


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


lowercase__	:Dict										=					[
    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)

lowercase__	:Optional[Any]										=					logging.getLogger()



def  UpperCamelCase					(							):
					'''simple docstring'''
					lowercase					=		argparse.ArgumentParser()
					parser.add_argument('''-f'''	)
					lowercase					=		parser.parse_args()
					return args.f



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__="eval"	):
					'''simple docstring'''
					lowercase					=		os.path.join(lowercase_				,    f'{split}_results.json'	)
					if os.path.exists(lowercase_	):
										with open(lowercase_				,    '''r'''	) as f:
															return json.load(lowercase_	)
					raise ValueError(f'can\'t find {path}'	)


lowercase__	:str										=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):




							def 				A__   (       self):
												lowercase					=		self.get_auto_remove_tmp_dir()
												lowercase					=		f'\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

												with patch.object(A__     ,'''argv'''     ,A__):
																	run_flax_glue.main()
																	lowercase					=		get_results(A__)
																	self.assertGreaterEqual(result['''eval_accuracy''']     ,0.75)




							@slow
							def 				A__   (       self):
												lowercase					=		self.get_auto_remove_tmp_dir()
												lowercase					=		f'\n            run_clm_flax.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 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

												with patch.object(A__     ,'''argv'''     ,A__):
																	run_clm_flax.main()
																	lowercase					=		get_results(A__)
																	self.assertLess(result['''eval_perplexity''']     ,1_0_0)




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

												with patch.object(A__     ,'''argv'''     ,A__):
																	run_summarization_flax.main()
																	lowercase					=		get_results(A__     ,split='''test''')
																	self.assertGreaterEqual(result['''test_rouge1''']     ,1_0)
																	self.assertGreaterEqual(result['''test_rouge2''']     ,2)
																	self.assertGreaterEqual(result['''test_rougeL''']     ,7)
																	self.assertGreaterEqual(result['''test_rougeLsum''']     ,7)




							@slow
							def 				A__   (       self):
												lowercase					=		self.get_auto_remove_tmp_dir()
												lowercase					=		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            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

												with patch.object(A__     ,'''argv'''     ,A__):
																	run_mlm_flax.main()
																	lowercase					=		get_results(A__)
																	self.assertLess(result['''eval_perplexity''']     ,4_2)




							@slow
							def 				A__   (       self):
												lowercase					=		self.get_auto_remove_tmp_dir()
												lowercase					=		f'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

												with patch.object(A__     ,'''argv'''     ,A__):
																	run_ta_mlm_flax.main()
																	lowercase					=		get_results(A__)
																	self.assertGreaterEqual(result['''eval_accuracy''']     ,0.42)




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

												lowercase					=		self.get_auto_remove_tmp_dir()
												lowercase					=		f'\n            run_flax_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            --logging_steps 2 --eval_steps 2\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_flax_ner.main()
																	lowercase					=		get_results(A__)
																	self.assertGreaterEqual(result['''eval_accuracy''']     ,0.75)
																	self.assertGreaterEqual(result['''eval_f1''']     ,0.3)





							@slow
							def 				A__   (       self):
												lowercase					=		self.get_auto_remove_tmp_dir()
												lowercase					=		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            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\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_qa.main()
																	lowercase					=		get_results(A__)
																	self.assertGreaterEqual(result['''eval_f1''']     ,3_0)
																	self.assertGreaterEqual(result['''eval_exact''']     ,3_0)






import os



def  UpperCamelCase					(							lowerCAmelCase__ = "input.txt"	):
					'''simple docstring'''

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

					lowercase					=		len(lowerCAmelCase__	)
					lowercase					=		len(matrix[0]	)

					lowercase					=		[[-1 for _ in range(lowerCAmelCase__	)] for _ in range(lowerCAmelCase__	)]
					for i in range(lowerCAmelCase__	):
										lowercase					=		matrix[i][0]

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

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

										for i in range(rows - 2				,    -1				,    -1	):
															lowercase					=		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() = }')




