

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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def   __UpperCAmelCase					(      __a :     Any						,__a :     str						,__a :     List[Any]=1_024						,__a :     Optional[int]=1_024						,__a :     int=False						,**__a :     str      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       Tuple   =						AutoTokenizer.from_pretrained(__a      )
					_a      :       int   =						SeqaSeqDataset(__a						,__a						,__a						,__a						,type_path='''train'''						,**__a      )
					_a      :       List[str]   =						tok.pad_token_id

					def get_lens(__a :     Any      ):
										_a      :       List[Any]   =						tqdm(
										    DataLoader(__a						,batch_size=512						,num_workers=8						,shuffle=__a						,collate_fn=ds.collate_fn      )						,desc=str(ds.len_file      )						,)
										_a      :       Optional[Any]   =						[]
										for batch in dl:
															_a      :       List[str]   =						batch['''input_ids'''].ne(__a      ).sum(1      ).tolist()
															_a      :       Optional[Any]   =						batch['''labels'''].ne(__a      ).sum(1      ).tolist()
															if consider_target:
																				for src, tgt in zip(__a						,__a      ):
																									max_lens.append(max(__a						,__a      )      )
															else:
																				max_lens.extend(__a      )
										return max_lens

					_a      :       List[str]   =						get_lens(__a      )
					_a      :       Tuple   =						SeqaSeqDataset(__a						,__a						,__a						,__a						,type_path='''val'''						,**__a      )
					_a      :       Union[str, Any]   =						get_lens(__a      )
					pickle_save(__a						,train_ds.len_file      )
					pickle_save(__a						,val_ds.len_file      )


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






import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

a__												=						'''Usage of script: script_name <size_of_canvas:int>'''

a__												=						[0] * 100 + [1] * 10
random.shuffle(choice)



def   __UpperCAmelCase					(      __a :     int      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       int   =						[[False for i in range(__a      )] for j in range(__a      )]
					return canvas



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	None:

					"""simple docstring"""

					for i, row in enumerate(__a      ):
										for j, _ in enumerate(__a      ):
															_a      :       Optional[int]   =						bool(random.getrandbits(1      )      )



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       Any   =						np.array(__a      )
					_a      :       Optional[int]   =						np.array(create_canvas(current_canvas.shape[0]      )      )
					for r, row in enumerate(__a      ):
										for c, pt in enumerate(__a      ):
															_a      :       Tuple   =						__judge_point(
															    __a						,current_canvas[r - 1 : r + 2, c - 1 : c + 2]      )

					_a      :       List[str]   =						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					_a      :       list[list[bool]]   =						current_canvas.tolist()
					return return_canvas



def   __UpperCAmelCase					(      __a :     bool						,__a :     list[list[bool]]      )     ->	bool:

					"""simple docstring"""

					_a      :       Optional[Any]   =						0
					_a      :       str   =						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					_a      :       Optional[int]   =						pt
					if pt:
										if alive < 2:
															_a      :       Dict   =						False
										elif alive == 2 or alive == 3:
															_a      :       Optional[Any]   =						True
										elif alive > 3:
															_a      :       str   =						False
					else:
										if alive == 3:
															_a      :       int   =						True

					return state


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise Exception(usage_doc)

			a__												=						int(sys.argv[1])
			# main working structure of this module.
			a__												=						create_canvas(canvas_size)
			seed(c)
			a__						,     a__												=						plt.subplots()
			fig.show()
			a__												=						ListedColormap(['''w''', '''k'''])
			try:
						while True:
									a__												=						run(c)
									ax.matshow(c, cmap=cmap)
									fig.canvas.draw()
									ax.cla()
			except KeyboardInterrupt:
						# do nothing.
						pass



