



import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
A__       =			logging.getLogger(__name__)


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					np.random.seed(args.seed     )
					torch.manual_seed(args.seed     )
					if args.n_gpu > 0:
										torch.cuda.manual_seed_all(args.seed     )


def 						_UpperCAmelCase							( ):





					"""simple docstring"""

					_lowerCAmelCase         =       argparse.ArgumentParser()
					parser.add_argument(
					    """--model_name_or_path"""      ,		default=snake_case      ,		type=snake_case      ,		required=snake_case      ,		)
					parser.add_argument("""--prompt"""      ,		type=snake_case      ,		default=""""""     )
					parser.add_argument("""--length"""      ,		type=snake_case      ,		default=20     )
					parser.add_argument("""--stop_token"""      ,		type=snake_case      ,		default=snake_case      ,		help="""Token at which text generation is stopped"""     )
					parser.add_argument(
					    """--temperature"""      ,		type=snake_case      ,		default=1.0      ,		help="""temperature of 1.0 has no effect, lower tend toward greedy sampling"""      ,		)
					parser.add_argument(
					    """--repetition_penalty"""      ,		type=snake_case      ,		default=1.0      ,		help="""primarily useful for CTRL model; in that case, use 1.2"""     )
					parser.add_argument("""--k"""      ,		type=snake_case      ,		default=0     )
					parser.add_argument("""--penalty_alpha"""      ,		type=snake_case      ,		default=0.0     )
					parser.add_argument("""--p"""      ,		type=snake_case      ,		default=0.9     )

					parser.add_argument("""--prefix"""      ,		type=snake_case      ,		default=""""""      ,		help="""Text added prior to input."""     )
					parser.add_argument("""--padding_text"""      ,		type=snake_case      ,		default=""""""      ,		help="""Deprecated, the use of `--prefix` is preferred."""     )
					parser.add_argument("""--xlm_language"""      ,		type=snake_case      ,		default=""""""      ,		help="""Optional language when used with the XLM model."""     )

					parser.add_argument("""--seed"""      ,		type=snake_case      ,		default=42      ,		help="""random seed for initialization"""     )
					parser.add_argument("""--no_cuda"""      ,		action="""store_true"""      ,		help="""Avoid using CUDA when available"""     )
					parser.add_argument(
					    """--fp16"""      ,		action="""store_true"""      ,		help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""      ,		)
					_lowerCAmelCase         =       parser.parse_args()

					_lowerCAmelCase         =       torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""     )
					_lowerCAmelCase         =       0 if args.no_cuda else torch.cuda.device_count()

					logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'     )

					set_seed(snake_case     )

					# Initialize the model and tokenizer
					_lowerCAmelCase         =       AutoTokenizer.from_pretrained(args.model_name_or_path     )
					_lowerCAmelCase         =       AutoModelForCausalLM.from_pretrained(args.model_name_or_path     )

					# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
					# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
					model.to(args.device     )

					if args.fpaa:
										model.half()

					logger.info(snake_case     )
					_lowerCAmelCase         =       args.prompt if args.prompt else input("""Model prompt >>> """     )

					_lowerCAmelCase         =       tokenizer(snake_case      ,		return_tensors="""pt"""      ,		add_special_tokens=snake_case     )
					_lowerCAmelCase         =       {key: value.to(args.device     ) for key, value in inputs.items()}

					_lowerCAmelCase         =       model.generate(
					    **snake_case      ,		max_length=args.length + len(inputs["""input_ids"""][0]     )      ,		penalty_alpha=args.penalty_alpha      ,		top_k=args.k      ,		)

					_lowerCAmelCase         =       []
					for generated_sequence_idx, generated_sequence in enumerate(snake_case     ):
										print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='     )
										_lowerCAmelCase         =       generated_sequence.tolist()

										# Decode text
										_lowerCAmelCase         =       tokenizer.decode(snake_case      ,		clean_up_tokenization_spaces=snake_case      ,		add_special_tokens=snake_case     )

										# Remove all text after the stop token
										_lowerCAmelCase         =       text[: text.find(args.stop_token     ) if args.stop_token else None]

										# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
										_lowerCAmelCase         =       (
										    prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0]      ,		clean_up_tokenization_spaces=snake_case     )     ) :]
										)

										generated_sequences.append(snake_case     )
										print(snake_case     )

					return generated_sequences


if __name__ == "__main__":
	main()









def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if not root or root not in binary_tree_mirror_dictionary:
    return
  a_    , a_		    =  binary_tree_mirror_dictionary[root][:2]
  a_		    =  [right_child, left_child]
  binary_tree_mirror_dict(_A      ,      _A			)
  binary_tree_mirror_dict(_A      ,      _A			)






def 				UpperCAmelCase__  (							_A      ,      _A = 1			):






  """simple docstring"""


  if not binary_tree:
    raise ValueError('''binary tree cannot be empty'''			)
  if root not in binary_tree:
    a_		    =  f"root {root} is not present in the binary_tree"
    raise ValueError(_A			)
  a_		    =  dict(_A			)
  binary_tree_mirror_dict(_A      ,      _A			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  UpperCamelCase__       				=	{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F"""Binary tree: {binary_tree}""")
  UpperCamelCase__       				=	binary_tree_mirror(binary_tree, 5)
  print(F"""Binary tree mirror: {binary_tree_mirror_dictionary}""")
