

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      snake_case_      (					_a      ,			unittest.TestCase				):


							"""simple docstring"""





							__UpperCAmelCase      =MPNetTokenizer
							__UpperCAmelCase      =MPNetTokenizerFast
							__UpperCAmelCase      =True
							__UpperCAmelCase      =True




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

												__lowerCAmelCase       =				[
												    '[UNK]',
												    '[CLS]',
												    '[SEP]',
												    '[PAD]',
												    '[MASK]',
												    'want',
												    '##want',
												    '##ed',
												    'wa',
												    'un',
												    'runn',
												    '##ing',
												    ',',
												    'low',
												    'lowest',
												]
												__lowerCAmelCase       =				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       ,    _A				):
												__lowerCAmelCase       =				'UNwant\u00E9d,running'
												__lowerCAmelCase       =				'unwanted, running'
												return input_text, output_text




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.tokenizer_class(self.vocab_file				)

												__lowerCAmelCase       =				tokenizer.tokenize('UNwant\u00E9d,running'				)
												self.assertListEqual(_A       ,    ['un', '##want', '##ed', ',', 'runn', '##ing']				)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_A				)       ,    [9, 6, 7, 1_2, 1_0, 1_1]				)




							@slow
							def 							A__	(			self				):
												__lowerCAmelCase       =				self.tokenizer_class.from_pretrained('microsoft/mpnet-base'				)

												__lowerCAmelCase       =				tokenizer.encode('sequence builders'       ,    add_special_tokens=_A				)
												__lowerCAmelCase       =				tokenizer.encode('multi-sequence build'       ,    add_special_tokens=_A				)

												__lowerCAmelCase       =				tokenizer.build_inputs_with_special_tokens(_A				)
												__lowerCAmelCase       =				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






