




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__A    :				Optional[int]    						=	logging.getLogger(__name__)




@dataclass
class       UpperCAmelCase_   :




							'''simple docstring'''



							a__    	=  field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}							)
							a__    	=  field(
							    default=__SCREAMING_SNAKE_CASE		,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}							)
							a__    	=  field(
							    default=__SCREAMING_SNAKE_CASE		,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}							)
							a__    	=  field(
							    default=__SCREAMING_SNAKE_CASE		,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,   )





@dataclass
class       UpperCAmelCase_   :




							'''simple docstring'''



							a__    	=  field(
							    metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()							)}							)
							a__    	=  field(
							    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}							)
							a__    	=  field(
							    default=1_2_8		,   metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }		,   )
							a__    	=  field(
							    default=__SCREAMING_SNAKE_CASE		,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}							)





def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       default_data_collator(_UpperCamelCase )
			SCREAMING_SNAKE_CASE     =       batch.pop("""pairID"""       ,						_UpperCamelCase )
			return batch
def        lowerCamelCase_     (     ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
			SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir )
			    and os.listdir(training_args.output_dir )
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
						    """ --overwrite_output_dir to overcome.""" )

			# Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""       ,						datefmt="""%m/%d/%Y %H:%M:%S"""       ,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,						)
			logger.warning(
			    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""       ,						training_args.local_rank       ,						training_args.device       ,						training_args.n_gpu       ,						bool(training_args.local_rank != -1 )       ,						training_args.fpaa       ,						)
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank ):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info("""Training/evaluation parameters %s"""       ,						_UpperCamelCase )

			# Set seed
			set_seed(training_args.seed )

			try:
						SCREAMING_SNAKE_CASE     =       hans_tasks_num_labels[data_args.task_name]
			except KeyError:
						raise ValueError("""Task not found: %s""" % (data_args.task_name) )

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			SCREAMING_SNAKE_CASE     =       AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,						num_labels=_UpperCamelCase       ,						finetuning_task=data_args.task_name       ,						cache_dir=model_args.cache_dir       ,						)
			SCREAMING_SNAKE_CASE     =       AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,						cache_dir=model_args.cache_dir       ,						)
			SCREAMING_SNAKE_CASE     =       AutoModelForSequenceClassification.from_pretrained(
			    model_args.model_name_or_path       ,						from_tf=bool(""".ckpt""" in model_args.model_name_or_path )       ,						config=_UpperCamelCase       ,						cache_dir=model_args.cache_dir       ,						)

			# Get datasets
			SCREAMING_SNAKE_CASE     =       (
			    HansDataset(
			        data_dir=data_args.data_dir       ,						tokenizer=_UpperCamelCase       ,						task=data_args.task_name       ,						max_seq_length=data_args.max_seq_length       ,						overwrite_cache=data_args.overwrite_cache       ,						)
			    if training_args.do_train
			    else None
			)
			SCREAMING_SNAKE_CASE     =       (
			    HansDataset(
			        data_dir=data_args.data_dir       ,						tokenizer=_UpperCamelCase       ,						task=data_args.task_name       ,						max_seq_length=data_args.max_seq_length       ,						overwrite_cache=data_args.overwrite_cache       ,						evaluate=_UpperCamelCase       ,						)
			    if training_args.do_eval
			    else None
			)

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE     =       Trainer(
			    model=_UpperCamelCase       ,						args=_UpperCamelCase       ,						train_dataset=_UpperCamelCase       ,						eval_dataset=_UpperCamelCase       ,						data_collator=_UpperCamelCase       ,						)

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_master():
									tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
			if training_args.do_eval:
						logger.info("""*** Evaluate ***""" )

						SCREAMING_SNAKE_CASE     =       trainer.predict(_UpperCamelCase )
						SCREAMING_SNAKE_CASE     =       output.predictions
						SCREAMING_SNAKE_CASE     =       np.argmax(_UpperCamelCase       ,						axis=1 )

						SCREAMING_SNAKE_CASE     =       [ex.pairID for ex in eval_dataset]
						SCREAMING_SNAKE_CASE     =       os.path.join(training_args.output_dir       ,						"""hans_predictions.txt""" )
						SCREAMING_SNAKE_CASE     =       eval_dataset.get_labels()
						if trainer.is_world_master():
									with open(_UpperCamelCase       ,						"""w""" ) as writer:
												writer.write("""pairID,gold_label\n""" )
												for pid, pred in zip(_UpperCamelCase       ,						_UpperCamelCase ):
															writer.write("""ex""" + str(_UpperCamelCase ) + """,""" + label_list[int(_UpperCamelCase )] + """\n""" )

						trainer._log(output.metrics )
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			main()


if __name__ == "__main__":
		main()










