





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


lowerCAmelCase_    =				logging.getLogger(__name__)



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     List[str]       			=      '''sequence-classification'''






			def __init__(self					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							if type(A_    ) == dict:
											snake_case_						:    Optional[Any]				=  Namespace(**A_    )
							snake_case_						:    Tuple				=  glue_output_modes[hparams.task]
							snake_case_						:    Tuple				=  glue_tasks_num_labels[hparams.task]

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






			def 			lowerCamelCase  (self					,							**__magic_name__    )     ->   Any:


							'''simple docstring'''



							return self.model(**A_    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

							snake_case_						:    Dict				=  self(**A_    )
							snake_case_						:    Tuple				=  outputs[0]

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






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.hparams
							snake_case_						:    List[str]				=  processors[args.task]()
							snake_case_						:    Dict				=  processor.get_labels()

							for mode in ["train", "dev"]:
											snake_case_						:    List[Any]				=  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    )
															snake_case_						:    str				=  (
															    processor.get_dev_examples(args.data_dir    )
															    if mode == '''dev'''
															    else processor.get_train_examples(args.data_dir    )
															)
															snake_case_						:    Union[str, Any]				=  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 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__ = False    )     ->   DataLoader:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  '''dev''' if mode == '''test''' else mode

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

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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

							snake_case_						:    Optional[int]				=  self(**A_    )
							snake_case_			,							snake_case_						:    Union[str, Any]				=  outputs[:2]
							snake_case_						:    str				=  logits.detach().cpu().numpy()
							snake_case_						:    Dict				=  inputs['''labels'''].detach().cpu().numpy()

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






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   tuple:


							'''simple docstring'''



							snake_case_						:    Any				=  torch.stack([x['''val_loss'''] for x in outputs]    ).mean().detach().cpu().item()
							snake_case_						:    int				=  np.concatenate([x['''pred'''] for x in outputs]					,							axis=0    )

							if self.hparams.glue_output_mode == "classification":
											snake_case_						:    Union[str, Any]				=  np.argmax(A_					,							axis=1    )
							elif self.hparams.glue_output_mode == "regression":
											snake_case_						:    Dict				=  np.squeeze(A_    )

							snake_case_						:    Optional[Any]				=  np.concatenate([x['''target'''] for x in outputs]					,							axis=0    )
							snake_case_						:    int				=  [[] for _ in range(out_label_ids.shape[0]    )]
							snake_case_						:    List[str]				=  [[] for _ in range(out_label_ids.shape[0]    )]

							snake_case_						:    Union[str, Any]				=  {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task					,							A_					,							A_    )}

							snake_case_						:    Union[str, Any]				=  dict(results.items()    )
							snake_case_						:    str				=  results
							return ret, preds_list, out_label_list






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   dict:


							'''simple docstring'''



							snake_case_			,							snake_case_			,							snake_case_						:    List[str]				=  self._eval_end(A_    )
							snake_case_						:    List[Any]				=  ret['''log''']
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   dict:


							'''simple docstring'''



							snake_case_			,							snake_case_			,							snake_case_						:    Optional[int]				=  self._eval_end(A_    )
							snake_case_						:    Tuple				=  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 			lowerCamelCase  (__magic_name__					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							BaseTransformer.add_model_specific_args(A_					,							A_    )
							parser.add_argument(
							    '''--max_seq_length'''					,							default=128					,							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      lowerCamelCase_						(				)		->						List[Any]:






				"""simple docstring"""


				snake_case_						:    Dict				=  argparse.ArgumentParser()
				add_generic_args(_UpperCamelCase ,				os.getcwd() )
				snake_case_						:    Any				=  GLUETransformer.add_model_specific_args(_UpperCamelCase ,				os.getcwd() )
				snake_case_						:    str				=  parser.parse_args()

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

				snake_case_						:    int				=  GLUETransformer(_UpperCamelCase )
				snake_case_						:    Union[str, Any]				=  generic_train(_UpperCamelCase ,				_UpperCamelCase )

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


if __name__ == "__main__":
	main()






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class   A(						unittest.TestCase   ):



 '''simple docstring'''


 def 				a__ (				self					:  Optional[Any]  )   ->	Optional[Any]:

       """simple docstring"""






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

 @property
 def 				a__ (				self					:  int  )   ->	int:

       """simple docstring"""






       lowerCamelCase_		     =    1
       lowerCamelCase_		     =    3
       lowerCamelCase_		     =    (32, 32)

       lowerCamelCase_		     =    floats_tensor((batch_size, num_channels) + sizes						,							rng=random.Random(0  )  ).to(A_  )
       return image

 @property
 def 				a__ (				self					:  Any  )   ->	List[Any]:

       """simple docstring"""






       torch.manual_seed(0  )
       lowerCamelCase_		     =    UNetaDConditionModel(
           block_out_channels=(32, 32, 64)						,							layers_per_block=2						,							sample_size=32						,							in_channels=7						,							out_channels=4						,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')						,							up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')						,							cross_attention_dim=32						,							attention_head_dim=8						,							use_linear_projection=A_						,							only_cross_attention=(True, True, False)						,							num_class_embeds=100						,							)
       return model

