






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,
)


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

__A      =				'\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      __A					(     _lowercase					,			_lowercase					,			_lowercase=8 ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase=5_12					,			_lowercase=5_12 ):

					'''simple docstring'''







					_A								= pil_image.resize((w, h)					,			resample=Image.BICUBIC					,			reducing_gap=1 )
					_A								= np.array(pil_image.convert('''RGB''' ) )
					_A								= arr.astype(np.floataa ) / 1_27.5 - 1
					_A								= np.transpose(lowercase__					,			[2, 0, 1] )
					_A								= torch.from_numpy(lowercase__ ).unsqueeze(0 )
					return image





class 						SCREAMING_SNAKE_CASE				( __snake_case					):





							"""simple docstring"""

							def __init__(  self:   int		,     __A:   Union[str, Any]		,     __A:   int		,     __A:   Optional[int]		,     )    ->				int:
												super().__init__()

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

							def       __A (  self:   Dict		,     __A:   Optional[Any]		,     __A:   Optional[Any]		,     __A:   int   )    ->				str:
												_A								= min(int(num_inference_steps * strength   )		,     __UpperCamelCase   )

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

												return timesteps, num_inference_steps - t_start

							def       __A (  self:   List[str]		,     __A:   str		,     __A:   List[str]		,     __A:   Optional[Any]		,     __A:   Dict		,     __A:   int		,     __A:   Union[str, Any]		,     __A:   Optional[int]=None   )    ->				Any:


												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   )}"""   )

												_A								= image.to(device=__UpperCamelCase		,     dtype=__UpperCamelCase   )

												_A								= batch_size * num_images_per_prompt

												if image.shape[1] == 4:
																	_A								= 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   ):
																						_A								= [
																						    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(__UpperCamelCase   )
																						]
																						_A								= torch.cat(__UpperCamelCase		,     dim=0   )
																	else:
																						_A								= self.movq.encode(__UpperCamelCase   ).latent_dist.sample(__UpperCamelCase   )

																	_A								= self.movq.config.scaling_factor * init_latents

												_A								= torch.cat([init_latents]		,     dim=0   )

												_A								= init_latents.shape
												_A								= randn_tensor(__UpperCamelCase		,     generator=__UpperCamelCase		,     device=__UpperCamelCase		,     dtype=__UpperCamelCase   )

												# get latents
												_A								= self.scheduler.add_noise(__UpperCamelCase		,     __UpperCamelCase		,     __UpperCamelCase   )

												_A								= init_latents

												return latents

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


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

												_A								= torch.device(f"""cuda:{gpu_id}"""   )

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

							def       __A (  self:   int		,     __A:   str=0   )    ->				List[Any]:


												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.'''   )

												_A								= 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)

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

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

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


												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:   Dict		,     __A:   str		,     __A:   List[Any]		,     __A:   List[Any]		,     __A:   Optional[int] = 5_12		,     __A:   Union[str, Any] = 5_12		,     __A:   List[str] = 1_00		,     __A:   Any = 4.0		,     __A:   Dict = 0.3		,     __A:   Optional[int] = 1		,     __A:   Union[str, Any] = None		,     __A:   Dict = "pil"		,     __A:   Dict = True		,     )    ->				List[Any]:
												_A								= self._execution_device

												_A								= guidance_scale > 1.0

												if isinstance(__UpperCamelCase		,     __UpperCamelCase   ):
																	_A								= torch.cat(__UpperCamelCase		,     dim=0   )
												_A								= image_embeds.shape[0]
												if isinstance(__UpperCamelCase		,     __UpperCamelCase   ):
																	_A								= torch.cat(__UpperCamelCase		,     dim=0   )

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

												_A								= torch.cat([negative_image_embeds, image_embeds]		,     dim=0   ).to(dtype=self.unet.dtype		,     device=__UpperCamelCase   )

												if not isinstance(__UpperCamelCase		,     __UpperCamelCase   ):
																	_A								= [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"""   )

												_A								= torch.cat([prepare_image(__UpperCamelCase		,     __UpperCamelCase		,     __UpperCamelCase   ) for i in image]		,     dim=0   )
												_A								= image.to(dtype=image_embeds.dtype		,     device=__UpperCamelCase   )

