



'''simple docstring'''

import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
			pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :						Any	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {
    '''kakaobrain/align-base''': '''https://huggingface.co/kakaobrain/align-base/resolve/main/config.json''',
}

class 							a__( _a     ):
			a_	: Tuple									=      '''align_text_model'''






			def __init__(     self					,   _UpperCAmelCase=3_0522					,   _UpperCAmelCase=768					,   _UpperCAmelCase=12					,   _UpperCAmelCase=12					,   _UpperCAmelCase=3072					,   _UpperCAmelCase="gelu"					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=512					,   _UpperCAmelCase=2					,   _UpperCAmelCase=0.02					,   _UpperCAmelCase=1E-12					,   _UpperCAmelCase=0					,   _UpperCAmelCase="absolute"					,   _UpperCAmelCase=True					,   **_UpperCAmelCase					,   )							->							List[str]:
					super().__init__(**__UpperCamelCase						)

					snake_case__	     =vocab_size
					snake_case__	     =hidden_size
					snake_case__	     =num_hidden_layers
					snake_case__	     =num_attention_heads
					snake_case__	     =hidden_act
					snake_case__	     =intermediate_size
					snake_case__	     =hidden_dropout_prob
					snake_case__	     =attention_probs_dropout_prob
					snake_case__	     =max_position_embeddings
					snake_case__	     =type_vocab_size
					snake_case__	     =initializer_range
					snake_case__	     =layer_norm_eps
					snake_case__	     =position_embedding_type
					snake_case__	     =use_cache
					snake_case__	     =pad_token_id






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Optional[Any]:
					cls._set_token_in_kwargs(__UpperCamelCase						)

					snake_case__     ,	snake_case__	     =cls.get_config_dict(__UpperCamelCase					,   **__UpperCamelCase						)

					# get the text config dict if we are loading from AlignConfig
					if config_dict.get('model_type'						) == "align":
							snake_case__	     =config_dict['text_config']

					if "model_type" in config_dict and hasattr(cls					,   'model_type'						) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
							    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""						)

					return cls.from_dict(__UpperCamelCase					,   **__UpperCamelCase						)





class 							a__( _a     ):
			a_	: List[Any]									=      '''align_vision_model'''






			def __init__(     self					,   _UpperCAmelCase = 3					,   _UpperCAmelCase = 600					,   _UpperCAmelCase = 2.0					,   _UpperCAmelCase = 3.1					,   _UpperCAmelCase = 8					,   _UpperCAmelCase = [3, 3, 5, 3, 5, 5, 3]					,   _UpperCAmelCase = [32, 16, 24, 40, 80, 112, 192]					,   _UpperCAmelCase = [16, 24, 40, 80, 112, 192, 320]					,   _UpperCAmelCase = []					,   _UpperCAmelCase = [1, 2, 2, 2, 1, 2, 1]					,   _UpperCAmelCase = [1, 2, 2, 3, 3, 4, 1]					,   _UpperCAmelCase = [1, 6, 6, 6, 6, 6, 6]					,   _UpperCAmelCase = 0.25					,   _UpperCAmelCase = "swish"					,   _UpperCAmelCase = 2560					,   _UpperCAmelCase = "mean"					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = 0.001					,   _UpperCAmelCase = 0.99					,   _UpperCAmelCase = 0.2					,   **_UpperCAmelCase					,   )							->							Dict:
					super().__init__(**__UpperCamelCase						)

					snake_case__	     =num_channels
					snake_case__	     =image_size
					snake_case__	     =width_coefficient
					snake_case__	     =depth_coefficient
					snake_case__	     =depth_divisor
					snake_case__	     =kernel_sizes
					snake_case__	     =in_channels
					snake_case__	     =out_channels
					snake_case__	     =depthwise_padding
					snake_case__	     =strides
					snake_case__	     =num_block_repeats
					snake_case__	     =expand_ratios
					snake_case__	     =squeeze_expansion_ratio
					snake_case__	     =hidden_act
					snake_case__	     =hidden_dim
					snake_case__	     =pooling_type
					snake_case__	     =initializer_range
					snake_case__	     =batch_norm_eps
					snake_case__	     =batch_norm_momentum
					snake_case__	     =drop_connect_rate
					snake_case__	     =sum(__UpperCamelCase						) * 4






