
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


SCREAMING_SNAKE_CASE				:List[Any]									=							logging.getLogger(__name__)





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	"sequence-classification"






	def __init__(			self   :					Tuple				,A   :					List[Any]					):
					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 							UpperCamelCase_    (			self   :					List[Any]				,**A   :					Optional[Any]					):
					return self.model(**A					)






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					int				,A   :					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 							UpperCamelCase_    (			self   :					Optional[int]					):
					__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 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					str				,A   :					int				,A   :					bool = False					):
					__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 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					Optional[Any]				,A   :					int					):
					__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 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					int					):
					__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 							UpperCamelCase_    (			self   :					Any				,A   :					list					):
					__A     ,				__A     ,				__A       =					self._eval_end(A					)
					__A       =					ret["log"]
					return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					Union[str, Any]					):
					__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 							UpperCamelCase_    (			A   :					Any				,A   :					List[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 		UpperCAmelCase     (      )						->      Any:







				"""simple docstring"""



				__A       =					argparse.ArgumentParser()
				add_generic_args(a_						,  os.getcwd()		)
				__A       =					GLUETransformer.add_model_specific_args(a_						,  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(a_		)
				__A       =					generic_train(a_						,  a_		)

				# 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=a_		)		)
								__A       =					model.load_from_checkpoint(checkpoints[-1]		)
								return trainer.test(a_		)


if __name__ == "__main__":
					main()






'''simple docstring'''



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





     warnings.warn(
         '''The preprocess method is deprecated and will be removed in a future version. Please'''
         ''' use VaeImageProcessor.preprocess instead''' ,						_lowerCamelCase ,						)
     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return image
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[image]

     if isinstance(image[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	image[0].size
          __snake_case       ,     __snake_case            =	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          __snake_case            =	[np.array(i.resize((w, h) ,						resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	np.array(_lowerCamelCase						).astype(np.floataa						) / 255.0
          __snake_case            =	image.transpose(0 ,						3 ,						1 ,						2						)
          __snake_case            =	2.0 * image - 1.0
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(image[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return mask
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[mask]

     if isinstance(mask[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	mask[0].size
          __snake_case       ,     __snake_case            =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          __snake_case            =	[np.array(m.convert('''L'''						).resize((w, h) ,						resample=PIL_INTERPOLATION['''nearest''']						)						)[None, :] for m in mask]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	mask.astype(np.floataa						) / 255.0
          __snake_case            =	0
          __snake_case            =	1
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(mask[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return mask







class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   RePaintScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            super().__init__()
            self.register_modules(unet=__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE	)




       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 250     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	)   ->	Union[ImagePipelineOutput, Tuple]:



            '''simple docstring'''




            __snake_case            =	image

            __snake_case            =	_preprocess_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	original_image.to(device=self.device     ,	dtype=self.unet.dtype	)
            __snake_case            =	_preprocess_mask(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	mask_image.to(device=self.device     ,	dtype=self.unet.dtype	)

            __snake_case            =	original_image.shape[0]

            # sample gaussian noise to begin the loop
            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.'''	)

            __snake_case            =	original_image.shape
            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=self.device     ,	dtype=self.unet.dtype	)

            # set step values
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	eta

            __snake_case            =	self.scheduler.timesteps[0] + 1
            __snake_case            =	generator[0] if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
                 if t < t_last:
                      # predict the noise residual
                      __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).sample
                      # compute previous image: x_t -> x_t-1
                      __snake_case            =	self.scheduler.step(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).prev_sample

                 else:
                      # compute the reverse: x_t-1 -> x_t
                      __snake_case            =	self.scheduler.undo_step(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	t

            __snake_case            =	(image / 2 + 0.5).clamp(0     ,	1	)
            __snake_case            =	image.cpu().permute(0     ,	2     ,	3     ,	1	).numpy()
            if output_type == "pil":
                 __snake_case            =	self.numpy_to_pil(__SCREAMING_SNAKE_CASE	)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)






