



'''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


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 		_snake_case		(					)			->						Dict:







	"""simple docstring"""
	lowerCAmelCase    =	argparse.ArgumentParser()
	parser.add_argument("""-f"""							)
	lowerCAmelCase    =	parser.parse_args()
	return args.f






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						str							)			->						Dict:







	"""simple docstring"""
	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 		_snake_case		(					)			->						List[str]:







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


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





class __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""      )
						def 				__snake_case    (					self      )	->	Dict:
							lowerCAmelCase    =	self.get_auto_remove_tmp_dir()
							lowerCAmelCase    =	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(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"""]	,       0.7_5      )


						def 				__snake_case    (					self      )	->	Optional[Any]:
							lowerCAmelCase    =	self.get_auto_remove_tmp_dir()
							lowerCAmelCase    =	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(A_	,       """argv"""	,       A_      ):
								run_clm.main()
								lowerCAmelCase    =	get_results(A_      )
								self.assertLess(result["""eval_perplexity"""]	,       100      )


						def 				__snake_case    (					self      )	->	List[Any]:
							lowerCAmelCase    =	self.get_auto_remove_tmp_dir()
							lowerCAmelCase    =	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(A_	,       """argv"""	,       A_      ):
								run_mlm.main()
								lowerCAmelCase    =	get_results(A_      )
								self.assertLess(result["""eval_perplexity"""]	,       42      )


						def 				__snake_case    (					self      )	->	Tuple:
							# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
							lowerCAmelCase    =	7 if get_gpu_count() > 1 else 2

							lowerCAmelCase    =	self.get_auto_remove_tmp_dir()
							lowerCAmelCase    =	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(A_	,       """argv"""	,       A_      ):
								run_ner.main()
								lowerCAmelCase    =	get_results(A_      )
								self.assertGreaterEqual(result["""accuracy"""]	,       0.7_5      )


						def 				__snake_case    (					self      )	->	Any:
							lowerCAmelCase    =	self.get_auto_remove_tmp_dir()
							lowerCAmelCase    =	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(A_	,       """argv"""	,       A_      ):
								run_squad.main()
								lowerCAmelCase    =	get_results(A_      )
								self.assertGreaterEqual(result["""f1"""]	,       30      )
								self.assertGreaterEqual(result["""exact"""]	,       30      )


						def 				__snake_case    (					self      )	->	int:
							lowerCAmelCase    =	self.get_auto_remove_tmp_dir()
							lowerCAmelCase    =	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(A_	,       """argv"""	,       A_      ):
								run_swag.main()
								lowerCAmelCase    =	get_results(A_      )
								self.assertGreaterEqual(result["""val_accuracy"""]	,       0.8      )


						@slow
						def 				__snake_case    (					self      )	->	List[Any]:
							lowerCAmelCase    =	self.get_auto_remove_tmp_dir()
							lowerCAmelCase    =	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(A_	,       """argv"""	,       A_      ):
								run_summarization.main()
								lowerCAmelCase    =	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 				__snake_case    (					self      )	->	Dict:
							lowerCAmelCase    =	self.get_auto_remove_tmp_dir()
							lowerCAmelCase    =	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(A_	,       """argv"""	,       A_      ):
								run_translation.main()
								lowerCAmelCase    =	get_results(A_      )
								self.assertGreaterEqual(result["""bleu"""]	,       30      )




						def 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	self.get_auto_remove_tmp_dir()
							lowerCAmelCase    =	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(A_	,       """argv"""	,       A_      ):
								run_image_classification.main()
								lowerCAmelCase    =	get_results(A_      )
								self.assertGreaterEqual(result["""accuracy"""]	,       0.7      )




'''simple docstring'''

from __future__ import annotations

from pathlib import Path






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						tuple[int, int]							,		_SCREAMING_SNAKE_CASE     :						tuple[int, int]							)			->						int:







	"""simple docstring"""
	return pointa[0] * pointa[1] - pointa[1] * pointa[0]






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							)			->						bool:







	"""simple docstring"""
	lowerCAmelCase    =	(xa, ya)
	lowerCAmelCase    =	(xa - xa, ya - ya)
	lowerCAmelCase    =	(xa - xa, ya - ya)
	lowerCAmelCase    =	-vector_product(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) / vector_product(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	+vector_product(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) / vector_product(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	return a > 0 and b > 0 and a + b < 1






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						str = "p102_triangles.txt"							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	Path(_SCREAMING_SNAKE_CASE							).parent.joinpath(_SCREAMING_SNAKE_CASE							).read_text(encoding="""utf-8"""							)

	lowerCAmelCase    =	[]
	for line in data.strip().split("""\n"""							):
		triangles.append([int(_SCREAMING_SNAKE_CASE							) for number in line.split(""","""							)]							)

	lowerCAmelCase    =	0
	lowerCAmelCase    =	42

	for triangle in triangles:
		ret += contains_origin(*_SCREAMING_SNAKE_CASE							)

	return ret


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