


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_lowercase      :      Optional[int]            =					logging.get_logger(__name__)

_lowercase      :      List[Any]            =					{
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}






class 	UpperCamelCase__(       _a	):
				__magic_name__    :				Optional[Any]  		=	"""conditional_detr"""
				__magic_name__    :				str  		=	["""past_key_values"""]
				__magic_name__    :				List[Any]  		=	{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				}




				def __init__(		self      :			List[Any]				,   lowerCAmelCase      :			Tuple=True				,   lowerCAmelCase      :			List[str]=None				,   lowerCAmelCase      :			str=3				,   lowerCAmelCase      :			List[Any]=300				,   lowerCAmelCase      :			Union[str, Any]=6				,   lowerCAmelCase      :			Optional[int]=2048				,   lowerCAmelCase      :			Tuple=8				,   lowerCAmelCase      :			Dict=6				,   lowerCAmelCase      :			Dict=2048				,   lowerCAmelCase      :			Union[str, Any]=8				,   lowerCAmelCase      :			Dict=0.0				,   lowerCAmelCase      :			Tuple=0.0				,   lowerCAmelCase      :			Optional[Any]=True				,   lowerCAmelCase      :			List[Any]="relu"				,   lowerCAmelCase      :			Dict=256				,   lowerCAmelCase      :			Tuple=0.1				,   lowerCAmelCase      :			Tuple=0.0				,   lowerCAmelCase      :			Any=0.0				,   lowerCAmelCase      :			List[Any]=0.02				,   lowerCAmelCase      :			Any=1.0				,   lowerCAmelCase      :			str=False				,   lowerCAmelCase      :			Dict="sine"				,   lowerCAmelCase      :			int="resnet50"				,   lowerCAmelCase      :			Any=True				,   lowerCAmelCase      :			Any=False				,   lowerCAmelCase      :			Dict=2				,   lowerCAmelCase      :			Tuple=5				,   lowerCAmelCase      :			Any=2				,   lowerCAmelCase      :			List[str]=1				,   lowerCAmelCase      :			Dict=1				,   lowerCAmelCase      :			Dict=2				,   lowerCAmelCase      :			Optional[int]=5				,   lowerCAmelCase      :			int=2				,   lowerCAmelCase      :			Optional[int]=0.25				,   **lowerCAmelCase      :			Tuple				,   )->					Dict:

										"""simple docstring"""




										if backbone_config is not None and use_timm_backbone:
																raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''				)

										if not use_timm_backbone:
																if backbone_config is None:
																						logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''				)
																						UpperCAmelCase					    =     CONFIG_MAPPING["""resnet"""](out_features=['''stage4''']				)
																elif isinstance(__lowerCamelCase				,   __lowerCamelCase				):
																						UpperCAmelCase					    =     backbone_config.get('''model_type'''				)
																						UpperCAmelCase					    =     CONFIG_MAPPING[backbone_model_type]
																						UpperCAmelCase					    =     config_class.from_dict(__lowerCamelCase				)

										UpperCAmelCase					    =     use_timm_backbone
										UpperCAmelCase					    =     backbone_config
										UpperCAmelCase					    =     num_channels
										UpperCAmelCase					    =     num_queries
										UpperCAmelCase					    =     d_model
										UpperCAmelCase					    =     encoder_ffn_dim
										UpperCAmelCase					    =     encoder_layers
										UpperCAmelCase					    =     encoder_attention_heads
										UpperCAmelCase					    =     decoder_ffn_dim
										UpperCAmelCase					    =     decoder_layers
										UpperCAmelCase					    =     decoder_attention_heads
										UpperCAmelCase					    =     dropout
										UpperCAmelCase					    =     attention_dropout
										UpperCAmelCase					    =     activation_dropout
										UpperCAmelCase					    =     activation_function
										UpperCAmelCase					    =     init_std
										UpperCAmelCase					    =     init_xavier_std
										UpperCAmelCase					    =     encoder_layerdrop
										UpperCAmelCase					    =     decoder_layerdrop
										UpperCAmelCase					    =     encoder_layers
										UpperCAmelCase					    =     auxiliary_loss
										UpperCAmelCase					    =     position_embedding_type
										UpperCAmelCase					    =     backbone
										UpperCAmelCase					    =     use_pretrained_backbone
										UpperCAmelCase					    =     dilation
										# Hungarian matcher
										UpperCAmelCase					    =     class_cost
										UpperCAmelCase					    =     bbox_cost
										UpperCAmelCase					    =     giou_cost
										# Loss coefficients
										UpperCAmelCase					    =     mask_loss_coefficient
										UpperCAmelCase					    =     dice_loss_coefficient
										UpperCAmelCase					    =     cls_loss_coefficient
										UpperCAmelCase					    =     bbox_loss_coefficient
										UpperCAmelCase					    =     giou_loss_coefficient
										UpperCAmelCase					    =     focal_alpha
										super().__init__(is_encoder_decoder=__lowerCamelCase				,   **__lowerCamelCase				)