												_A								= self.movq.encode(__UpperCamelCase   )['''latents''']
												_A								= latents.repeat_interleave(__UpperCamelCase		,     dim=0   )
												self.scheduler.set_timesteps(__UpperCamelCase		,     device=__UpperCamelCase   )
												_A							,_A								= self.get_timesteps(__UpperCamelCase		,     __UpperCamelCase		,     __UpperCamelCase   )
												_A								= timesteps[:1].repeat(batch_size * num_images_per_prompt   )
												_A							,_A								= downscale_height_and_width(__UpperCamelCase		,     __UpperCamelCase		,     self.movq_scale_factor   )
												_A								= 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
																	_A								= torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

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

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

																	# compute the previous noisy sample x_t -> x_t-1
																	_A								= self.scheduler.step(
																	    __UpperCamelCase		,     __UpperCamelCase		,     __UpperCamelCase		,     generator=__UpperCamelCase		,     )[0]

												# post-processing
												_A								= 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"]:
																	_A								= image * 0.5 + 0.5
																	_A								= image.clamp(0		,     1   )
																	_A								= image.cpu().permute(0		,     2		,     3		,     1   ).float().numpy()

												if output_type == "pil":
																	_A								= self.numpy_to_pil(__UpperCamelCase   )

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase   )











import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


__A      =				logging.getLogger(__name__)





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"sequence-classification"

							def __init__(  self:   str		,     __A:   Union[str, Any]   )    ->				List[str]:
												if type(__A   ) == dict:
																	_A								= Namespace(**__A   )
												_A								= glue_output_modes[hparams.task]
												_A								= glue_tasks_num_labels[hparams.task]

												super().__init__(__A		,     __A		,     self.mode   )

							def       __A (  self:   Optional[Any]		,     **__A:   Union[str, Any]   )    ->				Optional[int]:
												return self.model(**__A   )

							def       __A (  self:   Any		,     __A:   Union[str, Any]		,     __A:   int   )    ->				Optional[Any]:
												_A								= {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

												if self.config.model_type not in ["distilbert", "bart"]:
																	_A								= batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

												_A								= self(**__A   )
												_A								= outputs[0]

												_A								= self.trainer.lr_schedulers[0]['''scheduler''']
												_A								= {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
												return {"loss": loss, "log": tensorboard_logs}

							def       __A (  self:   List[str]   )    ->				Dict:
												_A								= self.hparams
												_A								= processors[args.task]()
												_A								= processor.get_labels()

												for mode in ["train", "dev"]:
																	_A								= self._feature_file(__A   )
																	if os.path.exists(__A   ) and not args.overwrite_cache:
																						logger.info('''Loading features from cached file %s'''		,     __A   )
																	else:
																						logger.info('''Creating features from dataset file at %s'''		,     args.data_dir   )
																						_A								= (
																						    processor.get_dev_examples(args.data_dir   )
																						    if mode == '''dev'''
																						    else processor.get_train_examples(args.data_dir   )
																						)
																						_A								= convert_examples_to_features(
																						    __A		,     self.tokenizer		,     max_length=args.max_seq_length		,     label_list=self.labels		,     output_mode=args.glue_output_mode		,     )
																						logger.info('''Saving features into cached file %s'''		,     __A   )
																						torch.save(__A		,     __A   )

							def       __A (  self:   List[str]		,     __A:   str		,     __A:   int		,     __A:   bool = False   )    ->				DataLoader:
												_A								= '''dev''' if mode == '''test''' else mode

