ,			3      ,			self.vocab_size       )
										A__					:    Optional[int]														= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,			dtype=np.intaa       ))      ,			-1       )

										A__					:    List[str]														= OPTConfig(
										    vocab_size=self.vocab_size      ,			hidden_size=self.hidden_size      ,			num_hidden_layers=self.num_hidden_layers      ,			num_attention_heads=self.num_attention_heads      ,			ffn_dim=self.intermediate_size      ,			dropout=self.hidden_dropout_prob      ,			attention_dropout=self.attention_probs_dropout_prob      ,			max_position_embeddings=self.max_position_embeddings      ,			eos_token_id=self.eos_token_id      ,			bos_token_id=self.bos_token_id      ,			pad_token_id=self.pad_token_id      ,			embed_dim=self.embed_dim      ,			is_encoder_decoder=snake_case_      ,			word_embed_proj_dim=self.word_embed_proj_dim      ,			initializer_range=self.initializer_range      ,			use_cache=snake_case_      ,			)
										A__					:    Optional[int]														= prepare_opt_inputs_dict(snake_case_      ,			snake_case_       )
										return config, inputs_dict





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    str														= self.prepare_config_and_inputs()
										return config, inputs_dict





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







										'''simple docstring'''


										A__					:    str														= 20
										A__					:    Union[str, Any]														= model_class_name(snake_case_       )

										A__					:    Union[str, Any]														= inputs_dict["""input_ids"""]
										A__					:    Dict														= inputs_dict["""attention_mask"""]

										A__					:    Tuple														= model.init_cache(input_ids.shape[0]      ,			snake_case_       )
										A__					:    Optional[Any]														= jnp.ones((input_ids.shape[0], max_length)      ,			dtype="""i4"""       )

										A__					:    Union[str, Any]														= jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1       )[None, :]      ,			(input_ids.shape[0], input_ids.shape[-1] - 1)      ,			)
										A__					:    List[str]														= model(
										    input_ids[:, :-1]      ,			attention_mask=snake_case_      ,			past_key_values=snake_case_      ,			position_ids=snake_case_      ,			)

										A__					:    Optional[int]														= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,			dtype="""i4"""       )
										A__					:    Optional[Any]														= model(
										    input_ids[:, -1:]      ,			attention_mask=snake_case_      ,			past_key_values=outputs_cache.past_key_values      ,			position_ids=snake_case_      ,			)

										A__					:    Tuple														= model(snake_case_       )

										A__					:    Optional[int]														= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
										self.parent.assertTrue(diff < 1E-3      ,			msg=F'''Max diff is {diff}'''       )





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







										'''simple docstring'''


										A__					:    Union[str, Any]														= 20
										A__					:    Any														= model_class_name(snake_case_       )

										A__							,       A__					:    int														= (
										    inputs_dict["""input_ids"""],
										    inputs_dict["""attention_mask"""],
										)

										A__					:    List[Any]														= jnp.concatenate(
										    [
										        attention_mask,
										        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])       ),
										    ]      ,			axis=-1      ,			)

										A__					:    Dict														= model.init_cache(input_ids.shape[0]      ,			snake_case_       )
										A__					:    Union[str, Any]														= jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1       )[None, :]      ,			(input_ids.shape[0], input_ids.shape[-1] - 1)      ,			)

										A__					:    Optional[Any]														= model(
										    input_ids[:, :-1]      ,			attention_mask=snake_case_      ,			past_key_values=snake_case_      ,			position_ids=snake_case_      ,			)
										A__					:    Optional[Any]														= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,			dtype="""i4"""       )
										A__					:    List[str]														= model(
										    input_ids[:, -1:]      ,			past_key_values=outputs_cache.past_key_values      ,			attention_mask=snake_case_      ,			position_ids=snake_case_      ,			)

										A__					:    str														= model(snake_case_      ,			attention_mask=snake_case_       )

										A__					:    List[str]														= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
										self.parent.assertTrue(diff < 1E-3      ,			msg=F'''Max diff is {diff}'''       )





