



import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if isinstance(lowerCAmelCase__  ,	torch.Tensor   ):
		return image
	elif isinstance(lowerCAmelCase__  ,	PIL.Image.Image   ):
		__lowercase		       =			[image]

	if isinstance(image[0]  ,	PIL.Image.Image   ):
		__lowercase		       =			[np.array(i.resize((w, h)  ,	resample=PIL_INTERPOLATION["lanczos"]   )   )[None, :] for i in image]
		__lowercase		       =			np.concatenate(lowerCAmelCase__  ,	axis=0   )
		__lowercase		       =			np.array(lowerCAmelCase__   ).astype(np.floataa   ) / 2_5_5.0
		__lowercase		       =			image.transpose(0  ,	3  ,	1  ,	2   )
		__lowercase		       =			2.0 * image - 1.0
		__lowercase		       =			torch.from_numpy(lowerCAmelCase__   )
	elif isinstance(image[0]  ,	torch.Tensor   ):
		__lowercase		       =			torch.cat(lowerCAmelCase__  ,	dim=0   )
	return image



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=0.9_9_9_5   ):
	if not isinstance(lowerCAmelCase__  ,	np.ndarray   ):
		__lowercase		       =			True
		__lowercase		       =			va.device
		__lowercase		       =			va.cpu().numpy()
		__lowercase		       =			va.cpu().numpy()

	__lowercase		       =			np.sum(va * va / (np.linalg.norm(lowerCAmelCase__   ) * np.linalg.norm(lowerCAmelCase__   ))   )
	if np.abs(lowerCAmelCase__   ) > DOT_THRESHOLD:
		__lowercase		       =			(1 - t) * va + t * va
	else:
		__lowercase		       =			np.arccos(lowerCAmelCase__   )
		__lowercase		       =			np.sin(lowerCAmelCase__   )
		__lowercase		       =			theta_a * t
		__lowercase		       =			np.sin(lowerCAmelCase__   )
		__lowercase		       =			np.sin(theta_a - theta_t   ) / sin_theta_a
		__lowercase		       =			sin_theta_t / sin_theta_a
		__lowercase		       =			sa * va + sa * va

	if inputs_are_torch:
		__lowercase		       =			torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )

	return va



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			F.normalize(lowerCAmelCase__  ,	dim=-1   )
	__lowercase		       =			F.normalize(lowerCAmelCase__  ,	dim=-1   )
	return (x - y).norm(dim=-1   ).div(2   ).arcsin().pow(2   ).mul(2   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	for param in model.parameters():
		__lowercase		       =			value

class   _A   ( __SCREAMING_SNAKE_CASE	):


						'''simple docstring'''





						def __init__(	self	:							int							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							Dict							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							int=None							,      lowerCamelCase	:							List[Any]=None							,      lowerCamelCase	:							List[str]=None							,      ):






							'''simple docstring'''



							super().__init__()
							self.register_modules(
							    vae=_a							,      text_encoder=_a							,      clip_model=_a							,      tokenizer=_a							,      unet=_a							,      scheduler=_a							,      feature_extractor=_a							,      coca_model=_a							,      coca_tokenizer=_a							,      coca_transform=_a							,      )
							__lowercase		       =			(
							    feature_extractor.size
							    if isinstance(feature_extractor.size							,      _a	)
							    else feature_extractor.size["shortest_edge"]
							)
							__lowercase		       =			transforms.Normalize(mean=feature_extractor.image_mean							,      std=feature_extractor.image_std	)
							set_requires_grad(self.text_encoder							,      _a	)
							set_requires_grad(self.clip_model							,      _a	)



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							int = "auto"	):






							'''simple docstring'''



							if slice_size == "auto":
								# half the attention head size is usually a good trade-off between
								# speed and memory
								__lowercase		       =			self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_a	)



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							self.enable_attention_slicing(_a	)



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							set_requires_grad(self.vae							,      _a	)



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							set_requires_grad(self.vae							,      _a	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							set_requires_grad(self.unet							,      _a	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							set_requires_grad(self.unet							,      _a	)



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			min(int(num_inference_steps * strength	)							,      _a	)

							__lowercase		       =			max(num_inference_steps - init_timestep							,      0	)
							__lowercase		       =			self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start



						def     _snake_case	(	self	:							Tuple							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							List[Any]							,      lowerCamelCase	:							Dict							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							str=None	):






							'''simple docstring'''



							if not isinstance(_a							,      torch.Tensor	):
								raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(_a	)}"""	)

