"""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)

_A   					=    logging.getLogger()




def 						SCREAMING_SNAKE_CASE					(  )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				argparse.ArgumentParser()
				parser.add_argument("-f"       )
				SCREAMING_SNAKE_CASE__		  =				parser.parse_args()
				return args.f




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Tuple:
				SCREAMING_SNAKE_CASE__		  =				{}
				SCREAMING_SNAKE_CASE__		  =				os.path.join(__UpperCamelCase  ,					"all_results.json"       )
				if os.path.exists(__UpperCamelCase       ):
								with open(__UpperCamelCase  ,					"r"       ) as f:
												SCREAMING_SNAKE_CASE__		  =				json.load(__UpperCamelCase       )
				else:
								raise ValueError(F"""can't find {path}"""       )
				return results




def 						SCREAMING_SNAKE_CASE					(  )							->   int:
				SCREAMING_SNAKE_CASE__		  =				torch.cuda.is_available() and torch_device == "cuda"
				return is_using_cuda and is_apex_available()


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

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@classmethod
					def 			lowerCAmelCase_					(						cls			:				Tuple		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE__		  =				os.path.join(cls.tmpdir   , "default_config.yml"		)
									write_basic_config(save_location=cls.configPath		)
									SCREAMING_SNAKE_CASE__		  =				["accelerate", "launch", "--config_file", cls.configPath]



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				List[str]		)							->       Optional[Any]:
									shutil.rmtree(cls.tmpdir		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				int		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__		  =				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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_SCREAMING_SNAKE_CASE		)
									self.assertGreaterEqual(result["eval_accuracy"]   , 0.75		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "glue_no_trainer"		)		)		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__		  =				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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_SCREAMING_SNAKE_CASE		)
									self.assertLess(result["perplexity"]   , 100		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "clm_no_trainer"		)		)		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				str		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__		  =				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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_SCREAMING_SNAKE_CASE		)
									self.assertLess(result["perplexity"]   , 42		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "mlm_no_trainer"		)		)		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				int		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				7 if get_gpu_count() > 1 else 2

									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__		  =				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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_SCREAMING_SNAKE_CASE		)
									self.assertGreaterEqual(result["eval_accuracy"]   , 0.75		)
									self.assertLess(result["train_loss"]   , 0.5		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "ner_no_trainer"		)		)		)



					@unittest.skip(reason="Fix me @muellerzr"		)
					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__		  =				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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_SCREAMING_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(_SCREAMING_SNAKE_CASE   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "qa_no_trainer"		)		)		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				str		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__		  =				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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_SCREAMING_SNAKE_CASE		)
									self.assertGreaterEqual(result["eval_accuracy"]   , 0.8		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "swag_no_trainer"		)		)		)



					@slow
					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__		  =				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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_SCREAMING_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(_SCREAMING_SNAKE_CASE   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "summarization_no_trainer"		)		)		)



					@slow
					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				str		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__		  =				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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_SCREAMING_SNAKE_CASE		)
									self.assertGreaterEqual(result["eval_bleu"]   , 30		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "epoch_0"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "translation_no_trainer"		)		)		)



					@slow
					def 			lowerCAmelCase_					(						self			:				str		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				logging.StreamHandler(sys.stdout		)
									logger.addHandler(_SCREAMING_SNAKE_CASE		)

									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__		  =				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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_SCREAMING_SNAKE_CASE		)
									self.assertGreaterEqual(result["eval_overall_accuracy"]   , 0.10		)



					@mock.patch.dict(os.environ   , {"WANDB_MODE": "offline"}		)
					def 			lowerCAmelCase_					(						self			:				int		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__		  =				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		)
									SCREAMING_SNAKE_CASE__		  =				get_results(_SCREAMING_SNAKE_CASE		)
									# The base model scores a 25%
									self.assertGreaterEqual(result["eval_accuracy"]   , 0.6		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "step_1"		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE   , "image_classification_no_trainer"		)		)		)





"""simple docstring"""






from math import pi




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
						print(arc_length(9_0, 1_0))




