"""simple docstring"""







from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



			'''simple docstring'''



			def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

						super().__init__()
						self.register_modules(unet=_SCREAMING_SNAKE_CASE   ,							scheduler=_SCREAMING_SNAKE_CASE  )







			@torch.no_grad()
			def __call__(  self   ,							_SCREAMING_SNAKE_CASE = 1   ,							_SCREAMING_SNAKE_CASE = None   ,							_SCREAMING_SNAKE_CASE = 50   ,							_SCREAMING_SNAKE_CASE = "pil"   ,							_SCREAMING_SNAKE_CASE = True   ,							**_SCREAMING_SNAKE_CASE   ,							):


						'''simple docstring'''

						__lowerCamelCase								=				torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,							generator=_SCREAMING_SNAKE_CASE   ,							)
						__lowerCamelCase								=				image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									__lowerCamelCase								=				self.unet(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__lowerCamelCase								=				self.scheduler.step(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ).prev_sample

						__lowerCamelCase								=				(image / 2 + 0.5).clamp(0   ,							1  )
						__lowerCamelCase								=				image.cpu().permute(0   ,							2   ,							3   ,							1  ).numpy()
						if output_type == "pil":
									__lowerCamelCase								=				self.numpy_to_pil(_SCREAMING_SNAKE_CASE  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE  ), "This is a local test"







"""simple docstring"""







import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE  :		List[str]        =      sys.argv[1]
_SCREAMING_SNAKE_CASE  :		List[str]        =      sys.argv[2]
_SCREAMING_SNAKE_CASE  :		Tuple        =      int(sys.argv[3])

_SCREAMING_SNAKE_CASE  :		List[str]        =      0

_SCREAMING_SNAKE_CASE  :		Optional[int]        =      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
     for line in f_p:
          _SCREAMING_SNAKE_CASE  :		List[Any]        =      line.rstrip()

          if not line:
               print(line)
               _SCREAMING_SNAKE_CASE  :		Union[str, Any]        =      0
               continue

          _SCREAMING_SNAKE_CASE  :		Dict        =      line.split()[0]

          _SCREAMING_SNAKE_CASE  :		Tuple        =      len(tokenizer.tokenize(token))

          # Token contains strange control characters like \x96 or \x95
          # Just filter out the complete line
          if current_subwords_len == 0:
               continue

          if (subword_len_counter + current_subwords_len) > max_len:
               print('''''')
               print(line)
               _SCREAMING_SNAKE_CASE  :		List[Any]        =      current_subwords_len
               continue

          subword_len_counter += current_subwords_len

          print(line)






