
'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

_lowercase :  str    							=       "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[Any]      ,      __SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=8							):



						"""simple docstring"""





						lowercase_	:     Dict													=       height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						lowercase_	:     int													=       width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: Any=512      ,      __SCREAMING_SNAKE_CASE					: Dict=512							):



						"""simple docstring"""





						lowercase_	:     str													=       pil_image.resize((w, h)      ,      resample=Image.BICUBIC      ,      reducing_gap=1							)
						lowercase_	:     Any													=       np.array(pil_image.convert('''RGB'''							)							)
						lowercase_	:     List[str]													=       arr.astype(np.floataa							) / 127.5 - 1
						lowercase_	:     List[str]													=       np.transpose(__SCREAMING_SNAKE_CASE      ,      [2, 0, 1]							)
						lowercase_	:     Union[str, Any]													=       torch.from_numpy(__SCREAMING_SNAKE_CASE							).unsqueeze(0							)
						return image






class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__()

											self.register_modules(
											    unet=__SCREAMING_SNAKE_CASE				,							scheduler=__SCREAMING_SNAKE_CASE				,							movq=__SCREAMING_SNAKE_CASE				,							)
											lowercase_	:     Optional[Any]													=       2 ** (len(self.movq.config.block_out_channels		) - 1)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       min(int(num_inference_steps * strength		)				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       max(num_inference_steps - init_timestep				,							0		)
											lowercase_	:     Tuple													=       self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE				,							(torch.Tensor, PIL.Image.Image, list)		):
																	raise ValueError(
																	    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__SCREAMING_SNAKE_CASE		)}'''		)

											lowercase_	:     Union[str, Any]													=       image.to(device=__SCREAMING_SNAKE_CASE				,							dtype=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       batch_size * num_images_per_prompt

											if image.shape[1] == 4:
																	lowercase_	:     Tuple													=       image

											else:
																	if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) and len(__SCREAMING_SNAKE_CASE		) != batch_size:
																							raise ValueError(
																							    F'''You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE		)}, but requested an effective batch'''
																							    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

																	elif isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																							lowercase_	:     Tuple													=       [
																							    self.movq.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(__SCREAMING_SNAKE_CASE		)
																							]
																							lowercase_	:     Any													=       torch.cat(__SCREAMING_SNAKE_CASE				,							dim=0		)
																	else:
																							lowercase_	:     List[Any]													=       self.movq.encode(__SCREAMING_SNAKE_CASE		).latent_dist.sample(__SCREAMING_SNAKE_CASE		)

																	lowercase_	:     Dict													=       self.movq.config.scaling_factor * init_latents

											lowercase_	:     List[Any]													=       torch.cat([init_latents]				,							dim=0		)

											lowercase_	:     List[Any]													=       init_latents.shape
											lowercase_	:     str													=       randn_tensor(__SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							device=__SCREAMING_SNAKE_CASE				,							dtype=__SCREAMING_SNAKE_CASE		)

											# get latents
											lowercase_	:     str													=       self.scheduler.add_noise(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Any													=       init_latents

											return latents






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE=0		):


											"""simple docstring"""





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

											lowercase_	:     Tuple													=       torch.device(F'''cuda:{gpu_id}'''		)

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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE=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.'''		)

											lowercase_	:     str													=       torch.device(F'''cuda:{gpu_id}'''		)

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

											lowercase_	:     str													=       None
											for cpu_offloaded_model in [self.unet, self.movq]:
																	lowercase_						, lowercase_	:     Optional[int]													=       cpu_offload_with_hook(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							prev_module_hook=__SCREAMING_SNAKE_CASE		)

											# We'll offload the last model manually.
											lowercase_	:     Any													=       hook






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


											"""simple docstring"""





											if not hasattr(self.unet				,							'''_hf_hook'''		):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(__SCREAMING_SNAKE_CASE				,							'''_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(__SCREAMING_SNAKE_CASE		)
					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = 5_12				,							__SCREAMING_SNAKE_CASE = 5_12				,							__SCREAMING_SNAKE_CASE = 1_00				,							__SCREAMING_SNAKE_CASE = 4.0				,							__SCREAMING_SNAKE_CASE = 0.3				,							__SCREAMING_SNAKE_CASE = 1				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "pil"				,							__SCREAMING_SNAKE_CASE = True				,							):


											"""simple docstring"""





											lowercase_	:     Tuple													=       self._execution_device

											lowercase_	:     str													=       guidance_scale > 1.0

											if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     str													=       torch.cat(__SCREAMING_SNAKE_CASE				,							dim=0		)
											lowercase_	:     Union[str, Any]													=       image_embeds.shape[0]
											if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     str													=       torch.cat(__SCREAMING_SNAKE_CASE				,							dim=0		)

											if do_classifier_free_guidance:
																	lowercase_	:     List[Any]													=       image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE				,							dim=0		)
																	lowercase_	:     Optional[Any]													=       negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE				,							dim=0		)

