





'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class      SCREAMING_SNAKE_CASE__      (   snake_case__							,unittest.TestCase		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	PaintByExamplePipeline
					SCREAMING_SNAKE_CASE							=	IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
					SCREAMING_SNAKE_CASE							=	IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					SCREAMING_SNAKE_CASE							=	frozenset([]		)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							torch.manual_seed(0		)
							__UpperCAmelCase	:							Dict     =     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		,	)
							__UpperCAmelCase	:							Optional[Any]     =     PNDMScheduler(skip_prk_steps=UpperCAmelCase_		)
							torch.manual_seed(0		)
							__UpperCAmelCase	:							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		)
							__UpperCAmelCase	:							Tuple     =     CLIPVisionConfig(
							    hidden_size=32		,	projection_dim=32		,	intermediate_size=37		,	layer_norm_eps=1e-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	image_size=32		,	patch_size=4		,	)
							__UpperCAmelCase	:							str     =     PaintByExampleImageEncoder(UpperCAmelCase_		,	proj_size=32		)
							__UpperCAmelCase	:							Any     =     CLIPImageProcessor(crop_size=32		,	size=32		)

							__UpperCAmelCase	:							Optional[int]     =     {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "safety_checker": None,
							    "feature_extractor": feature_extractor,
							}
							return components
					def 		lowerCamelCase_   (   self       :      Optional[int]		,	UpperCAmelCase_       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							int     =     np.array(image.convert("RGB"		)		)
							__UpperCAmelCase	:							List[str]     =     image[None].transpose(0		,	3		,	1		,	2		)
							__UpperCAmelCase	:							Any     =     torch.from_numpy(UpperCAmelCase_		).to(dtype=torch.floataa		) / 127.5 - 1.0
							return image
					def 		lowerCamelCase_   (   self       :      Any		,	UpperCAmelCase_       :      Tuple="cpu"		,	UpperCAmelCase_       :      List[str]=0		):







							"""simple docstring"""
							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
							__UpperCAmelCase	:							Dict     =     floats_tensor((1, 3, 32, 32)		,	rng=random.Random(UpperCAmelCase_		)		).to(UpperCAmelCase_		)
							__UpperCAmelCase	:							Dict     =     image.cpu().permute(0		,	2		,	3		,	1		)[0]
							__UpperCAmelCase	:							Tuple     =     Image.fromarray(np.uinta(UpperCAmelCase_		)		).convert("RGB"		).resize((64, 64)		)
							__UpperCAmelCase	:							Tuple     =     Image.fromarray(np.uinta(image + 4		)		).convert("RGB"		).resize((64, 64)		)
							__UpperCAmelCase	:							str     =     Image.fromarray(np.uinta(UpperCAmelCase_		)		).convert("RGB"		).resize((32, 32)		)

							if str(UpperCAmelCase_		).startswith("mps"		):
									__UpperCAmelCase	:							List[Any]     =     torch.manual_seed(UpperCAmelCase_		)
							else:
									__UpperCAmelCase	:							Any     =     torch.Generator(device=UpperCAmelCase_		).manual_seed(UpperCAmelCase_		)
							__UpperCAmelCase	:							Dict     =     {
							    "example_image": example_image,
							    "image": init_image,
							    "mask_image": mask_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}
							return inputs
					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							__UpperCAmelCase	:							Any     =     PaintByExamplePipeline(**UpperCAmelCase_		)
							__UpperCAmelCase	:							Tuple     =     pipe.to("cpu"		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase_		)

							__UpperCAmelCase	:							List[Any]     =     self.get_dummy_inputs()
							__UpperCAmelCase	:							str     =     pipe(**UpperCAmelCase_		)
							__UpperCAmelCase	:							Union[str, Any]     =     output.images

							__UpperCAmelCase	:							List[str]     =     image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							__UpperCAmelCase	:							List[Any]     =     np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]		)

							assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
					def 		lowerCamelCase_   (   self       :      int		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     "cpu"
							__UpperCAmelCase	:							int     =     self.get_dummy_inputs()
							inputs.pop("mask_image"		)
							__UpperCAmelCase	:							List[str]     =     self.convert_to_pt(inputs.pop("image"		)		)
							__UpperCAmelCase	:							List[str]     =     image.clamp(0		,	1		) / 2

							# make sure here that pndm scheduler skips prk
							__UpperCAmelCase	:							Any     =     PaintByExamplePipeline(**self.get_dummy_components()		)
							__UpperCAmelCase	:							Tuple     =     pipe.to(UpperCAmelCase_		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase_		)

							__UpperCAmelCase	:							Any     =     pipe(image=UpperCAmelCase_		,	mask_image=mask_image[:, 0]		,	**UpperCAmelCase_		)
							__UpperCAmelCase	:							Optional[Any]     =     output.images

							__UpperCAmelCase	:							Dict     =     image.cpu().permute(0		,	2		,	3		,	1		)[0]
							__UpperCAmelCase	:							Optional[int]     =     mask_image.cpu().permute(0		,	2		,	3		,	1		)[0]

							__UpperCAmelCase	:							int     =     Image.fromarray(np.uinta(UpperCAmelCase_		)		).convert("RGB"		)
							__UpperCAmelCase	:							Optional[Any]     =     Image.fromarray(np.uinta(UpperCAmelCase_		)		).convert("RGB"		)

							__UpperCAmelCase	:							Optional[Any]     =     pipe(**self.get_dummy_inputs()		)
							__UpperCAmelCase	:							Optional[int]     =     output.images

							assert out_a.shape == (1, 64, 64, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()		).max() < 5e-2






