


'''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,
)


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

lowerCamelCase__     						=  '\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 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase=8 ):
		_UpperCAmelCase		:							Any             =   height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		_UpperCAmelCase		:							Dict             =   width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase=512   ,			__lowerCAmelCase=512 ):
		_UpperCAmelCase		:							Tuple             =   pil_image.resize((w, h)   ,			resample=Image.BICUBIC   ,			reducing_gap=1 )
		_UpperCAmelCase		:							Optional[int]             =   np.array(pil_image.convert("RGB" ) )
		_UpperCAmelCase		:							Tuple             =   arr.astype(np.floataa ) / 1_2_7.5 - 1
		_UpperCAmelCase		:							Any             =   np.transpose(__lowerCAmelCase   ,			[2, 0, 1] )
		_UpperCAmelCase		:							List[str]             =   torch.from_numpy(__lowerCAmelCase ).unsqueeze(0 )
		return image






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




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



					'''simple docstring'''







					super().__init__()

					self.register_modules(
					    unet=lowerCamelCase__						,	scheduler=lowerCamelCase__						,	movq=lowerCamelCase__						,	)
					_UpperCAmelCase		:							Optional[int]             =   2 ** (len(self.movq.config.block_out_channels			) - 1)




			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  int						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  Union[str, Any]			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   min(int(num_inference_steps * strength			)						,	lowerCamelCase__			)

					_UpperCAmelCase		:							int             =   max(num_inference_steps - init_timestep						,	0			)
					_UpperCAmelCase		:							Union[str, Any]             =   self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start




			def 				lowerCAmelCase__     (		self	:  Tuple						,	lowerCamelCase__	:  Optional[Any]						,	lowerCamelCase__	:  Optional[Any]						,	lowerCamelCase__	:  Optional[Any]						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  Dict=None			) ->List[Any]:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__						,	(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(lowerCamelCase__			)}"""			)

					_UpperCAmelCase		:							Union[str, Any]             =   image.to(device=lowerCamelCase__						,	dtype=lowerCamelCase__			)

					_UpperCAmelCase		:							Optional[Any]             =   batch_size * num_images_per_prompt

					if image.shape[1] == 4:
							_UpperCAmelCase		:							int             =   image

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

							elif isinstance(lowerCamelCase__						,	lowerCamelCase__			):
									_UpperCAmelCase		:							str             =   [
									    self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(lowerCamelCase__			)
									]
									_UpperCAmelCase		:							Optional[int]             =   torch.cat(lowerCamelCase__						,	dim=0			)
							else:
									_UpperCAmelCase		:							Dict             =   self.movq.encode(lowerCamelCase__			).latent_dist.sample(lowerCamelCase__			)

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

					_UpperCAmelCase		:							List[str]             =   torch.cat([init_latents]						,	dim=0			)

					_UpperCAmelCase		:							Union[str, Any]             =   init_latents.shape
					_UpperCAmelCase		:							Union[str, Any]             =   randn_tensor(lowerCamelCase__						,	generator=lowerCamelCase__						,	device=lowerCamelCase__						,	dtype=lowerCamelCase__			)

					# get latents
					_UpperCAmelCase		:							Optional[int]             =   self.scheduler.add_noise(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__			)

					_UpperCAmelCase		:							Optional[int]             =   init_latents

					return latents




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



					'''simple docstring'''







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

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

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




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



					'''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."			)

					_UpperCAmelCase		:							Union[str, Any]             =   torch.device(F"""cuda:{gpu_id}"""			)

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

					_UpperCAmelCase		:							List[str]             =   None
					for cpu_offloaded_model in [self.unet, self.movq]:
							_UpperCAmelCase       ,     _UpperCAmelCase		:							List[Any]             =   cpu_offload_with_hook(lowerCamelCase__						,	lowerCamelCase__						,	prev_module_hook=lowerCamelCase__			)

					# We'll offload the last model manually.
					_UpperCAmelCase		:							Union[str, Any]             =   hook




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



					'''simple docstring'''







					if not hasattr(self.unet						,	"_hf_hook"			):
							return self.device
					for module in self.unet.modules():
							if (
							    hasattr(lowerCamelCase__						,	"_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(lowerCamelCase__			)
			def __call__(		self	:  Optional[Any]						,	lowerCamelCase__	:  Union[torch.FloatTensor, List[torch.FloatTensor]]						,	lowerCamelCase__	:  Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,	lowerCamelCase__	:  Union[torch.FloatTensor, List[torch.FloatTensor]]						,	lowerCamelCase__	:  torch.FloatTensor						,	lowerCamelCase__	:  int = 5_12						,	lowerCamelCase__	:  int = 5_12						,	lowerCamelCase__	:  int = 1_00						,	lowerCamelCase__	:  float = 4.0						,	lowerCamelCase__	:  float = 0.3						,	lowerCamelCase__	:  int = 1						,	lowerCamelCase__	:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,	lowerCamelCase__	:  Optional[str] = "pil"						,	lowerCamelCase__	:  bool = True						,	) ->str:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   self._execution_device

