'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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):




					"""simple docstring"""


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



			@property
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  1
					lowerCAmelCase   					=  3
					lowerCAmelCase   					=  (32, 32)

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



			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase   					=  UNetaDConditionModel(
					    block_out_channels=(32, 64)       ,    layers_per_block=2       ,    sample_size=32       ,    in_channels=4       ,    out_channels=4       ,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,    up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,    cross_attention_dim=32       ,    )
					return model



			@property
			def 							a_       (   self):




					"""simple docstring"""


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



			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase   					=  RobertaSeriesConfig(
					    hidden_size=32       ,    project_dim=32       ,    intermediate_size=37       ,    layer_norm_eps=1E-0_5       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    pad_token_id=1       ,    vocab_size=5006       ,    )
					return RobertaSeriesModelWithTransformation(__lowerCAmelCase)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					def extract(*__lowerCAmelCase       ,    **__lowerCAmelCase):






							class 				a__:


										'''simple docstring'''






										def __init__(   self):




												"""simple docstring"""


												lowerCAmelCase   					=  torch.ones([0])







										def 							a_       (   self       ,    __lowerCAmelCase):




												"""simple docstring"""


												self.pixel_values.to(__lowerCAmelCase)
												return self







							return Out()

					return extract



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
					lowerCAmelCase   					=  self.dummy_cond_unet
					lowerCAmelCase   					=  PNDMScheduler(skip_prk_steps=__lowerCAmelCase)
					lowerCAmelCase   					=  self.dummy_vae
					lowerCAmelCase   					=  self.dummy_text_encoder
					lowerCAmelCase   					=  XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""")
					lowerCAmelCase   					=  77

					lowerCAmelCase   					=  self.dummy_image.to(__lowerCAmelCase)
					lowerCAmelCase   					=  init_image / 2 + 0.5

					# make sure here that pndm scheduler skips prk
					lowerCAmelCase   					=  AltDiffusionImgaImgPipeline(
					    unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase       ,    vae=__lowerCAmelCase       ,    text_encoder=__lowerCAmelCase       ,    tokenizer=__lowerCAmelCase       ,    safety_checker=__lowerCAmelCase       ,    feature_extractor=self.dummy_extractor       ,    )
					lowerCAmelCase   					=  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,    do_normalize=__lowerCAmelCase)
					lowerCAmelCase   					=  alt_pipe.to(__lowerCAmelCase)
					alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  """A painting of a squirrel eating a burger"""
					lowerCAmelCase   					=  torch.Generator(device=__lowerCAmelCase).manual_seed(0)
					lowerCAmelCase   					=  alt_pipe(
					    [prompt]       ,    generator=__lowerCAmelCase       ,    guidance_scale=6.0       ,    num_inference_steps=2       ,    output_type="""np"""       ,    image=__lowerCAmelCase       ,    )

					lowerCAmelCase   					=  output.images

					lowerCAmelCase   					=  torch.Generator(device=__lowerCAmelCase).manual_seed(0)
					lowerCAmelCase   					=  alt_pipe(
					    [prompt]       ,    generator=__lowerCAmelCase       ,    guidance_scale=6.0       ,    num_inference_steps=2       ,    output_type="""np"""       ,    image=__lowerCAmelCase       ,    return_dict=__lowerCAmelCase       ,    )[0]

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

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5E-3



			@unittest.skipIf(torch_device != """cuda"""       ,    """This test requires a GPU""")
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.dummy_cond_unet
					lowerCAmelCase   					=  PNDMScheduler(skip_prk_steps=__lowerCAmelCase)
					lowerCAmelCase   					=  self.dummy_vae
					lowerCAmelCase   					=  self.dummy_text_encoder
					lowerCAmelCase   					=  XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""")
					lowerCAmelCase   					=  77

					lowerCAmelCase   					=  self.dummy_image.to(__lowerCAmelCase)

					# put models in fp16
					lowerCAmelCase   					=  unet.half()
					lowerCAmelCase   					=  vae.half()
					lowerCAmelCase   					=  bert.half()

					# make sure here that pndm scheduler skips prk
					lowerCAmelCase   					=  AltDiffusionImgaImgPipeline(
					    unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase       ,    vae=__lowerCAmelCase       ,    text_encoder=__lowerCAmelCase       ,    tokenizer=__lowerCAmelCase       ,    safety_checker=__lowerCAmelCase       ,    feature_extractor=self.dummy_extractor       ,    )
					lowerCAmelCase   					=  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,    do_normalize=__lowerCAmelCase)
					lowerCAmelCase   					=  alt_pipe.to(__lowerCAmelCase)
					alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  """A painting of a squirrel eating a burger"""
					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  alt_pipe(
					    [prompt]       ,    generator=__lowerCAmelCase       ,    num_inference_steps=2       ,    output_type="""np"""       ,    image=__lowerCAmelCase       ,    ).images

					assert image.shape == (1, 32, 32, 3)







