





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCAmelCase__						:			str									=False

class       __A     (						unittest.TestCase    ):
				pass




@nightly
@require_torch_gpu
class       __A     (						unittest.TestCase    ):


				def _snake_case       (		self							):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


				def _snake_case       (		self							):
								lowerCamelCase	 =VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(__lowercase							)
								pipe.set_progress_bar_config(disable=__lowercase							)

								lowerCamelCase	 ="""A painting of a squirrel eating a burger """
								lowerCamelCase	 =torch.manual_seed(0							)
								lowerCamelCase	 =pipe(
								    prompt=__lowercase   , generator=__lowercase   , guidance_scale=7.5   , num_inference_steps=2   , output_type="""numpy"""							).images

								with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(__lowercase							)
												lowerCamelCase	 =VersatileDiffusionTextToImagePipeline.from_pretrained(__lowercase							)
								pipe.to(__lowercase							)
								pipe.set_progress_bar_config(disable=__lowercase							)

								lowerCamelCase	 =generator.manual_seed(0							)
								lowerCamelCase	 =pipe(
								    prompt=__lowercase   , generator=__lowercase   , guidance_scale=7.5   , num_inference_steps=2   , output_type="""numpy"""							).images

								assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"


				def _snake_case       (		self							):
								lowerCamelCase	 =VersatileDiffusionTextToImagePipeline.from_pretrained(
								    """shi-labs/versatile-diffusion"""   , torch_dtype=torch.floataa							)
								pipe.to(__lowercase							)
								pipe.set_progress_bar_config(disable=__lowercase							)

								lowerCamelCase	 ="""A painting of a squirrel eating a burger """
								lowerCamelCase	 =torch.manual_seed(0							)
								lowerCamelCase	 =pipe(
								    prompt=__lowercase   , generator=__lowercase   , guidance_scale=7.5   , num_inference_steps=50   , output_type="""numpy"""							).images

								lowerCamelCase	 =image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase	 =np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]							)

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












import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=False			,      **_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =AutoTokenizer.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""train"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =tok.pad_token_id

				def get_lens(_UpperCAmelCase				):
								lowerCamelCase	 =tqdm(
								    DataLoader(_UpperCAmelCase			,      batch_size=5_12			,      num_workers=8			,      shuffle=_UpperCAmelCase			,      collate_fn=ds.collate_fn				)			,      desc=str(ds.len_file				)			,      )
								lowerCamelCase	 =[]
								for batch in dl:
												lowerCamelCase	 =batch["""input_ids"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												lowerCamelCase	 =batch["""labels"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												if consider_target:
																for src, tgt in zip(_UpperCAmelCase			,      _UpperCAmelCase				):
																				max_lens.append(max(_UpperCAmelCase			,      _UpperCAmelCase				)				)
												else:
																max_lens.extend(_UpperCAmelCase				)
								return max_lens

				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""val"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				pickle_save(_UpperCAmelCase			,      train_ds.len_file				)
				pickle_save(_UpperCAmelCase			,      val_ds.len_file				)


if __name__ == "__main__":
							fire.Fire(save_len_file)





