
"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


_A            = logging.get_logger(__name__)

_A            = {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	'plbart'
	lowercase_        =	['past_key_values']
	lowercase_        =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




	def __init__( self		, UpperCAmelCase_=50005		, UpperCAmelCase_=1024		, UpperCAmelCase_=6		, UpperCAmelCase_=3072		, UpperCAmelCase_=12		, UpperCAmelCase_=6		, UpperCAmelCase_=3072		, UpperCAmelCase_=12		, UpperCAmelCase_=0.0		, UpperCAmelCase_=0.0		, UpperCAmelCase_=True		, UpperCAmelCase_=True		, UpperCAmelCase_="gelu"		, UpperCAmelCase_=768		, UpperCAmelCase_=0.1		, UpperCAmelCase_=0.1		, UpperCAmelCase_=0.0		, UpperCAmelCase_=0.02		, UpperCAmelCase_=0.0		, UpperCAmelCase_=True		, UpperCAmelCase_=1		, UpperCAmelCase_=0		, UpperCAmelCase_=2		, UpperCAmelCase_=2		, **UpperCAmelCase_		, )		->							Union[str, Any]:
			lowerCamelCase     :			List[str]		=    vocab_size
			lowerCamelCase     :			Optional[Any]		=    max_position_embeddings
			lowerCamelCase     :			Union[str, Any]		=    d_model
			lowerCamelCase     :			Optional[Any]		=    encoder_ffn_dim
			lowerCamelCase     :			Any		=    encoder_layers
			lowerCamelCase     :			Dict		=    encoder_attention_heads
			lowerCamelCase     :			Optional[Any]		=    decoder_ffn_dim
			lowerCamelCase     :			int		=    decoder_layers
			lowerCamelCase     :			int		=    decoder_attention_heads
			lowerCamelCase     :			Union[str, Any]		=    dropout
			lowerCamelCase     :			Optional[Any]		=    attention_dropout
			lowerCamelCase     :			Dict		=    activation_dropout
			lowerCamelCase     :			str		=    activation_function
			lowerCamelCase     :			int		=    init_std
			lowerCamelCase     :			Any		=    encoder_layerdrop
			lowerCamelCase     :			int		=    decoder_layerdrop
			lowerCamelCase     :			Dict		=    classifier_dropout
			lowerCamelCase     :			Optional[Any]		=    use_cache
			lowerCamelCase     :			Tuple		=    encoder_layers
			lowerCamelCase     :			List[str]		=    scale_embedding  # scale factor will be sqrt(d_model) if True
			super().__init__(
			    pad_token_id=UpperCAmelCase_		, bos_token_id=UpperCAmelCase_		, eos_token_id=UpperCAmelCase_		, is_encoder_decoder=UpperCAmelCase_		, forced_eos_token_id=UpperCAmelCase_		, **UpperCAmelCase_		, )






class        _lowercase   (				__UpperCAmelCase		):




	@property
	def 			_UpperCamelCase    ( self					)		->							Mapping[str, Mapping[int, str]]:
			return OrderedDict(
			    [
			        ('input_ids', {0: 'batch', 1: 'sequence'}),
			        ('attention_mask', {0: 'batch', 1: 'sequence'}),
			    ]					)




	@property
	def 			_UpperCamelCase    ( self					)		->							Mapping[str, Mapping[int, str]]:
			if self.use_past:
					return OrderedDict(
					    [
					        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
					        ('past_keys', {0: 'batch', 2: 'sequence'}),
					        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
					    ]					)
			else:
					return OrderedDict(
					    [
					        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
					        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
					    ]					)








"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	RePaintPipeline
	lowercase_        =	IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
	lowercase_        =	PipelineTesterMixin.required_optional_params - {
	    'latents',
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	lowercase_        =	IMAGE_INPAINTING_BATCH_PARAMS
	lowercase_        =	False




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			torch.manual_seed(0					)
			torch.manual_seed(0					)
			lowerCamelCase     :			Optional[Any]		=    UNetaDModel(
			    block_out_channels=(32, 64)		, layers_per_block=2		, sample_size=32		, in_channels=3		, out_channels=3		, down_block_types=('DownBlock2D', 'AttnDownBlock2D')		, up_block_types=('AttnUpBlock2D', 'UpBlock2D')		, )
			lowerCamelCase     :			Union[str, Any]		=    RePaintScheduler()
			lowerCamelCase     :			Dict		=    {'unet': unet, 'scheduler': scheduler}
			return components




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_=0					)		->							Optional[int]:
			if str(UpperCAmelCase_					).startswith('mps'					):
					lowerCamelCase     :			str		=    torch.manual_seed(UpperCAmelCase_					)
			else:
					lowerCamelCase     :			Tuple		=    torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)
			lowerCamelCase     :			str		=    np.random.RandomState(UpperCAmelCase_					).standard_normal((1, 3, 32, 32)					)
			lowerCamelCase     :			Tuple		=    torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_		, dtype=torch.floataa					)
			lowerCamelCase     :			Optional[int]		=    (image > 0).to(device=UpperCAmelCase_		, dtype=torch.floataa					)
			lowerCamelCase     :			Optional[Any]		=    {
			    'image': image,
			    'mask_image': mask,
			    'generator': generator,
			    'num_inference_steps': 5,
			    'eta': 0.0,
			    'jump_length': 2,
			    'jump_n_sample': 2,
			    'output_type': 'numpy',
			}
			return inputs




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Any		=    'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase     :			Union[str, Any]		=    self.get_dummy_components()
			lowerCamelCase     :			List[Any]		=    RePaintPipeline(**UpperCAmelCase_					)
			lowerCamelCase     :			int		=    sd_pipe.to(UpperCAmelCase_					)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    self.get_dummy_inputs(UpperCAmelCase_					)
			lowerCamelCase     :			Dict		=    sd_pipe(**UpperCAmelCase_					).images
			lowerCamelCase     :			List[Any]		=    image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCamelCase     :			Optional[Any]		=    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			return super().test_save_load_local()




	@unittest.skip('non-deterministic pipeline'					)
	def 			_UpperCamelCase    ( self					)		->							int:
			return super().test_inference_batch_single_identical()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							int:
			return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			return super().test_save_load_optional_components()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class        _lowercase   (				unittest.TestCase		):




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			Optional[int]		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256.png'					)
			lowerCamelCase     :			str		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'					)
			lowerCamelCase     :			Optional[Any]		=    load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256_result.npy'					)

			lowerCamelCase     :			Any		=    'google/ddpm-ema-celebahq-256'
			lowerCamelCase     :			List[Any]		=    UNetaDModel.from_pretrained(UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    RePaintScheduler.from_pretrained(UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    RePaintPipeline(unet=UpperCAmelCase_		, scheduler=UpperCAmelCase_					).to(UpperCAmelCase_					)
			repaint.set_progress_bar_config(disable=UpperCAmelCase_					)
			repaint.enable_attention_slicing()

			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)
			lowerCamelCase     :			str		=    repaint(
			    UpperCAmelCase_		, UpperCAmelCase_		, num_inference_steps=250		, eta=0.0		, jump_length=10		, jump_n_sample=10		, generator=UpperCAmelCase_		, output_type='np'		, )
			lowerCamelCase     :			Optional[int]		=    output.images[0]

			assert image.shape == (256, 256, 3)
			assert np.abs(expected_image - image					).mean() < 1E-2






