


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class        lowercase	(      __lowerCamelCase ,						unittest.TestCase	):
							lowercase_ : Dict       =LEDTokenizer
							lowercase_ : List[Any]       =LEDTokenizerFast
							lowercase_ : Tuple       =True




							def 				A__   (       self):
												super().setUp()
												lowercase					=		[
												    '''l''',
												    '''o''',
												    '''w''',
												    '''e''',
												    '''r''',
												    '''s''',
												    '''t''',
												    '''i''',
												    '''d''',
												    '''n''',
												    '''\u0120''',
												    '''\u0120l''',
												    '''\u0120n''',
												    '''\u0120lo''',
												    '''\u0120low''',
												    '''er''',
												    '''\u0120lowest''',
												    '''\u0120newer''',
												    '''\u0120wider''',
												    '''<unk>''',
												]
												lowercase					=		dict(zip(SCREAMING_SNAKE_CASE_     ,range(len(SCREAMING_SNAKE_CASE_))))
												lowercase					=		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
												lowercase					=		{'''unk_token''': '''<unk>'''}

												lowercase					=		os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES['''vocab_file'''])
												lowercase					=		os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES['''merges_file'''])
												with open(self.vocab_file     ,'''w'''     ,encoding='''utf-8''') as fp:
																	fp.write(json.dumps(SCREAMING_SNAKE_CASE_) + '''\n''')
												with open(self.merges_file     ,'''w'''     ,encoding='''utf-8''') as fp:
																	fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_))




							def 				A__   (       self     ,**A__):
												kwargs.update(self.special_tokens_map)
												return self.tokenizer_class.from_pretrained(self.tmpdirname     ,**SCREAMING_SNAKE_CASE_)




							def 				A__   (       self     ,**A__):
												kwargs.update(self.special_tokens_map)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,**SCREAMING_SNAKE_CASE_)




							def 				A__   (       self     ,A__):
												return "lower newer", "lower newer"




							@cached_property
							def 				A__   (       self):
												return LEDTokenizer.from_pretrained('''allenai/led-base-16384''')




							@cached_property
							def 				A__   (       self):
												return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''')




							@require_torch
							def 				A__   (       self):
												lowercase					=		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
												lowercase					=		[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowercase					=		tokenizer(SCREAMING_SNAKE_CASE_     ,max_length=len(SCREAMING_SNAKE_CASE_)     ,padding=SCREAMING_SNAKE_CASE_     ,return_tensors='''pt''')
																	self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_)

																	self.assertEqual((2, 9)     ,batch.input_ids.shape)
																	self.assertEqual((2, 9)     ,batch.attention_mask.shape)
																	lowercase					=		batch.input_ids.tolist()[0]
																	self.assertListEqual(SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_)




							@require_torch
							def 				A__   (       self):
												lowercase					=		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowercase					=		tokenizer(SCREAMING_SNAKE_CASE_     ,padding=SCREAMING_SNAKE_CASE_     ,return_tensors='''pt''')
																	self.assertIn('''input_ids'''     ,SCREAMING_SNAKE_CASE_)
																	self.assertIn('''attention_mask'''     ,SCREAMING_SNAKE_CASE_)
																	self.assertNotIn('''labels'''     ,SCREAMING_SNAKE_CASE_)
																	self.assertNotIn('''decoder_attention_mask'''     ,SCREAMING_SNAKE_CASE_)




							@require_torch
							def 				A__   (       self):
												lowercase					=		[
												    '''Summary of the text.''',
												    '''Another summary.''',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowercase					=		tokenizer(text_target=SCREAMING_SNAKE_CASE_     ,max_length=3_2     ,padding='''max_length'''     ,return_tensors='''pt''')
																	self.assertEqual(3_2     ,targets['''input_ids'''].shape[1])




							@require_torch
							def 				A__   (       self):
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowercase					=		tokenizer(
																	    ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']     ,padding=SCREAMING_SNAKE_CASE_     ,truncation=SCREAMING_SNAKE_CASE_     ,return_tensors='''pt''')
																	self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_)
																	self.assertEqual(batch.input_ids.shape     ,(2, 5_1_2_2))




							@require_torch
							def 				A__   (       self):
												lowercase					=		['''A long paragraph for summarization.''']
												lowercase					=		[
												    '''Summary of the text.''',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowercase					=		tokenizer(SCREAMING_SNAKE_CASE_     ,return_tensors='''pt''')
																	lowercase					=		tokenizer(text_target=SCREAMING_SNAKE_CASE_     ,return_tensors='''pt''')
																	lowercase					=		inputs['''input_ids''']
																	lowercase					=		targets['''input_ids''']
																	self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
																	self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())




							@require_torch
							def 				A__   (       self):
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowercase					=		['''Summary of the text.''', '''Another summary.''']
																	lowercase					=		[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																	lowercase					=		tokenizer(SCREAMING_SNAKE_CASE_     ,padding=SCREAMING_SNAKE_CASE_)
																	lowercase					=		[[0] * len(SCREAMING_SNAKE_CASE_) for x in encoded_output['''input_ids''']]
																	lowercase					=		tokenizer.pad(SCREAMING_SNAKE_CASE_)
																	self.assertSequenceEqual(outputs['''global_attention_mask''']     ,SCREAMING_SNAKE_CASE_)




							def 				A__   (       self):
												pass





							def 				A__   (       self):
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
																						lowercase					=		self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_     ,**SCREAMING_SNAKE_CASE_)
																						lowercase					=		self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_     ,**SCREAMING_SNAKE_CASE_)
																						lowercase					=		'''A, <mask> AllenNLP sentence.'''
																						lowercase					=		tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_     ,add_special_tokens=SCREAMING_SNAKE_CASE_     ,return_token_type_ids=SCREAMING_SNAKE_CASE_)
																						lowercase					=		tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_     ,add_special_tokens=SCREAMING_SNAKE_CASE_     ,return_token_type_ids=SCREAMING_SNAKE_CASE_)
																						self.assertEqual(sum(tokens_r['''token_type_ids'''])     ,sum(tokens_p['''token_type_ids''']))
																						self.assertEqual(
																						    sum(tokens_r['''attention_mask''']) / len(tokens_r['''attention_mask'''])     ,sum(tokens_p['''attention_mask''']) / len(tokens_p['''attention_mask'''])     ,)

																						lowercase					=		tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''])
																						lowercase					=		tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''])
																						self.assertSequenceEqual(tokens_p['''input_ids''']     ,[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])
																						self.assertSequenceEqual(tokens_r['''input_ids''']     ,[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])

																						self.assertSequenceEqual(
																						    SCREAMING_SNAKE_CASE_     ,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''])
																						self.assertSequenceEqual(
																						    SCREAMING_SNAKE_CASE_     ,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''])






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() = }')