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







			'''simple docstring'''
			_UpperCamelCase     :	Any				    =							(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
			_UpperCamelCase     :	Any				    =							() if is_flax_available() else ()





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= FlaxOPTModelTester(self       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    Optional[Any]														= self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward(snake_case_      ,			snake_case_      ,			snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    List[str]														= self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward_with_attn_mask(snake_case_      ,			snake_case_      ,			snake_case_       )





			@slow
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										for model_class_name in self.all_model_classes:
																	A__					:    Any														= model_class_name.from_pretrained("""facebook/opt-125m"""       )
																	A__					:    List[Any]														= np.ones((1, 1)       ) * model.config.eos_token_id
																	A__					:    Any														= model(snake_case_       )
																	self.assertIsNotNone(snake_case_       )





@require_sentencepiece
@require_flax
class 	__UpperCAmelCase						(unittest.TestCase    ):







			'''simple docstring'''





			@slow
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= FlaxOPTModel.from_pretrained("""facebook/opt-350m"""       )
										A__					:    Optional[int]														= jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]       )
										A__					:    Optional[int]														= model(input_ids=snake_case_       ).last_hidden_state
										A__					:    Union[str, Any]														= (1, 11, 512)
										self.assertEqual(output.shape      ,			snake_case_       )
										A__					:    Optional[int]														= jnp.array(
										    [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]       )
										self.assertTrue(jnp.allclose(output[:, :3, :3]      ,			snake_case_      ,			atol=4E-2       )       )





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







			'''simple docstring'''





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										super().setUp()
										A__					:    List[Any]														= """facebook/opt-350m"""





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= FlaxOPTForCausalLM.from_pretrained(self.path_model       )
										A__					:    Optional[Any]														= GPTaTokenizer.from_pretrained(self.path_model       )

										A__					:    Dict														= [
										    """Today is a beautiful day and I want to""",
										    """In the city of""",
										    """Paris is the capital of France and""",
										    """Computers and mobile phones have taken""",
										]
										# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
										A__					:    Dict														= tokenizer(snake_case_      ,			return_tensors="""jax"""      ,			padding=snake_case_      ,			add_special_tokens=snake_case_       )
										A__					:    Optional[Any]														= model(inputs.input_ids      ,			attention_mask=inputs.attention_mask       )[0].mean(axis=-1       )
										A__					:    str														= jnp.array(
										    [
										        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
										        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
										        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
										        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
										    ]       )
										self.assertTrue(jnp.allclose(snake_case_      ,			snake_case_      ,			atol=4E-2       )       )

										A__					:    int														= jax.jit(snake_case_       )
										A__					:    Dict														= model(inputs.input_ids      ,			attention_mask=inputs.attention_mask       )[0].mean(axis=-1       )
										self.assertTrue(jnp.allclose(snake_case_      ,			snake_case_      ,			atol=4E-2       )       )





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







			'''simple docstring'''





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return [
										    "Today is a beautiful day and I want",
										    "In the city of",
										    "Paris is the capital of France and",
										    "Computers and mobile phones have taken",
										]





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= """facebook/opt-125m"""

										A__					:    List[Any]														= [
										    """Today is a beautiful day and I want to""",
										    """In the city of New York, the city""",
										    """Paris is the capital of France and the capital""",
										    """Computers and mobile phones have taken over the""",
										]

										A__					:    Optional[int]														= []

										A__					:    List[Any]														= FlaxOPTForCausalLM.from_pretrained(snake_case_       )
										A__					:    Tuple														= GPTaTokenizer.from_pretrained(snake_case_       )

										for prompt in self.prompts:
																	A__					:    Optional[Any]														= tokenizer(snake_case_      ,			return_tensors="""jax"""       ).input_ids

																	A__					:    List[Any]														= model.generate(snake_case_      ,			max_length=10       )
																	A__					:    List[Any]														= generated_ids[0]

																	A__					:    Optional[Any]														= tokenizer.batch_decode(snake_case_      ,			skip_special_tokens=snake_case_       )
																	predicted_outputs += generated_string

										self.assertListEqual(snake_case_      ,			snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= """facebook/opt-350m"""

