


"""simple docstring"""







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 				UpperCamelCase_			(				UpperCamelCase_    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Tuple					      =    LEDTokenizer
	snake_case__       :						Dict					      =    LEDTokenizerFast
	snake_case__       :						Any					      =    True




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

						__SCREAMING_SNAKE_CASE		     =       os.path.join(self.tmpdirname    ,    VOCAB_FILES_NAMES["vocab_file"]     )
						__SCREAMING_SNAKE_CASE		     =       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(UpperCamelCase__     ) + "\n"     )
						with open(self.merges_file    ,    "w"    ,    encoding="utf-8"     ) as fp:
											fp.write("\n".join(UpperCamelCase__     )     )




	def 						UpperCAmelCase_   (				self    :       Dict    ,    **UpperCAmelCase__    :       Optional[Any]     ) ->			List[str]:
						kwargs.update(self.special_tokens_map     )
						return self.tokenizer_class.from_pretrained(self.tmpdirname    ,    **UpperCamelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    **UpperCAmelCase__    :       int     ) ->			Tuple:
						kwargs.update(self.special_tokens_map     )
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,    **UpperCamelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       int     ) ->			int:
						return "lower newer", "lower newer"




	@cached_property
	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Tuple:
						return LEDTokenizer.from_pretrained("allenai/led-base-16384"     )




	@cached_property
	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Tuple:
						return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"     )




	@require_torch
	def 						UpperCAmelCase_   (				self    :       Any     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
						__SCREAMING_SNAKE_CASE		     =       [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]:
											__SCREAMING_SNAKE_CASE		     =       tokenizer(UpperCamelCase__    ,    max_length=len(UpperCamelCase__     )    ,    padding=UpperCamelCase__    ,    return_tensors="pt"     )
											self.assertIsInstance(UpperCamelCase__    ,    UpperCamelCase__     )

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




	@require_torch
	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__SCREAMING_SNAKE_CASE		     =       tokenizer(UpperCamelCase__    ,    padding=UpperCamelCase__    ,    return_tensors="pt"     )
											self.assertIn("input_ids"    ,    UpperCamelCase__     )
											self.assertIn("attention_mask"    ,    UpperCamelCase__     )
											self.assertNotIn("labels"    ,    UpperCamelCase__     )
											self.assertNotIn("decoder_attention_mask"    ,    UpperCamelCase__     )




	@require_torch
	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       [
						    '''Summary of the text.''',
						    '''Another summary.''',
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__SCREAMING_SNAKE_CASE		     =       tokenizer(text_target=UpperCamelCase__    ,    max_length=3_2    ,    padding="max_length"    ,    return_tensors="pt"     )
											self.assertEqual(3_2    ,    targets["input_ids"].shape[1]     )




	@require_torch
	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			str:





						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__SCREAMING_SNAKE_CASE		     =       tokenizer(
											    ["I am a small frog" * 1_0_2_4, "I am a small frog"]    ,    padding=UpperCamelCase__    ,    truncation=UpperCamelCase__    ,    return_tensors="pt"     )
											self.assertIsInstance(UpperCamelCase__    ,    UpperCamelCase__     )
											self.assertEqual(batch.input_ids.shape    ,    (2, 5_1_2_2)     )




	@require_torch
	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       ['''A long paragraph for summarization.''']
						__SCREAMING_SNAKE_CASE		     =       [
						    '''Summary of the text.''',
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__SCREAMING_SNAKE_CASE		     =       tokenizer(UpperCamelCase__    ,    return_tensors="pt"     )
											__SCREAMING_SNAKE_CASE		     =       tokenizer(text_target=UpperCamelCase__    ,    return_tensors="pt"     )
											__SCREAMING_SNAKE_CASE		     =       inputs['''input_ids''']
											__SCREAMING_SNAKE_CASE		     =       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 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			int:





						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__SCREAMING_SNAKE_CASE		     =       ['''Summary of the text.''', '''Another summary.''']
											__SCREAMING_SNAKE_CASE		     =       [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			List[str]:
						pass




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Union[str, Any]:





						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																__SCREAMING_SNAKE_CASE		     =       self.rust_tokenizer_class.from_pretrained(UpperCamelCase__    ,    **UpperCamelCase__     )
																__SCREAMING_SNAKE_CASE		     =       self.tokenizer_class.from_pretrained(UpperCamelCase__    ,    **UpperCamelCase__     )
																__SCREAMING_SNAKE_CASE		     =       '''A, <mask> AllenNLP sentence.'''
																__SCREAMING_SNAKE_CASE		     =       tokenizer_r.encode_plus(UpperCamelCase__    ,    add_special_tokens=UpperCamelCase__    ,    return_token_type_ids=UpperCamelCase__     )
																__SCREAMING_SNAKE_CASE		     =       tokenizer_p.encode_plus(UpperCamelCase__    ,    add_special_tokens=UpperCamelCase__    ,    return_token_type_ids=UpperCamelCase__     )
																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"]     )    ,    )

																__SCREAMING_SNAKE_CASE		     =       tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]     )
																__SCREAMING_SNAKE_CASE		     =       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(
																    UpperCamelCase__    ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]     )
																self.assertSequenceEqual(
																    UpperCamelCase__    ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]     )




"""simple docstring"""







import functools




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					if not isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for day in days     ):
										raise ValueError("The parameter days should be a list of integers"     )

					if len(lowerCAmelCase_     ) != 3 or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for cost in costs     ):
										raise ValueError("The parameter costs should be a list of three integers"     )

					if len(lowerCAmelCase_     ) == 0:
										return 0

					if min(lowerCAmelCase_     ) <= 0:
										raise ValueError("All days elements should be greater than 0"     )

					if max(lowerCAmelCase_     ) >= 366:
										raise ValueError("All days elements should be less than 366"     )

					__SCREAMING_SNAKE_CASE		     =       set(lowerCAmelCase_     )

					@functools.cache
					def dynamic_programming(lowerCAmelCase_     ) -> int:
										if index > 365:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1     )

										return min(
										    costs[0] + dynamic_programming(index + 1     )						,		costs[1] + dynamic_programming(index + 7     )						,		costs[2] + dynamic_programming(index + 30     )						,		)

					return dynamic_programming(1     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
