






'''simple docstring'''




import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase__          =						{
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

lowerCAmelCase__          =						logging.get_logger(__name__)







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  '''mask2former'''
	_lowerCamelCase              =  ['''swin''']
	_lowerCamelCase              =  {'''hidden_size''': '''hidden_dim'''}

	def __init__(      self   :						str  ,					UpperCAmelCase_   :						Optional[Dict] = None  ,					UpperCAmelCase_   :						int = 256  ,					UpperCAmelCase_   :						int = 256  ,					UpperCAmelCase_   :						int = 256  ,					UpperCAmelCase_   :						int = 1_024  ,					UpperCAmelCase_   :						str = "relu"  ,					UpperCAmelCase_   :						int = 6  ,					UpperCAmelCase_   :						int = 10  ,					UpperCAmelCase_   :						int = 8  ,					UpperCAmelCase_   :						float = 0.0  ,					UpperCAmelCase_   :						int = 2_048  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						int = 4  ,					UpperCAmelCase_   :						int = 255  ,					UpperCAmelCase_   :						int = 100  ,					UpperCAmelCase_   :						float = 0.1  ,					UpperCAmelCase_   :						float = 2.0  ,					UpperCAmelCase_   :						float = 5.0  ,					UpperCAmelCase_   :						float = 5.0  ,					UpperCAmelCase_   :						int = 12_544  ,					UpperCAmelCase_   :						float = 3.0  ,					UpperCAmelCase_   :						float = 0.75  ,					UpperCAmelCase_   :						float = 0.02  ,					UpperCAmelCase_   :						float = 1.0  ,					UpperCAmelCase_   :						bool = True  ,					UpperCAmelCase_   :						List[int] = [4, 8, 16, 32]  ,					UpperCAmelCase_   :						bool = None  ,					**UpperCAmelCase_   :						Optional[int]  ,					):
			if backbone_config is None:
					logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.')
					UpperCamelCase__				:					Tuple					=					CONFIG_MAPPING['swin'](
					    image_size=224  ,					in_channels=3  ,					patch_size=4  ,					embed_dim=96  ,					depths=[2, 2, 18, 2]  ,					num_heads=[3, 6, 12, 24]  ,					window_size=7  ,					drop_path_rate=0.3  ,					use_absolute_embeddings=UpperCAmelCase_  ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']  ,					)

			if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					UpperCamelCase__				:					Optional[Any]					=					backbone_config.pop('model_type')
					UpperCamelCase__				:					Union[str, Any]					=					CONFIG_MAPPING[backbone_model_type]
					UpperCamelCase__				:					Dict					=					config_class.from_dict(UpperCAmelCase_)

			# 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 Mask2Former. '
					    F'Supported model types: {",".join(self.backbones_supported)}')

			UpperCamelCase__				:					Optional[Any]					=					backbone_config
			UpperCamelCase__				:					List[Any]					=					feature_size
			UpperCamelCase__				:					Dict					=					mask_feature_size
			UpperCamelCase__				:					Optional[Any]					=					hidden_dim
			UpperCamelCase__				:					Any					=					encoder_feedforward_dim
			UpperCamelCase__				:					Optional[Any]					=					activation_function
			UpperCamelCase__				:					Tuple					=					encoder_layers
			UpperCamelCase__				:					Optional[int]					=					decoder_layers
			UpperCamelCase__				:					int					=					num_attention_heads
			UpperCamelCase__				:					Optional[int]					=					dropout
			UpperCamelCase__				:					Dict					=					dim_feedforward
			UpperCamelCase__				:					List[str]					=					pre_norm
			UpperCamelCase__				:					Dict					=					enforce_input_projection
			UpperCamelCase__				:					Union[str, Any]					=					common_stride
			UpperCamelCase__				:					Dict					=					ignore_value
			UpperCamelCase__				:					List[Any]					=					num_queries
			UpperCamelCase__				:					List[Any]					=					no_object_weight
			UpperCamelCase__				:					Optional[int]					=					class_weight
			UpperCamelCase__				:					Dict					=					mask_weight
			UpperCamelCase__				:					Tuple					=					dice_weight
			UpperCamelCase__				:					Any					=					train_num_points
			UpperCamelCase__				:					str					=					oversample_ratio
			UpperCamelCase__				:					Any					=					importance_sample_ratio
			UpperCamelCase__				:					Union[str, Any]					=					init_std
			UpperCamelCase__				:					str					=					init_xavier_std
			UpperCamelCase__				:					List[Any]					=					use_auxiliary_loss
			UpperCamelCase__				:					List[str]					=					feature_strides
			UpperCamelCase__				:					str					=					output_auxiliary_logits
			UpperCamelCase__				:					List[Any]					=					decoder_layers

			super().__init__(**UpperCAmelCase_)

