'''simple docstring'''

from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
							from ..models.auto import AutoTokenizer
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[Any]:
								raise NotImplementedError()






		def 		UpperCamelCase__       (   self				) ->     str:
								raise NotImplementedError()

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A = False		,						**__A				) ->     Dict:
								_lowerCAmelCase										=tokenizer
								_lowerCAmelCase										=skip_prompt
								_lowerCAmelCase										=decode_kwargs

								# variables used in the streaming process
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=0
								_lowerCAmelCase										=True






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:
								if len(value.shape				) > 1 and value.shape[0] > 1:
														raise ValueError('TextStreamer only supports batch size 1'				)
								elif len(value.shape				) > 1:
														_lowerCAmelCase										=value[0]

								if self.skip_prompt and self.next_tokens_are_prompt:
														_lowerCAmelCase										=False
														return

								# Add the new token to the cache and decodes the entire thing.
								self.token_cache.extend(value.tolist()				)
								_lowerCAmelCase										=self.tokenizer.decode(self.token_cache		,						**self.decode_kwargs				)

								# After the symbol for a new line, we flush the cache.
								if text.endswith('\n'				):
														_lowerCAmelCase										=text[self.print_len :]
														_lowerCAmelCase										=[]
														_lowerCAmelCase										=0
								# If the last token is a CJK character, we print the characters.
								elif len(__A				) > 0 and self._is_chinese_char(ord(text[-1]				)				):
														_lowerCAmelCase										=text[self.print_len :]
														self.print_len += len(__A				)
								# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
								# which may change with the subsequent token -- there are probably smarter ways to do this!)
								else:
														_lowerCAmelCase										=text[self.print_len : text.rfind(' '				) + 1]
														self.print_len += len(__A				)

								self.on_finalized_text(__A				)






		def 		UpperCamelCase__       (   self				) ->     int:
								# Flush the cache, if it exists
								if len(self.token_cache				) > 0:
														_lowerCAmelCase										=self.tokenizer.decode(self.token_cache		,						**self.decode_kwargs				)
														_lowerCAmelCase										=text[self.print_len :]
														_lowerCAmelCase										=[]
														_lowerCAmelCase										=0
								else:
														_lowerCAmelCase										=''

								_lowerCAmelCase										=True
								self.on_finalized_text(__A		,						stream_end=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A = False				) ->     Tuple:
								print(__A		,						flush=__A		,						end='' if not stream_end else None				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     str:
								# This defines a "chinese character" as anything in the CJK Unicode block:
								#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
								#
								# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
								# despite its name. The modern Korean Hangul alphabet is a different block,
								# as is Japanese Hiragana and Katakana. Those alphabets are used to write
								# space-separated words, so they are not treated specially and handled
								# like the all of the other languages.
								if (
								    (cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
								    or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
								    or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
								    or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
								    or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
								    or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
								    or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
								    or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
								):  #
														return True

								return False

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A = False		,						__A = None		,						**__A				) ->     List[Any]:
								super().__init__(__A		,						__A		,						**__A				)
								_lowerCAmelCase										=Queue()
								_lowerCAmelCase										=None
								_lowerCAmelCase										=timeout






		def 		UpperCamelCase__       (   self		,						__A		,						__A = False				) ->     Any:
								self.text_queue.put(__A		,						timeout=self.timeout				)
								if stream_end:
														self.text_queue.put(self.stop_signal		,						timeout=self.timeout				)






		def __iter__(   self				) ->     Optional[int]:
								return self






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=self.text_queue.get(timeout=self.timeout				)
								if value == self.stop_signal:
														raise StopIteration()
								else:
														return value






'''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
lowercase_     		= False
lowercase_     		= False



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return TrainCommand(a__      )


class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@staticmethod
		def 		UpperCamelCase__       (   __A				) ->     Tuple:
								_lowerCAmelCase										=parser.add_parser('train'		,						help='CLI tool to train a model on a task.'				)

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

								train_parser.add_argument(
								    '--task'		,						type=__A		,						default='text_classification'		,						help='Task to train the model on.'				)
								train_parser.add_argument(
								    '--model'		,						type=__A		,						default='bert-base-uncased'		,						help='Model\'s name or path to stored model.'				)
								train_parser.add_argument('--train_batch_size'		,						type=__A		,						default=32		,						help='Batch size for training.'				)
								train_parser.add_argument('--valid_batch_size'		,						type=__A		,						default=64		,						help='Batch size for validation.'				)
								train_parser.add_argument('--learning_rate'		,						type=__A		,						default=3E-5		,						help='Learning rate.'				)
								train_parser.add_argument('--adam_epsilon'		,						type=__A		,						default=1E-08		,						help='Epsilon for Adam optimizer.'				)
								train_parser.set_defaults(func=__A				)






		def __init__(   self		,						__A				) ->     List[str]:
								_lowerCAmelCase										=logging.get_logger('transformers-cli/training'				)

								_lowerCAmelCase										='tf' if is_tf_available() else 'torch'

								os.makedirs(args.output		,						exist_ok=__A				)
								_lowerCAmelCase										=args.output

								_lowerCAmelCase										=args.column_label
								_lowerCAmelCase										=args.column_text
								_lowerCAmelCase										=args.column_id

								self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''				)
								if args.task == "text_classification":
														_lowerCAmelCase										=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										=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										=None
								if args.validation_data:
														self.logger.info(F'''Loading validation dataset from {args.validation_data}'''				)
														_lowerCAmelCase										=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										=args.validation_split
								_lowerCAmelCase										=args.train_batch_size
								_lowerCAmelCase										=args.valid_batch_size
								_lowerCAmelCase										=args.learning_rate
								_lowerCAmelCase										=args.adam_epsilon






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								if self.framework == "tf":
														return self.run_tf()
								return self.run_torch()






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								raise NotImplementedError






		def 		UpperCamelCase__       (   self				) ->     List[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				)