 @property
 def 				a__ (				self					:  Optional[int]  )   ->	Optional[int]:

       """simple docstring"""






       torch.manual_seed(0  )
       lowerCamelCase_		     =    AutoencoderKL(
           block_out_channels=[32, 32, 64]						,							in_channels=3						,							out_channels=3						,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']						,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']						,							latent_channels=4						,							)
       return model

 @property
 def 				a__ (				self					:  Union[str, Any]  )   ->	Union[str, Any]:

       """simple docstring"""






       torch.manual_seed(0  )
       lowerCamelCase_		     =    CLIPTextConfig(
           bos_token_id=0						,							eos_token_id=2						,							hidden_size=32						,							intermediate_size=37						,							layer_norm_eps=1E-05						,							num_attention_heads=4						,							num_hidden_layers=5						,							pad_token_id=1						,							vocab_size=1000						,							hidden_act='gelu'						,							projection_dim=512						,							)
       return CLIPTextModel(A_  )

 def 				a__ (				self					:  Optional[int]  )   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase_		     =    self.dummy_cond_unet_upscale
       lowerCamelCase_		     =    DDPMScheduler()
       lowerCamelCase_		     =    DDIMScheduler(prediction_type='v_prediction'  )
       lowerCamelCase_		     =    self.dummy_vae
       lowerCamelCase_		     =    self.dummy_text_encoder
       lowerCamelCase_		     =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

       lowerCamelCase_		     =    self.dummy_image.cpu().permute(0						,							2						,							3						,							1  )[0]
       lowerCamelCase_		     =    Image.fromarray(np.uinta(A_  )  ).convert('RGB'  ).resize((64, 64)  )

       # make sure here that pndm scheduler skips prk
       lowerCamelCase_		     =    StableDiffusionUpscalePipeline(
           unet=A_						,							low_res_scheduler=A_						,							scheduler=A_						,							vae=A_						,							text_encoder=A_						,							tokenizer=A_						,							max_noise_level=350						,							)
       lowerCamelCase_		     =    sd_pipe.to(A_  )
       sd_pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_		     =    'A painting of a squirrel eating a burger'
       lowerCamelCase_		     =    torch.Generator(device=A_  ).manual_seed(0  )
       lowerCamelCase_		     =    sd_pipe(
           [prompt]						,							image=A_						,							generator=A_						,							guidance_scale=6.0						,							noise_level=20						,							num_inference_steps=2						,							output_type='np'						,							)

       lowerCamelCase_		     =    output.images

       lowerCamelCase_		     =    torch.Generator(device=A_  ).manual_seed(0  )
       lowerCamelCase_		     =    sd_pipe(
           [prompt]						,							image=A_						,							generator=A_						,							guidance_scale=6.0						,							noise_level=20						,							num_inference_steps=2						,							output_type='np'						,							return_dict=A_						,							)[0]

       lowerCamelCase_		     =    image[0, -3:, -3:, -1]
       lowerCamelCase_		     =    image_from_tuple[0, -3:, -3:, -1]

       lowerCamelCase_		     =    low_res_image.size[0] * 4
       assert image.shape == (1, expected_height_width, expected_height_width, 3)
       lowerCamelCase_		     =    np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2

 def 				a__ (				self					:  List[Any]  )   ->	int:

       """simple docstring"""






       lowerCamelCase_		     =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase_		     =    self.dummy_cond_unet_upscale
       lowerCamelCase_		     =    DDPMScheduler()
       lowerCamelCase_		     =    DDIMScheduler(prediction_type='v_prediction'  )
       lowerCamelCase_		     =    self.dummy_vae
       lowerCamelCase_		     =    self.dummy_text_encoder
       lowerCamelCase_		     =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

       lowerCamelCase_		     =    self.dummy_image.cpu().permute(0						,							2						,							3						,							1  )[0]
       lowerCamelCase_		     =    Image.fromarray(np.uinta(A_  )  ).convert('RGB'  ).resize((64, 64)  )

       # make sure here that pndm scheduler skips prk
       lowerCamelCase_		     =    StableDiffusionUpscalePipeline(
           unet=A_						,							low_res_scheduler=A_						,							scheduler=A_						,							vae=A_						,							text_encoder=A_						,							tokenizer=A_						,							max_noise_level=350						,							)
       lowerCamelCase_		     =    sd_pipe.to(A_  )
       sd_pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_		     =    'A painting of a squirrel eating a burger'
       lowerCamelCase_		     =    sd_pipe(
           2 * [prompt]						,							image=2 * [low_res_image]						,							guidance_scale=6.0						,							noise_level=20						,							num_inference_steps=2						,							output_type='np'						,							)
       lowerCamelCase_		     =    output.images
       assert image.shape[0] == 2

       lowerCamelCase_		     =    torch.Generator(device=A_  ).manual_seed(0  )
       lowerCamelCase_		     =    sd_pipe(
           [prompt]						,							image=A_						,							generator=A_						,							num_images_per_prompt=2						,							guidance_scale=6.0						,							noise_level=20						,							num_inference_steps=2						,							output_type='np'						,							)
       lowerCamelCase_		     =    output.images
       assert image.shape[0] == 2