				@property
				def 					a__(		self      :			Optional[Any]				)->					str:

										"""simple docstring"""




										return self.encoder_attention_heads




				@property
				def 					a__(		self      :			Optional[int]				)->					Tuple:

										"""simple docstring"""




										return self.d_model





				def 					a__(		self      :			str				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     copy.deepcopy(self.__dict__				)
										if self.backbone_config is not None:
																UpperCAmelCase					    =     self.backbone_config.to_dict()
										UpperCAmelCase					    =     self.__class__.model_type
										return output







class 	UpperCamelCase__(       _a	):
				__magic_name__    :				Optional[int]  		=	version.parse("1.11"	)




				@property
				def 					a__(		self      :			Optional[Any]				)->					Tuple:

										"""simple docstring"""




										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										        ('''pixel_mask''', {0: '''batch'''}),
										    ]				)




				@property
				def 					a__(		self      :			Any				)->					Optional[Any]:

										"""simple docstring"""




										return 1E-5





				@property
				def 					a__(		self      :			Any				)->					Optional[Any]:

										"""simple docstring"""




										return 12









'''simple docstring'''



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				List[str]  		=	StableDiffusionSAGPipeline
				__magic_name__    :				str  		=	TEXT_TO_IMAGE_PARAMS
				__magic_name__    :				Any  		=	TEXT_TO_IMAGE_BATCH_PARAMS
				__magic_name__    :				Optional[int]  		=	TEXT_TO_IMAGE_IMAGE_PARAMS
				__magic_name__    :				List[Any]  		=	TEXT_TO_IMAGE_IMAGE_PARAMS
				__magic_name__    :				str  		=	False




				def 					a__(		self      :			Union[str, Any]				)->					Tuple:

										"""simple docstring"""




										torch.manual_seed(0				)
										UpperCAmelCase					    =     UNetaDConditionModel(
										    block_out_channels=(32, 64)				,   layers_per_block=2				,   sample_size=32				,   in_channels=4				,   out_channels=4				,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,   cross_attention_dim=32				,   )
										UpperCAmelCase					    =     DDIMScheduler(
										    beta_start=0.00085				,   beta_end=0.012				,   beta_schedule='''scaled_linear'''				,   clip_sample=lowerCAmelCase				,   set_alpha_to_one=lowerCAmelCase				,   )
										torch.manual_seed(0				)
										UpperCAmelCase					    =     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					    =     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=1000				,   )
										UpperCAmelCase					    =     CLIPTextModel(lowerCAmelCase				)
										UpperCAmelCase					    =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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




				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Tuple=0				)->					str:

										"""simple docstring"""




										if str(lowerCAmelCase				).startswith('''mps'''				):
																UpperCAmelCase					    =     torch.manual_seed(lowerCAmelCase				)
										else:
																UpperCAmelCase					    =     torch.Generator(device=lowerCAmelCase				).manual_seed(lowerCAmelCase				)
										UpperCAmelCase					    =     {
										    '''prompt''': '''.''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 1.0,
										    '''sag_scale''': 1.0,
										    '''output_type''': '''numpy''',
										}
										return inputs





				def 					a__(		self      :			Any				)->					List[str]:

										"""simple docstring"""




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







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




				def 					a__(		self      :			Union[str, Any]				)->					Any:

										"""simple docstring"""




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




				def 					a__(		self      :			Union[str, Any]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				)

										UpperCAmelCase					    =     output.images

										UpperCAmelCase					    =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										UpperCAmelCase					    =     np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2




				def 					a__(		self      :			int				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				)

										UpperCAmelCase					    =     output.images

										UpperCAmelCase					    =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										UpperCAmelCase					    =     np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2





				def 					a__(		self      :			Optional[Any]				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   width=768				,   height=512				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				,   )

										UpperCAmelCase					    =     output.images

										assert image.shape == (1, 512, 768, 3)