	@classmethod
	def      __UpperCamelCase							(      cls   :						Optional[int]  ,					UpperCAmelCase_   :						PretrainedConfig  ,					**UpperCAmelCase_   :						Any):
			return cls(
			    backbone_config=UpperCAmelCase_  ,					**UpperCAmelCase_  ,					)





	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					Tuple					=					copy.deepcopy(self.__dict__)
			UpperCamelCase__				:					Union[str, Any]					=					self.backbone_config.to_dict()
			UpperCamelCase__				:					Any					=					self.__class__.model_type
			return output












'''simple docstring'''




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class       __lowercase					:

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			torch.manual_seed(0)
			UpperCamelCase__				:					Dict					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Union[str, Any]					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					UNetaDConditionModel(
			    sample_size=32  ,					layers_per_block=1  ,					block_out_channels=[32, 64]  ,					down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]  ,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,					in_channels=3  ,					out_channels=6  ,					cross_attention_dim=32  ,					encoder_hid_dim=32  ,					attention_head_dim=8  ,					addition_embed_type='text'  ,					addition_embed_type_num_heads=2  ,					cross_attention_norm='group_norm'  ,					resnet_time_scale_shift='scale_shift'  ,					act_fn='gelu'  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					thresholding=UpperCAmelCase_  ,					dynamic_thresholding_ratio=0.95  ,					sample_max_value=1.0  ,					prediction_type='epsilon'  ,					variance_type='learned_range'  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}

	def      __UpperCamelCase							(      self   :						Dict):
			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					UNetaDConditionModel(
			    sample_size=32  ,					layers_per_block=[1, 2]  ,					block_out_channels=[32, 64]  ,					down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]  ,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,					in_channels=6  ,					out_channels=6  ,					cross_attention_dim=32  ,					encoder_hid_dim=32  ,					attention_head_dim=8  ,					addition_embed_type='text'  ,					addition_embed_type_num_heads=2  ,					cross_attention_norm='group_norm'  ,					resnet_time_scale_shift='scale_shift'  ,					act_fn='gelu'  ,					class_embed_type='timestep'  ,					mid_block_scale_factor=1.4_14  ,					time_embedding_act_fn='gelu'  ,					time_embedding_dim=32  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					str					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					thresholding=UpperCAmelCase_  ,					dynamic_thresholding_ratio=0.95  ,					sample_max_value=1.0  ,					prediction_type='epsilon'  ,					variance_type='learned_range'  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "image_noising_scheduler": image_noising_scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Dict					=					self.get_dummy_components()
			UpperCamelCase__				:					List[Any]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					inputs['prompt']
			UpperCamelCase__				:					List[Any]					=					inputs['generator']
			UpperCamelCase__				:					Tuple					=					inputs['num_inference_steps']
			UpperCamelCase__				:					List[Any]					=					inputs['output_type']

			if "image" in inputs:
					UpperCamelCase__				:					Tuple					=					inputs['image']
			else:
					UpperCamelCase__				:					Union[str, Any]					=					None

			if "mask_image" in inputs:
					UpperCamelCase__				:					Optional[int]					=					inputs['mask_image']
			else:
					UpperCamelCase__				:					int					=					None

			if "original_image" in inputs:
					UpperCamelCase__				:					List[Any]					=					inputs['original_image']
			else:
					UpperCamelCase__				:					Optional[Any]					=					None

			UpperCamelCase__,	UpperCamelCase__				:					Any					=					pipe.encode_prompt(UpperCAmelCase_)

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					List[Any]					=					{
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					UpperCamelCase__				:					Dict					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Optional[int]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					original_image

			# set all optional components to None
			for optional_component in pipe._optional_components:
					setattr(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

			UpperCamelCase__				:					int					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					self.pipeline_class.from_pretrained(UpperCAmelCase_)
					pipe_loaded.to(UpperCAmelCase_)
					pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			for optional_component in pipe._optional_components:
					self.assertTrue(
					    getattr(UpperCAmelCase_  ,					UpperCAmelCase_) is None  ,					F'`{optional_component}` did not stay set to None after loading.'  ,					)

			UpperCamelCase__				:					Optional[int]					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					inputs['generator']
			UpperCamelCase__				:					List[Any]					=					inputs['num_inference_steps']
			UpperCamelCase__				:					Optional[int]					=					inputs['output_type']

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					Any					=					{
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					UpperCamelCase__				:					Tuple					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					str					=					original_image

			UpperCamelCase__				:					Union[str, Any]					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Dict					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)





	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Any					=					self.get_dummy_components()
			UpperCamelCase__				:					List[str]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					self.pipeline_class.from_pretrained(UpperCAmelCase_)
					pipe_loaded.to(UpperCAmelCase_)
					pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			UpperCamelCase__				:					Any					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Optional[int]					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)




