
from __future__ import annotations

import sys




class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	def __init__(						self			:					Union[str, Any]    ,						lowercase			:					str    ,						lowercase			:					int			):


				'''simple docstring'''


				_snake_case						=    letter
				_snake_case						=    freq
				_snake_case						=    {}





	def __repr__(						self			:					Tuple			):


				'''simple docstring'''


				return f'''{self.letter}:{self.freq}'''




class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	def __init__(						self			:					Union[str, Any]    ,						lowercase			:					int    ,						lowercase			:					Letter | TreeNode    ,						lowercase			:					Letter | TreeNode			):


				'''simple docstring'''


				_snake_case						=    freq
				_snake_case						=    left
				_snake_case						=    right






def  a_	( __lowercase     :			str				)				->						list[Letter]:
			_snake_case						=    {}
			with open(__lowercase				) as f:
						while True:
									_snake_case						=    f.read(1				)
									if not c:
												break
									_snake_case						=    chars[c] + 1 if c in chars else 1
			return sorted((Letter(__lowercase ,      __lowercase				) for c, f in chars.items()) ,      key=lambda __lowercase				: x.freq				)






def  a_	( __lowercase     :			list[Letter]				)				->						Letter | TreeNode:
			_snake_case						=    letters  # type: ignore
			while len(__lowercase				) > 1:
						_snake_case						=    response.pop(0				)
						_snake_case						=    response.pop(0				)
						_snake_case						=    left.freq + right.freq
						_snake_case						=    TreeNode(__lowercase ,      __lowercase ,      __lowercase				)
						response.append(__lowercase				)
						response.sort(key=lambda __lowercase				: x.freq				)
			return response[0]






def  a_	( __lowercase     :			Letter | TreeNode ,      __lowercase     :			str				)				->						list[Letter]:
			if isinstance(__lowercase ,      __lowercase				):
						_snake_case						=    bitstring
						return [root]
			_snake_case						=    root  # type: ignore
			_snake_case						=    []
			letters += traverse_tree(treenode.left ,      bitstring + '0'				)
			letters += traverse_tree(treenode.right ,      bitstring + '1'				)
			return letters






def  a_	( __lowercase     :			str				)				->						None:
			_snake_case						=    parse_file(__lowercase				)
			_snake_case						=    build_tree(__lowercase				)
			_snake_case						=    {
			    k: v for letter in traverse_tree(__lowercase ,      ''				) for k, v in letter.bitstring.items()
			}
			print(f'''Huffman Coding  of {file_path}: '''				)
			with open(__lowercase				) as f:
						while True:
									_snake_case						=    f.read(1				)
									if not c:
												break
									print(letters[c] ,      end=' '				)
			print()


if __name__ == "__main__":
			# pass the file path to the huffman function
			huffman(sys.argv[1])

import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(__lowercase ,      __lowercase ,      __lowercase				)

			# Save pytorch-model
			_snake_case						=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_snake_case						=    pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
			torch.save(model.state_dict() ,      __lowercase				)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''				)
			with open(__lowercase ,      'w' ,      encoding='utf-8'				) as f:
						f.write(config.to_json_string()				)


if __name__ == "__main__":
			_lowerCamelCase :       List[Any]     					=    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--imagegpt_checkpoint_path''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path to the TensorFlow checkpoint path.''',
			)
			parser.add_argument(
			    '''--model_size''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Size of the model (can be either \'small\', \'medium\' or \'large\').''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_lowerCamelCase :       Optional[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)