										A__					:    List[Any]														= [
										    """Today is a beautiful day and I want to""",
										    """In the city of San Francisco, the city""",
										    """Paris is the capital of France and the capital""",
										    """Computers and mobile phones have taken over the""",
										]

										A__					:    Union[str, Any]														= []
										A__					:    Optional[int]														= FlaxOPTForCausalLM.from_pretrained(snake_case_       )
										A__					:    Tuple														= GPTaTokenizer.from_pretrained(snake_case_       )

										for prompt in self.prompts:
																	A__					:    Dict														= tokenizer(snake_case_      ,			return_tensors="""jax"""       ).input_ids

																	A__					:    Optional[Any]														= model.generate(snake_case_      ,			max_length=10       )
																	A__					:    Union[str, Any]														= generated_ids[0]

																	A__					:    List[str]														= tokenizer.batch_decode(snake_case_      ,			skip_special_tokens=snake_case_       )
																	predicted_outputs += generated_string

										self.assertListEqual(snake_case_      ,			snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= """facebook/opt-125m"""
										A__					:    Any														= [
										    """Today is a beautiful day and I want to thank""",
										    """In the city of Rome Canaver Canaver Canaver Canaver""",
										]
										A__					:    Any														= FlaxOPTForCausalLM.from_pretrained(snake_case_       )
										A__					:    List[Any]														= GPTaTokenizer.from_pretrained(snake_case_       )
										A__					:    Tuple														= tokenizer(
										    [
										        """Today is a beautiful day and I want to""",
										        """In the city of""",
										    ]      ,			return_tensors="""jax"""      ,			padding=snake_case_      ,			)

										A__					:    int														= jax.jit(model.generate       )

										A__					:    Union[str, Any]														= jit_generate(inputs["""input_ids"""]      ,			attention_mask=inputs["""attention_mask"""]       ).sequences

										A__					:    Dict														= tokenizer.batch_decode(snake_case_      ,			skip_special_tokens=snake_case_       )

										self.assertIsNotNone(snake_case_      ,			snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= """facebook/opt-350m"""

										A__					:    int														= GPTaTokenizer.from_pretrained(snake_case_       )
										A__					:    List[Any]														= FlaxOPTForCausalLM.from_pretrained(snake_case_       )

										A__					:    Optional[int]														= """left"""

										# use different length sentences to test batching
										A__					:    Any														= [
										    """Hello, my dog is a little""",
										    """Today, I""",
										]

										A__					:    int														= tokenizer(snake_case_      ,			return_tensors="""jax"""      ,			padding=snake_case_       )
										A__					:    List[str]														= inputs["""input_ids"""]

										A__					:    int														= model.generate(input_ids=snake_case_      ,			attention_mask=inputs["""attention_mask"""]      ,			trace=snake_case_       )

										A__					:    str														= tokenizer(sentences[0]      ,			return_tensors="""jax"""       ).input_ids
										A__					:    int														= model.generate(input_ids=snake_case_       )

										A__					:    Optional[int]														= inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
										A__					:    Dict														= tokenizer(sentences[1]      ,			return_tensors="""jax"""       ).input_ids
										A__					:    str														= model.generate(input_ids=snake_case_      ,			max_length=model.config.max_length - num_paddings       )

										A__					:    Optional[Any]														= tokenizer.batch_decode(outputs[0]      ,			skip_special_tokens=snake_case_       )
										A__					:    List[str]														= tokenizer.decode(output_non_padded[0][0]      ,			skip_special_tokens=snake_case_       )
										A__					:    List[Any]														= tokenizer.decode(output_padded[0][0]      ,			skip_special_tokens=snake_case_       )

										A__					:    Tuple														= [
										    """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
										    """Today, I was in the middle of a conversation with a friend about the""",
										]
										self.assertListEqual(snake_case_      ,			snake_case_       )
										self.assertListEqual(snake_case_      ,			[non_padded_sentence, padded_sentence]       )






"""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





