





'''simple docstring'''







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


snake_case_  :							Any            =	logging.getLogger(__name__)


@dataclass
class        __a   :
							__a       : str          =							field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}  )
							__a       : Optional[Any]          =							field(
							    default=lowerCamelCase							,		metadata={"help": "Pretrained config name or path if not the same as model_name"}  )
							__a       : Optional[int]          =							field(
							    default=lowerCamelCase							,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}  )
							__a       : Optional[Any]          =							field(
							    default=lowerCamelCase							,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,		)





@dataclass
class        __a   :
							__a       : str          =							field(
							    metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()  )}  )
							__a       : Any          =							field(
							    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}  )
							__a       : Any          =							field(
							    default=128							,		metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }							,		)
							__a       : Tuple          =							field(
							    default=lowerCamelCase							,		metadata={"help": "Overwrite the cached training and evaluation sets"}  )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[InputFeatures] )			->						Dict[str, torch.Tensor]:
						UpperCAmelCase_      :   Tuple      =	default_data_collator(UpperCAmelCase__ )
						UpperCAmelCase_      :   Optional[Any]      =	batch.pop('''pairID''',      UpperCAmelCase__ )
						return batch





def 							lowerCamelCase_   (	)			->						List[str]:
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						UpperCAmelCase_      :   Any      =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
						UpperCAmelCase_      :   Dict      =	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:
												UpperCAmelCase_      :   Optional[int]      =	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.

						UpperCAmelCase_      :   Optional[int]      =	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,      )
						UpperCAmelCase_      :   Optional[int]      =	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,      )
						UpperCAmelCase_      :   Optional[Any]      =	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
						UpperCAmelCase_      :   Any      =	(
						    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
						)
						UpperCAmelCase_      :   Optional[Any]      =	(
						    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
						UpperCAmelCase_      :   Union[str, Any]      =	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 ***''' )

												UpperCAmelCase_      :   Union[str, Any]      =	trainer.predict(UpperCAmelCase__ )
												UpperCAmelCase_      :   Dict      =	output.predictions
												UpperCAmelCase_      :   Optional[int]      =	np.argmax(UpperCAmelCase__,      axis=1 )

												UpperCAmelCase_      :   List[Any]      =	[ex.pairID for ex in eval_dataset]
												UpperCAmelCase_      :   List[Any]      =	os.path.join(training_args.output_dir,      '''hans_predictions.txt''' )
												UpperCAmelCase_      :   List[str]      =	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__   :      Optional[Any] )			->						int:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
					main()













"""simple docstring"""



import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


A_     =				'''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''






class 				__SCREAMING_SNAKE_CASE    (					unittest.TestCase							,		UpperCamelCase		):


							def     _UpperCamelCase							(      self   :    int				):
													'''simple docstring'''





													A__    :	Any			=	load_tool("""summarization"""				)
													self.tool.setup()
													A__    :	List[Any]			=	load_tool("""summarization"""      ,     remote=snake_case				)


							def     _UpperCamelCase							(      self   :    Any				):
													'''simple docstring'''





													A__    :	List[Any]			=	self.tool(snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )


							def     _UpperCamelCase							(      self   :    Optional[int]				):
													'''simple docstring'''





													A__    :	List[str]			=	self.remote_tool(snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )


							def     _UpperCamelCase							(      self   :    Dict				):
													'''simple docstring'''





													A__    :	List[Any]			=	self.tool(text=snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )


							def     _UpperCamelCase							(      self   :    Union[str, Any]				):
													'''simple docstring'''





													A__    :	Optional[int]			=	self.remote_tool(text=snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )






