
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   :					List[str]    =			[
    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   :					Dict    =			logging.getLogger()


def  A			(   )  ->    Dict:
		'''simple docstring'''

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


def  A			(   lowercase					)  ->    List[str]:
		'''simple docstring'''

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


def  A			(   )  ->    Any:
		'''simple docstring'''

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


_UpperCAmelCase   :					Tuple    =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):



		@skip('Skipping until shape inference for to_tf_dataset PR is merged.'			)
		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	self.get_auto_remove_tmp_dir()
				UpperCamelCase            =	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'			)
						UpperCamelCase            =	get_results(A_			)
						self.assertGreaterEqual(result['eval_accuracy']							, 0.75			)



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.get_auto_remove_tmp_dir()
				UpperCamelCase            =	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()
						UpperCamelCase            =	get_results(A_			)
						self.assertLess(result['eval_perplexity']							, 100			)



		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	self.get_auto_remove_tmp_dir()
				UpperCamelCase            =	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()
						UpperCamelCase            =	get_results(A_			)
						self.assertLess(result['eval_perplexity']							, 42			)



		def __UpperCamelCase					(					self			)    ->   str:
				"""simple docstring"""
				# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
				UpperCamelCase            =	7 if get_gpu_count() > 1 else 2

				UpperCamelCase            =	self.get_auto_remove_tmp_dir()
				UpperCamelCase            =	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()
						UpperCamelCase            =	get_results(A_			)
						self.assertGreaterEqual(result['accuracy']							, 0.75			)



		def __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	self.get_auto_remove_tmp_dir()
				UpperCamelCase            =	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()
						UpperCamelCase            =	get_results(A_			)
						self.assertGreaterEqual(result['f1']							, 30			)
						self.assertGreaterEqual(result['exact']							, 30			)



		def __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	self.get_auto_remove_tmp_dir()
				UpperCamelCase            =	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()
						UpperCamelCase            =	get_results(A_			)
						self.assertGreaterEqual(result['val_accuracy']							, 0.8			)



		@slow
		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	self.get_auto_remove_tmp_dir()
				UpperCamelCase            =	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()
						UpperCamelCase            =	get_results(A_			)
						self.assertGreaterEqual(result['rouge1']							, 10			)
						self.assertGreaterEqual(result['rouge2']							, 2			)
						self.assertGreaterEqual(result['rougeL']							, 7			)
						self.assertGreaterEqual(result['rougeLsum']							, 7			)



		@slow
		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	self.get_auto_remove_tmp_dir()
				UpperCamelCase            =	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()
						UpperCamelCase            =	get_results(A_			)
						self.assertGreaterEqual(result['bleu']							, 30			)



		def __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	self.get_auto_remove_tmp_dir()
				UpperCamelCase            =	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()
						UpperCamelCase            =	get_results(A_			)
						self.assertGreaterEqual(result['accuracy']							, 0.7			)


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

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

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

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


def  A			(   lowercase					)  ->    bool:
		'''simple docstring'''

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


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					_UpperCAmelCase   :					List[str]    =			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.''')
