


"""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


SCREAMING_SNAKE_CASE					:       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)

SCREAMING_SNAKE_CASE					:       Any							   =							logging.getLogger()






def 						lowercase				(	)	->Tuple:
	"""simple docstring"""





	__snake_case      :		int         =							argparse.ArgumentParser()
	parser.add_argument('''-f'''	)
	__snake_case      :		Tuple         =							parser.parse_args()
	return args.f






def 						lowercase				(	_snake_case		: Optional[Any]	)	->Tuple:
	"""simple docstring"""





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






def 						lowercase				(	)	->Optional[Any]:
	"""simple docstring"""





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


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



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

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





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							self.get_auto_remove_tmp_dir()
			__snake_case      :		str         =							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'''   )
				__snake_case      :		List[Any]         =							get_results(a_   )
				self.assertGreaterEqual(result['''eval_accuracy''']							,			0.75   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							self.get_auto_remove_tmp_dir()
			__snake_case      :		Any         =							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()
				__snake_case      :		Tuple         =							get_results(a_   )
				self.assertLess(result['''eval_perplexity''']							,			1_00   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							self.get_auto_remove_tmp_dir()
			__snake_case      :		Optional[int]         =							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()
				__snake_case      :		Optional[int]         =							get_results(a_   )
				self.assertLess(result['''eval_perplexity''']							,			42   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		str         =							7 if get_gpu_count() > 1 else 2

			__snake_case      :		int         =							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(a_							,			'''argv'''							,			a_   ):
				run_ner.main()
				__snake_case      :		Dict         =							get_results(a_   )
				self.assertGreaterEqual(result['''accuracy''']							,			0.75   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		str         =							self.get_auto_remove_tmp_dir()
			__snake_case      :		Tuple         =							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()
				__snake_case      :		Tuple         =							get_results(a_   )
				self.assertGreaterEqual(result['''f1''']							,			30   )
				self.assertGreaterEqual(result['''exact''']							,			30   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							self.get_auto_remove_tmp_dir()
			__snake_case      :		Optional[int]         =							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()
				__snake_case      :		int         =							get_results(a_   )
				self.assertGreaterEqual(result['''val_accuracy''']							,			0.8   )



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[Any]         =							self.get_auto_remove_tmp_dir()
			__snake_case      :		Optional[int]         =							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()
				__snake_case      :		Optional[int]         =							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 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[Any]         =							self.get_auto_remove_tmp_dir()
			__snake_case      :		List[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(a_							,			'''argv'''							,			a_   ):
				run_translation.main()
				__snake_case      :		int         =							get_results(a_   )
				self.assertGreaterEqual(result['''bleu''']							,			30   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							self.get_auto_remove_tmp_dir()
			__snake_case      :		int         =							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()
				__snake_case      :		Dict         =							get_results(a_   )
				self.assertGreaterEqual(result['''accuracy''']							,			0.7   )









"""simple docstring"""
from __future__ import annotations

import math
import random






def 						lowercase				(	_snake_case		: int	,  _snake_case		: int	,  _snake_case		: int	)	->list[int]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							d * e - 1
	__snake_case      :		List[str]         =							0
	__snake_case      :		Dict         =							0
	while p == 0:
		__snake_case      :		str         =							random.randint(2	,  n - 1	)
		__snake_case      :		int         =							k
		while True:
			if t % 2 == 0:
				__snake_case      :		int         =							t // 2
				__snake_case      :		int         =							(g**t) % n
				__snake_case      :		Optional[Any]         =							math.gcd(x - 1	,  _snake_case	)
				if x > 1 and y > 1:
					__snake_case      :		Tuple         =							y
					__snake_case      :		int         =							n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()