					_UpperCAmelCase		:							int             =   guidance_scale > 1.0

					if isinstance(lowerCamelCase__						,	lowerCamelCase__			):
							_UpperCAmelCase		:							Optional[Any]             =   torch.cat(lowerCamelCase__						,	dim=0			)
					if isinstance(lowerCamelCase__						,	lowerCamelCase__			):
							_UpperCAmelCase		:							List[Any]             =   torch.cat(lowerCamelCase__						,	dim=0			)
					if isinstance(lowerCamelCase__						,	lowerCamelCase__			):
							_UpperCAmelCase		:							List[Any]             =   torch.cat(lowerCamelCase__						,	dim=0			)

					_UpperCAmelCase		:							List[str]             =   image_embeds.shape[0]

					if do_classifier_free_guidance:
							_UpperCAmelCase		:							Optional[int]             =   image_embeds.repeat_interleave(lowerCamelCase__						,	dim=0			)
							_UpperCAmelCase		:							int             =   negative_image_embeds.repeat_interleave(lowerCamelCase__						,	dim=0			)
							_UpperCAmelCase		:							Union[str, Any]             =   hint.repeat_interleave(lowerCamelCase__						,	dim=0			)

					_UpperCAmelCase		:							Dict             =   torch.cat([negative_image_embeds, image_embeds]						,	dim=0			).to(dtype=self.unet.dtype						,	device=lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[Any]             =   torch.cat([hint, hint]						,	dim=0			).to(dtype=self.unet.dtype						,	device=lowerCamelCase__			)

					if not isinstance(lowerCamelCase__						,	lowerCamelCase__			):
							_UpperCAmelCase		:							Union[str, Any]             =   [image]
					if not all(isinstance(lowerCamelCase__						,	(PIL.Image.Image, torch.Tensor)			) for i in image			):
							raise ValueError(
							    F"""Input is in incorrect format: {[type(lowerCamelCase__			) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""			)

					_UpperCAmelCase		:							Optional[Any]             =   torch.cat([prepare_image(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__			) for i in image]						,	dim=0			)
					_UpperCAmelCase		:							Union[str, Any]             =   image.to(dtype=image_embeds.dtype						,	device=lowerCamelCase__			)

					_UpperCAmelCase		:							Any             =   self.movq.encode(lowerCamelCase__			)["latents"]
					_UpperCAmelCase		:							Tuple             =   latents.repeat_interleave(lowerCamelCase__						,	dim=0			)
					self.scheduler.set_timesteps(lowerCamelCase__						,	device=lowerCamelCase__			)
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[int]             =   self.get_timesteps(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__			)
					_UpperCAmelCase		:							List[str]             =   timesteps[:1].repeat(batch_size * num_images_per_prompt			)
					_UpperCAmelCase       ,     _UpperCAmelCase		:							List[str]             =   downscale_height_and_width(lowerCamelCase__						,	lowerCamelCase__						,	self.movq_scale_factor			)
					_UpperCAmelCase		:							str             =   self.prepare_latents(
					    lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	image_embeds.dtype						,	lowerCamelCase__						,	lowerCamelCase__			)
					for i, t in enumerate(self.progress_bar(lowerCamelCase__			)			):
							# expand the latents if we are doing classifier free guidance
							_UpperCAmelCase		:							List[str]             =   torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

							_UpperCAmelCase		:							List[Any]             =   {"image_embeds": image_embeds, "hint": hint}
							_UpperCAmelCase		:							List[str]             =   self.unet(
							    sample=lowerCamelCase__						,	timestep=lowerCamelCase__						,	encoder_hidden_states=lowerCamelCase__						,	added_cond_kwargs=lowerCamelCase__						,	return_dict=lowerCamelCase__						,	)[0]

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

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

							_UpperCAmelCase		:							List[str]             =   self.scheduler.step(
							    lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	generator=lowerCamelCase__						,	)[0]

