
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


SCREAMING_SNAKE_CASE :		Dict       			=							logging.get_logger(__name__)


SCREAMING_SNAKE_CASE :		List[str]       			=							R"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"






class        __lowercase		(       _a							):


					@add_start_docstrings(A_    )
					def __call__(						self							,							a__							,							a__							,							**a__    )		->				Optional[Any]:





									'''simple docstring'''
									raise NotImplementedError('''StoppingCriteria needs to be subclassed'''    )






class        __lowercase		(       _a							):


					def __init__(						self							,							a__							,							a__ = None    )		->				Optional[Any]:





									'''simple docstring'''
									A_													=	max_length
									A_													=	max_position_embeddings


					@add_start_docstrings(A_    )
					def __call__(						self							,							a__							,							a__							,							**a__    )		->				str:





									'''simple docstring'''
									A_													=	input_ids.shape[-1]
									A_													=	cur_len >= self.max_length
									if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
													logger.warning_once(
													    '''This is a friendly reminder - the current text generation call will exceed the model\'s predefined '''
													    F"maximum length ({self.max_position_embeddings}). Depending on the model, you may observe "
													    '''exceptions, performance degradation, or nothing at all.'''    )
									return is_done






class        __lowercase		(       _a							):


					def __init__(						self							,							a__							,							a__    )		->				Optional[Any]:





									'''simple docstring'''
									warnings.warn(
									    '''The class `MaxNewTokensCriteria` is deprecated. '''
									    F"Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` "
									    '''with `max_length = start_length + max_new_tokens` instead.'''							,							A_							,							)
									A_													=	start_length
									A_													=	max_new_tokens
									A_													=	start_length + max_new_tokens


					@add_start_docstrings(A_    )
					def __call__(						self							,							a__							,							a__							,							**a__    )		->				Any:





									'''simple docstring'''
									return input_ids.shape[-1] >= self.max_length






class        __lowercase		(       _a							):


					def __init__(						self							,							a__							,							a__ = None    )		->				str:





									'''simple docstring'''
									A_													=	max_time
									A_													=	time.time() if initial_timestamp is None else initial_timestamp


					@add_start_docstrings(A_    )
					def __call__(						self							,							a__							,							a__							,							**a__    )		->				Optional[int]:





									'''simple docstring'''
									return time.time() - self.initial_timestamp > self.max_time






class        __lowercase		(       _a							):


					@add_start_docstrings(A_    )
					def __call__(						self							,							a__							,							a__							,							**a__    )		->				int:





									'''simple docstring'''
									return any(criteria(A_							,							A_    ) for criteria in self    )


					@property
					def 							lowerCAmelCase_     (						self    )		->				int:





									'''simple docstring'''


									for stopping_criterium in self:
													if isinstance(A_							,							A_    ):
																	return stopping_criterium.max_length
													elif isinstance(A_							,							A_    ):
																	return stopping_criterium.max_length
									return None



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase  ):
				A_													=	stopping_criteria.max_length
				A_													=	deepcopy(UpperCamelCase__  )
				if stopping_max_length is not None and stopping_max_length != max_length:
								warnings.warn('''You set different `max_length` for stopping criteria and `max_length` parameter'''			,    UpperCamelCase__  )
				elif stopping_max_length is None:
								new_stopping_criteria.append(MaxLengthCriteria(max_length=UpperCamelCase__  )  )
				return new_stopping_criteria



"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_   (					UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			Any			):






 """simple docstring"""







 print(f'''Vertex\tShortest Distance from vertex {src}'''			)
 for i, d in enumerate(UpperCamelCase__			):
  print(f'''{i}\t\t{d}'''			)


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 for j in range(UpperCamelCase__			):
  __lowercase				,		__lowercase				,		__lowercase						=						(graph[j][k] for k in ["""src""", """dst""", """weight"""])
  if distance[u] != float("""inf"""			) and distance[u] + w < distance[v]:
   return True
 return False


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						[float("""inf"""			)] * vertex_count
 __lowercase						=						0.0

 for _ in range(vertex_count - 1			):
  for j in range(UpperCamelCase__			):
   __lowercase				,		__lowercase				,		__lowercase						=						(graph[j][k] for k in ["""src""", """dst""", """weight"""])

   if distance[u] != float("""inf"""			) and distance[u] + w < distance[v]:
    __lowercase						=						distance[u] + w

 __lowercase						=						check_negative_cycle(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
 if negative_cycle_exists:
  raise Exception("""Negative cycle found"""			)

 return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       UpperCAmelCase__					=int(input("Enter number of vertices: ").strip())
       UpperCAmelCase__					=int(input("Enter number of edges: ").strip())

       UpperCAmelCase__					=[{} for _ in range(E)]

       for i in range(E):
              print("Edge ", i + 1)
              UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__					=(
                  int(x)
                  for x in input("Enter source, destination, weight: ").strip().split(" ")
              )
              UpperCAmelCase__					={"src": src, "dst": dest, "weight": weight}

       UpperCAmelCase__					=int(input("\nEnter shortest path source:").strip())
       UpperCAmelCase__					=bellman_ford(graph, V, E, source)
       print_distance(shortest_distance, 0)
