


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A	:		List[Any]    =       logging.get_logger(__name__)  # pylint: disable=invalid-name

A	:		str    =       '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'






def 			UpperCamelCase (     __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							Optional[int]   ,   __magic_name__					:							Optional[int]=8					)       ->  Optional[int]:
		"""simple docstring"""




		lowercase__					      =		height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowercase__					      =		width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor







class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						Optional[Any]				, _UpperCAmelCase   :						UNetaDConditionModel				, _UpperCAmelCase   :						DDPMScheduler				, _UpperCAmelCase   :						VQModel				, )						->       str:




							"""simple docstring"""


							super().__init__()

							self.register_modules(
							    unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase				, movq=_UpperCAmelCase				, )
							lowercase__					      =		2 ** (len(self.movq.config.block_out_channels		) - 1)

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						Any				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						Dict				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						str		)						->       List[str]:




							"""simple docstring"""


							if latents is None:
									lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase				, device=_UpperCAmelCase				, dtype=_UpperCAmelCase		)
							else:
									if latents.shape != shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
									lowercase__					      =		latents.to(_UpperCAmelCase		)

							lowercase__					      =		latents * scheduler.init_noise_sigma
							return latents

					def   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						List[Any]=0		)						->       List[Any]:




							"""simple docstring"""


							if is_accelerate_available():
									from accelerate import cpu_offload
							else:
									raise ImportError("""Please install accelerate via `pip install accelerate`"""		)

							lowercase__					      =		torch.device(f'''cuda:{gpu_id}'''		)

							lowercase__					      =		[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
											cpu_offload(_UpperCAmelCase				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[int]				, _UpperCAmelCase   :						List[str]=0		)						->       int:




							"""simple docstring"""


							if is_accelerate_available() and is_accelerate_version(""">="""				, """0.17.0.dev0"""		):
									from accelerate import cpu_offload_with_hook
							else:
									raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""		)

							lowercase__					      =		torch.device(f'''cuda:{gpu_id}'''		)

							if self.device.type != "cpu":
									self.to("""cpu"""				, silence_dtype_warnings=_UpperCAmelCase		)
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							lowercase__					      =		None
							for cpu_offloaded_model in [self.unet, self.movq]:
									lowercase__      ,     lowercase__					      =		cpu_offload_with_hook(_UpperCAmelCase				, _UpperCAmelCase				, prev_module_hook=_UpperCAmelCase		)

							# We'll offload the last model manually.
							lowercase__					      =		hook

					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def   lowerCamelCase__					(self   :						Tuple		)						->       Tuple:




							"""simple docstring"""


							if not hasattr(self.unet				, """_hf_hook"""		):
									return self.device
							for module in self.unet.modules():
									if (
									    hasattr(_UpperCAmelCase				, """_hf_hook"""		)
									    and hasattr(module._hf_hook				, """execution_device"""		)
									    and module._hf_hook.execution_device is not None
									):
											return torch.device(module._hf_hook.execution_device		)
							return self.device


					@torch.no_grad()
					@replace_example_docstring(_UpperCAmelCase		)
					def __call__(self   :						Dict				, _UpperCAmelCase   :						Union[torch.FloatTensor, List[torch.FloatTensor]]				, _UpperCAmelCase   :						Union[torch.FloatTensor, List[torch.FloatTensor]]				, _UpperCAmelCase   :						int = 512				, _UpperCAmelCase   :						int = 512				, _UpperCAmelCase   :						int = 100				, _UpperCAmelCase   :						float = 4.0				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[torch.FloatTensor] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, )						->       Dict:




							"""simple docstring"""


							lowercase__					      =		self._execution_device

							lowercase__					      =		guidance_scale > 1.0

							if isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
									lowercase__					      =		torch.cat(_UpperCAmelCase				, dim=0		)
							lowercase__					      =		image_embeds.shape[0] * num_images_per_prompt
							if isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
									lowercase__					      =		torch.cat(_UpperCAmelCase				, dim=0		)

							if do_classifier_free_guidance:
									lowercase__					      =		image_embeds.repeat_interleave(_UpperCAmelCase				, dim=0		)
									lowercase__					      =		negative_image_embeds.repeat_interleave(_UpperCAmelCase				, dim=0		)

							lowercase__					      =		torch.cat([negative_image_embeds, image_embeds]				, dim=0		).to(dtype=self.unet.dtype				, device=_UpperCAmelCase		)

							self.scheduler.set_timesteps(_UpperCAmelCase				, device=_UpperCAmelCase		)
							lowercase__					      =		self.scheduler.timesteps

							lowercase__					      =		self.unet.config.in_channels

							lowercase__      ,     lowercase__					      =		downscale_height_and_width(_UpperCAmelCase				, _UpperCAmelCase				, self.movq_scale_factor		)

