






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,
)


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

__UpperCAmelCase										=		'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> 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"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'




def   lowercase__   (  __snake_case					:							Optional[int]  , __snake_case					:							Union[str, Any]  , __snake_case					:							Dict=8						):


					'''simple docstring'''


					UpperCAmelCase_					:   List[Any]   =		height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					UpperCAmelCase_					:   Union[str, Any]   =		width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor




def   lowercase__   (  __snake_case					:							str  , __snake_case					:							Optional[int]=512  , __snake_case					:							Optional[int]=512						):


					'''simple docstring'''


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






class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		def __init__(							self       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	)			->							Optional[Any]:
							super().__init__()

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



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase     )			->							Dict:
							# get the original timestep using init_timestep
							UpperCAmelCase_					:   List[str]   =		min(int(num_inference_steps * strength     )       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Dict   =		max(num_inference_steps - init_timestep       ,	0     )
							UpperCAmelCase_					:   Any   =		self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase=None     )			->							int:
							if not isinstance(_UpperCamelCase       ,	(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(_UpperCamelCase     )}"     )

							UpperCAmelCase_					:   List[Any]   =		image.to(device=_UpperCamelCase       ,	dtype=_UpperCamelCase     )

							UpperCAmelCase_					:   Optional[int]   =		batch_size * num_images_per_prompt

							if image.shape[1] == 4:
												UpperCAmelCase_					:   Dict   =		image

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

												elif isinstance(_UpperCamelCase       ,	_UpperCamelCase     ):
																	UpperCAmelCase_					:   int   =		[
																	    self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(_UpperCamelCase     )
																	]
																	UpperCAmelCase_					:   Union[str, Any]   =		torch.cat(_UpperCamelCase       ,	dim=0     )
												else:
																	UpperCAmelCase_					:   List[Any]   =		self.movq.encode(_UpperCamelCase     ).latent_dist.sample(_UpperCamelCase     )

												UpperCAmelCase_					:   Optional[Any]   =		self.movq.config.scaling_factor * init_latents

							UpperCAmelCase_					:   Optional[Any]   =		torch.cat([init_latents]       ,	dim=0     )

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

							# get latents
							UpperCAmelCase_					:   Union[str, Any]   =		self.scheduler.add_noise(_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Optional[Any]   =		init_latents

							return latents



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase=0     )			->							List[Any]:
							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('Please install accelerate via `pip install accelerate`'     )

							UpperCAmelCase_					:   Optional[Any]   =		torch.device(f"cuda:{gpu_id}"     )

							UpperCAmelCase_					:   List[str]   =		[
							    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     )			->							int:
							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.'     )

							UpperCAmelCase_					:   int   =		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)

							UpperCAmelCase_					:   Optional[Any]   =		None
							for cpu_offloaded_model in [self.unet, self.movq]:
												UpperCAmelCase_ ,						UpperCAmelCase_					:   Tuple   =		cpu_offload_with_hook(_UpperCamelCase       ,	_UpperCamelCase       ,	prev_module_hook=_UpperCamelCase     )

							# We'll offload the last model manually.
							UpperCAmelCase_					:   Dict   =		hook



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def       __UpperCAmelCase    (							self     )			->							Tuple:
							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       ,	_UpperCamelCase = 5_1_2       ,	_UpperCamelCase = 5_1_2       ,	_UpperCamelCase = 1_0_0       ,	_UpperCamelCase = 4.0       ,	_UpperCamelCase = 0.3       ,	_UpperCamelCase = 1       ,	_UpperCamelCase = None       ,	_UpperCamelCase = "pil"       ,	_UpperCamelCase = True       ,	)			->							Tuple:
							UpperCAmelCase_					:   Optional[int]   =		self._execution_device

							UpperCAmelCase_					:   Union[str, Any]   =		guidance_scale > 1.0

							if isinstance(_UpperCamelCase       ,	_UpperCamelCase     ):
												UpperCAmelCase_					:   Any   =		torch.cat(_UpperCamelCase       ,	dim=0     )
							if isinstance(_UpperCamelCase       ,	_UpperCamelCase     ):
												UpperCAmelCase_					:   Optional[int]   =		torch.cat(_UpperCamelCase       ,	dim=0     )
							if isinstance(_UpperCamelCase       ,	_UpperCamelCase     ):
												UpperCAmelCase_					:   str   =		torch.cat(_UpperCamelCase       ,	dim=0     )

