



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 				_a						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Union[str, Any] =       LEDTokenizer
							A				:Dict =       LEDTokenizerFast
							A				:List[Any] =       True



							def 		_A	(							self				):







											"""simple docstring"""



											super().setUp()
											a__	:							Any      						= [
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											a__	:							int      						= dict(zip(__UpperCAmelCase ,					range(len(__UpperCAmelCase				)				)				)				)
											a__	:							Dict      						= ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											a__	:							Tuple      						= {"unk_token": "<unk>"}

											a__	:							Any      						= os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["vocab_file"]				)
											a__	:							Union[str, Any]      						= 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 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



											kwargs.update(self.special_tokens_map				)
											return self.tokenizer_class.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



											kwargs.update(self.special_tokens_map				)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											return "lower newer", "lower newer"



							@cached_property
							def 		_A	(							self				):







											"""simple docstring"""



											return LEDTokenizer.from_pretrained("allenai/led-base-16384"				)



							@cached_property
							def 		_A	(							self				):







											"""simple docstring"""



											return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"				)



							@require_torch
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= ["A long paragraph for summarization.", "Another paragraph for summarization."]
											a__	:							str      						= [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__	:							Union[str, Any]      						= 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				)
															a__	:							Any      						= batch.input_ids.tolist()[0]
															self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase				)



							@require_torch
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= ["A long paragraph for summarization.", "Another paragraph for summarization."]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__	:							List[str]      						= 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 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= [
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__	:							Dict      						= tokenizer(text_target=__UpperCAmelCase ,					max_length=32 ,					padding="max_length" ,					return_tensors="pt"				)
															self.assertEqual(32 ,					targets["input_ids"].shape[1]				)



							@require_torch
							def 		_A	(							self				):







											"""simple docstring"""



											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__	:							int      						= tokenizer(
															    ["I am a small frog" * 1024, "I am a small frog"] ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					return_tensors="pt"				)
															self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase				)
															self.assertEqual(batch.input_ids.shape ,					(2, 5122)				)



							@require_torch
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= ["A long paragraph for summarization."]
											a__	:							Union[str, Any]      						= [
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__	:							str      						= tokenizer(__UpperCAmelCase ,					return_tensors="pt"				)
															a__	:							List[str]      						= tokenizer(text_target=__UpperCAmelCase ,					return_tensors="pt"				)
															a__	:							Union[str, Any]      						= inputs["input_ids"]
															a__	:							Tuple      						= 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				):







											"""simple docstring"""



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

															a__	:							str      						= tokenizer(__UpperCAmelCase ,					padding=__UpperCAmelCase				)
															a__	:							Optional[int]      						= [[0] * len(__UpperCAmelCase				) for x in encoded_output["input_ids"]]
															a__	:							Any      						= tokenizer.pad(__UpperCAmelCase				)
															self.assertSequenceEqual(outputs["global_attention_mask"] ,					__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											pass




							def 		_A	(							self				):







											"""simple docstring"""



											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																			a__	:							Any      						= self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase				)
																			a__	:							int      						= self.tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase				)
																			a__	:							List[str]      						= "A, <mask> AllenNLP sentence."
																			a__	:							Any      						= tokenizer_r.encode_plus(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase				)
																			a__	:							Optional[int]      						= 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"]				) ,					)

																			a__	:							int      						= tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]				)
																			a__	:							Any      						= tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]				)
																			self.assertSequenceEqual(tokens_p["input_ids"] ,					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]				)
																			self.assertSequenceEqual(tokens_r["input_ids"] ,					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]				)

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







import unittest

from knapsack import knapsack as k


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= 0
											a__	:							Union[str, Any]      						= [0]
											a__	:							Optional[Any]      						= [0]
											a__	:							Tuple      						= len(__UpperCAmelCase				)
											self.assertEqual(k.knapsack(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				) ,					0				)

											a__	:							Optional[Any]      						= [60]
											a__	:							Tuple      						= [10]
											a__	:							Optional[Any]      						= len(__UpperCAmelCase				)
											self.assertEqual(k.knapsack(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				) ,					0				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= 3
											a__	:							Optional[int]      						= [1, 2, 3]
											a__	:							List[Any]      						= [3, 2, 1]
											a__	:							List[Any]      						= len(__UpperCAmelCase				)
											self.assertEqual(k.knapsack(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				) ,					5				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= 50
											a__	:							Union[str, Any]      						= [60, 100, 120]
											a__	:							str      						= [10, 20, 30]
											a__	:							Union[str, Any]      						= len(__UpperCAmelCase				)
											self.assertEqual(k.knapsack(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				) ,					220				)


if __name__ == "__main__":
						unittest.main()


