
"""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


__lowerCAmelCase							:  Optional[int] 				=		[
    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)

__lowerCAmelCase							:  Optional[Any] 				=		logging.getLogger()

def __snake_case (      )       ->    int:


		"""simple docstring"""





		a__   		=     argparse.ArgumentParser()
		parser.add_argument('''-f'''    )
		a__   		=     parser.parse_args()
		return args.f

def __snake_case (      UpperCamelCase    )       ->    Tuple:


		"""simple docstring"""





		a__   		=     {}
		a__   		=     os.path.join(UpperCamelCase						,					'''all_results.json'''    )
		if os.path.exists(UpperCamelCase    ):
				with open(UpperCamelCase						,					'''r'''    ) as f:
						a__   		=     json.load(UpperCamelCase    )
		else:
				raise ValueError(f"can't find {path}"    )
		return results

def __snake_case (      )       ->    Optional[Any]:


		"""simple docstring"""





		return bool(tf.config.list_physical_devices('''GPU'''    )    )


__lowerCAmelCase							:  Dict 				=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''			)
	def    _UpperCamelCase							(  self :Tuple			)			->   List[str]:

			'''simple docstring'''






			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

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

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					run_text_classification.main()
					# Reset the mixed precision policy so we don't break other tests
					tf.keras.mixed_precision.set_global_policy('''float32'''			)
					a__   		=     get_results(__magic_name__			)
					self.assertGreaterEqual(result['''eval_accuracy''']       ,    0.75			)


	def    _UpperCamelCase							(  self :str			)			->   str:

			'''simple docstring'''






			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".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(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					run_clm.main()
					a__   		=     get_results(__magic_name__			)
					self.assertLess(result['''eval_perplexity''']       ,    100			)


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   Any:

			'''simple docstring'''






			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_mlm.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            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					run_mlm.main()
					a__   		=     get_results(__magic_name__			)
					self.assertLess(result['''eval_perplexity''']       ,    42			)


	def    _UpperCamelCase							(  self :Tuple			)			->   List[str]:

			'''simple docstring'''






			a__   		=     7 if get_gpu_count() > 1 else 2

			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_ner.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            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\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        ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					run_ner.main()
					a__   		=     get_results(__magic_name__			)
					self.assertGreaterEqual(result['''accuracy''']       ,    0.75			)


	def    _UpperCamelCase							(  self :List[str]			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_qa.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            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					run_squad.main()
					a__   		=     get_results(__magic_name__			)
					self.assertGreaterEqual(result['''f1''']       ,    30			)
					self.assertGreaterEqual(result['''exact''']       ,    30			)


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Any:

			'''simple docstring'''






			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_swag.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            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					run_swag.main()
					a__   		=     get_results(__magic_name__			)
					self.assertGreaterEqual(result['''val_accuracy''']       ,    0.8			)


	@slow
	def    _UpperCamelCase							(  self :List[Any]			)			->   Any:

			'''simple docstring'''






			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_summarization.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            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

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


	@slow
	def    _UpperCamelCase							(  self :Dict			)			->   str:

			'''simple docstring'''






			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_translation.py\n            --model_name_or_path Rocketknight1/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            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\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        ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					run_translation.main()
					a__   		=     get_results(__magic_name__			)
					self.assertGreaterEqual(result['''bleu''']       ,    30			)






	def    _UpperCamelCase							(  self :Any			)			->   int:

			'''simple docstring'''






			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					run_image_classification.main()
					a__   		=     get_results(__magic_name__			)
					self.assertGreaterEqual(result['''accuracy''']       ,    0.7			)






"""simple docstring"""




def __snake_case (      UpperCamelCase = 10**6    )       ->    int:


		"""simple docstring"""





		if n <= 0:
				raise ValueError('''Please enter an integer greater than 0'''    )

		a__   		=     list(range(n + 1    )    )
		for number in range(2						,					n + 1    ):
				if phi[number] == number:
						phi[number] -= 1
						for multiple in range(number * 2						,					n + 1						,					UpperCamelCase    ):
								a__   		=     (phi[multiple] // number) * (number - 1)

		a__   		=     1
		for number in range(1						,					n + 1    ):
				if (answer / phi[answer]) < (number / phi[number]):
						a__   		=     number

		return answer


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