							__lowercase		       =			image.to(device=_a							,      dtype=_a	)

							if isinstance(_a							,      _a	):
								__lowercase		       =			[
								    self.vae.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(_a	)
								]
								__lowercase		       =			torch.cat(_a							,      dim=0	)
							else:
								__lowercase		       =			self.vae.encode(_a	).latent_dist.sample(_a	)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							__lowercase		       =			0.1_8215 * init_latents
							__lowercase		       =			init_latents.repeat_interleave(_a							,      dim=0	)

							__lowercase		       =			randn_tensor(init_latents.shape							,      generator=_a							,      device=_a							,      dtype=_a	)

							# get latents
							__lowercase		       =			self.scheduler.add_noise(_a							,      _a							,      _a	)
							__lowercase		       =			init_latents

							return latents



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			self.coca_transform(_a	).unsqueeze(0	)
							with torch.no_grad(), torch.cuda.amp.autocast():
								__lowercase		       =			self.coca_model.generate(transformed_image.to(device=self.device							,      dtype=self.coca_model.dtype	)	)
							__lowercase		       =			self.coca_tokenizer.decode(generated[0].cpu().numpy()	)
							return generated.split("<end_of_text>"	)[0].replace("<start_of_text>"							,      ""	).rstrip(" .,"	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							str							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			self.feature_extractor.preprocess(_a	)
							__lowercase		       =			torch.from_numpy(clip_image_input["pixel_values"][0]	).unsqueeze(0	).to(self.device	).half()
							__lowercase		       =			self.clip_model.get_image_features(_a	)
							__lowercase		       =			image_embeddings_clip / image_embeddings_clip.norm(p=2							,      dim=-1							,      keepdim=_a	)
							__lowercase		       =			image_embeddings_clip.repeat_interleave(_a							,      dim=0	)
							return image_embeddings_clip



						@torch.enable_grad()
						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							List[Any]							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							str							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Dict							,      ):






							'''simple docstring'''



							__lowercase		       =			latents.detach().requires_grad_()

							__lowercase		       =			self.scheduler.scale_model_input(_a							,      _a	)

							# predict the noise residual
							__lowercase		       =			self.unet(_a							,      _a							,      encoder_hidden_states=_a	).sample

							if isinstance(self.scheduler							,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)	):
								__lowercase		       =			self.scheduler.alphas_cumprod[timestep]
								__lowercase		       =			1 - alpha_prod_t
								# compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								__lowercase		       =			(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

								__lowercase		       =			torch.sqrt(_a	)
								__lowercase		       =			pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler							,      _a	):
								__lowercase		       =			self.scheduler.sigmas[index]
								__lowercase		       =			latents - sigma * noise_pred
							else:
								raise ValueError(f"""scheduler type {type(self.scheduler	)} not supported"""	)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							__lowercase		       =			1 / 0.1_8215 * sample
							__lowercase		       =			self.vae.decode(_a	).sample
							__lowercase		       =			(image / 2 + 0.5).clamp(0							,      1	)

							__lowercase		       =			transforms.Resize(self.feature_extractor_size	)(_a	)
							__lowercase		       =			self.normalize(_a	).to(latents.dtype	)

							__lowercase		       =			self.clip_model.get_image_features(_a	)
							__lowercase		       =			image_embeddings_clip / image_embeddings_clip.norm(p=2							,      dim=-1							,      keepdim=_a	)

							__lowercase		       =			spherical_dist_loss(_a							,      _a	).mean() * clip_guidance_scale

							__lowercase		       =			-torch.autograd.grad(_a							,      _a	)[0]

							if isinstance(self.scheduler							,      _a	):
								__lowercase		       =			latents.detach() + grads * (sigma**2)
								__lowercase		       =			noise_pred_original
							else:
								__lowercase		       =			noise_pred_original - torch.sqrt(_a	) * grads
							return noise_pred, latents



						@torch.no_grad()
						def __call__(	self	:							Optional[Any]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							List[str] = None							,      lowerCamelCase	:							List[Any] = 512							,      lowerCamelCase	:							int = 512							,      lowerCamelCase	:							Union[str, Any] = 0.6							,      lowerCamelCase	:							int = 50							,      lowerCamelCase	:							Tuple = 7.5							,      lowerCamelCase	:							List[str] = 1							,      lowerCamelCase	:							str = 0.0							,      lowerCamelCase	:							Dict = 100							,      lowerCamelCase	:							Optional[Any] = None							,      lowerCamelCase	:							List[str] = "pil"							,      lowerCamelCase	:							Optional[int] = True							,      lowerCamelCase	:							str = 0.8							,      lowerCamelCase	:							List[Any] = 0.1							,      lowerCamelCase	:							Union[str, Any] = 0.1							,      ):