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Tuple:
					cls._set_token_in_kwargs(__UpperCamelCase						)

					snake_case__     ,	snake_case__	     =cls.get_config_dict(__UpperCamelCase					,   **__UpperCamelCase						)

					# get the vision config dict if we are loading from AlignConfig
					if config_dict.get('model_type'						) == "align":
							snake_case__	     =config_dict['vision_config']

					if "model_type" in config_dict and hasattr(cls					,   'model_type'						) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
							    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""						)

					return cls.from_dict(__UpperCamelCase					,   **__UpperCamelCase						)





class 							a__( _a     ):
			a_	: Optional[Any]									=      '''align'''
			a_	: int									=      True






			def __init__(     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase=640					,   _UpperCAmelCase=1.0					,   _UpperCAmelCase=0.02					,   **_UpperCAmelCase					,   )							->							int:
					super().__init__(**__UpperCamelCase						)

					if text_config is None:
							snake_case__	     ={}
							logger.info('text_config is None. Initializing the AlignTextConfig with default values.'						)

					if vision_config is None:
							snake_case__	     ={}
							logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'						)

					snake_case__	     =AlignTextConfig(**__UpperCamelCase						)
					snake_case__	     =AlignVisionConfig(**__UpperCamelCase						)

					snake_case__	     =projection_dim
					snake_case__	     =temperature_init_value
					snake_case__	     =initializer_range






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							List[str]:
					return cls(text_config=text_config.to_dict()					,   vision_config=vision_config.to_dict()					,   **__UpperCamelCase						)






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =copy.deepcopy(self.__dict__						)
					snake_case__	     =self.text_config.to_dict()
					snake_case__	     =self.vision_config.to_dict()
					snake_case__	     =self.__class__.model_type
					return output






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		: Tuple								=     logging.get_logger(__name__)  # pylint: disable=invalid-name

A		: Optional[Any]								=     '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	int=8       )  ->     Optional[int]:
						_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_ (			_a    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			):
									super().__init__()

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									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    UpperCamelCase_				(      self				,			__UpperCamelCase=0  ):
									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    UpperCamelCase_				(      self				,			__UpperCamelCase=0  ):
									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    UpperCamelCase_				(      self  ):
									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				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase = 512				,			__UpperCamelCase = 512				,			__UpperCamelCase = 100				,			__UpperCamelCase = 4.0				,			__UpperCamelCase = 1				,			__UpperCamelCase = None				,			__UpperCamelCase = None				,			__UpperCamelCase = "pil"				,			__UpperCamelCase = True				,			):
									_lowercase					       =					self._execution_device

									_lowercase					       =					guidance_scale > 1.0

									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )
									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )
									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )

									_lowercase					       =					image_embeds.shape[0] * num_images_per_prompt

									if do_classifier_free_guidance:
															_lowercase					       =					image_embeds.repeat_interleave(__UpperCamelCase				,			dim=0  )
															_lowercase					       =					negative_image_embeds.repeat_interleave(__UpperCamelCase				,			dim=0  )
															_lowercase					       =					hint.repeat_interleave(__UpperCamelCase				,			dim=0  )

									_lowercase					       =					torch.cat([negative_image_embeds, image_embeds]				,			dim=0  ).to(dtype=self.unet.dtype				,			device=__UpperCamelCase  )
									_lowercase					       =					torch.cat([hint, hint]				,			dim=0  ).to(dtype=self.unet.dtype				,			device=__UpperCamelCase  )
									self.scheduler.set_timesteps(__UpperCamelCase				,			device=__UpperCamelCase  )
									_lowercase					       =					self.scheduler.timesteps

									_lowercase					       =					self.movq.config.latent_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, """hint""": hint}
															_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  )