




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =StableDiffusionInpaintPipeline
						lowerCAmelCase__			      =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						lowerCAmelCase__			      =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						lowerCAmelCase__			      =frozenset(
						    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						lowerCAmelCase__			      =frozenset([] )



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE_					      =							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					,							attention_head_dim=(2, 4)					,							use_linear_projection=snake_case__					,							)
								SCREAMING_SNAKE_CASE_					      =							PNDMScheduler(skip_prk_steps=snake_case__  )
								torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE_					      =							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					,							sample_size=1_28					,							)
								torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE_					      =							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=10_00					,							hidden_act='gelu'					,							projection_dim=5_12					,							)
								SCREAMING_SNAKE_CASE_					      =							CLIPTextModel(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

								SCREAMING_SNAKE_CASE_					      =							{
								    'unet': unet,
								    'scheduler': scheduler,
								    'vae': vae,
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'safety_checker': None,
								    'feature_extractor': None,
								}
								return components



						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   Dict					,							snake_case__	:   List[str]=0  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							floats_tensor((1, 3, 32, 32)					,							rng=random.Random(snake_case__  )  ).to(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							image.cpu().permute(0					,							2					,							3					,							1  )[0]
								SCREAMING_SNAKE_CASE_					      =							Image.fromarray(np.uinta(snake_case__  )  ).convert('RGB'  ).resize((64, 64)  )
								SCREAMING_SNAKE_CASE_					      =							Image.fromarray(np.uinta(image + 4  )  ).convert('RGB'  ).resize((64, 64)  )
								if str(snake_case__  ).startswith('mps'  ):
										SCREAMING_SNAKE_CASE_					      =							torch.manual_seed(snake_case__  )
								else:
										SCREAMING_SNAKE_CASE_					      =							torch.Generator(device=snake_case__  ).manual_seed(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							{
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': init_image,
								    'mask_image': mask_image,
								    'generator': generator,
								    'num_inference_steps': 2,
								    'guidance_scale': 6.0,
								    'output_type': 'numpy',
								}
								return inputs



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'cpu'  # ensure determinism for the device-dependent torch.Generator
								SCREAMING_SNAKE_CASE_					      =							self.get_dummy_components()
								SCREAMING_SNAKE_CASE_					      =							StableDiffusionInpaintPipeline(**snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							sd_pipe.to(snake_case__  )
								sd_pipe.set_progress_bar_config(disable=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							self.get_dummy_inputs(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							sd_pipe(**snake_case__  ).images
								SCREAMING_SNAKE_CASE_					      =							image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								SCREAMING_SNAKE_CASE_					      =							np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2






						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



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



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/sd2-inpaint/init_image.png'  )
								SCREAMING_SNAKE_CASE_					      =							load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'  )
								SCREAMING_SNAKE_CASE_					      =							load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
								    '/yellow_cat_sitting_on_a_park_bench.npy'  )

								SCREAMING_SNAKE_CASE_					      =							'stabilityai/stable-diffusion-2-inpainting'
								SCREAMING_SNAKE_CASE_					      =							StableDiffusionInpaintPipeline.from_pretrained(snake_case__					,							safety_checker=snake_case__  )
								pipe.to(snake_case__  )
								pipe.set_progress_bar_config(disable=snake_case__  )
								pipe.enable_attention_slicing()

								SCREAMING_SNAKE_CASE_					      =							'Face of a yellow cat, high resolution, sitting on a park bench'

								SCREAMING_SNAKE_CASE_					      =							torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE_					      =							pipe(
								    prompt=snake_case__					,							image=snake_case__					,							mask_image=snake_case__					,							generator=snake_case__					,							output_type='np'					,							)
								SCREAMING_SNAKE_CASE_					      =							output.images[0]

								assert image.shape == (5_12, 5_12, 3)
								assert np.abs(expected_image - image  ).max() < 9e-3



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/sd2-inpaint/init_image.png'  )
								SCREAMING_SNAKE_CASE_					      =							load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'  )
								SCREAMING_SNAKE_CASE_					      =							load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
								    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'  )

								SCREAMING_SNAKE_CASE_					      =							'stabilityai/stable-diffusion-2-inpainting'
								SCREAMING_SNAKE_CASE_					      =							StableDiffusionInpaintPipeline.from_pretrained(
								    snake_case__					,							torch_dtype=torch.floataa					,							safety_checker=snake_case__					,							)
								pipe.to(snake_case__  )
								pipe.set_progress_bar_config(disable=snake_case__  )
								pipe.enable_attention_slicing()

								SCREAMING_SNAKE_CASE_					      =							'Face of a yellow cat, high resolution, sitting on a park bench'

								SCREAMING_SNAKE_CASE_					      =							torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE_					      =							pipe(
								    prompt=snake_case__					,							image=snake_case__					,							mask_image=snake_case__					,							generator=snake_case__					,							output_type='np'					,							)
								SCREAMING_SNAKE_CASE_					      =							output.images[0]

								assert image.shape == (5_12, 5_12, 3)
								assert np.abs(expected_image - image  ).max() < 5e-1






						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								SCREAMING_SNAKE_CASE_					      =							load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/sd2-inpaint/init_image.png'  )
								SCREAMING_SNAKE_CASE_					      =							load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'  )

								SCREAMING_SNAKE_CASE_					      =							'stabilityai/stable-diffusion-2-inpainting'
								SCREAMING_SNAKE_CASE_					      =							PNDMScheduler.from_pretrained(snake_case__					,							subfolder='scheduler'  )
								SCREAMING_SNAKE_CASE_					      =							StableDiffusionInpaintPipeline.from_pretrained(
								    snake_case__					,							safety_checker=snake_case__					,							scheduler=snake_case__					,							torch_dtype=torch.floataa					,							)
								pipe.to(snake_case__  )
								pipe.set_progress_bar_config(disable=snake_case__  )
								pipe.enable_attention_slicing(1  )
								pipe.enable_sequential_cpu_offload()

								SCREAMING_SNAKE_CASE_					      =							'Face of a yellow cat, high resolution, sitting on a park bench'

								SCREAMING_SNAKE_CASE_					      =							torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE_					      =							pipe(
								    prompt=snake_case__					,							image=snake_case__					,							mask_image=snake_case__					,							generator=snake_case__					,							num_inference_steps=2					,							output_type='np'					,							)

								SCREAMING_SNAKE_CASE_					      =							torch.cuda.max_memory_allocated()
								# make sure that less than 2.65 GB is allocated
								assert mem_bytes < 2.65 * 10**9





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Tuple									= ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Any									= [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Dict									= [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     str									= [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     Any									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)