



'''simple docstring'''






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,
)
lowerCamelCase__	  =		logging.getLogger(__name__)





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     str  )		->List[Any]:






							'''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 				_SCREAMING_SNAKE_CASE(					)		->Any:






							'''simple docstring'''
							_lowercase							:		Optional[Any]				=   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''' ,       )
							_lowercase							:		Optional[Any]				=   parser.parse_args()

							_lowercase							:		List[Any]				=   torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''  )
							_lowercase							:		Union[str, Any]				=   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
							_lowercase							:		Optional[int]				=   AutoTokenizer.from_pretrained(args.model_name_or_path  )
							_lowercase							:		str				=   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_  )
							_lowercase							:		Union[str, Any]				=   args.prompt if args.prompt else input('''Model prompt >>> '''  )

							_lowercase							:		Optional[int]				=   tokenizer(snake_case_ ,       return_tensors='''pt''' ,       add_special_tokens=snake_case_  )
							_lowercase							:		Optional[Any]				=   {key: value.to(args.device  ) for key, value in inputs.items()}

							_lowercase							:		Optional[int]				=   model.generate(
							    **snake_case_ ,       max_length=args.length + len(inputs['''input_ids'''][0]  ) ,       penalty_alpha=args.penalty_alpha ,       top_k=args.k ,       )

							_lowercase							:		Tuple				=   []
							for generated_sequence_idx, generated_sequence in enumerate(snake_case_  ):
														print(F"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="  )
														_lowercase							:		Any				=   generated_sequence.tolist()

														# Decode text
														_lowercase							:		Tuple				=   tokenizer.decode(snake_case_ ,       clean_up_tokenization_spaces=snake_case_ ,       add_special_tokens=snake_case_  )

														# Remove all text after the stop token
														_lowercase							:		Any				=   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
														_lowercase							:		Optional[Any]				=   (
														    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()











"""simple docstring"""





UpperCamelCase					=     float("""inf""")







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_)			->   List[str]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     [0] * n
          lowercase__:							Dict         =     [0] * n
          lowercase__:							List[Any]         =     [0] * n
          lowercase__:							str         =     [[] for _ in range(UpperCAmelCase_)]





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_=0)			->   Optional[int]:




          '''simple docstring'''




          self.adj[a].append([b, len(self.adj[b]), c, 0])
          self.adj[b].append([a, len(self.adj[a]) - 1, rcap, 0])





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   Union[str, Any]:




          '''simple docstring'''




          if vertex == sink or not flow:
                 return flow

          for i in range(self.ptr[vertex]					, len(self.adj[vertex])):
                 lowercase__:							Optional[Any]         =     self.adj[vertex][i]
                 if self.lvl[e[0]] == self.lvl[vertex] + 1:
                        lowercase__:							Optional[int]         =     self.depth_first_search(e[0]					, UpperCAmelCase_					, min(UpperCAmelCase_					, e[2] - e[3]))
                        if p:
                               self.adj[vertex][i][3] += p
                               self.adj[e[0]][e[1]][3] -= p
                               return p
                 lowercase__:							int         =     self.ptr[vertex] + 1
          return 0





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__    ,      lowercase__:							Union[str, Any]         =     0, source
          for l in range(31):  # noqa: E741  l = 30 maybe faster for random data
                 while True:
                        lowercase__    ,      lowercase__:							Union[str, Any]         =     [0] * len(self.q), [0] * len(self.q)
                        lowercase__    ,      lowercase__    ,      lowercase__:							str         =     0, 1, 1
                        while qi < qe and not self.lvl[sink]:
                               lowercase__:							Tuple         =     self.q[qi]
                               qi += 1
                               for e in self.adj[v]:
                                      if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
                                             lowercase__:							Optional[int]         =     e[0]
                                             qe += 1
                                             lowercase__:							str         =     self.lvl[v] + 1

                        lowercase__:							List[str]         =     self.depth_first_search(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)
                        while p:
                               flow += p
                               lowercase__:							Any         =     self.depth_first_search(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)

                        if not self.lvl[sink]:
                               break

          return flow
UpperCamelCase					=     Dinic(10)
UpperCamelCase					=     0
UpperCamelCase					=     9
for vertex in range(1, 5):
  graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
  graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
  graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))






