
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case      :							Tuple     	=    logging.get_logger(__name__)

_snake_case      :							Union[str, Any]     	=    {"vocab_file": "spm_char.model"}

_snake_case      :							Union[str, Any]     	=    {
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

_snake_case      :							Any     	=    {
    "microsoft/speecht5_asr": 10_24,
    "microsoft/speecht5_tts": 10_24,
    "microsoft/speecht5_vc": 10_24,
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  List[str]				   =					VOCAB_FILES_NAMES
						UpperCamelCase      :  Union[str, Any]				   =					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      :  Optional[Any]				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      :  Tuple				   =					['''input_ids''', '''attention_mask''']





						def __init__(			self							:str		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Tuple="<s>"		,    lowerCAmelCase__							:Tuple="</s>"		,    lowerCAmelCase__							:List[Any]="<unk>"		,    lowerCAmelCase__							:List[str]="<pad>"		,    lowerCAmelCase__							:Optional[Dict[str, Any]] = None		,    **lowerCAmelCase__							:Any		,    )  ->None:
										lowercase         =      {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=lowerCAmelCase__		,    eos_token=lowerCAmelCase__		,    unk_token=lowerCAmelCase__		,    pad_token=lowerCAmelCase__		,    sp_model_kwargs=self.sp_model_kwargs		,    **lowerCAmelCase__		,    )

										lowercase         =      vocab_file

										lowercase         =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
										self.sp_model.Load(lowerCAmelCase__     )





						@property
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[Any]:
										return self.sp_model.get_piece_size()





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Union[str, Any]:
										lowercase         =      {self.convert_ids_to_tokens(lowerCAmelCase__     ): i for i in range(self.vocab_size     )}
										vocab.update(self.added_tokens_encoder     )
										return vocab





						def __getstate__(			self							:Optional[int]     )  ->int:
										lowercase         =      self.__dict__.copy()
										lowercase         =      None
										return state





						def __setstate__(			self							:Tuple		,    lowerCAmelCase__							:Union[str, Any]     )  ->List[str]:
										lowercase         =      d

										# for backward compatibility
										if not hasattr(self		,    "sp_model_kwargs"     ):
														lowercase         =      {}

										lowercase         =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
										self.sp_model.Load(self.vocab_file     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:str     )  ->List[str]:
										return self.sp_model.encode(lowerCAmelCase__		,    out_type=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Union[str, Any]     )  ->Optional[int]:
										return self.sp_model.piece_to_id(lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:Optional[Any]     )  ->Union[str, Any]:
										lowercase         =      self.sp_model.IdToPiece(lowerCAmelCase__     )
										return token





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Union[str, Any]     )  ->Union[str, Any]:
										lowercase         =      []
										lowercase         =      ""
										for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																		out_string += self.sp_model.decode(lowerCAmelCase__     ) + token
																		lowercase         =      []
														else:
																		current_sub_tokens.append(lowerCAmelCase__     )
										out_string += self.sp_model.decode(lowerCAmelCase__     )
										return out_string.strip()





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Union[str, Any]=None     )  ->List[int]:
										if token_ids_a is None:
														return token_ids_a + [self.eos_token_id]
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return token_ids_a + token_ids_a + [self.eos_token_id]





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None		,    lowerCAmelCase__							:bool = False     )  ->List[int]:
										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowerCAmelCase__		,    token_ids_a=lowerCAmelCase__		,    already_has_special_tokens=lowerCAmelCase__     )

										lowercase         =      [1]
										if token_ids_a is None:
														return ([0] * len(lowerCAmelCase__     )) + suffix_ones
										return ([0] * len(lowerCAmelCase__     )) + ([0] * len(lowerCAmelCase__     )) + suffix_ones





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[str] = None     )  ->Tuple[str]:
										if not os.path.isdir(lowerCAmelCase__     ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
														return
										lowercase         =      os.path.join(
										    lowerCAmelCase__		,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

										if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowerCAmelCase__     ) and os.path.isfile(self.vocab_file     ):
														copyfile(self.vocab_file		,    lowerCAmelCase__     )
										elif not os.path.isfile(self.vocab_file     ):
														with open(lowerCAmelCase__		,    "wb"     ) as fi:
																		lowercase         =      self.sp_model.serialized_model_proto()
																		fi.write(lowerCAmelCase__     )

										return (out_vocab_file,)


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
_snake_case      :							Any     	=    False
_snake_case      :							int     	=    False

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				return TrainCommand(__magic_name__						)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:ArgumentParser     )  ->str:
										lowercase         =      parser.add_parser("train"		,    help="CLI tool to train a model on a task."     )

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

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





						def __init__(			self							:Optional[int]		,    lowerCAmelCase__							:Namespace     )  ->str:
										lowercase         =      logging.get_logger("transformers-cli/training"     )

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

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase__     )
										lowercase         =      args.output

										lowercase         =      args.column_label
										lowercase         =      args.column_text
										lowercase         =      args.column_id

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

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





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





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Dict:
										raise NotImplementedError





						def        SCREAMING_SNAKE_CASE(			self							: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     )
