





'''simple docstring'''




import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


a__					:			int          =     [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-classification''',
        '''language-modeling''',
        '''summarization''',
        '''token-classification''',
        '''question-answering''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
			import run_clm_flax
			import run_flax_glue
			import run_flax_ner
			import run_mlm_flax
			import run_qa
			import run_summarization_flax
			import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

a__					:			Union[str, Any]          =     logging.getLogger()




def 						__lowerCamelCase			(	)   ->Optional[int]:
	snake_case__	       =   argparse.ArgumentParser()
	parser.add_argument('-f'							)
	snake_case__	       =   parser.parse_args()
	return args.f




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_="eval"							)   ->Optional[int]:
	snake_case__	       =   os.path.join(UpperCAmelCase_					,				f'''{split}_results.json'''							)
	if os.path.exists(UpperCAmelCase_							):
		with open(UpperCAmelCase_					,				'r'							) as f:
			return json.load(UpperCAmelCase_							)
	raise ValueError(f'''can\'t find {path}'''							)


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







class 				__snake_case     (					__magic_name__							):





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   self.get_auto_remove_tmp_dir()
		snake_case__	       =   F'''
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

		with patch.object(UpperCamelCase_      ,       'argv'      ,       UpperCamelCase_		):
			run_flax_glue.main()
			snake_case__	       =   get_results(UpperCamelCase_		)
			self.assertGreaterEqual(result['eval_accuracy']      ,       0.7_5		)





	@slow
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   self.get_auto_remove_tmp_dir()
		snake_case__	       =   F'''
            run_clm_flax.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 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

		with patch.object(UpperCamelCase_      ,       'argv'      ,       UpperCamelCase_		):
			run_clm_flax.main()
			snake_case__	       =   get_results(UpperCamelCase_		)
			self.assertLess(result['eval_perplexity']      ,       100		)





	@slow
	def   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   self.get_auto_remove_tmp_dir()
		snake_case__	       =   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
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        '''.split()

		with patch.object(UpperCamelCase_      ,       'argv'      ,       UpperCamelCase_		):
			run_summarization_flax.main()
			snake_case__	       =   get_results(UpperCamelCase_      ,       split='test'		)
			self.assertGreaterEqual(result['test_rouge1']      ,       10		)
			self.assertGreaterEqual(result['test_rouge2']      ,       2		)
			self.assertGreaterEqual(result['test_rougeL']      ,       7		)
			self.assertGreaterEqual(result['test_rougeLsum']      ,       7		)





	@slow
	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   self.get_auto_remove_tmp_dir()
		snake_case__	       =   F'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        '''.split()

		with patch.object(UpperCamelCase_      ,       'argv'      ,       UpperCamelCase_		):
			run_mlm_flax.main()
			snake_case__	       =   get_results(UpperCamelCase_		)
			self.assertLess(result['eval_perplexity']      ,       42		)





	@slow
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.get_auto_remove_tmp_dir()
		snake_case__	       =   F'''
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

		with patch.object(UpperCamelCase_      ,       'argv'      ,       UpperCamelCase_		):
			run_ta_mlm_flax.main()
			snake_case__	       =   get_results(UpperCamelCase_		)
			self.assertGreaterEqual(result['eval_accuracy']      ,       0.4_2		)





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

		snake_case__	       =   self.get_auto_remove_tmp_dir()
		snake_case__	       =   F'''
            run_flax_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
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

		with patch.object(UpperCamelCase_      ,       'argv'      ,       UpperCamelCase_		):
			run_flax_ner.main()
			snake_case__	       =   get_results(UpperCamelCase_		)
			self.assertGreaterEqual(result['eval_accuracy']      ,       0.7_5		)
			self.assertGreaterEqual(result['eval_f1']      ,       0.3		)





	@slow
	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   self.get_auto_remove_tmp_dir()
		snake_case__	       =   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
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

		with patch.object(UpperCamelCase_      ,       'argv'      ,       UpperCamelCase_		):
			run_qa.main()
			snake_case__	       =   get_results(UpperCamelCase_		)
			self.assertGreaterEqual(result['eval_f1']      ,       30		)
			self.assertGreaterEqual(result['eval_exact']      ,       30		)










'''simple docstring'''




import os




def 						__lowerCamelCase			(	UpperCAmelCase_ = "input.txt"							)   ->int:

	with open(os.path.join(os.path.dirname(UpperCAmelCase_							)					,				UpperCAmelCase_							)							) as input_file:
		snake_case__	       =   [
		    [int(UpperCAmelCase_							) for element in line.split(','							)]
		    for line in input_file.readlines()
		]

	snake_case__	       =   len(UpperCAmelCase_							)
	snake_case__	       =   len(matrix[0]							)

	snake_case__	       =   [[-1 for _ in range(UpperCAmelCase_							)] for _ in range(UpperCAmelCase_							)]
	for i in range(UpperCAmelCase_							):
		snake_case__	       =   matrix[i][0]

	for j in range(1					,				UpperCAmelCase_							):
		for i in range(UpperCAmelCase_							):
			snake_case__	       =   minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1					,				UpperCAmelCase_							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i - 1][j] + matrix[i][j]							)

		for i in range(rows - 2					,				-1					,				-1							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i + 1][j] + matrix[i][j]							)

	return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums							)


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



