


'''simple docstring'''



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)

__UpperCAmelCase    :Tuple				       =     logging.getLogger()




def 			_a			(		):





     '''simple docstring'''






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




def 			_a			(		_lowercase      :			str						):





     '''simple docstring'''






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




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    torch.cuda.is_available() and torch_device == '''cuda'''
     return is_using_cuda and is_apex_available()


__UpperCAmelCase    :Optional[Any]				       =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class 			a       (	_a ):


     """simple docstring"""






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

     @classmethod
     def 				lowerCamelCase__				(       cls  :	Tuple    )		->							int:
          shutil.rmtree(cls.tmpdir    )

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

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

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

     @mock.patch.dict(os.environ							,				{'''WANDB_MODE''': '''offline'''}    )
     def 				lowerCamelCase__				(       self  :	Dict    )		->							Tuple:
          __UpperCAmelCase			:				str													=    self.get_auto_remove_tmp_dir()
          __UpperCAmelCase			:				List[Any]													=    f'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.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			:				Tuple													=    get_results(snake_case    )
          self.assertLess(result['''perplexity''']							,				100    )
          self.assertTrue(os.path.exists(os.path.join(snake_case							,				'''epoch_0'''    )    )    )
          self.assertTrue(os.path.exists(os.path.join(snake_case							,				'''clm_no_trainer'''    )    )    )

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

          run_command(self._launch_args + testargs    )
          __UpperCAmelCase			:				List[str]													=    get_results(snake_case    )
          self.assertLess(result['''perplexity''']							,				42    )
          self.assertTrue(os.path.exists(os.path.join(snake_case							,				'''epoch_0'''    )    )    )
          self.assertTrue(os.path.exists(os.path.join(snake_case							,				'''mlm_no_trainer'''    )    )    )

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

          __UpperCAmelCase			:				Tuple													=    self.get_auto_remove_tmp_dir()
          __UpperCAmelCase			:				List[str]													=    f'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

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

     @unittest.skip(reason='''Fix me @muellerzr'''    )
     @mock.patch.dict(os.environ							,				{'''WANDB_MODE''': '''offline'''}    )
     def 				lowerCamelCase__				(       self  :	Tuple    )		->							int:
          __UpperCAmelCase			:				Tuple													=    self.get_auto_remove_tmp_dir()
          __UpperCAmelCase			:				Any													=    f'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

          run_command(self._launch_args + testargs    )
          __UpperCAmelCase			:				str													=    get_results(snake_case    )
          # 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(snake_case							,				'''epoch_0'''    )    )    )
          self.assertTrue(os.path.exists(os.path.join(snake_case							,				'''qa_no_trainer'''    )    )    )

     @mock.patch.dict(os.environ							,				{'''WANDB_MODE''': '''offline'''}    )
     def 				lowerCamelCase__				(       self  :	Any    )		->							Optional[int]:
          __UpperCAmelCase			:				Union[str, Any]													=    self.get_auto_remove_tmp_dir()
          __UpperCAmelCase			:				Any													=    f'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

          run_command(self._launch_args + testargs    )
          __UpperCAmelCase			:				Optional[int]													=    get_results(snake_case    )
          self.assertGreaterEqual(result['''eval_accuracy''']							,				0.8    )
          self.assertTrue(os.path.exists(os.path.join(snake_case							,				'''swag_no_trainer'''    )    )    )

     @slow
     @mock.patch.dict(os.environ							,				{'''WANDB_MODE''': '''offline'''}    )
     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Optional[Any]:
          __UpperCAmelCase			:				int													=    self.get_auto_remove_tmp_dir()
          __UpperCAmelCase			:				List[str]													=    f'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

          run_command(self._launch_args + testargs    )
          __UpperCAmelCase			:				str													=    get_results(snake_case    )
          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(snake_case							,				'''epoch_0'''    )    )    )
          self.assertTrue(os.path.exists(os.path.join(snake_case							,				'''summarization_no_trainer'''    )    )    )

     @slow
     @mock.patch.dict(os.environ							,				{'''WANDB_MODE''': '''offline'''}    )
     def 				lowerCamelCase__				(       self  :	Any    )		->							Optional[Any]:
          __UpperCAmelCase			:				Union[str, Any]													=    self.get_auto_remove_tmp_dir()
          __UpperCAmelCase			:				List[Any]													=    f'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

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

     @slow
     def 				lowerCamelCase__				(       self  :	Tuple    )		->							List[Any]:
          __UpperCAmelCase			:				List[str]													=    logging.StreamHandler(sys.stdout    )
          logger.addHandler(snake_case    )

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

          run_command(self._launch_args + testargs    )
          __UpperCAmelCase			:				List[Any]													=    get_results(snake_case    )
          self.assertGreaterEqual(result['''eval_overall_accuracy''']							,				0.10    )






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

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

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



'''simple docstring'''



def 			_a			(		_lowercase      :			int = 4000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				Tuple													=    []
     __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    0, 1
     while b <= n:
          if b % 2 == 0:
               even_fibs.append(_lowercase						)
          __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    b, a + b
     return sum(_lowercase						)


if __name__ == "__main__":
      print(f"""{solution() = }""")