												_A								= self._feature_file(__A   )
												logger.info('''Loading features from cached file %s'''		,     __A   )
												_A								= torch.load(__A   )
												_A								= torch.tensor([f.input_ids for f in features]		,     dtype=torch.long   )
												_A								= torch.tensor([f.attention_mask for f in features]		,     dtype=torch.long   )
												_A								= torch.tensor([f.token_type_ids for f in features]		,     dtype=torch.long   )
												if self.hparams.glue_output_mode == "classification":
																	_A								= torch.tensor([f.label for f in features]		,     dtype=torch.long   )
												elif self.hparams.glue_output_mode == "regression":
																	_A								= torch.tensor([f.label for f in features]		,     dtype=torch.float   )

												return DataLoader(
												    TensorDataset(__A		,     __A		,     __A		,     __A   )		,     batch_size=__A		,     shuffle=__A		,     )

							def       __A (  self:   List[str]		,     __A:   str		,     __A:   Tuple   )    ->				str:
												_A								= {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

												if self.config.model_type not in ["distilbert", "bart"]:
																	_A								= batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

												_A								= self(**__A   )
												_A							,_A								= outputs[:2]
												_A								= logits.detach().cpu().numpy()
												_A								= inputs['''labels'''].detach().cpu().numpy()

												return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

							def       __A (  self:   str		,     __A:   Dict   )    ->				tuple:
												_A								= torch.stack([x['''val_loss'''] for x in outputs]   ).mean().detach().cpu().item()
												_A								= np.concatenate([x['''pred'''] for x in outputs]		,     axis=0   )

												if self.hparams.glue_output_mode == "classification":
																	_A								= np.argmax(__A		,     axis=1   )
												elif self.hparams.glue_output_mode == "regression":
																	_A								= np.squeeze(__A   )

												_A								= np.concatenate([x['''target'''] for x in outputs]		,     axis=0   )
												_A								= [[] for _ in range(out_label_ids.shape[0]   )]
												_A								= [[] for _ in range(out_label_ids.shape[0]   )]

												_A								= {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task		,     __A		,     __A   )}

												_A								= dict(results.items()   )
												_A								= results
												return ret, preds_list, out_label_list

							def       __A (  self:   Any		,     __A:   list   )    ->				dict:
												_A							,_A							,_A								= self._eval_end(__A   )
												_A								= ret['''log''']
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

							def       __A (  self:   int		,     __A:   Union[str, Any]   )    ->				dict:
												_A							,_A							,_A								= self._eval_end(__A   )
												_A								= ret['''log''']
												# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
												return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



							@staticmethod
							def       __A (  __A:   Optional[Any]		,     __A:   Optional[Any]   )    ->				Optional[Any]:
												BaseTransformer.add_model_specific_args(__A		,     __A   )
												parser.add_argument(
												    '''--max_seq_length'''		,     default=1_28		,     type=__A		,     help=(
												        '''The maximum total input sequence length after tokenization. Sequences longer '''
												        '''than this will be truncated, sequences shorter will be padded.'''
												    )		,     )

												parser.add_argument(
												    '''--task'''		,     default=''''''		,     type=__A		,     required=__A		,     help='''The GLUE task to run'''		,     )
												parser.add_argument(
												    '''--gpus'''		,     default=0		,     type=__A		,     help='''The number of GPUs allocated for this, it is by default 0 meaning none'''		,     )

												parser.add_argument(
												    '''--overwrite_cache'''		,     action='''store_true'''		,     help='''Overwrite the cached training and evaluation sets'''   )

												return parser




def      __A					(     ):

					'''simple docstring'''







					_A								= argparse.ArgumentParser()
					add_generic_args(_lowercase					,			os.getcwd() )
					_A								= GLUETransformer.add_model_specific_args(_lowercase					,			os.getcwd() )
					_A								= parser.parse_args()

					# If output_dir not provided, a folder will be generated in pwd
					if args.output_dir is None:
										_A								= os.path.join(
										    '''./results'''					,			f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}"""					,			)
										os.makedirs(args.output_dir )

					_A								= GLUETransformer(_lowercase )
					_A								= generic_train(_lowercase					,			_lowercase )

					# Optionally, predict on dev set and write to output_dir
					if args.do_predict:
										_A								= sorted(glob.glob(os.path.join(args.output_dir					,			'''checkpoint-epoch=*.ckpt''' )					,			recursive=_lowercase ) )
										_A								= model.load_from_checkpoint(checkpoints[-1] )
										return trainer.test(_lowercase )


if __name__ == "__main__":
				main()