							UpperCAmelCase_					:   Any   =		image_embeds.shape[0]

							if do_classifier_free_guidance:
												UpperCAmelCase_					:   List[str]   =		image_embeds.repeat_interleave(_UpperCamelCase       ,	dim=0     )
												UpperCAmelCase_					:   List[str]   =		negative_image_embeds.repeat_interleave(_UpperCamelCase       ,	dim=0     )
												UpperCAmelCase_					:   Dict   =		hint.repeat_interleave(_UpperCamelCase       ,	dim=0     )

							UpperCAmelCase_					:   List[Any]   =		torch.cat([negative_image_embeds, image_embeds]       ,	dim=0     ).to(dtype=self.unet.dtype       ,	device=_UpperCamelCase     )
							UpperCAmelCase_					:   Tuple   =		torch.cat([hint, hint]       ,	dim=0     ).to(dtype=self.unet.dtype       ,	device=_UpperCamelCase     )

							if not isinstance(_UpperCamelCase       ,	_UpperCamelCase     ):
												UpperCAmelCase_					:   int   =		[image]
							if not all(isinstance(_UpperCamelCase       ,	(PIL.Image.Image, torch.Tensor)     ) for i in image     ):
												raise ValueError(
												    f"Input is in incorrect format: {[type(_UpperCamelCase     ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"     )

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

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

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

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

												# compute the previous noisy sample x_t -> x_t-1

												UpperCAmelCase_					:   Any   =		self.scheduler.step(
												    _UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	generator=_UpperCamelCase       ,	)[0]

							# post-processing
							UpperCAmelCase_					:   Dict   =		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"]:
												UpperCAmelCase_					:   Union[str, Any]   =		image * 0.5 + 0.5
												UpperCAmelCase_					:   List[Any]   =		image.clamp(0       ,	1     )
												UpperCAmelCase_					:   Union[str, Any]   =		image.cpu().permute(0       ,	2       ,	3       ,	1     ).float().numpy()

							if output_type == "pil":
												UpperCAmelCase_					:   Optional[int]   =		self.numpy_to_pil(_UpperCamelCase     )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase     )














import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 						lowerCamelCase   (_snake_case				,						unittest.TestCase    ):







		'''simple docstring'''



		_snake_case					:					Tuple 		=						BertweetTokenizer
		_snake_case					:					List[str] 		=						False



		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCAmelCase_					:   Optional[int]   =		['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
							UpperCAmelCase_					:   Dict   =		dict(zip(_UpperCamelCase       ,	range(len(_UpperCamelCase     )     )     )     )
							UpperCAmelCase_					:   List[Any]   =		['#version: 0.2', 'a m</w>']
							UpperCAmelCase_					:   Union[str, Any]   =		{'unk_token': '<unk>'}

							UpperCAmelCase_					:   Optional[Any]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['vocab_file']     )
							UpperCAmelCase_					:   List[str]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['merges_file']     )
							with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												for token in vocab_tokens:
																	fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												fp.write('\n'.join(_UpperCamelCase     )     )



		def       __UpperCAmelCase    (							self       ,	**_UpperCamelCase     )			->							List[Any]:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname       ,	**_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							UpperCAmelCase_					:   Any   =		'I am VinAI Research'
							UpperCAmelCase_					:   Any   =		'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
							return input_text, output_text



		def       __UpperCAmelCase    (							self     )			->							Tuple:
							UpperCAmelCase_					:   Union[str, Any]   =		BertweetTokenizer(self.vocab_file       ,	self.merges_file       ,	**self.special_tokens_map     )
							UpperCAmelCase_					:   Optional[Any]   =		'I am VinAI Research'
							UpperCAmelCase_					:   str   =		'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
							UpperCAmelCase_					:   Any   =		tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Optional[int]   =		tokens + [tokenizer.unk_token]

							UpperCAmelCase_					:   List[str]   =		[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )       ,	_UpperCamelCase     )






