




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


UpperCamelCase__            =     [
    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)

UpperCamelCase__            =     logging.getLogger()







def       _a (		):
					__lowerCAmelCase							  =			argparse.ArgumentParser()
					parser.add_argument("-f"	)
					__lowerCAmelCase							  =			parser.parse_args()
					return args.f







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					__lowerCAmelCase							  =			{}
					__lowerCAmelCase							  =			os.path.join(SCREAMING_SNAKE_CASE_ , "all_results.json"	)
					if os.path.exists(SCREAMING_SNAKE_CASE_	):
										with open(SCREAMING_SNAKE_CASE_ , "r"	) as f:
															__lowerCAmelCase							  =			json.load(SCREAMING_SNAKE_CASE_	)
					else:
										raise ValueError(F"""can't find {path}"""	)
					return results







def       _a (		):
					return bool(tf.config.list_physical_devices("GPU"	)	)


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


class      a__       (   snake_case__ ):






						@skip("Skipping until shape inference for to_tf_dataset PR is merged."     )
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



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

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

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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.get_auto_remove_tmp_dir()
											__lowerCAmelCase							  =			f"""
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.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(_A						,     "argv"						,     _A     ):
																run_clm.main()
																__lowerCAmelCase							  =			get_results(_A     )
																self.assertLess(result["eval_perplexity"]						,     1_0_0     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.get_auto_remove_tmp_dir()
											__lowerCAmelCase							  =			f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

											with patch.object(_A						,     "argv"						,     _A     ):
																run_mlm.main()
																__lowerCAmelCase							  =			get_results(_A     )
																self.assertLess(result["eval_perplexity"]						,     4_2     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			7 if get_gpu_count() > 1 else 2

											__lowerCAmelCase							  =			self.get_auto_remove_tmp_dir()
											__lowerCAmelCase							  =			f"""
            run_ner.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}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

											with patch.object(_A						,     "argv"						,     _A     ):
																run_ner.main()
																__lowerCAmelCase							  =			get_results(_A     )
																self.assertGreaterEqual(result["accuracy"]						,     0.75     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.get_auto_remove_tmp_dir()
											__lowerCAmelCase							  =			f"""
            run_qa.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}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

											with patch.object(_A						,     "argv"						,     _A     ):
																run_squad.main()
																__lowerCAmelCase							  =			get_results(_A     )
																self.assertGreaterEqual(result["f1"]						,     3_0     )
																self.assertGreaterEqual(result["exact"]						,     3_0     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.get_auto_remove_tmp_dir()
											__lowerCAmelCase							  =			f"""
            run_swag.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}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

											with patch.object(_A						,     "argv"						,     _A     ):
																run_swag.main()
																__lowerCAmelCase							  =			get_results(_A     )
																self.assertGreaterEqual(result["val_accuracy"]						,     0.8     )






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.get_auto_remove_tmp_dir()
											__lowerCAmelCase							  =			f"""
            run_summarization.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}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

											with patch.object(_A						,     "argv"						,     _A     ):
																run_summarization.main()
																__lowerCAmelCase							  =			get_results(_A     )
																self.assertGreaterEqual(result["rouge1"]						,     1_0     )
																self.assertGreaterEqual(result["rouge2"]						,     2     )
																self.assertGreaterEqual(result["rougeL"]						,     7     )
																self.assertGreaterEqual(result["rougeLsum"]						,     7     )






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.get_auto_remove_tmp_dir()
											__lowerCAmelCase							  =			f"""
            run_translation.py
            --model_name_or_path Rocketknight1/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}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

											with patch.object(_A						,     "argv"						,     _A     ):
																run_translation.main()
																__lowerCAmelCase							  =			get_results(_A     )
																self.assertGreaterEqual(result["bleu"]						,     3_0     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



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

											with patch.object(_A						,     "argv"						,     _A     ):
																run_image_classification.main()
																__lowerCAmelCase							  =			get_results(_A     )
																self.assertGreaterEqual(result["accuracy"]						,     0.7     )










def       _a (		SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int	):
					if a < b:
										return gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					if a % b == 0:
										return b
					return gcd(SCREAMING_SNAKE_CASE_ , a % b	)







def       _a (		SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int	):
					if y == 0:
										return 1
					__lowerCAmelCase							  =			power(SCREAMING_SNAKE_CASE_ , y // 2 , SCREAMING_SNAKE_CASE_	) % mod
					__lowerCAmelCase							  =			(temp * temp) % mod
					if y % 2 == 1:
										__lowerCAmelCase							  =			(temp * x) % mod
					return temp







def       _a (		SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			2
					while b < n:
										if gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	) == 1 and power(SCREAMING_SNAKE_CASE_ , n - 1 , SCREAMING_SNAKE_CASE_	) != 1:
															return False
										b += 1
					return True


if __name__ == "__main__":
					UpperCamelCase__            =     int(input("""Enter number: """).strip())
					if is_carmichael_number(number):
										print(f'''{number} is a Carmichael Number.''')
					else:
										print(f'''{number} is not a Carmichael Number.''')




