
"""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


__A 		= logging.getLogger(__name__)







@dataclass
class 	lowerCamelCase__      :





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







@dataclass
class 	lowerCamelCase__      :





   '''simple docstring'''
   lowerCamelCase						      =	field(
       metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()       )}       )
   lowerCamelCase						      =	field(
       metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}       )
   lowerCamelCase						      =	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.'''
           )
       }				,			)
   lowerCamelCase						      =	field(
       default=__lowerCamelCase				,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )







def 					_lowerCamelCase(__UpperCamelCase )       ->				Dict[str, torch.Tensor]:
 _lowerCAmelCase			=default_data_collator(__lowerCamelCase )
 _lowerCAmelCase			=batch.pop("""pairID"""					,				__lowerCamelCase )
 return batch



def 					_lowerCamelCase()       ->				Optional[Any]:
 _lowerCAmelCase			=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
 _lowerCAmelCase			=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"""					,				__lowerCamelCase )

 # Set seed
 set_seed(training_args.seed )

 try:
  _lowerCAmelCase			=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.

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

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

 # Initialize our Trainer
 _lowerCAmelCase			=Trainer(
     model=__lowerCamelCase					,				args=__lowerCamelCase					,				train_dataset=__lowerCamelCase					,				eval_dataset=__lowerCamelCase					,				data_collator=__lowerCamelCase					,				)

 # 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 ***""" )

  _lowerCAmelCase			=trainer.predict(__lowerCamelCase )
  _lowerCAmelCase			=output.predictions
  _lowerCAmelCase			=np.argmax(__lowerCamelCase					,				axis=1 )

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

  trainer._log(output.metrics )



def 					_lowerCamelCase(__UpperCamelCase )       ->				Optional[Any]:
 main()


if __name__ == "__main__":
   main()





"""simple docstring"""


import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__A 		= [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
			import run_clm
			import run_image_classification
			import run_mlm
			import run_ner
			import run_qa as run_squad
			import run_summarization
			import run_swag
			import run_text_classification
			import run_translation


logging.basicConfig(level=logging.DEBUG)

__A 		= logging.getLogger()



def 					_lowerCamelCase()       ->				str:
	_lowerCAmelCase			=argparse.ArgumentParser()
	parser.add_argument("""-f""" )
	_lowerCAmelCase			=parser.parse_args()
	return args.f



def 					_lowerCamelCase(__UpperCamelCase )       ->				List[str]:
	_lowerCAmelCase			={}
	_lowerCAmelCase			=os.path.join(__UpperCamelCase					,				"""all_results.json""" )
	if os.path.exists(__UpperCamelCase ):
		with open(__UpperCamelCase					,				"""r""" ) as f:
			_lowerCAmelCase			=json.load(__UpperCamelCase )
	else:
		raise ValueError(F'''can\'t find {path}''' )
	return results



def 					_lowerCamelCase()       ->				List[Any]:
	return bool(tf.config.list_physical_devices("""GPU""" ) )


__A 		= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""						)
			def       _lowerCAmelCase	(							self						)				->  List[str]:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

				if is_cuda_available():
					testargs.append("""--fp16"""						)

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_text_classification.main()
					# Reset the mixed precision policy so we don't break other tests
					tf.keras.mixed_precision.set_global_policy("""float32"""						)
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""eval_accuracy"""]  ,  0.7_5						)



			def       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

				if len(tf.config.list_physical_devices("""GPU"""						)						) > 1:
					# Skipping because there are not enough batches to train the model + would need a drop_last to work.
					return

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_clm.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertLess(result["""eval_perplexity"""]  ,  1_00						)



			def       _lowerCAmelCase	(							self						)				->  str:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_mlm.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertLess(result["""eval_perplexity"""]  ,  42						)



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
				_lowerCAmelCase			=7 if get_gpu_count() > 1 else 2

				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_ner.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""accuracy"""]  ,  0.7_5						)



			def       _lowerCAmelCase	(							self						)				->  str:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_squad.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""f1"""]  ,  30						)
					self.assertGreaterEqual(result["""exact"""]  ,  30						)



			def       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_swag.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""val_accuracy"""]  ,  0.8						)



			@slow
			def       _lowerCAmelCase	(							self						)				->  Tuple:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_summarization.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""rouge1"""]  ,  10						)
					self.assertGreaterEqual(result["""rouge2"""]  ,  2						)
					self.assertGreaterEqual(result["""rougeL"""]  ,  7						)
					self.assertGreaterEqual(result["""rougeLsum"""]  ,  7						)



			@slow
			def       _lowerCAmelCase	(							self						)				->  Any:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_translation.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""bleu"""]  ,  30						)



			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_image_classification.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""accuracy"""]  ,  0.7						)



