


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__UpperCAmelCase       :       Dict       	= logging.get_logger(__name__)

__UpperCAmelCase       :       List[Any]       	= ['UtteranceLevel']


@torch.no_grad()
def    a		(							SCREAMING_SNAKE_CASE_					:						Dict							,	SCREAMING_SNAKE_CASE_					:						Dict							,	SCREAMING_SNAKE_CASE_					:						int							,	SCREAMING_SNAKE_CASE_					:						Union[str, Any]				):






       """simple docstring"""





       UpperCamelCase			:		Optional[int]      		=						torch.load(__lowercase							,	map_location='''cpu'''				)
       if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
              raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""				)

       UpperCamelCase			:		List[str]      		=						checkpoint["Downstream"]

       UpperCamelCase			:		List[str]      		=						HubertConfig.from_pretrained(__lowercase				)
       UpperCamelCase			:		Union[str, Any]      		=						HubertForSequenceClassification.from_pretrained(__lowercase							,	config=__lowercase				)
       UpperCamelCase			:		Union[str, Any]      		=						WavaVecaFeatureExtractor.from_pretrained(
           __lowercase							,	return_attention_mask=__lowercase							,	do_normalize=__lowercase				)

       if hf_congfig.use_weighted_layer_sum:
              UpperCamelCase			:		Optional[int]      		=						checkpoint["Featurizer"]["weights"]

       UpperCamelCase			:		str      		=						downstream_dict["projector.weight"]
       UpperCamelCase			:		Tuple      		=						downstream_dict["projector.bias"]
       UpperCamelCase			:		List[str]      		=						downstream_dict["model.post_net.linear.weight"]
       UpperCamelCase			:		str      		=						downstream_dict["model.post_net.linear.bias"]

       hf_feature_extractor.save_pretrained(__lowercase				)
       hf_model.save_pretrained(__lowercase				)


if __name__ == "__main__":
  __UpperCAmelCase       :       Optional[int]       	= argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase       :       str       	= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__SCREAMING_SNAKE_CASE						:    Optional[int]     = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
def 					snake_case						(__lowercase						)       ->       list[int]:

						'''simple docstring'''



						return [int(__lowercase						) for c in F"""{ruleset:08}"""[:8]]
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       list[int]:

						'''simple docstring'''



						_snake_case						:     Any				=    len(cells[0]						)  # 31
						_snake_case						:     str				=    []
						for i in range(__lowercase						):
												# Get the neighbors of each cell
												# Handle neighbours outside bounds by using 0 as their value
												_snake_case						:     Any				=    0 if i == 0 else cells[time][i - 1]
												_snake_case						:     List[str]				=    0 if i == population - 1 else cells[time][i + 1]
												# Define a new cell and add it to the new generation
												_snake_case						:     str				=    7 - int(F"""{left_neighbor}{cells[time][i]}{right_neighbor}"""					,			2						)
												next_generation.append(rule[situation]						)
						return next_generation
def 					snake_case						(__lowercase						)       ->       Image.Image:

						'''simple docstring'''



						_snake_case						:     str				=    Image.new("RGB"					,			(len(cells[0]						), len(__lowercase						))						)
						_snake_case						:     int				=    img.load()
						# Generates image
						for w in range(img.width						):
												for h in range(img.height						):
																		_snake_case						:     List[Any]				=    255 - int(255 * cells[h][w]						)
																		_snake_case						:     Union[str, Any]				=    (color, color, color)
						return img


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Any     = bin(int(input('Rule:\n').strip()))[2:]
				__SCREAMING_SNAKE_CASE						:    Tuple     = format_ruleset(int(rule_num))
				for time in range(1_6):
								CELLS.append(new_generation(CELLS, rule, time))
				__SCREAMING_SNAKE_CASE						:    List[Any]     = generate_image(CELLS)
				# Uncomment to save the image
				# img.save(f"rule_{rule_num}.png")
				img.show()