'''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


A_        =   [
    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)

A_        =   logging.getLogger()


def 					UpperCAmelCase__      (							)						->		int:



						"""simple docstring"""

						snake_case   :					List[str]							=						argparse.ArgumentParser()
						parser.add_argument("-f")
						snake_case   :					int							=						parser.parse_args()
						return args.f


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Any)						->		Optional[int]:



						"""simple docstring"""

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


def 					UpperCAmelCase__      (							)						->		Tuple:



						"""simple docstring"""

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


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




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			@skip("Skipping until shape inference for to_tf_dataset PR is merged."				)
			def      lowerCamelCase   (       self				):
									snake_case   :					Union[str, Any]							=						self.get_auto_remove_tmp_dir()
									snake_case   :					Union[str, Any]							=						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(snake_case_							, "argv"							, 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"				)
															snake_case   :					Optional[int]							=						get_results(snake_case_				)
															self.assertGreaterEqual(result["eval_accuracy"]							, 0.75				)



			def      lowerCamelCase   (       self				):
									snake_case   :					Optional[int]							=						self.get_auto_remove_tmp_dir()
									snake_case   :					int							=						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(snake_case_							, "argv"							, snake_case_				):
															run_clm.main()
															snake_case   :					List[str]							=						get_results(snake_case_				)
															self.assertLess(result["eval_perplexity"]							, 100				)



			def      lowerCamelCase   (       self				):
									snake_case   :					Union[str, Any]							=						self.get_auto_remove_tmp_dir()
									snake_case   :					List[Any]							=						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(snake_case_							, "argv"							, snake_case_				):
															run_mlm.main()
															snake_case   :					List[str]							=						get_results(snake_case_				)
															self.assertLess(result["eval_perplexity"]							, 42				)



			def      lowerCamelCase   (       self				):
									# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
									snake_case   :					Optional[Any]							=						7 if get_gpu_count() > 1 else 2

									snake_case   :					Tuple							=						self.get_auto_remove_tmp_dir()
									snake_case   :					Tuple							=						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(snake_case_							, "argv"							, snake_case_				):
															run_ner.main()
															snake_case   :					Tuple							=						get_results(snake_case_				)
															self.assertGreaterEqual(result["accuracy"]							, 0.75				)



			def      lowerCamelCase   (       self				):
									snake_case   :					Dict							=						self.get_auto_remove_tmp_dir()
									snake_case   :					Optional[int]							=						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(snake_case_							, "argv"							, snake_case_				):
															run_squad.main()
															snake_case   :					List[str]							=						get_results(snake_case_				)
															self.assertGreaterEqual(result["f1"]							, 30				)
															self.assertGreaterEqual(result["exact"]							, 30				)



			def      lowerCamelCase   (       self				):
									snake_case   :					Union[str, Any]							=						self.get_auto_remove_tmp_dir()
									snake_case   :					Optional[Any]							=						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(snake_case_							, "argv"							, snake_case_				):
															run_swag.main()
															snake_case   :					str							=						get_results(snake_case_				)
															self.assertGreaterEqual(result["val_accuracy"]							, 0.8				)



			@slow
			def      lowerCamelCase   (       self				):
									snake_case   :					Union[str, Any]							=						self.get_auto_remove_tmp_dir()
									snake_case   :					List[Any]							=						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(snake_case_							, "argv"							, snake_case_				):
															run_summarization.main()
															snake_case   :					List[str]							=						get_results(snake_case_				)
															self.assertGreaterEqual(result["rouge1"]							, 10				)
															self.assertGreaterEqual(result["rouge2"]							, 2				)
															self.assertGreaterEqual(result["rougeL"]							, 7				)
															self.assertGreaterEqual(result["rougeLsum"]							, 7				)



			@slow
			def      lowerCamelCase   (       self				):
									snake_case   :					Dict							=						self.get_auto_remove_tmp_dir()
									snake_case   :					Union[str, Any]							=						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(snake_case_							, "argv"							, snake_case_				):
															run_translation.main()
															snake_case   :					Dict							=						get_results(snake_case_				)
															self.assertGreaterEqual(result["bleu"]							, 30				)






			def      lowerCamelCase   (       self				):
									snake_case   :					Tuple							=						self.get_auto_remove_tmp_dir()
									snake_case   :					Any							=						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(snake_case_							, "argv"							, snake_case_				):
															run_image_classification.main()
															snake_case   :					Union[str, Any]							=						get_results(snake_case_				)
															self.assertGreaterEqual(result["accuracy"]							, 0.7				)






'''simple docstring'''

import math


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5,			int(math.sqrt(_lowerCAmelCase) + 1),			6):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int = 200_0000)						->		int:



						"""simple docstring"""

						return sum(num for num in range(3,			_lowerCAmelCase,			2) if is_prime(_lowerCAmelCase)) + 2 if n > 2 else 0


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





