
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors




@require_tokenizers
class 				A__ (			lowercase_ ,  unittest.TestCase):
		A_		: Optional[int]            =			MvpTokenizer
		A_		: Optional[int]            =			MvpTokenizerFast
		A_		: Optional[int]            =			True
		A_		: Tuple            =			filter_roberta_detectors

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

			__lowerCAmelCase    :		Optional[Any]		    =   os.path.join(self.tmpdirname ,	VOCAB_FILES_NAMES['vocab_file']				)
			__lowerCAmelCase    :		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(_SCREAMING_SNAKE_CASE				) + '\n'				)
			with open(self.merges_file ,	'w' ,	encoding='utf-8'				) as fp:
				fp.write('\n'.join(_SCREAMING_SNAKE_CASE				)				)

		def 							__lowerCamelCase							(   self ,	**_SCREAMING_SNAKE_CASE				):
			kwargs.update(self.special_tokens_map				)
			return self.tokenizer_class.from_pretrained(self.tmpdirname ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	**_SCREAMING_SNAKE_CASE				):
			kwargs.update(self.special_tokens_map				)
			return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return "lower newer", "lower newer"

		@cached_property
		def 							__lowerCamelCase							(   self				):
			return MvpTokenizer.from_pretrained('RUCAIBox/mvp'				)

		@cached_property
		def 							__lowerCamelCase							(   self				):
			return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'				)

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
			__lowerCAmelCase    :		Union[str, Any]		    =   [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				__lowerCAmelCase    :		Tuple		    =   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				)
				__lowerCAmelCase    :		Dict		    =   batch.input_ids.tolist()[0]
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
				# Test that special tokens are reset

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				__lowerCAmelCase    :		Dict		    =   tokenizer(_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				)
				# check if input_ids are returned and no labels
				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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   [
			    'Summary of the text.',
			    'Another summary.',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				__lowerCAmelCase    :		Optional[Any]		    =   tokenizer(text_target=_SCREAMING_SNAKE_CASE ,	max_length=32 ,	padding='max_length' ,	return_tensors='pt'				)
				self.assertEqual(32 ,	targets['input_ids'].shape[1]				)

		@require_torch
		def 							__lowerCamelCase							(   self				):
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				__lowerCAmelCase    :		Optional[Any]		    =   tokenizer(
				    ['I am a small frog' * 10_24, '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, 10_24)				)

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   ['A long paragraph for summarization.']
			__lowerCAmelCase    :		str		    =   [
			    'Summary of the text.',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				__lowerCAmelCase    :		Dict		    =   tokenizer(_SCREAMING_SNAKE_CASE ,	text_target=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				)
				__lowerCAmelCase    :		Optional[Any]		    =   inputs['input_ids']
				__lowerCAmelCase    :		Union[str, Any]		    =   inputs['labels']
				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()				)

		def 							__lowerCamelCase							(   self				):
			pass







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

					# token_type_ids should put 0 everywhere
					self.assertEqual(sum(tokens_r['token_type_ids']				) ,	sum(tokens_p['token_type_ids']				)				)

					# attention_mask should put 1 everywhere, so sum over length should be 1
					self.assertEqual(
					    sum(tokens_r['attention_mask']				) / len(tokens_r['attention_mask']				) ,	sum(tokens_p['attention_mask']				) / len(tokens_p['attention_mask']				) ,	)

					__lowerCAmelCase    :		Optional[Any]		    =   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']				)
					__lowerCAmelCase    :		Optional[Any]		    =   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']				)

					# Rust correctly handles the space before the mask while python doesnt
					self.assertSequenceEqual(tokens_p['input_ids'] ,	[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)
					self.assertSequenceEqual(tokens_r['input_ids'] ,	[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 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>']				)

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   len(_UpperCamelCase	)
	__lowerCAmelCase    :		Tuple		    =   [[0] * n for i in range(_UpperCamelCase	)]
	for i in range(_UpperCamelCase	):
		__lowerCAmelCase    :		Any		    =   y_points[i]

	for i in range(2 ,						_UpperCamelCase	):
		for j in range(_UpperCamelCase ,						_UpperCamelCase	):
			__lowerCAmelCase    :		int		    =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()