			@unittest.skipIf(torch_device != """cuda"""       ,    """This test requires a GPU""")
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/img2img/sketch-mountains-input.jpg""")
					# resize to resolution that is divisible by 8 but not 16 or 32
					lowerCAmelCase   					=  init_image.resize((760, 504))

					lowerCAmelCase   					=  """BAAI/AltDiffusion"""
					lowerCAmelCase   					=  AltDiffusionImgaImgPipeline.from_pretrained(
					    __lowerCAmelCase       ,    safety_checker=__lowerCAmelCase       ,    )
					pipe.to(__lowerCAmelCase)
					pipe.set_progress_bar_config(disable=__lowerCAmelCase)
					pipe.enable_attention_slicing()

					lowerCAmelCase   					=  """A fantasy landscape, trending on artstation"""

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pipe(
					    prompt=__lowerCAmelCase       ,    image=__lowerCAmelCase       ,    strength=0.75       ,    guidance_scale=7.5       ,    generator=__lowerCAmelCase       ,    output_type="""np"""       ,    )
					lowerCAmelCase   					=  output.images[0]

					lowerCAmelCase   					=  image[255:258, 383:386, -1]

					assert image.shape == (504, 760, 3)
					lowerCAmelCase   					=  np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000])

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







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


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


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







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/img2img/sketch-mountains-input.jpg""")
					lowerCAmelCase   					=  init_image.resize((768, 512))
					lowerCAmelCase   					=  load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy""")

					lowerCAmelCase   					=  """BAAI/AltDiffusion"""
					lowerCAmelCase   					=  AltDiffusionImgaImgPipeline.from_pretrained(
					    __lowerCAmelCase       ,    safety_checker=__lowerCAmelCase       ,    )
					pipe.to(__lowerCAmelCase)
					pipe.set_progress_bar_config(disable=__lowerCAmelCase)
					pipe.enable_attention_slicing()

					lowerCAmelCase   					=  """A fantasy landscape, trending on artstation"""

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pipe(
					    prompt=__lowerCAmelCase       ,    image=__lowerCAmelCase       ,    strength=0.75       ,    guidance_scale=7.5       ,    generator=__lowerCAmelCase       ,    output_type="""np"""       ,    )
					lowerCAmelCase   					=  output.images[0]

					assert image.shape == (512, 768, 3)
					# img2img is flaky across GPUs even in fp32, so using MAE here
					assert np.abs(expected_image - image).max() < 1E-2





'''simple docstring'''




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


__lowercase          =  logging.getLogger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Any      =       '''sequence-classification'''



			def __init__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					if type(__lowerCAmelCase) == dict:
							lowerCAmelCase   					=  Namespace(**__lowerCAmelCase)
					lowerCAmelCase   					=  glue_output_modes[hparams.task]
					lowerCAmelCase   					=  glue_tasks_num_labels[hparams.task]

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



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return self.model(**__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

					lowerCAmelCase   					=  self(**__lowerCAmelCase)
					lowerCAmelCase   					=  outputs[0]

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.hparams
					lowerCAmelCase   					=  processors[args.task]()
					lowerCAmelCase   					=  processor.get_labels()

					for mode in ["train", "dev"]:
							lowerCAmelCase   					=  self._feature_file(__lowerCAmelCase)
							if os.path.exists(__lowerCAmelCase) and not args.overwrite_cache:
									logger.info("""Loading features from cached file %s"""       ,    __lowerCAmelCase)
							else:
									logger.info("""Creating features from dataset file at %s"""       ,    args.data_dir)
									lowerCAmelCase   					=  (
									    processor.get_dev_examples(args.data_dir)
									    if mode == """dev"""
									    else processor.get_train_examples(args.data_dir)
									)
									lowerCAmelCase   					=  convert_examples_to_features(
									    __lowerCAmelCase       ,    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"""       ,    __lowerCAmelCase)
									torch.save(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = False):




					"""simple docstring"""


					lowerCAmelCase   					=  """dev""" if mode == """test""" else mode

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

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



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

					lowerCAmelCase   					=  self(**__lowerCAmelCase)
					lowerCAmelCase					,     lowerCAmelCase   					=  outputs[:2]
					lowerCAmelCase   					=  logits.detach().cpu().numpy()
					lowerCAmelCase   					=  inputs["""labels"""].detach().cpu().numpy()

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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  torch.stack([x["""val_loss"""] for x in outputs]).mean().detach().cpu().item()
					lowerCAmelCase   					=  np.concatenate([x["""pred"""] for x in outputs]       ,    axis=0)

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

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

					lowerCAmelCase   					=  {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task       ,    __lowerCAmelCase       ,    __lowerCAmelCase)}

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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  self._eval_end(__lowerCAmelCase)
					lowerCAmelCase   					=  ret["""log"""]
					return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  self._eval_end(__lowerCAmelCase)
					lowerCAmelCase   					=  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_       (   __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					BaseTransformer.add_model_specific_args(__lowerCAmelCase       ,    __lowerCAmelCase)
					parser.add_argument(
					    """--max_seq_length"""       ,    default=128       ,    type=__lowerCAmelCase       ,    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=__lowerCAmelCase       ,    required=__lowerCAmelCase       ,    help="""The GLUE task to run"""       ,    )
					parser.add_argument(
					    """--gpus"""       ,    default=0       ,    type=__lowerCAmelCase       ,    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 				snake_case__							(			)    ->	Optional[Any]:

		'''simple docstring'''


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

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

		lowerCAmelCase   					=  GLUETransformer(_A )
		lowerCAmelCase   					=  generic_train(_A      ,   _A )

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


if __name__ == "__main__":
						main()