 @unittest.skipIf(torch_device != 'cuda'						,							'This test requires a GPU'  )
 def 				a__ (				self					:  List[Any]  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    self.dummy_cond_unet_upscale
       lowerCamelCase_		     =    DDPMScheduler()
       lowerCamelCase_		     =    DDIMScheduler(prediction_type='v_prediction'  )
       lowerCamelCase_		     =    self.dummy_vae
       lowerCamelCase_		     =    self.dummy_text_encoder
       lowerCamelCase_		     =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

       lowerCamelCase_		     =    self.dummy_image.cpu().permute(0						,							2						,							3						,							1  )[0]
       lowerCamelCase_		     =    Image.fromarray(np.uinta(A_  )  ).convert('RGB'  ).resize((64, 64)  )

       # put models in fp16, except vae as it overflows in fp16
       lowerCamelCase_		     =    unet.half()
       lowerCamelCase_		     =    text_encoder.half()

       # make sure here that pndm scheduler skips prk
       lowerCamelCase_		     =    StableDiffusionUpscalePipeline(
           unet=A_						,							low_res_scheduler=A_						,							scheduler=A_						,							vae=A_						,							text_encoder=A_						,							tokenizer=A_						,							max_noise_level=350						,							)
       lowerCamelCase_		     =    sd_pipe.to(A_  )
       sd_pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_		     =    'A painting of a squirrel eating a burger'
       lowerCamelCase_		     =    torch.manual_seed(0  )
       lowerCamelCase_		     =    sd_pipe(
           [prompt]						,							image=A_						,							generator=A_						,							num_inference_steps=2						,							output_type='np'						,							).images

       lowerCamelCase_		     =    low_res_image.size[0] * 4
       assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class   A(						unittest.TestCase   ):



 '''simple docstring'''


 def 				a__ (				self					:  Tuple  )   ->	str:

       """simple docstring"""






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

 def 				a__ (				self					:  int  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/sd2-upscale/low_res_cat.png'  )
       lowerCamelCase_		     =    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
           '/upsampled_cat.npy'  )

       lowerCamelCase_		     =    'stabilityai/stable-diffusion-x4-upscaler'
       lowerCamelCase_		     =    StableDiffusionUpscalePipeline.from_pretrained(A_  )
       pipe.to(A_  )
       pipe.set_progress_bar_config(disable=A_  )
       pipe.enable_attention_slicing()

       lowerCamelCase_		     =    'a cat sitting on a park bench'

       lowerCamelCase_		     =    torch.manual_seed(0  )
       lowerCamelCase_		     =    pipe(
           prompt=A_						,							image=A_						,							generator=A_						,							output_type='np'						,							)
       lowerCamelCase_		     =    output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image  ).max() < 1E-3

 def 				a__ (				self					:  Dict  )   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/sd2-upscale/low_res_cat.png'  )
       lowerCamelCase_		     =    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
           '/upsampled_cat_fp16.npy'  )

       lowerCamelCase_		     =    'stabilityai/stable-diffusion-x4-upscaler'
       lowerCamelCase_		     =    StableDiffusionUpscalePipeline.from_pretrained(
           A_						,							torch_dtype=torch.floataa						,							)
       pipe.to(A_  )
       pipe.set_progress_bar_config(disable=A_  )
       pipe.enable_attention_slicing()

       lowerCamelCase_		     =    'a cat sitting on a park bench'

       lowerCamelCase_		     =    torch.manual_seed(0  )
       lowerCamelCase_		     =    pipe(
           prompt=A_						,							image=A_						,							generator=A_						,							output_type='np'						,							)
       lowerCamelCase_		     =    output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image  ).max() < 5E-1





 def 				a__ (				self					:  Union[str, Any]  )   ->	Union[str, Any]:

       """simple docstring"""






       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       lowerCamelCase_		     =    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/sd2-upscale/low_res_cat.png'  )

       lowerCamelCase_		     =    'stabilityai/stable-diffusion-x4-upscaler'
       lowerCamelCase_		     =    StableDiffusionUpscalePipeline.from_pretrained(
           A_						,							torch_dtype=torch.floataa						,							)
       pipe.to(A_  )
       pipe.set_progress_bar_config(disable=A_  )
       pipe.enable_attention_slicing(1  )
       pipe.enable_sequential_cpu_offload()

       lowerCamelCase_		     =    'a cat sitting on a park bench'

       lowerCamelCase_		     =    torch.manual_seed(0  )
       lowerCamelCase_		     =    pipe(
           prompt=A_						,							image=A_						,							generator=A_						,							num_inference_steps=5						,							output_type='np'						,							)

       lowerCamelCase_		     =    torch.cuda.max_memory_allocated()
       # make sure that less than 2.9 GB is allocated
       assert mem_bytes < 2.9 * 10**9



