
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 							a		(				snake_case__:   int		,  snake_case__:   List[Any]		,  snake_case__:   Union[str, Any]		,  snake_case__:   Dict=1_024   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   [], []

				lowercase_	    =   list(zip(snake_case__		,  snake_case__   )   )
				lowercase_    ,      lowercase_	    =   sorted_examples[0]

				def is_too_big(snake_case__:   List[str]   ):
								return tok(snake_case__		,  return_tensors='''pt'''   ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]   ):
								lowercase_	    =   new_src + ''' ''' + src
								lowercase_	    =   new_tgt + ''' ''' + tgt
								if is_too_big(snake_case__   ) or is_too_big(snake_case__   ):  # cant fit, finalize example
												finished_src.append(snake_case__   )
												finished_tgt.append(snake_case__   )
												lowercase_    ,      lowercase_	    =   src, tgt
								else:  # can fit, keep adding
												lowercase_    ,      lowercase_	    =   cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(snake_case__   )
								finished_tgt.append(snake_case__   )
				return finished_src, finished_tgt




def 							a		(				snake_case__:   List[Any]		,  snake_case__:   Path		,  snake_case__:   int		,  snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   Path(snake_case__   )
				save_path.mkdir(exist_ok=snake_case__   )
				for split in ["train"]:
								lowercase_    ,      lowercase_	    =   data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
								lowercase_	    =   [x.rstrip() for x in Path(snake_case__   ).open().readlines()]
								lowercase_	    =   [x.rstrip() for x in Path(snake_case__   ).open().readlines()]
								lowercase_    ,      lowercase_	    =   pack_examples(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
								print(F'''packed {split} split from {len(snake_case__   )} examples -> {len(snake_case__   )}.'''   )
								Path(save_path / F'''{split}.source'''   ).open('''w'''   ).write('''\n'''.join(snake_case__   )   )
								Path(save_path / F'''{split}.target'''   ).open('''w'''   ).write('''\n'''.join(snake_case__   )   )
				for split in ["val", "test"]:
								lowercase_    ,      lowercase_	    =   data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
								shutil.copyfile(snake_case__		,  save_path / F'''{split}.source'''   )
								shutil.copyfile(snake_case__		,  save_path / F'''{split}.target'''   )




def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   argparse.ArgumentParser()
				parser.add_argument('''--tok_name'''		,  type=snake_case__		,  help='''like facebook/bart-large-cnn,t5-base, etc.'''   )
				parser.add_argument('''--max_seq_len'''		,  type=snake_case__		,  default=128   )
				parser.add_argument('''--data_dir'''		,  type=snake_case__   )
				parser.add_argument('''--save_path'''		,  type=snake_case__   )
				lowercase_	    =   parser.parse_args()
				lowercase_	    =   AutoTokenizer.from_pretrained(args.tok_name   )
				return pack_data_dir(snake_case__		,  Path(args.data_dir   )		,  args.max_seq_len		,  args.save_path   )


if __name__ == "__main__":
						packer_cli()








"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
@slow
@require_torch_gpu
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]      )

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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-1



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_dpmpp_2m"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe(
										    [prompt]       ,  generator=__a       ,  guidance_scale=7.5       ,  num_inference_steps=15       ,  output_type="np"       ,  use_karras_sigmas=__a       ,  )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array(
										    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]      )

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