							'''simple docstring'''



							if isinstance(_a							,      _a	) and len(_a	) != batch_size:
								raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(_a	)} generators."""	)

							if height % 8 != 0 or width % 8 != 0:
								raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""	)

							if isinstance(_a							,      torch.Generator	) and batch_size > 1:
								__lowercase		       =			[generator] + [None] * (batch_size - 1)

							__lowercase		       =			[
							    ("model", self.coca_model is None),
							    ("tokenizer", self.coca_tokenizer is None),
							    ("transform", self.coca_transform is None),
							]
							__lowercase		       =			[x[0] for x in coca_is_none if x[1]]
							__lowercase		       =			", ".join(_a	)
							# generate prompts with coca model if prompt is None
							if content_prompt is None:
								if len(_a	):
									raise ValueError(
									    f"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
									    f"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""	)
								__lowercase		       =			self.get_image_description(_a	)
							if style_prompt is None:
								if len(_a	):
									raise ValueError(
									    f"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
									    f""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""	)
								__lowercase		       =			self.get_image_description(_a	)

							# get prompt text embeddings for content and style
							__lowercase		       =			self.tokenizer(
							    _a							,      padding="max_length"							,      max_length=self.tokenizer.model_max_length							,      truncation=_a							,      return_tensors="pt"							,      )
							__lowercase		       =			self.text_encoder(content_text_input.input_ids.to(self.device	)	)[0]

							__lowercase		       =			self.tokenizer(
							    _a							,      padding="max_length"							,      max_length=self.tokenizer.model_max_length							,      truncation=_a							,      return_tensors="pt"							,      )
							__lowercase		       =			self.text_encoder(style_text_input.input_ids.to(self.device	)	)[0]

							__lowercase		       =			slerp(_a							,      _a							,      _a	)

							# duplicate text embeddings for each generation per prompt
							__lowercase		       =			text_embeddings.repeat_interleave(_a							,      dim=0	)

							# set timesteps
							__lowercase		       =			"offset" in set(inspect.signature(self.scheduler.set_timesteps	).parameters.keys()	)
							__lowercase		       =			{}
							if accepts_offset:
								__lowercase		       =			1

							self.scheduler.set_timesteps(_a							,      **_a	)
							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							self.scheduler.timesteps.to(self.device	)

							__lowercase       ,    __lowercase		       =			self.get_timesteps(_a							,      _a							,      self.device	)
							__lowercase		       =			timesteps[:1].repeat(_a	)

							# Preprocess image
							__lowercase		       =			preprocess(_a							,      _a							,      _a	)
							__lowercase		       =			self.prepare_latents(
							    _a							,      _a							,      _a							,      text_embeddings.dtype							,      self.device							,      _a	)

							__lowercase		       =			preprocess(_a							,      _a							,      _a	)
							__lowercase		       =			self.prepare_latents(
							    _a							,      _a							,      _a							,      text_embeddings.dtype							,      self.device							,      _a	)

							__lowercase		       =			slerp(_a							,      _a							,      _a	)

							if clip_guidance_scale > 0:
								__lowercase		       =			self.get_clip_image_embeddings(_a							,      _a	)
								__lowercase		       =			self.get_clip_image_embeddings(_a							,      _a	)
								__lowercase		       =			slerp(
								    _a							,      _a							,      _a	)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							__lowercase		       =			guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
								__lowercase		       =			content_text_input.input_ids.shape[-1]
								__lowercase		       =			self.tokenizer([""]							,      padding="max_length"							,      max_length=_a							,      return_tensors="pt"	)
								__lowercase		       =			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]
								# duplicate unconditional embeddings for each generation per prompt
								__lowercase		       =			uncond_embeddings.repeat_interleave(_a							,      dim=0	)

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								__lowercase		       =			torch.cat([uncond_embeddings, text_embeddings]	)

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							__lowercase		       =			(batch_size, self.unet.config.in_channels, height // 8, width // 8)
							__lowercase		       =			text_embeddings.dtype
							if latents is None:
								if self.device.type == "mps":
									# randn does not work reproducibly on mps
									__lowercase		       =			torch.randn(_a							,      generator=_a							,      device="cpu"							,      dtype=_a	).to(
									    self.device	)
								else:
									__lowercase		       =			torch.randn(_a							,      generator=_a							,      device=self.device							,      dtype=_a	)
							else:
								if latents.shape != latents_shape:
									raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
								__lowercase		       =			latents.to(self.device	)