					# post-processing
					_UpperCAmelCase		:							int             =   self.movq.decode(lowerCamelCase__						,	force_not_quantize=lowerCamelCase__			)["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		:							Tuple             =   image * 0.5 + 0.5
							_UpperCAmelCase		:							List[Any]             =   image.clamp(0						,	1			)
							_UpperCAmelCase		:							List[str]             =   image.cpu().permute(0						,	2						,	3						,	1			).float().numpy()

					if output_type == "pil":
							_UpperCAmelCase		:							Optional[Any]             =   self.numpy_to_pil(lowerCamelCase__			)

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=lowerCamelCase__			)







'''simple docstring'''


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							Dict             =   filter(lambda __lowerCAmelCase : p.requires_grad   ,			model.parameters() )
		_UpperCAmelCase		:							List[Any]             =   sum([np.prod(p.size() ) for p in model_parameters] )
		return params


lowerCamelCase__     						=  logging.getLogger(__name__)






class 						lowerCAmelCase__      (      pl.Callback						):




			def 				lowerCAmelCase__     (		self	:  Optional[Any]						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Union[str, Any]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
					pl_module.logger.log_metrics(lowerCamelCase__			)




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  Any=True			) ->None:



					'''simple docstring'''







					logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
					_UpperCAmelCase		:							Tuple             =   trainer.callback_metrics
					trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}			)
					# Log results
					_UpperCAmelCase		:							Optional[int]             =   Path(pl_module.hparams.output_dir			)
					if type_path == "test":
							_UpperCAmelCase		:							Optional[Any]             =   od / "test_results.txt"
							_UpperCAmelCase		:							str             =   od / "test_generations.txt"
					else:
							# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
							# If people want this it will be easy enough to add back.
							_UpperCAmelCase		:							Dict             =   od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
							_UpperCAmelCase		:							List[str]             =   od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
							results_file.parent.mkdir(exist_ok=lowerCamelCase__			)
							generations_file.parent.mkdir(exist_ok=lowerCamelCase__			)
					with open(lowerCamelCase__						,	"a+"			) as writer:
							for key in sorted(lowerCamelCase__			):
									if key in ["log", "progress_bar", "preds"]:
											continue
									_UpperCAmelCase		:							Union[str, Any]             =   metrics[key]
									if isinstance(lowerCamelCase__						,	torch.Tensor			):
											_UpperCAmelCase		:							int             =   val.item()
									_UpperCAmelCase		:							List[str]             =   F"""{key}: {val:.6f}\n"""
									writer.write(lowerCamelCase__			)

					if not save_generations:
							return

					if "preds" in metrics:
							_UpperCAmelCase		:							Dict             =   "\n".join(metrics["preds"]			)
							generations_file.open("w+"			).write(lowerCamelCase__			)




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[Any]						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  int			) ->Optional[int]:



					'''simple docstring'''







					try:
							_UpperCAmelCase		:							int             =   pl_module.model.model.num_parameters()
					except AttributeError:
							_UpperCAmelCase		:							Dict             =   pl_module.model.num_parameters()

					_UpperCAmelCase		:							Dict             =   count_trainable_parameters(lowerCamelCase__			)
					# mp stands for million parameters
					trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6}			)




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule			) ->int:



					'''simple docstring'''







					save_json(pl_module.metrics						,	pl_module.metrics_save_path			)
					return self._write_logs(lowerCamelCase__						,	lowerCamelCase__						,	"test"			)






			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  Tuple			) ->Dict:



					'''simple docstring'''







					save_json(pl_module.metrics						,	pl_module.metrics_save_path			)
					# Uncommenting this will save val generations
					# return self._write_logs(trainer, pl_module, "valid")






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase=1   ,			__lowerCAmelCase=False ):
		if metric == "rouge2":
				_UpperCAmelCase		:							Tuple             =   "{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				_UpperCAmelCase		:							List[str]             =   "{val_avg_bleu:.4f}-{step_count}"
		elif metric == "loss":
				_UpperCAmelCase		:							Union[str, Any]             =   "{val_avg_loss:.4f}-{step_count}"
		else:
				raise NotImplementedError(
				    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
				    " this function." )

		_UpperCAmelCase		:							Optional[int]             =   ModelCheckpoint(
		    dirpath=__lowerCAmelCase   ,			filename=__lowerCAmelCase   ,			monitor=F"""val_{metric}"""   ,			mode="min" if "loss" in metric else "max"   ,			save_top_k=__lowerCAmelCase   ,			)
		return checkpoint_callback






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		return EarlyStopping(
		    monitor=F"""val_{metric}"""   ,			mode="min" if "loss" in metric else "max"   ,			patience=__lowerCAmelCase   ,			verbose=__lowerCAmelCase   ,			)