							# create initial latent
							lowercase__					      =		self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)				, image_embeds.dtype				, _UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, self.scheduler				, )

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

									lowercase__					      =		{"""image_embeds""": image_embeds}
									lowercase__					      =		self.unet(
									    sample=_UpperCAmelCase				, timestep=_UpperCAmelCase				, encoder_hidden_states=_UpperCAmelCase				, added_cond_kwargs=_UpperCAmelCase				, return_dict=_UpperCAmelCase				, )[0]

									if do_classifier_free_guidance:
											lowercase__      ,     lowercase__					      =		noise_pred.split(latents.shape[1]				, dim=1		)
											lowercase__      ,     lowercase__					      =		noise_pred.chunk(2		)
											lowercase__      ,     lowercase__					      =		variance_pred.chunk(2		)
											lowercase__					      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
											lowercase__					      =		torch.cat([noise_pred, variance_pred_text]				, dim=1		)

									if not (
									    hasattr(self.scheduler.config				, """variance_type"""		)
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
											lowercase__      ,     lowercase__					      =		noise_pred.split(latents.shape[1]				, dim=1		)

									# compute the previous noisy sample x_t -> x_t-1
									lowercase__					      =		self.scheduler.step(
									    _UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase				, )[0]
							# post-processing
							lowercase__					      =		self.movq.decode(_UpperCAmelCase				, force_not_quantize=_UpperCAmelCase		)["""sample"""]

							if output_type not in ["pt", "np", "pil"]:
									raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''		)

							if output_type in ["np", "pil"]:
									lowercase__					      =		image * 0.5 + 0.5
									lowercase__					      =		image.clamp(0				, 1		)
									lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		).float().numpy()

							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)





import warnings
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


A	:		List[str]    =       logging.get_logger(__name__)

A	:		List[Any]    =       {
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			'''segformer'''

					def __init__(self   :						Dict				, _UpperCAmelCase   :						Optional[int]=3				, _UpperCAmelCase   :						Dict=4				, _UpperCAmelCase   :						Union[str, Any]=[2, 2, 2, 2]				, _UpperCAmelCase   :						List[str]=[8, 4, 2, 1]				, _UpperCAmelCase   :						str=[32, 64, 160, 256]				, _UpperCAmelCase   :						Optional[int]=[7, 3, 3, 3]				, _UpperCAmelCase   :						int=[4, 2, 2, 2]				, _UpperCAmelCase   :						str=[1, 2, 5, 8]				, _UpperCAmelCase   :						Optional[int]=[4, 4, 4, 4]				, _UpperCAmelCase   :						List[Any]="gelu"				, _UpperCAmelCase   :						Optional[Any]=0.0				, _UpperCAmelCase   :						Dict=0.0				, _UpperCAmelCase   :						Dict=0.1				, _UpperCAmelCase   :						List[str]=0.02				, _UpperCAmelCase   :						str=0.1				, _UpperCAmelCase   :						Optional[Any]=1E-6				, _UpperCAmelCase   :						Optional[Any]=256				, _UpperCAmelCase   :						Any=255				, **_UpperCAmelCase   :						str				, )						->       Tuple:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase		)

							if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
									warnings.warn(
									    """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
									    """ removed, as the behaviour will default to that of reshape_last_stage = True."""				, _UpperCAmelCase				, )

							lowercase__					      =		num_channels
							lowercase__					      =		num_encoder_blocks
							lowercase__					      =		depths
							lowercase__					      =		sr_ratios
							lowercase__					      =		hidden_sizes
							lowercase__					      =		patch_sizes
							lowercase__					      =		strides
							lowercase__					      =		mlp_ratios
							lowercase__					      =		num_attention_heads
							lowercase__					      =		hidden_act
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		classifier_dropout_prob
							lowercase__					      =		initializer_range
							lowercase__					      =		drop_path_rate
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		decoder_hidden_size
							lowercase__					      =		kwargs.get("""reshape_last_stage"""				, _UpperCAmelCase		)
							lowercase__					      =		semantic_loss_ignore_index






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			version.parse('''1.11'''							)

					@property
					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


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

					@property
					def   lowerCamelCase__					(self   :						Optional[int]		)						->       float:




							"""simple docstring"""


							return 1E-4


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




							"""simple docstring"""


							return 12

