


'''simple docstring'''

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__				( __A							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		List[Any]	=	MvpTokenizer
				__UpperCamelCase    :		List[Any]	=	MvpTokenizerFast
				__UpperCamelCase    :		Any	=	True
				__UpperCamelCase    :		List[Any]	=	filter_roberta_detectors




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

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




				def 					_snake_case  (self       ,			**__lowercase		):
										kwargs.update(self.special_tokens_map		)
										return self.tokenizer_class.from_pretrained(self.tmpdirname       ,			**__lowercase		)




				def 					_snake_case  (self       ,			**__lowercase		):
										kwargs.update(self.special_tokens_map		)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										return "lower newer", "lower newer"




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




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




				@require_torch
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										__lowerCAmelCase							       =				[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							       =				tokenizer(__lowercase       ,			max_length=len(__lowercase		)       ,			padding=__lowercase       ,			return_tensors='''pt'''		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)

																self.assertEqual((2, 9)       ,			batch.input_ids.shape		)
																self.assertEqual((2, 9)       ,			batch.attention_mask.shape		)
																__lowerCAmelCase							       =				batch.input_ids.tolist()[0]
																self.assertListEqual(__lowercase       ,			__lowercase		)
																# Test that special tokens are reset




				@require_torch
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__lowerCAmelCase							       =				tokenizer(__lowercase       ,			padding=__lowercase       ,			return_tensors='''pt'''		)
																# check if input_ids are returned and no labels
																self.assertIn('''input_ids'''       ,			__lowercase		)
																self.assertIn('''attention_mask'''       ,			__lowercase		)
																self.assertNotIn('''labels'''       ,			__lowercase		)
																self.assertNotIn('''decoder_attention_mask'''       ,			__lowercase		)




				@require_torch
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				[
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__lowerCAmelCase							       =				tokenizer(text_target=__lowercase       ,			max_length=32       ,			padding='''max_length'''       ,			return_tensors='''pt'''		)
																self.assertEqual(32       ,			targets['''input_ids'''].shape[1]		)




				@require_torch
				def 					_snake_case  (self		):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__lowerCAmelCase							       =				tokenizer(
																    ['''I am a small frog''' * 10_24, '''I am a small frog''']       ,			padding=__lowercase       ,			truncation=__lowercase       ,			return_tensors='''pt'''		)
																self.assertIsInstance(__lowercase       ,			__lowercase		)
																self.assertEqual(batch.input_ids.shape       ,			(2, 10_24)		)




				@require_torch
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				['''A long paragraph for summarization.''']
										__lowerCAmelCase							       =				[
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__lowerCAmelCase							       =				tokenizer(__lowercase       ,			text_target=__lowercase       ,			return_tensors='''pt'''		)
																__lowerCAmelCase							       =				inputs['''input_ids''']
																__lowerCAmelCase							       =				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 					_snake_case  (self		):
										pass




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

																						# 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							       =				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
																						__lowerCAmelCase							       =				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(
																						    __lowercase       ,			['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)
																						self.assertSequenceEqual(
																						    __lowercase       ,			['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)





'''simple docstring'''

from torch import nn

def 							__magic_name__(						lowerCamelCase):
						if act_fn in ["swish", "silu"]:
												return nn.SiLU()
						elif act_fn == "mish":
												return nn.Mish()
						elif act_fn == "gelu":
												return nn.GELU()
						else:
												raise ValueError(F"""Unsupported activation function: {act_fn}""")