											lowercase_	:     Union[str, Any]													=       torch.cat([negative_image_embeds, image_embeds]				,							dim=0		).to(dtype=self.unet.dtype				,							device=__SCREAMING_SNAKE_CASE		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Dict													=       [image]
											if not all(isinstance(__SCREAMING_SNAKE_CASE				,							(PIL.Image.Image, torch.Tensor)		) for i in image		):
																	raise ValueError(
																	    F'''Input is in incorrect format: {[type(__SCREAMING_SNAKE_CASE		) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''		)

											lowercase_	:     Any													=       torch.cat([prepare_image(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) for i in image]				,							dim=0		)
											lowercase_	:     Optional[Any]													=       image.to(dtype=image_embeds.dtype				,							device=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     List[str]													=       self.movq.encode(__SCREAMING_SNAKE_CASE		)['''latents''']
											lowercase_	:     List[Any]													=       latents.repeat_interleave(__SCREAMING_SNAKE_CASE				,							dim=0		)
											self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE				,							device=__SCREAMING_SNAKE_CASE		)
											lowercase_						, lowercase_	:     List[str]													=       self.get_timesteps(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       timesteps[:1].repeat(batch_size * num_images_per_prompt		)
											lowercase_						, lowercase_	:     Optional[Any]													=       downscale_height_and_width(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							self.movq_scale_factor		)
											lowercase_	:     Tuple													=       self.prepare_latents(
											    __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							image_embeds.dtype				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE		)		):
																	# expand the latents if we are doing classifier free guidance
																	lowercase_	:     List[Any]													=       torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

																	lowercase_	:     Optional[int]													=       {'''image_embeds''': image_embeds}
																	lowercase_	:     List[Any]													=       self.unet(
																	    sample=__SCREAMING_SNAKE_CASE				,							timestep=__SCREAMING_SNAKE_CASE				,							encoder_hidden_states=__SCREAMING_SNAKE_CASE				,							added_cond_kwargs=__SCREAMING_SNAKE_CASE				,							return_dict=__SCREAMING_SNAKE_CASE				,							)[0]

																	if do_classifier_free_guidance:
																							lowercase_						, lowercase_	:     List[Any]													=       noise_pred.split(latents.shape[1]				,							dim=1		)
																							lowercase_						, lowercase_	:     str													=       noise_pred.chunk(2		)
																							lowercase_						, lowercase_	:     Optional[int]													=       variance_pred.chunk(2		)
																							lowercase_	:     Union[str, Any]													=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																							lowercase_	:     Dict													=       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_	:     str													=       noise_pred.split(latents.shape[1]				,							dim=1		)

																	# compute the previous noisy sample x_t -> x_t-1
																	lowercase_	:     Tuple													=       self.scheduler.step(
																	    __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							)[0]

											# post-processing
											lowercase_	:     Optional[int]													=       self.movq.decode(__SCREAMING_SNAKE_CASE				,							force_not_quantize=__SCREAMING_SNAKE_CASE		)['''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_	:     Tuple													=       image * 0.5 + 0.5
																	lowercase_	:     int													=       image.clamp(0				,							1		)
																	lowercase_	:     Any													=       image.cpu().permute(0				,							2				,							3				,							1		).float().numpy()

											if output_type == "pil":
																	lowercase_	:     Optional[Any]													=       self.numpy_to_pil(__SCREAMING_SNAKE_CASE		)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE		)





'''simple docstring'''







import torch

from transformers import AutoModel






class  lowerCAmelCase__   (  torch.nn.Module     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"		):


											"""simple docstring"""





											super(__SCREAMING_SNAKE_CASE				,							self		).__init__()

											lowercase_	:     Tuple													=       AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE				,							return_dict=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       torch.nn.CosineSimilarity(3				,							1E-0_8		)
											lowercase_	:     Optional[Any]													=       torch.nn.Softmax(dim=1		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.bert(**__SCREAMING_SNAKE_CASE		).last_hidden_state






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return token_embeddings.sum(2				,							keepdim=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=1		):


											"""simple docstring"""





											return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       W_supports['''sizes'''].tolist()
											lowercase_	:     Dict													=       W_supports['''start_token_id'''].item()
											lowercase_	:     List[Any]													=       W_supports['''end_token_id'''].item()

											del W_supports["sizes"]
											del W_supports["start_token_id"]
											del W_supports["end_token_id"]

											lowercase_	:     List[str]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       None
											lowercase_	:     Dict													=       None

											lowercase_	:     Tuple													=       W_supports['''input_ids'''] == start_token_id
											lowercase_	:     Any													=       W_supports['''input_ids'''] == end_token_id

											for i, size in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == 0:
																							lowercase_	:     List[str]													=       0
																	else:
																							lowercase_	:     List[Any]													=       support_sizes[i - 1]

																	lowercase_	:     str													=       S[s : s + size][start_token_masks[s : s + size]]
																	lowercase_	:     Optional[int]													=       S[s : s + size][end_token_masks[s : s + size]]

																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_start.T		).sum(1		).softmax(0		)
																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_end.T		).sum(1		).softmax(0		)

																	if p_starts is not None:
																							lowercase_	:     Tuple													=       torch.vstack((p_starts, p_start)		)
																							lowercase_	:     Optional[Any]													=       torch.vstack((p_ends, p_end)		)
																	else:
																							lowercase_	:     str													=       p_start
																							lowercase_	:     int													=       p_end

											return p_starts, p_ends



