


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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						List[Any]=1024					,    UpperCAmelCase_		:						Union[str, Any]=1024					,    UpperCAmelCase_		:						int=False					,    **UpperCAmelCase_		:						Optional[Any]		):




      """simple docstring"""





      a		:Any									=			AutoTokenizer.from_pretrained(UpperCAmelCase_		)
      a		:Tuple									=			SeqaSeqDataset(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_					,    type_path='''train'''					,    **UpperCAmelCase_		)
      a		:List[Any]									=			tok.pad_token_id

      def get_lens(UpperCAmelCase_		:						Any		):
            a		:Dict									=			tqdm(
                DataLoader(UpperCAmelCase_					,    batch_size=512					,    num_workers=8					,    shuffle=UpperCAmelCase_					,    collate_fn=ds.collate_fn		)					,    desc=str(ds.len_file		)					,    )
            a		:Union[str, Any]									=			[]
            for batch in dl:
                  a		:Union[str, Any]									=			batch['''input_ids'''].ne(UpperCAmelCase_		).sum(1		).tolist()
                  a		:List[Any]									=			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

      a		:Tuple									=			get_lens(UpperCAmelCase_		)
      a		:List[Any]									=			SeqaSeqDataset(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_					,    type_path='''val'''					,    **UpperCAmelCase_		)
      a		:Any									=			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)









import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[Any]									=			UNetaDModel(
           block_out_channels=(32, 64)   ,    layers_per_block=2   ,    sample_size=32   ,    in_channels=3   ,    out_channels=3   ,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')   ,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')   ,    )
       return model




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

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







@slow
@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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





