import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

__lowercase							 =					logging.getLogger()



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		argparse.ArgumentParser()
    parser.add_argument('''-f'''	)
    __UpperCamelCase     :Any			  =		parser.parse_args()
    return args.f



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		{}
    __UpperCamelCase     :str			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			'''all_results.json'''	)
    if os.path.exists(SCREAMING_SNAKE_CASE	):
        with open(SCREAMING_SNAKE_CASE  ,			'''r'''	) as f:
            __UpperCamelCase     :Optional[Any]			  =		json.load(SCREAMING_SNAKE_CASE	)
    else:
        raise ValueError(f"""can't find {path}"""	)
    return results



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		torch.cuda.is_available() and torch_device == '''cuda'''
    return is_using_cuda and is_apex_available()


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


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    @classmethod
    def 			UpperCamelCase__   (	cls)       ->	int:
        # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
        __UpperCamelCase     :List[Any]			  =		tempfile.mkdtemp()
        __UpperCamelCase     :List[Any]			  =		os.path.join(cls.tmpdir , '''default_config.yml''')
        write_basic_config(save_location=cls.configPath)
        __UpperCamelCase     :int			  =		['''accelerate''', '''launch''', '''--config_file''', cls.configPath]







    @classmethod
    def 			UpperCamelCase__   (	cls)       ->	Union[str, Any]:
        shutil.rmtree(cls.tmpdir)







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :List[str]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Any			  =		f"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        if is_cuda_and_apex_available():
            testargs.append('''--fp16''')

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Optional[Any]			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''glue_no_trainer''')))







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase     :Any			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :List[str]			  =		f"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        if torch.cuda.device_count() > 1:
            # Skipping because there are not enough batches to train the model + would need a drop_last to work.
            return

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Dict			  =		get_results(__lowercase)
        self.assertLess(result['''perplexity'''] , 100)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''clm_no_trainer''')))







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :List[Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :List[Any]			  =		f"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Union[str, Any]			  =		get_results(__lowercase)
        self.assertLess(result['''perplexity'''] , 42)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''mlm_no_trainer''')))







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
        __UpperCamelCase     :Union[str, Any]			  =		7 if get_gpu_count() > 1 else 2

        __UpperCamelCase     :List[Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :List[Any]			  =		f"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Optional[Any]			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75)
        self.assertLess(result['''train_loss'''] , 0.5)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''ner_no_trainer''')))







    @unittest.skip(reason='''Fix me @muellerzr''')
    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Union[str, Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :int			  =		f"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Any			  =		get_results(__lowercase)
        # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
        self.assertGreaterEqual(result['''eval_f1'''] , 28)
        self.assertGreaterEqual(result['''eval_exact'''] , 28)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''qa_no_trainer''')))







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :int			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :int			  =		f"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.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}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Dict			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_accuracy'''] , 0.8)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''swag_no_trainer''')))







    @slow
    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :str			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Dict			  =		f"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Tuple			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_rouge1'''] , 10)
        self.assertGreaterEqual(result['''eval_rouge2'''] , 2)
        self.assertGreaterEqual(result['''eval_rougeL'''] , 7)
        self.assertGreaterEqual(result['''eval_rougeLsum'''] , 7)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''summarization_no_trainer''')))







    @slow
    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Optional[int]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Dict			  =		f"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/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}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Optional[int]			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_bleu'''] , 30)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''epoch_0''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''translation_no_trainer''')))







    @slow
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Tuple			  =		logging.StreamHandler(sys.stdout)
        logger.addHandler(__lowercase)

        __UpperCamelCase     :int			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Any			  =		f"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :List[str]			  =		get_results(__lowercase)
        self.assertGreaterEqual(result['''eval_overall_accuracy'''] , 0.10)







    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''})
    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :Union[str, Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Optional[Any]			  =		f"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

        if is_cuda_and_apex_available():
            testargs.append('''--fp16''')

        run_command(self._launch_args + testargs)
        __UpperCamelCase     :Optional[Any]			  =		get_results(__lowercase)
        # The base model scores a 25%
        self.assertGreaterEqual(result['''eval_accuracy'''] , 0.6)
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''step_1''')))
        self.assertTrue(os.path.exists(os.path.join(__lowercase , '''image_classification_no_trainer''')))












"""simple docstring"""




import os

# Precomputes a list of the 100 first triangular numbers
lowerCamelCase_     :						List[str]						=					[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Union[str, Any]										=  os.path.dirname(os.path.realpath(_UpperCAmelCase					)					)
  A_  :       Tuple										=  os.path.join(_UpperCAmelCase ,					'words.txt'					)

  A_  :       List[Any]										=  ''
  with open(_UpperCAmelCase					) as f:
    A_  :       int										=  f.readline()

  A_  :       Optional[Any]										=  [word.strip('"'					) for word in words.strip('\r\n'					).split(','					)]
  A_  :       Dict										=  [
      word
      for word in [sum(ord(_UpperCAmelCase					) - 64 for x in word					) for word in words]
      if word in TRIANGULAR_NUMBERS
  ]
  return len(_UpperCAmelCase					)


if __name__ == "__main__":
   print(solution())