



import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_lowerCamelCase    :		List[Any]		      =    {
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_lowerCamelCase    :		Any		      =    logging.get_logger(__name__)


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		List[str]		=		'maskformer'
						UpperCAmelCase			:		Optional[Any]		=		{'hidden_size': 'mask_feature_size'}
						UpperCAmelCase			:		Union[str, Any]		=		['resnet', 'swin']
						UpperCAmelCase			:		List[str]		=		['detr']







						def __init__(  self    :				List[str]       ,	snake_case    :				int = 256       ,	snake_case    :				int = 256       ,	snake_case    :				float = 0.1       ,	snake_case    :				bool = False       ,	snake_case    :				Optional[Dict] = None       ,	snake_case    :				Optional[Dict] = None       ,	snake_case    :				float = 0.02       ,	snake_case    :				float = 1.0       ,	snake_case    :				float = 1.0       ,	snake_case    :				float = 1.0       ,	snake_case    :				float = 20.0       ,	snake_case    :				Optional[bool] = None       ,	**snake_case    :				Union[str, Any]       ,	):



											'''simple docstring'''


											if backbone_config is None:
																# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																SCREAMING_SNAKE_CASE							:     List[Any]						=				SwinConfig(
																    image_size=384       ,	in_channels=3       ,	patch_size=4       ,	embed_dim=128       ,	depths=[2, 2, 18, 2]       ,	num_heads=[4, 8, 16, 32]       ,	window_size=12       ,	drop_path_rate=0.3       ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,	)

											if isinstance(snake_case       ,	snake_case	):
																SCREAMING_SNAKE_CASE							:     List[str]						=				backbone_config.pop('model_type'	)
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				CONFIG_MAPPING[backbone_model_type]
																SCREAMING_SNAKE_CASE							:     Any						=				config_class.from_dict(snake_case	)

											# verify that the backbone is supported
											if backbone_config.model_type not in self.backbones_supported:
																logger.warning_once(
																    f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
																    f'''Supported model types: {','.join(self.backbones_supported	)}'''	)

											if decoder_config is None:
																# fall back to https://huggingface.co/facebook/detr-resnet-50
																SCREAMING_SNAKE_CASE							:     Any						=				DetrConfig()
											else:
																# verify that the decoder is supported
																SCREAMING_SNAKE_CASE							:     Dict						=				(
																    decoder_config.pop('model_type'	) if isinstance(snake_case       ,	snake_case	) else decoder_config.model_type
																)
																if decoder_type not in self.decoders_supported:
																					raise ValueError(
																					    f'''Transformer Decoder {decoder_type} not supported, please use one of'''
																					    f''' {','.join(self.decoders_supported	)}'''	)
																if isinstance(snake_case       ,	snake_case	):
																					SCREAMING_SNAKE_CASE							:     Optional[int]						=				CONFIG_MAPPING[decoder_type]
																					SCREAMING_SNAKE_CASE							:     str						=				config_class.from_dict(snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				backbone_config
											SCREAMING_SNAKE_CASE							:     str						=				decoder_config
											# main feature dimension for the model
											SCREAMING_SNAKE_CASE							:     str						=				fpn_feature_size
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				mask_feature_size
											# initializer
											SCREAMING_SNAKE_CASE							:     str						=				init_std
											SCREAMING_SNAKE_CASE							:     Dict						=				init_xavier_std
											# Hungarian matcher && loss
											SCREAMING_SNAKE_CASE							:     Dict						=				cross_entropy_weight
											SCREAMING_SNAKE_CASE							:     List[Any]						=				dice_weight
											SCREAMING_SNAKE_CASE							:     int						=				mask_weight
											SCREAMING_SNAKE_CASE							:     List[Any]						=				use_auxiliary_loss
											SCREAMING_SNAKE_CASE							:     Any						=				no_object_weight
											SCREAMING_SNAKE_CASE							:     List[Any]						=				output_auxiliary_logits

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.decoder_config.encoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.decoder_config.num_hidden_layers
											super().__init__(**snake_case	)







						@classmethod
						def       lowerCamelCase_				(  cls    :				List[Any]       ,	snake_case    :				PretrainedConfig       ,	snake_case    :				PretrainedConfig       ,	**snake_case    :				Union[str, Any]	):



											'''simple docstring'''


											return cls(
											    backbone_config=snake_case       ,	decoder_config=snake_case       ,	**snake_case       ,	)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				copy.deepcopy(self.__dict__	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.backbone_config.to_dict()
											SCREAMING_SNAKE_CASE							:     Any						=				self.decoder_config.to_dict()
											SCREAMING_SNAKE_CASE							:     Dict						=				self.__class__.model_type
											return output




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase    :		Any		      =    False


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						pass


@nightly
@require_torch_gpu
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(snake_case	)
																SCREAMING_SNAKE_CASE							:     Any						=				VersatileDiffusionPipeline.from_pretrained(snake_case       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				generator.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'cyberpunk 2077'
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Any						=				pipe.dual_guided(
											    prompt=snake_case       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'       ,	).images

											SCREAMING_SNAKE_CASE							:     List[Any]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Tuple						=				np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]	)

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

											SCREAMING_SNAKE_CASE							:     Any						=				'A painting of a squirrel eating a burger '
											SCREAMING_SNAKE_CASE							:     Any						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				pipe.text_to_image(
											    prompt=snake_case       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Dict						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

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

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				pipe.image_variation(snake_case       ,	generator=snake_case       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]	)

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