							# scale the initial noise by the standard deviation required by the scheduler
							__lowercase		       =			latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							__lowercase		       =			"eta" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
							__lowercase		       =			{}
							if accepts_eta:
								__lowercase		       =			eta

							# check if the scheduler accepts generator
							__lowercase		       =			"generator" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
							if accepts_generator:
								__lowercase		       =			generator

							with self.progress_bar(total=_a	):
								for i, t in enumerate(_a	):
									# expand the latents if we are doing classifier free guidance
									__lowercase		       =			torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
									__lowercase		       =			self.scheduler.scale_model_input(_a							,      _a	)

									# predict the noise residual
									__lowercase		       =			self.unet(_a							,      _a							,      encoder_hidden_states=_a	).sample

									# perform classifier free guidance
									if do_classifier_free_guidance:
										__lowercase       ,    __lowercase		       =			noise_pred.chunk(2	)
										__lowercase		       =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# perform clip guidance
									if clip_guidance_scale > 0:
										__lowercase		       =			(
										    text_embeddings.chunk(2	)[1] if do_classifier_free_guidance else text_embeddings
										)
										__lowercase       ,    __lowercase		       =			self.cond_fn(
										    _a							,      _a							,      _a							,      _a							,      _a							,      _a							,      _a							,      )

									# compute the previous noisy sample x_t -> x_t-1
									__lowercase		       =			self.scheduler.step(_a							,      _a							,      _a							,      **_a	).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							__lowercase		       =			1 / 0.1_8215 * latents
							__lowercase		       =			self.vae.decode(_a	).sample

							__lowercase		       =			(image / 2 + 0.5).clamp(0							,      1	)
							__lowercase		       =			image.cpu().permute(0							,      2							,      3							,      1	).numpy()

							if output_type == "pil":
								__lowercase		       =			self.numpy_to_pil(_a	)

							if not return_dict:
								return (image, None)

							return StableDiffusionPipelineOutput(images=_a							,      nsfw_content_detected=_a	)








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


snake_case__					:     int    		=   logging.get_logger(__name__)

snake_case__					:     Optional[int]    		=   {
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      """conditional_detr"""
						_snake_case			:     Union[str, Any]      							=      ["""past_key_values"""]
						_snake_case			:     Optional[int]      							=      {
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}



						def __init__(	self	:							Optional[Any]							,      lowerCamelCase	:							int=True							,      lowerCamelCase	:							Tuple=None							,      lowerCamelCase	:							Optional[int]=3							,      lowerCamelCase	:							Optional[int]=300							,      lowerCamelCase	:							List[Any]=6							,      lowerCamelCase	:							str=2_048							,      lowerCamelCase	:							Any=8							,      lowerCamelCase	:							List[str]=6							,      lowerCamelCase	:							Any=2_048							,      lowerCamelCase	:							List[Any]=8							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							List[str]=0.0							,      lowerCamelCase	:							List[Any]=True							,      lowerCamelCase	:							str="relu"							,      lowerCamelCase	:							int=256							,      lowerCamelCase	:							Dict=0.1							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							Dict=0.0							,      lowerCamelCase	:							Tuple=0.02							,      lowerCamelCase	:							int=1.0							,      lowerCamelCase	:							Tuple=False							,      lowerCamelCase	:							List[str]="sine"							,      lowerCamelCase	:							List[Any]="resnet50"							,      lowerCamelCase	:							Any=True							,      lowerCamelCase	:							Any=False							,      lowerCamelCase	:							List[Any]=2							,      lowerCamelCase	:							List[Any]=5							,      lowerCamelCase	:							str=2							,      lowerCamelCase	:							Dict=1							,      lowerCamelCase	:							List[str]=1							,      lowerCamelCase	:							Union[str, Any]=2							,      lowerCamelCase	:							Dict=5							,      lowerCamelCase	:							List[Any]=2							,      lowerCamelCase	:							Tuple=0.25							,      **lowerCamelCase	:							List[str]							,      ):






							'''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."	)
									__lowercase		       =			CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
								elif isinstance(lowerCamelCase							,      lowerCamelCase	):
									__lowercase		       =			backbone_config.get("model_type"	)
									__lowercase		       =			CONFIG_MAPPING[backbone_model_type]
									__lowercase		       =			config_class.from_dict(lowerCamelCase	)

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



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return self.encoder_attention_heads



						@property
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							return self.d_model



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



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

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Any      							=      version.parse("""1.11"""	)



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



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



						@property
						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							return 1e-5



						@property
						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							return 12



