






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,
)


__snake_case    :    Optional[int]         =     logging.get_logger(__name__)  # pylint: disable=invalid-name

__snake_case    :    List[str]         =     """
    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   (    UpperCamelCase_       :					int    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					List[Any]=8		)  ->   int:

						"""simple docstring"""



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


class     __SCREAMING_SNAKE_CASE					(   __lowercase):






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						):






												"""simple docstring"""
												super().__init__()

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






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												if latents is None:
																		lowerCAmelCase__    						=						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}"   )
																		lowerCAmelCase__    						=						latents.to(_UpperCamelCase   )

												lowerCAmelCase__    						=						latents * scheduler.init_noise_sigma
												return latents






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=0   ):






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

												lowerCAmelCase__    						=						torch.device(F"cuda:{gpu_id}"   )

												lowerCAmelCase__    						=						[
												    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   ):






												"""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.'   )

												lowerCAmelCase__    						=						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)

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

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






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def       UpperCamelCase__      (	self   ):






												"""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     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = 5_12     ,						_UpperCamelCase = 5_12     ,						_UpperCamelCase = 1_00     ,						_UpperCamelCase = 4.0     ,						_UpperCamelCase = 1     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						_UpperCamelCase = "pil"     ,						_UpperCamelCase = True     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						self._execution_device

												lowerCAmelCase__    						=						guidance_scale > 1.0

												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						torch.cat(_UpperCamelCase     ,						dim=0   )
												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						torch.cat(_UpperCamelCase     ,						dim=0   )
												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						torch.cat(_UpperCamelCase     ,						dim=0   )

												lowerCAmelCase__    						=						image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																		lowerCAmelCase__    						=						image_embeds.repeat_interleave(_UpperCamelCase     ,						dim=0   )
																		lowerCAmelCase__    						=						negative_image_embeds.repeat_interleave(_UpperCamelCase     ,						dim=0   )
																		lowerCAmelCase__    						=						hint.repeat_interleave(_UpperCamelCase     ,						dim=0   )

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

												lowerCAmelCase__    						=						self.movq.config.latent_channels

												lowerCAmelCase__						,       lowerCAmelCase__    						=						downscale_height_and_width(_UpperCamelCase     ,						_UpperCamelCase     ,						self.movq_scale_factor   )

												# create initial latent
												lowerCAmelCase__    						=						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
																		lowerCAmelCase__    						=						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

																		lowerCAmelCase__    						=						{'image_embeds': image_embeds, 'hint': hint}
																		lowerCAmelCase__    						=						self.unet(
																		    sample=_UpperCamelCase     ,						timestep=_UpperCamelCase     ,						encoder_hidden_states=_UpperCamelCase     ,						added_cond_kwargs=_UpperCamelCase     ,						return_dict=_UpperCamelCase     ,						)[0]

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

																		# compute the previous noisy sample x_t -> x_t-1
																		lowerCAmelCase__    						=						self.scheduler.step(
																		    _UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						generator=_UpperCamelCase     ,						)[0]
												# post-processing
												lowerCAmelCase__    						=						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"]:
																		lowerCAmelCase__    						=						image * 0.5 + 0.5
																		lowerCAmelCase__    						=						image.clamp(0     ,						1   )
																		lowerCAmelCase__    						=						image.cpu().permute(0     ,						2     ,						3     ,						1   ).float().numpy()

												if output_type == "pil":
																		lowerCAmelCase__    						=						self.numpy_to_pil(_UpperCamelCase   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_UpperCamelCase   )













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__snake_case    :    Dict         =     {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Union[str, Any]         =     ["""SpeechEncoderDecoderModel"""]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    str         =     ["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
					from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
					import sys

					__snake_case    :    Tuple         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