					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""simple docstring"""
							super().test_inference_batch_single_identical(expected_max_diff=3e-3		)






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







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							# make sure here that pndm scheduler skips prk
							__UpperCAmelCase	:							Any     =     load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/dog_in_bucket.png"		)
							__UpperCAmelCase	:							Optional[int]     =     load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/mask.png"		)
							__UpperCAmelCase	:							Any     =     load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/panda.jpg"		)

							__UpperCAmelCase	:							List[str]     =     PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"		)
							__UpperCAmelCase	:							Tuple     =     pipe.to(UpperCAmelCase_		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase_		)

							__UpperCAmelCase	:							str     =     torch.manual_seed(321		)
							__UpperCAmelCase	:							Optional[int]     =     pipe(
							    image=UpperCAmelCase_		,	mask_image=UpperCAmelCase_		,	example_image=UpperCAmelCase_		,	generator=UpperCAmelCase_		,	guidance_scale=5.0		,	num_inference_steps=50		,	output_type="np"		,	)

							__UpperCAmelCase	:							Union[str, Any]     =     output.images

							__UpperCAmelCase	:							int     =     image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							__UpperCAmelCase	:							List[Any]     =     np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]		)

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










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A    :				List[str]    						=	logging.get_logger(__name__)

__A    :				List[Any]    						=	{
    """mnaylor/mega-base-wikitext""": """https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json""",
}




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							a__    	=  '''mega'''





							def __init__(       self							:						Any							,  a							:						List[str]=30_522							,  a							:						Dict=128							,  a							:						Dict=4							,  a							:						Union[str, Any]=256							,  a							:						Tuple=16							,  a							:						Any=True							,  a							:						Dict=64							,  a							:						Union[str, Any]=False							,  a							:						Tuple=-1							,  a							:						str=None							,  a							:						int=True							,  a							:						Optional[int]="scalenorm"							,  a							:						Optional[int]=True							,  a							:						Dict="silu"							,  a							:						Optional[Any]="softmax"							,  a							:						Any=0.1							,  a							:						List[Any]=0.1							,  a							:						Tuple=0.1							,  a							:						Optional[int]=False							,  a							:						List[str]=True							,  a							:						Optional[int]=256							,  a							:						List[str]=True							,  a							:						int=0.1							,  a							:						str=2_048							,  a							:						Tuple=False							,  a							:						Optional[int]=2							,  a							:						Optional[int]=0.02							,  a							:						Optional[int]=0.2							,  a							:						Union[str, Any]=0.02							,  a							:						str=1.0							,  a							:						Dict=1							,  a							:						Union[str, Any]=0							,  a							:						Dict=2							,  a							:						List[str]="rotary"							,  a							:						Union[str, Any]=None							,  a							:						List[Any]=True							,  a							:						Tuple=True							,  **a							:						Optional[int]							,  )      ->       Dict:
										super().__init__(pad_token_id=a							,  bos_token_id=a							,  eos_token_id=a							,  **a      )

										SCREAMING_SNAKE_CASE     =       vocab_size
										SCREAMING_SNAKE_CASE     =       hidden_size
										SCREAMING_SNAKE_CASE     =       num_hidden_layers
										SCREAMING_SNAKE_CASE     =       activation
										SCREAMING_SNAKE_CASE     =       attention_activation
										SCREAMING_SNAKE_CASE     =       intermediate_size
										SCREAMING_SNAKE_CASE     =       ema_projection_size
										SCREAMING_SNAKE_CASE     =       bidirectional
										SCREAMING_SNAKE_CASE     =       shared_representation_size
										SCREAMING_SNAKE_CASE     =       use_chunking
										SCREAMING_SNAKE_CASE     =       chunk_size
										SCREAMING_SNAKE_CASE     =       truncation
										SCREAMING_SNAKE_CASE     =       normalize_before_mega
										SCREAMING_SNAKE_CASE     =       normalization_type
										SCREAMING_SNAKE_CASE     =       norm_affine
										SCREAMING_SNAKE_CASE     =       dropout_prob
										SCREAMING_SNAKE_CASE     =       hidden_dropout_prob
										SCREAMING_SNAKE_CASE     =       attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE     =       use_feature_dropout
										SCREAMING_SNAKE_CASE     =       use_normalized_ffn
										SCREAMING_SNAKE_CASE     =       nffn_hidden_size
										SCREAMING_SNAKE_CASE     =       normalize_before_ffn
										SCREAMING_SNAKE_CASE     =       nffn_activation_dropout_prob
										SCREAMING_SNAKE_CASE     =       max_positions
										SCREAMING_SNAKE_CASE     =       add_token_type_embeddings
										SCREAMING_SNAKE_CASE     =       type_vocab_size
										SCREAMING_SNAKE_CASE     =       initializer_range
										SCREAMING_SNAKE_CASE     =       ema_delta_alpha_range
										SCREAMING_SNAKE_CASE     =       ema_beta_range
										SCREAMING_SNAKE_CASE     =       ema_gamma_omega_range
										SCREAMING_SNAKE_CASE     =       relative_positional_bias
										SCREAMING_SNAKE_CASE     =       use_cache
										SCREAMING_SNAKE_CASE     =       classifier_dropout
										SCREAMING_SNAKE_CASE     =       add_lm_hidden_dense_layer
										SCREAMING_SNAKE_CASE     =       1  # not used but required by Hugging Face





class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							@property
							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
													SCREAMING_SNAKE_CASE     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
													SCREAMING_SNAKE_CASE     =       {0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]      )






