






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
lowerCamelCase     :							List[Any]    =False
lowerCamelCase     :							Union[str, Any]    =False







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Union[str, Any]:
     return TrainCommand(__lowerCAmelCase      )





class       __a  (   A__	):







 @staticmethod
 def  __lowercase (		SCREAMING_SNAKE_CASE :       ArgumentParser						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  parser.add_parser("train"							,       help="CLI tool to train a model on a task."						)

      train_parser.add_argument(
          "--train_data"							,       type=SCREAMING_SNAKE_CASE							,       required=SCREAMING_SNAKE_CASE							,       help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."							,       )
      train_parser.add_argument(
          "--column_label"							,       type=SCREAMING_SNAKE_CASE							,       default=0							,       help="Column of the dataset csv file with example labels."						)
      train_parser.add_argument(
          "--column_text"							,       type=SCREAMING_SNAKE_CASE							,       default=1							,       help="Column of the dataset csv file with example texts."						)
      train_parser.add_argument(
          "--column_id"							,       type=SCREAMING_SNAKE_CASE							,       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=SCREAMING_SNAKE_CASE							,       default=""							,       help="path to validation dataset."						)
      train_parser.add_argument(
          "--validation_split"							,       type=SCREAMING_SNAKE_CASE							,       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=SCREAMING_SNAKE_CASE							,       default="./"							,       help="path to saved the trained model."						)

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







 def __init__(		self :       str							,       SCREAMING_SNAKE_CASE :       Namespace						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  logging.get_logger("transformers-cli/training"						)

      UpperCamelCase__      :       List[Any]	    =  "tf" if is_tf_available() else "torch"

      os.makedirs(args.output							,       exist_ok=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Tuple	    =  args.output

      UpperCamelCase__      :       Union[str, Any]	    =  args.column_label
      UpperCamelCase__      :       List[Any]	    =  args.column_text
      UpperCamelCase__      :       Tuple	    =  args.column_id

      self.logger.info(F'Loading {args.task} pipeline for {args.model}'						)
      if args.task == "text_classification":
           UpperCamelCase__      :       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}'						)
      UpperCamelCase__      :       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							,       )
      UpperCamelCase__      :       List[str]	    =  None
      if args.validation_data:
           self.logger.info(F'Loading validation dataset from {args.validation_data}'						)
           UpperCamelCase__      :       Optional[int]	    =  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							,       )

      UpperCamelCase__      :       str	    =  args.validation_split
      UpperCamelCase__      :       List[str]	    =  args.train_batch_size
      UpperCamelCase__      :       List[str]	    =  args.valid_batch_size
      UpperCamelCase__      :       Any	    =  args.learning_rate
      UpperCamelCase__      :       Optional[int]	    =  args.adam_epsilon







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






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







 def  __lowercase (		self :       int						):


      '''simple docstring'''






      raise NotImplementedError







 def  __lowercase (		self :       Any						):


      '''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						)







lowerCamelCase     :							Optional[int]    ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     list[str]:
     UpperCamelCase__      :       Optional[Any]	    =  set()
     # keep track of all the paths to be checked
     UpperCamelCase__      :       Optional[Any]	    =  [[start]]

     # return path if start is goal
     if start == goal:
          return [start]

     # keeps looping until all possible paths have been checked
     while queue:
          # pop the first path from the queue
          UpperCamelCase__      :       int	    =  queue.pop(0      )
          # get the last node from the path
          UpperCamelCase__      :       Dict	    =  path[-1]
          if node not in explored:
               UpperCamelCase__      :       Tuple	    =  graph[node]
               # go through all neighbour nodes, construct a new path and
               # push it into the queue
               for neighbour in neighbours:
                    UpperCamelCase__      :       List[str]	    =  list(__lowerCAmelCase      )
                    new_path.append(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    # return path if neighbour is goal
                    if neighbour == goal:
                         return new_path

            # mark node as explored
               explored.add(__lowerCAmelCase      )

    # in case there's no path between the 2 nodes
     return []







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     int:
     if not graph or start not in graph or target not in graph:
          return -1
     if start == target:
          return 0
     UpperCamelCase__      :       Tuple	    =  [start]
     UpperCamelCase__      :       Optional[int]	    =  set(__lowerCAmelCase      )
     # Keep tab on distances from `start` node.
     UpperCamelCase__      :       str	    =  {start: 0, target: -1}
     while queue:
          UpperCamelCase__      :       Any	    =  queue.pop(0      )
          if node == target:
               UpperCamelCase__      :       Union[str, Any]	    =  (
                   dist[node] if dist[target] == -1 else min(dist[target] ,   dist[node]      )
               )
          for adjacent in graph[node]:
               if adjacent not in visited:
                    visited.add(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    UpperCamelCase__      :       List[Any]	    =  dist[node] + 1
     return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4