
'''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


__magic_name__   :   Tuple  							=[
    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)

__magic_name__   :   List[Any]  							=logging.getLogger()




def      __snake_case       (       ):
				'''simple docstring'''




				__magic_name__	  = argparse.ArgumentParser()
				parser.add_argument("-f"						)
				__magic_name__	  = parser.parse_args()
				return args.f




def      __snake_case       (       lowerCamelCase_ :				Tuple						):
				'''simple docstring'''




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




def      __snake_case       (       ):
				'''simple docstring'''




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


__magic_name__   :   Any  							=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						@skip("Skipping until shape inference for to_tf_dataset PR is merged."  )
						def 					__A					( self				:					str  )							->					List[Any]:
										__magic_name__	  = self.get_auto_remove_tmp_dir()
										__magic_name__	  = 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(_lowerCamelCase	,				"argv"	,				_lowerCamelCase  ):
														run_text_classification.main()
														# Reset the mixed precision policy so we don't break other tests
														tf.keras.mixed_precision.set_global_policy("float32"  )
														__magic_name__	  = get_results(_lowerCamelCase  )
														self.assertGreaterEqual(result["eval_accuracy"]	,				0.75  )
						def 					__A					( self				:					Optional[Any]  )							->					Union[str, Any]:
										__magic_name__	  = self.get_auto_remove_tmp_dir()
										__magic_name__	  = 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(_lowerCamelCase	,				"argv"	,				_lowerCamelCase  ):
														run_clm.main()
														__magic_name__	  = get_results(_lowerCamelCase  )
														self.assertLess(result["eval_perplexity"]	,				1_00  )
						def 					__A					( self				:					str  )							->					str:
										__magic_name__	  = self.get_auto_remove_tmp_dir()
										__magic_name__	  = 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(_lowerCamelCase	,				"argv"	,				_lowerCamelCase  ):
														run_mlm.main()
														__magic_name__	  = get_results(_lowerCamelCase  )
														self.assertLess(result["eval_perplexity"]	,				42  )
						def 					__A					( self				:					Any  )							->					int:
										# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
										__magic_name__	  = 7 if get_gpu_count() > 1 else 2

										__magic_name__	  = self.get_auto_remove_tmp_dir()
										__magic_name__	  = 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(_lowerCamelCase	,				"argv"	,				_lowerCamelCase  ):
														run_ner.main()
														__magic_name__	  = get_results(_lowerCamelCase  )
														self.assertGreaterEqual(result["accuracy"]	,				0.75  )
						def 					__A					( self				:					int  )							->					Any:
										__magic_name__	  = self.get_auto_remove_tmp_dir()
										__magic_name__	  = 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(_lowerCamelCase	,				"argv"	,				_lowerCamelCase  ):
														run_squad.main()
														__magic_name__	  = get_results(_lowerCamelCase  )
														self.assertGreaterEqual(result["f1"]	,				30  )
														self.assertGreaterEqual(result["exact"]	,				30  )
						def 					__A					( self				:					Any  )							->					Union[str, Any]:
										__magic_name__	  = self.get_auto_remove_tmp_dir()
										__magic_name__	  = 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(_lowerCamelCase	,				"argv"	,				_lowerCamelCase  ):
														run_swag.main()
														__magic_name__	  = get_results(_lowerCamelCase  )
														self.assertGreaterEqual(result["val_accuracy"]	,				0.8  )
						@slow
						def 					__A					( self				:					Optional[Any]  )							->					Union[str, Any]:
										__magic_name__	  = self.get_auto_remove_tmp_dir()
										__magic_name__	  = 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(_lowerCamelCase	,				"argv"	,				_lowerCamelCase  ):
														run_summarization.main()
														__magic_name__	  = get_results(_lowerCamelCase  )
														self.assertGreaterEqual(result["rouge1"]	,				10  )
														self.assertGreaterEqual(result["rouge2"]	,				2  )
														self.assertGreaterEqual(result["rougeL"]	,				7  )
														self.assertGreaterEqual(result["rougeLsum"]	,				7  )
						@slow
						def 					__A					( self				:					Union[str, Any]  )							->					Dict:
										__magic_name__	  = self.get_auto_remove_tmp_dir()
										__magic_name__	  = 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(_lowerCamelCase	,				"argv"	,				_lowerCamelCase  ):
														run_translation.main()
														__magic_name__	  = get_results(_lowerCamelCase  )
														self.assertGreaterEqual(result["bleu"]	,				30  )

						def 					__A					( self				:					str  )							->					Optional[Any]:
										__magic_name__	  = self.get_auto_remove_tmp_dir()
										__magic_name__	  = 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(_lowerCamelCase	,				"argv"	,				_lowerCamelCase  ):
														run_image_classification.main()
														__magic_name__	  = get_results(_lowerCamelCase  )
														self.assertGreaterEqual(result["accuracy"]	,				0.7  )







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				barcode //= 10  # exclude the last digit
				__magic_name__	  = False
				__magic_name__	  = 0

				# extract and check each digit
				while barcode != 0:
								__magic_name__	  = 1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								__magic_name__	  = not checker

				return (10 - (s % 10)) % 10




def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				return len(str(lowerCamelCase_						)						) == 13 and get_check_digit(lowerCamelCase_						) == barcode % 10




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




				if str(lowerCamelCase_						).isalpha():
								__magic_name__	  = F'Barcode \'{barcode}\' has alphabetic characters.'
								raise ValueError(lowerCamelCase_						)
				elif int(lowerCamelCase_						) < 0:
								raise ValueError("The entered barcode has a negative value. Try again."						)
				else:
								return int(lowerCamelCase_						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__magic_name__   :   Optional[int]  							=get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(F'''\'{barcode}\' is a valid barcode.''')
						else:
												print(F'''\'{barcode}\' is NOT a valid barcode.''')



