



'''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


SCREAMING_SNAKE_CASE__     : 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)

SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.getLogger()







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Any										=      argparse.ArgumentParser()
		parser.add_argument('-f'     )
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      parser.parse_args()
		return args.f







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







def  SCREAMING_SNAKE_CASE__ (				):
		return bool(tf.config.list_physical_devices('GPU'     )     )


SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			@skip('Skipping until shape inference for to_tf_dataset PR is merged.'	)
			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[str]										=      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(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_text_classification.main()
							# Reset the mixed precision policy so we don't break other tests
							tf.keras.mixed_precision.set_global_policy('float32'	)
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['eval_accuracy']      ,       0.75	)






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:str										=      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(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_clm.main()
							SCREAMING_SNAKE_CASE_						:Any										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertLess(result['eval_perplexity']      ,       100	)






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:int										=      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(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_mlm.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertLess(result['eval_perplexity']      ,       42	)






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      7 if get_gpu_count() > 1 else 2

					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:str										=      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(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_ner.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['accuracy']      ,       0.75	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Any										=      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(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_squad.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['f1']      ,       30	)
							self.assertGreaterEqual(result['exact']      ,       30	)






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_swag.main()
							SCREAMING_SNAKE_CASE_						:int										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['val_accuracy']      ,       0.8	)






			@slow
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      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(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_summarization.main()
							SCREAMING_SNAKE_CASE_						:Dict										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['rouge1']      ,       10	)
							self.assertGreaterEqual(result['rouge2']      ,       2	)
							self.assertGreaterEqual(result['rougeL']      ,       7	)
							self.assertGreaterEqual(result['rougeLsum']      ,       7	)






			@slow
			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[Any]										=      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(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_translation.main()
							SCREAMING_SNAKE_CASE_						:str										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['bleu']      ,       30	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Any										=      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(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_image_classification.main()
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['accuracy']      ,       0.7	)





'''simple docstring'''







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		assert b != 0, "This cannot accept modulo that is == 0"
		if n == 0:
				return 1

		if n % 2 == 1:
				return (bin_exp_mod(SCREAMING_SNAKE_CASE ,		n - 1 ,		SCREAMING_SNAKE_CASE     ) * a) % b

		SCREAMING_SNAKE_CASE_						:List[str]										=      bin_exp_mod(SCREAMING_SNAKE_CASE ,		n / 2 ,		SCREAMING_SNAKE_CASE     )
		return (r * r) % b


if __name__ == "__main__":
							try:
														SCREAMING_SNAKE_CASE__     : Any				=   int(input("""Enter Base : """).strip())
														SCREAMING_SNAKE_CASE__     : str				=   int(input("""Enter Power : """).strip())
														SCREAMING_SNAKE_CASE__     : List[Any]				=   int(input("""Enter Modulo : """).strip())
							except ValueError:
														print("""Invalid literal for integer""")

							print(bin_exp_mod(BASE, POWER, MODULO))
