
import gc
import unittest

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

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

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


enable_full_determinism()







class        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: Optional[int]	=		LDMTextToImagePipeline
						UpperCamelCase_							: Optional[Any]	=		TEXT_TO_IMAGE_PARAMS - {
						    "negative_prompt",
						    "negative_prompt_embeds",
						    "cross_attention_kwargs",
						    "prompt_embeds",
						}
						UpperCamelCase_							: Optional[Any]	=		PipelineTesterMixin.required_optional_params - {
						    "num_images_per_prompt",
						    "callback",
						    "callback_steps",
						}
						UpperCamelCase_							: Optional[int]	=		TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCamelCase_							: str	=		False

						def  A_				(			self				)					->      Any:


								'''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=0.0_0085       ,							beta_end=0.012       ,							beta_schedule="""scaled_linear"""       ,							clip_sample=a       ,							set_alpha_to_one=a       ,							)
								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=10_00       ,							)
								_UpperCamelCase						    =	CLIPTextModel(a				)
								_UpperCamelCase						    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

								_UpperCamelCase						    =	{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vqvae""": vae,
								    """bert""": text_encoder,
								    """tokenizer""": tokenizer,
								}
								return components

						def  A_				(			self       ,							a       ,							a=0				)					->      Union[str, Any]:


								'''simple docstring'''

								if str(a				).startswith("""mps"""				):
										_UpperCamelCase						    =	torch.manual_seed(a				)
								else:
										_UpperCamelCase						    =	torch.Generator(device=a				).manual_seed(a				)
								_UpperCamelCase						    =	{
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}
								return inputs







						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

								_UpperCamelCase						    =	self.get_dummy_components()
								_UpperCamelCase						    =	LDMTextToImagePipeline(**a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)
								_UpperCamelCase						    =	pipe(**a				).images
								_UpperCamelCase						    =	image[0, -3:, -3:, -1]

								assert image.shape == (1, 16, 16, 3)
								_UpperCamelCase						    =	np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]				)

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







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

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

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

						def  A_				(			self       ,							a       ,							a=torch.floataa       ,							a=0				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	torch.manual_seed(a				)
								_UpperCamelCase						    =	np.random.RandomState(a				).standard_normal((1, 4, 32, 32)				)
								_UpperCamelCase						    =	torch.from_numpy(a				).to(device=a       ,							dtype=a				)
								_UpperCamelCase						    =	{
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """latents""": latents,
								    """generator""": generator,
								    """num_inference_steps""": 3,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}
								return inputs







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


								'''simple docstring'''

								_UpperCamelCase						    =	LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""				).to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_inputs(a				)
								_UpperCamelCase						    =	pipe(**a				).images
								_UpperCamelCase						    =	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 2_56, 2_56, 3)
								_UpperCamelCase						    =	np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878]				)
								_UpperCamelCase						    =	np.abs(expected_slice - image_slice				).max()
								assert max_diff < 1e-3







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

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

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

						def  A_				(			self       ,							a       ,							a=torch.floataa       ,							a=0				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	torch.manual_seed(a				)
								_UpperCamelCase						    =	np.random.RandomState(a				).standard_normal((1, 4, 32, 32)				)
								_UpperCamelCase						    =	torch.from_numpy(a				).to(device=a       ,							dtype=a				)
								_UpperCamelCase						    =	{
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """latents""": latents,
								    """generator""": generator,
								    """num_inference_steps""": 50,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}
								return inputs







						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""				).to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_inputs(a				)
								_UpperCamelCase						    =	pipe(**a				).images[0]

								_UpperCamelCase						    =	load_numpy(
								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"""				)
								_UpperCamelCase						    =	np.abs(expected_image - image				).max()
								assert max_diff < 1e-3





import copy

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


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: int	=		"deta"
						UpperCamelCase_							: Union[str, Any]	=		{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}

						def __init__(			self       ,							a=None       ,							a=9_00       ,							a=20_48       ,							a=6       ,							a=20_48       ,							a=8       ,							a=6       ,							a=10_24       ,							a=8       ,							a=0.0       ,							a=True       ,							a="relu"       ,							a=2_56       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=1.0       ,							a=True       ,							a=False       ,							a="sine"       ,							a=5       ,							a=4       ,							a=4       ,							a=True       ,							a=3_00       ,							a=True       ,							a=True       ,							a=1       ,							a=5       ,							a=2       ,							a=1       ,							a=1       ,							a=5       ,							a=2       ,							a=0.1       ,							a=0.25       ,							**a       ,							)					->      Dict:


								'''simple docstring'''

								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=["""stage2""", """stage3""", """stage4"""]				)
								else:
										if isinstance(a       ,							a				):
												_UpperCamelCase						    =	backbone_config.pop("""model_type"""				)
												_UpperCamelCase						    =	CONFIG_MAPPING[backbone_model_type]
												_UpperCamelCase						    =	config_class.from_dict(a				)

								_UpperCamelCase						    =	backbone_config
								_UpperCamelCase						    =	num_queries
								_UpperCamelCase						    =	max_position_embeddings
								_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						    =	auxiliary_loss
								_UpperCamelCase						    =	position_embedding_type
								# deformable attributes
								_UpperCamelCase						    =	num_feature_levels
								_UpperCamelCase						    =	encoder_n_points
								_UpperCamelCase						    =	decoder_n_points
								_UpperCamelCase						    =	two_stage
								_UpperCamelCase						    =	two_stage_num_proposals
								_UpperCamelCase						    =	with_box_refine
								_UpperCamelCase						    =	assign_first_stage
								if two_stage is True and with_box_refine is False:
										raise ValueError("""If two_stage is True, with_box_refine must be True."""				)
								# Hungarian matcher
								_UpperCamelCase						    =	class_cost
								_UpperCamelCase						    =	bbox_cost
								_UpperCamelCase						    =	giou_cost
								# Loss coefficients
								_UpperCamelCase						    =	mask_loss_coefficient
								_UpperCamelCase						    =	dice_loss_coefficient
								_UpperCamelCase						    =	bbox_loss_coefficient
								_UpperCamelCase						    =	giou_loss_coefficient
								_UpperCamelCase						    =	eos_coefficient
								_UpperCamelCase						    =	focal_alpha
								super().__init__(is_encoder_decoder=a       ,							**a				)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.encoder_attention_heads

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.d_model







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	copy.deepcopy(self.__dict__				)
								_UpperCamelCase						    =	self.backbone_config.to_dict()
								_UpperCamelCase						    =	self.__class__.model_type
								return output



