

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 						lowercase     (					_a     ,				unittest.TestCase				):


		"""simple docstring"""




		a__       							=	MPNetTokenizer
		a__       							=	MPNetTokenizerFast
		a__       							=	True
		a__       							=	True


		def 		A__       (					self):
			super().setUp()

			_UpperCamelCase :		Dict								=    [
			    '[UNK]',
			    '[CLS]',
			    '[SEP]',
			    '[PAD]',
			    '[MASK]',
			    'want',
			    '##want',
			    '##ed',
			    'wa',
			    'un',
			    'runn',
			    '##ing',
			    ',',
			    'low',
			    'lowest',
			]
			_UpperCamelCase :		int								=    os.path.join(self.tmpdirname						,							VOCAB_FILES_NAMES['vocab_file'])
			with open(self.vocab_file						,							'w'						,							encoding='utf-8') as vocab_writer:
				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))


		def 		A__       (					self						,							__snake_case):
			_UpperCamelCase :		Optional[int]								=    'UNwant\u00E9d,running'
			_UpperCamelCase :		List[Any]								=    'unwanted, running'
			return input_text, output_text


		def 		A__       (					self):
			_UpperCamelCase :		str								=    self.tokenizer_class(self.vocab_file)

			_UpperCamelCase :		Dict								=    tokenizer.tokenize('UNwant\u00E9d,running')
			self.assertListEqual(_A						,							['un', '##want', '##ed', ',', 'runn', '##ing'])
			self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)						,							[9, 6, 7, 12, 10, 11])






		@slow
		def 		A__       (					self):
			_UpperCamelCase :		Optional[int]								=    self.tokenizer_class.from_pretrained('microsoft/mpnet-base')

			_UpperCamelCase :		Union[str, Any]								=    tokenizer.encode('sequence builders'						,							add_special_tokens=_A)
			_UpperCamelCase :		Dict								=    tokenizer.encode('multi-sequence build'						,							add_special_tokens=_A)

			_UpperCamelCase :		str								=    tokenizer.build_inputs_with_special_tokens(_A)
			_UpperCamelCase :		List[Any]								=    tokenizer.build_inputs_with_special_tokens(_A						,							_A)

			assert encoded_sentence == [0] + text + [2]
			assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""fsmt_decoder"""




							def __init__(			self       ,    _A=0       ,    _A=0				):
												super().__init__()
												__lowerCAmelCase       =				vocab_size
												__lowerCAmelCase       =				bos_token_id





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""fsmt"""
							__UpperCAmelCase      ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




							def __init__(			self       ,    _A=["en", "de"]       ,    _A=4_2_0_2_4       ,    _A=4_2_0_2_4       ,    _A="relu"       ,    _A=1_0_2_4       ,    _A=2_0_0       ,    _A=1_0_2_4       ,    _A=4_0_9_6       ,    _A=1_2       ,    _A=1_6       ,    _A=0.0       ,    _A=4_0_9_6       ,    _A=1_2       ,    _A=1_6       ,    _A=0.0       ,    _A=0.0       ,    _A=0.1       ,    _A=0.0       ,    _A=0.02       ,    _A=2       ,    _A=True       ,    _A=True       ,    _A=False       ,    _A=5       ,    _A=1.0       ,    _A=False       ,    _A=True       ,    _A=1       ,    _A=0       ,    _A=2       ,    _A=2       ,    **_A       ,    ):
												__lowerCAmelCase       =				langs
												__lowerCAmelCase       =				src_vocab_size
												__lowerCAmelCase       =				tgt_vocab_size
												__lowerCAmelCase       =				d_model  # encoder_embed_dim and decoder_embed_dim

												__lowerCAmelCase       =				encoder_ffn_dim
												__lowerCAmelCase       =				__lowerCAmelCase       =				encoder_layers
												__lowerCAmelCase       =				encoder_attention_heads
												__lowerCAmelCase       =				encoder_layerdrop
												__lowerCAmelCase       =				decoder_layerdrop
												__lowerCAmelCase       =				decoder_ffn_dim
												__lowerCAmelCase       =				decoder_layers
												__lowerCAmelCase       =				decoder_attention_heads
												__lowerCAmelCase       =				max_position_embeddings
												__lowerCAmelCase       =				init_std  # Normal(0, this parameter)
												__lowerCAmelCase       =				activation_function

												__lowerCAmelCase       =				DecoderConfig(vocab_size=_A       ,    bos_token_id=_A				)
												if "decoder" in common_kwargs:
																	del common_kwargs["decoder"]

												__lowerCAmelCase       =				scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												__lowerCAmelCase       =				attention_dropout
												__lowerCAmelCase       =				activation_dropout
												__lowerCAmelCase       =				dropout

												__lowerCAmelCase       =				use_cache
												super().__init__(
												    pad_token_id=_A       ,    bos_token_id=_A       ,    eos_token_id=_A       ,    decoder_start_token_id=_A       ,    is_encoder_decoder=_A       ,    tie_word_embeddings=_A       ,    forced_eos_token_id=_A       ,    max_length=_A       ,    num_beams=_A       ,    length_penalty=_A       ,    early_stopping=_A       ,    **_A       ,    )




							def 							A__	(			self				):
												__lowerCAmelCase       =				copy.deepcopy(self.__dict__				)
												__lowerCAmelCase       =				self.decoder.to_dict()
												__lowerCAmelCase       =				self.__class__.model_type
												return output






