


"""simple docstring"""







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__    :							List[Any]            =					False
a__    :							Union[str, Any]            =					False




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					return TrainCommand(lowerCAmelCase_       )




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	def   UpperCAmelCase_				(			UpperCAmelCase__      :    ArgumentParser       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    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=3_2      ,      help="Batch size for training."       )
						train_parser.add_argument("--valid_batch_size"      ,      type=UpperCAmelCase__      ,      default=6_4      ,      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      :    Union[str, Any]      ,      UpperCAmelCase__      :    Namespace       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    logging.get_logger("transformers-cli/training"       )

						__SCREAMING_SNAKE_CASE						       =    "tf" if is_tf_available() else "torch"

						os.makedirs(args.output      ,      exist_ok=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    args.output

						__SCREAMING_SNAKE_CASE						       =    args.column_label
						__SCREAMING_SNAKE_CASE						       =    args.column_text
						__SCREAMING_SNAKE_CASE						       =    args.column_id

						self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""       )
						if args.task == "text_classification":
											__SCREAMING_SNAKE_CASE						       =    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}"""       )
						__SCREAMING_SNAKE_CASE						       =    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      ,      )
						__SCREAMING_SNAKE_CASE						       =    None
						if args.validation_data:
											self.logger.info(F"""Loading validation dataset from {args.validation_data}"""       )
											__SCREAMING_SNAKE_CASE						       =    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      ,      )

						__SCREAMING_SNAKE_CASE						       =    args.validation_split
						__SCREAMING_SNAKE_CASE						       =    args.train_batch_size
						__SCREAMING_SNAKE_CASE						       =    args.valid_batch_size
						__SCREAMING_SNAKE_CASE						       =    args.learning_rate
						__SCREAMING_SNAKE_CASE						       =    args.adam_epsilon




	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     List[Any]:
						if self.framework == "tf":
											return self.run_tf()
						return self.run_torch()




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Dict:
						raise NotImplementedError




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




"""simple docstring"""







from __future__ import annotations




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					for j in range(lowerCAmelCase_       ):
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    (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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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

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

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

					__SCREAMING_SNAKE_CASE						       =    check_negative_cycle(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
					if negative_cycle_exists:
										raise Exception("Negative cycle found"       )

					return distance


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a__    :							Union[str, Any]            =					int(input('''Enter number of vertices: ''').strip())
		a__    :							Any            =					int(input('''Enter number of edges: ''').strip())

		a__    :							list[dict[str, int]]            =					[{} for _ in range(E)]

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

		a__    :							str            =					int(input('''\nEnter shortest path source:''').strip())
		a__    :							List[Any]            =					bellman_ford(graph, V, E, source)
		print_distance(shortest_distance, 0)
