





import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
     raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
__A     =    False
__A     =    False





def 					lowerCAmelCase_			(	__a		)					->		Any:







  """simple docstring"""





  return TrainCommand(_UpperCAmelCase		)






class        _SCREAMING_SNAKE_CASE						(     SCREAMING_SNAKE_CASE__   ):







 '''simple docstring'''

 @staticmethod
 def 							SCREAMING_SNAKE_CASE_ (UpperCAmelCase_    :       ArgumentParser)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =parser.add_parser("train"	,			help="CLI tool to train a model on a task.")

   train_parser.add_argument(
       "--train_data"	,			type=UpperCAmelCase_	,			required=UpperCAmelCase_	,			help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."	,			)
   train_parser.add_argument(
       "--column_label"	,			type=UpperCAmelCase_	,			default=0	,			help="Column of the dataset csv file with example labels.")
   train_parser.add_argument(
       "--column_text"	,			type=UpperCAmelCase_	,			default=1	,			help="Column of the dataset csv file with example texts.")
   train_parser.add_argument(
       "--column_id"	,			type=UpperCAmelCase_	,			default=2	,			help="Column of the dataset csv file with example ids.")
   train_parser.add_argument(
       "--skip_first_row"	,			action="store_true"	,			help="Skip the first row of the csv file (headers).")

   train_parser.add_argument("--validation_data"	,			type=UpperCAmelCase_	,			default=""	,			help="path to validation dataset.")
   train_parser.add_argument(
       "--validation_split"	,			type=UpperCAmelCase_	,			default=0.1	,			help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."	,			)

   train_parser.add_argument("--output"	,			type=UpperCAmelCase_	,			default="./"	,			help="path to saved the trained model.")

   train_parser.add_argument(
       "--task"	,			type=UpperCAmelCase_	,			default="text_classification"	,			help="Task to train the model on.")
   train_parser.add_argument(
       "--model"	,			type=UpperCAmelCase_	,			default="bert-base-uncased"	,			help="Model\'s name or path to stored model.")
   train_parser.add_argument("--train_batch_size"	,			type=UpperCAmelCase_	,			default=32	,			help="Batch size for training.")
   train_parser.add_argument("--valid_batch_size"	,			type=UpperCAmelCase_	,			default=64	,			help="Batch size for validation.")
   train_parser.add_argument("--learning_rate"	,			type=UpperCAmelCase_	,			default=3E-5	,			help="Learning rate.")
   train_parser.add_argument("--adam_epsilon"	,			type=UpperCAmelCase_	,			default=1E-0_8	,			help="Epsilon for Adam optimizer.")
   train_parser.set_defaults(func=UpperCAmelCase_)

 def __init__(self    :       str	,			UpperCAmelCase_    :       Namespace)	->int:



   '''simple docstring'''



   lowerCamelCase__:				str        =logging.get_logger("transformers-cli/training")

   lowerCamelCase__:				Dict        ='tf' if is_tf_available() else 'torch'

   os.makedirs(args.output	,			exist_ok=UpperCAmelCase_)
   lowerCamelCase__:				int        =args.output

   lowerCamelCase__:				Optional[int]        =args.column_label
   lowerCamelCase__:				Union[str, Any]        =args.column_text
   lowerCamelCase__:				List[str]        =args.column_id

   self.logger.info(F"""Loading {args.task} pipeline for {args.model}""")
   if args.task == "text_classification":
     lowerCamelCase__:				Any        =TextClassificationPipeline.from_pretrained(args.model)
   elif args.task == "token_classification":
     raise NotImplementedError
   elif args.task == "question_answering":
     raise NotImplementedError

   self.logger.info(F"""Loading dataset from {args.train_data}""")
   lowerCamelCase__:				Optional[int]        =Processor.create_from_csv(
       args.train_data	,			column_label=args.column_label	,			column_text=args.column_text	,			column_id=args.column_id	,			skip_first_row=args.skip_first_row	,			)
   lowerCamelCase__:				Dict        =None
   if args.validation_data:
     self.logger.info(F"""Loading validation dataset from {args.validation_data}""")
     lowerCamelCase__:				Optional[Any]        =Processor.create_from_csv(
         args.validation_data	,			column_label=args.column_label	,			column_text=args.column_text	,			column_id=args.column_id	,			skip_first_row=args.skip_first_row	,			)

   lowerCamelCase__:				Tuple        =args.validation_split
   lowerCamelCase__:				Union[str, Any]        =args.train_batch_size
   lowerCamelCase__:				str        =args.valid_batch_size
   lowerCamelCase__:				str        =args.learning_rate
   lowerCamelCase__:				Optional[int]        =args.adam_epsilon

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Tuple:



   '''simple docstring'''



   if self.framework == "tf":
     return self.run_tf()
   return self.run_torch()

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Dict:



   '''simple docstring'''



   raise NotImplementedError







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->int:



   '''simple docstring'''



   self.pipeline.fit(
       self.train_dataset	,			validation_data=self.valid_dataset	,			validation_split=self.validation_split	,			learning_rate=self.learning_rate	,			adam_epsilon=self.adam_epsilon	,			train_batch_size=self.train_batch_size	,			valid_batch_size=self.valid_batch_size	,			)

   # Save trained pipeline
   self.pipeline.save_pretrained(self.output)









from __future__ import annotations





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		List[Any]:







  """simple docstring"""





  print(F"""Vertex\tShortest Distance from vertex {src}"""		)
  for i, d in enumerate(__a		):
    print(F"""{i}\t\t{d}"""		)





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		Tuple:







  """simple docstring"""





  for j in range(__a		):
    lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				str        =(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_			(	__a     ,   __a     ,   __a     ,   __a		)					->		list[float]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =[float("inf"		)] * vertex_count
  lowerCamelCase__:				List[str]        =0.0

  for _ in range(vertex_count - 1		):
    for j in range(__a		):
      lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =(graph[j][k] for k in ["src", "dst", "weight"])

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

  lowerCamelCase__:				Tuple        =check_negative_cycle(__a     ,   __a     ,   __a		)
  if negative_cycle_exists:
    raise Exception("Negative cycle found"		)

  return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

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

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

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

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