"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__a      			= logging.get_logger(__name__)

__a      			= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a      			= {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

__a      			= {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

__a      			= {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE              =   PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE              =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE              =   ["""input_ids""", """attention_mask""", """token_type_ids"""]
							SCREAMING_SNAKE_CASE              =   DebertaTokenizer






							def __init__(    self			:  Dict		,			lowerCamelCase_			:  Dict=None		,			lowerCamelCase_			:  Union[str, Any]=None		,			lowerCamelCase_			:  Tuple=None		,			lowerCamelCase_			:  Dict="replace"		,			lowerCamelCase_			:  List[Any]="[CLS]"		,			lowerCamelCase_			:  Optional[Any]="[SEP]"		,			lowerCamelCase_			:  List[Any]="[SEP]"		,			lowerCamelCase_			:  str="[CLS]"		,			lowerCamelCase_			:  List[str]="[UNK]"		,			lowerCamelCase_			:  Any="[PAD]"		,			lowerCamelCase_			:  Tuple="[MASK]"		,			lowerCamelCase_			:  List[str]=False		,			**lowerCamelCase_			:  List[str]		,			):
													super().__init__(
													    lowerCamelCase_		,			lowerCamelCase_		,			tokenizer_file=lowerCamelCase_		,			errors=lowerCamelCase_		,			bos_token=lowerCamelCase_		,			eos_token=lowerCamelCase_		,			unk_token=lowerCamelCase_		,			sep_token=lowerCamelCase_		,			cls_token=lowerCamelCase_		,			pad_token=lowerCamelCase_		,			mask_token=lowerCamelCase_		,			add_prefix_space=lowerCamelCase_		,			**lowerCamelCase_		,			)
													UpperCAmelCase__												=     kwargs.pop('''add_bos_token'''		,			lowerCamelCase_					)

													UpperCAmelCase__												=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
													if pre_tok_state.get('''add_prefix_space'''		,			lowerCamelCase_					) != add_prefix_space:
																			UpperCAmelCase__												=     getattr(lowerCamelCase_		,			pre_tok_state.pop('''type'''					)					)
																			UpperCAmelCase__												=     add_prefix_space
																			UpperCAmelCase__												=     pre_tok_class(**lowerCamelCase_					)

													UpperCAmelCase__												=     add_prefix_space






							@property
							def        lowerCAmelCase  (    self			:  int					):
													if self._mask_token is None:
																			if self.verbose:
																									logger.error('''Using mask_token, but it is not set yet.'''					)
																			return None
													return str(self._mask_token					)






							@mask_token.setter
							def        lowerCAmelCase  (    self			:  Union[str, Any]		,			lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     AddedToken(lowerCamelCase_		,			lstrip=lowerCamelCase_		,			rstrip=lowerCamelCase_					) if isinstance(lowerCamelCase_		,			lowerCamelCase_					) else value
													UpperCAmelCase__												=     value






							def        lowerCAmelCase  (    self			:  int		,			lowerCamelCase_			:  List[int]		,			lowerCamelCase_			:  Optional[List[int]] = None					):
													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													UpperCAmelCase__												=     [self.cls_token_id]
													UpperCAmelCase__												=     [self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  List[int]		,			lowerCamelCase_			:  Optional[List[int]] = None					):
													UpperCAmelCase__												=     [self.sep_token_id]
													UpperCAmelCase__												=     [self.cls_token_id]

													if token_ids_a is None:
																			return len(cls + token_ids_a + sep					) * [0]
													return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






							def        lowerCAmelCase  (    self			:  Tuple		,			*lowerCamelCase_			:  Optional[int]		,			**lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     kwargs.get('''is_split_into_words'''		,			lowerCamelCase_					)
													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._batch_encode_plus(*lowerCamelCase_		,			**lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Tuple		,			*lowerCamelCase_			:  str		,			**lowerCamelCase_			:  Any					):
													UpperCAmelCase__												=     kwargs.get('''is_split_into_words'''		,			lowerCamelCase_					)

													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._encode_plus(*lowerCamelCase_		,			**lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Dict		,			lowerCamelCase_			:  str		,			lowerCamelCase_			:  Optional[str] = None					):
													UpperCAmelCase__												=     self._tokenizer.model.save(lowerCamelCase_		,			name=lowerCamelCase_					)
													return tuple(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  "Conversation"					):
													UpperCAmelCase__												=     []
													for is_user, text in conversation.iter_texts():
																			input_ids.extend(self.encode(lowerCamelCase_		,			add_special_tokens=lowerCamelCase_					) + [self.eos_token_id]					)

													if len(lowerCamelCase_					) > self.model_max_length:
																			UpperCAmelCase__												=     input_ids[-self.model_max_length :]
													return input_ids