




'''simple docstring'''







from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			_a  (	__a ):
       def __init__(	self						:	str   ,				lowercase						:	TransformeraDModel   ,				lowercase						:	AutoencoderKL   ,				lowercase						:	KarrasDiffusionSchedulers   ,				lowercase						:	Optional[Dict[int, str]] = None   ,				):



           '''simple docstring'''

           super().__init__()
           self.register_modules(transformer=lowercase   ,				vae=lowercase   ,				scheduler=lowercase				)

           # create a imagenet -> id dictionary for easier use
           UpperCAmelCase			       =		{}
           if idalabel is not None:
               for key, value in idalabel.items():
                   for label in value.split(''','''				):
                       UpperCAmelCase			       =		int(lowercase				)
               UpperCAmelCase			       =		dict(sorted(self.labels.items()				)				)
       def     A      (	self						:	Tuple   ,				lowercase						:	Union[str, List[str]]				):



           '''simple docstring'''

           if not isinstance(lowercase   ,				lowercase				):
               UpperCAmelCase			       =		list(lowercase				)

           for l in label:
               if l not in self.labels:
                   raise ValueError(
                       f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."				)

           return [self.labels[l] for l in label]







       @torch.no_grad()
       def __call__(	self						:	str   ,				lowercase						:	List[int]   ,				lowercase						:	float = 4.0   ,				lowercase						:	Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,				lowercase						:	int = 50   ,				lowercase						:	Optional[str] = "pil"   ,				lowercase						:	bool = True   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		len(lowercase				)
           UpperCAmelCase			       =		self.transformer.config.sample_size
           UpperCAmelCase			       =		self.transformer.config.in_channels

           UpperCAmelCase			       =		randn_tensor(
               shape=(batch_size, latent_channels, latent_size, latent_size)   ,				generator=lowercase   ,				device=self.device   ,				dtype=self.transformer.dtype   ,				)
           UpperCAmelCase			       =		torch.cat([latents] * 2				) if guidance_scale > 1 else latents

           UpperCAmelCase			       =		torch.tensor(lowercase   ,				device=self.device				).reshape(-1				)
           UpperCAmelCase			       =		torch.tensor([1_000] * batch_size   ,				device=self.device				)
           UpperCAmelCase			       =		torch.cat([class_labels, class_null]   ,				0				) if guidance_scale > 1 else class_labels

           # set step values
           self.scheduler.set_timesteps(lowercase				)

           for t in self.progress_bar(self.scheduler.timesteps				):
               if guidance_scale > 1:
                   UpperCAmelCase			       =		latent_model_input[: len(lowercase				) // 2]
                   UpperCAmelCase			       =		torch.cat([half, half]   ,				dim=0				)
               UpperCAmelCase			       =		self.scheduler.scale_model_input(lowercase   ,				lowercase				)

               UpperCAmelCase			       =		t
               if not torch.is_tensor(lowercase				):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   UpperCAmelCase			       =		latent_model_input.device.type == '''mps'''
                   if isinstance(lowercase   ,				lowercase				):
                       UpperCAmelCase			       =		torch.floataa if is_mps else torch.floataa
                   else:
                       UpperCAmelCase			       =		torch.intaa if is_mps else torch.intaa
                   UpperCAmelCase			       =		torch.tensor([timesteps]   ,				dtype=lowercase   ,				device=latent_model_input.device				)
               elif len(timesteps.shape				) == 0:
                   UpperCAmelCase			       =		timesteps[None].to(latent_model_input.device				)
               # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
               UpperCAmelCase			       =		timesteps.expand(latent_model_input.shape[0]				)
               # predict noise model_output
               UpperCAmelCase			       =		self.transformer(
                   lowercase   ,				timestep=lowercase   ,				class_labels=lowercase				).sample

               # perform guidance
               if guidance_scale > 1:
                   UpperCAmelCase       ,     UpperCAmelCase			       =		noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   UpperCAmelCase       ,     UpperCAmelCase			       =		torch.split(lowercase   ,				len(lowercase				) // 2   ,				dim=0				)

                   UpperCAmelCase			       =		uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   UpperCAmelCase			       =		torch.cat([half_eps, half_eps]   ,				dim=0				)

                   UpperCAmelCase			       =		torch.cat([eps, rest]   ,				dim=1				)

               # learned sigma
               if self.transformer.config.out_channels // 2 == latent_channels:
                   UpperCAmelCase       ,     UpperCAmelCase			       =		torch.split(lowercase   ,				lowercase   ,				dim=1				)
               else:
                   UpperCAmelCase			       =		noise_pred

               # compute previous image: x_t -> x_t-1
               UpperCAmelCase			       =		self.scheduler.step(lowercase   ,				lowercase   ,				lowercase				).prev_sample

           if guidance_scale > 1:
               UpperCAmelCase       ,     UpperCAmelCase			       =		latent_model_input.chunk(2   ,				dim=0				)
           else:
               UpperCAmelCase			       =		latent_model_input

           UpperCAmelCase			       =		1 / self.vae.config.scaling_factor * latents
           UpperCAmelCase			       =		self.vae.decode(lowercase				).sample

           UpperCAmelCase			       =		(samples / 2 + 0.5).clamp(0   ,				1				)

           # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
           UpperCAmelCase			       =		samples.cpu().permute(0   ,				2   ,				3   ,				1				).float().numpy()

           if output_type == "pil":
               UpperCAmelCase			       =		self.numpy_to_pil(lowercase				)

           if not return_dict:
               return (samples,)

           return ImagePipelineOutput(images=lowercase				)








"""simple docstring"""
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


SCREAMING_SNAKE_CASE :    Dict									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Optional[int]									=				{
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='yolos'



		def __init__(self				,       a_=7_68				,       a_=12				,       a_=12				,       a_=30_72				,       a_="gelu"				,       a_=0.0				,       a_=0.0				,       a_=0.02				,       a_=1E-12				,       a_=[5_12, 8_64]				,       a_=16				,       a_=3				,       a_=True				,       a_=1_00				,       a_=True				,       a_=False				,       a_=1				,       a_=5				,       a_=2				,       a_=5				,       a_=2				,       a_=0.1				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(**a_					)

			__snake_case	:						Union[str, Any]						=				hidden_size
			__snake_case	:						Union[str, Any]						=				num_hidden_layers
			__snake_case	:						Dict						=				num_attention_heads
			__snake_case	:						str						=				intermediate_size
			__snake_case	:						List[str]						=				hidden_act
			__snake_case	:						Tuple						=				hidden_dropout_prob
			__snake_case	:						Optional[int]						=				attention_probs_dropout_prob
			__snake_case	:						Union[str, Any]						=				initializer_range
			__snake_case	:						Tuple						=				layer_norm_eps
			__snake_case	:						List[Any]						=				image_size
			__snake_case	:						Tuple						=				patch_size
			__snake_case	:						str						=				num_channels
			__snake_case	:						Tuple						=				qkv_bias
			__snake_case	:						Union[str, Any]						=				num_detection_tokens
			__snake_case	:						List[str]						=				use_mid_position_embeddings
			__snake_case	:						Tuple						=				auxiliary_loss
			# Hungarian matcher
			__snake_case	:						List[str]						=				class_cost
			__snake_case	:						int						=				bbox_cost
			__snake_case	:						int						=				giou_cost
			# Loss coefficients
			__snake_case	:						Optional[int]						=				bbox_loss_coefficient
			__snake_case	:						List[str]						=				giou_loss_coefficient
			__snake_case	:						List[Any]						=				eos_coefficient



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=version.parse('1.11'				)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 1E-4



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 12





