




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
SCREAMING_SNAKE_CASE__       :      Tuple  =   False
SCREAMING_SNAKE_CASE__       :      int  =   False

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Tuple:
       return TrainCommand(_SCREAMING_SNAKE_CASE    )




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    @staticmethod
    def     _lowercase     (    UpperCamelCase__     )     ->      Union[str, Any]:
           lowerCamelCase   :      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-08     ,  help="Epsilon for Adam optimizer."     )
           train_parser.set_defaults(func=UpperCamelCase__     )







    def __init__(    self     ,  UpperCamelCase__     )     ->      Optional[Any]:
           lowerCamelCase   :      List[Any]			 =					logging.get_logger("transformers-cli/training"     )

           lowerCamelCase   :      str			 =					"tf" if is_tf_available() else "torch"

           os.makedirs(args.output     ,  exist_ok=UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					args.output

           lowerCamelCase   :      List[Any]			 =					args.column_label
           lowerCamelCase   :      List[Any]			 =					args.column_text
           lowerCamelCase   :      List[Any]			 =					args.column_id

           self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''     )
           if args.task == "text_classification":
                  lowerCamelCase   :      Tuple			 =					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   :      Dict			 =					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   :      Optional[Any]			 =					None
           if args.validation_data:
                  self.logger.info(F'''Loading validation dataset from {args.validation_data}'''     )
                  lowerCamelCase   :      Dict			 =					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   :      Optional[int]			 =					args.validation_split
           lowerCamelCase   :      Dict			 =					args.train_batch_size
           lowerCamelCase   :      Union[str, Any]			 =					args.valid_batch_size
           lowerCamelCase   :      Any			 =					args.learning_rate
           lowerCamelCase   :      Optional[int]			 =					args.adam_epsilon







    def     _lowercase     (    self     )     ->      Any:
           if self.framework == "tf":
                  return self.run_tf()
           return self.run_torch()







    def     _lowercase     (    self     )     ->      Any:
           raise NotImplementedError







    def     _lowercase     (    self     )     ->      Any:
           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

_SCREAMING_SNAKE_CASE										=						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					None:
        _A        =  graph
        # mapping node to its parent in resulting breadth first tree
        _A        =  {}
        _A        =  source_vertex



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  {self.source_vertex}
        _A        =  None
        _A        =  [self.source_vertex]  # first in first out queue

        while queue:
               _A        =  queue.pop(0							)
               for adjacent_vertex in self.graph[vertex]:
                      if adjacent_vertex not in visited:
                             visited.add(lowerCAmelCase_							)
                             _A        =  vertex
                             queue.append(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if target_vertex == self.source_vertex:
               return self.source_vertex

        _A        =  self.parent.get(lowerCAmelCase_							)
        if target_vertex_parent is None:
               _A        =  (
                   F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
               )
               raise ValueError(lowerCAmelCase_							)

        return self.shortest_path(lowerCAmelCase_							) + F'''->{target_vertex}'''


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))




