
'''simple docstring'''

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCAmelCase												=   logging.get_logger(__name__)


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= 'mt5'
   A		= ['past_key_values']






   def __init__(       self    ,__SCREAMING_SNAKE_CASE=250112    ,__SCREAMING_SNAKE_CASE=512    ,__SCREAMING_SNAKE_CASE=64    ,__SCREAMING_SNAKE_CASE=1024    ,__SCREAMING_SNAKE_CASE=8    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=6    ,__SCREAMING_SNAKE_CASE=32    ,__SCREAMING_SNAKE_CASE=128    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=1e-6    ,__SCREAMING_SNAKE_CASE=1.0    ,__SCREAMING_SNAKE_CASE="gated-gelu"    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE="T5Tokenizer"    ,__SCREAMING_SNAKE_CASE=False    ,__SCREAMING_SNAKE_CASE=0    ,__SCREAMING_SNAKE_CASE=1    ,__SCREAMING_SNAKE_CASE=0    ,**__SCREAMING_SNAKE_CASE    ,):
    super().__init__(
        is_encoder_decoder=__SCREAMING_SNAKE_CASE    ,tokenizer_class=__SCREAMING_SNAKE_CASE    ,tie_word_embeddings=__SCREAMING_SNAKE_CASE    ,pad_token_id=__SCREAMING_SNAKE_CASE    ,eos_token_id=__SCREAMING_SNAKE_CASE    ,decoder_start_token_id=__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE    ,)
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		vocab_size
    SCREAMING_SNAKE_CASE  :  Tuple								=		d_model
    SCREAMING_SNAKE_CASE  :  str								=		d_kv
    SCREAMING_SNAKE_CASE  :  Dict								=		d_ff
    SCREAMING_SNAKE_CASE  :  int								=		num_layers
    SCREAMING_SNAKE_CASE  :  Any								=		(
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    SCREAMING_SNAKE_CASE  :  List[Any]								=		num_heads
    SCREAMING_SNAKE_CASE  :  Any								=		relative_attention_num_buckets
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		relative_attention_max_distance
    SCREAMING_SNAKE_CASE  :  Any								=		dropout_rate
    SCREAMING_SNAKE_CASE  :  List[Any]								=		layer_norm_epsilon
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		initializer_factor
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		feed_forward_proj
    SCREAMING_SNAKE_CASE  :  int								=		use_cache

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.feed_forward_proj.split('-'      )
    SCREAMING_SNAKE_CASE  :  str								=		act_info[-1]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		act_info[0] == 'gated'

    if len(__SCREAMING_SNAKE_CASE      ) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE      ) > 2:
     raise ValueError(
         f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
         'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
         '\'gated-gelu\' or \'relu\''      )

    # for backwards compatibility
    if feed_forward_proj == "gated-gelu":
     SCREAMING_SNAKE_CASE  :  Tuple								=		'gelu_new'






   @property
   def 					__a			(       self      ):
    return self.d_model






   @property
   def 					__a			(       self      ):
    return self.num_heads






   @property
   def 					__a			(       self      ):
    return self.num_layers



class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{
        'input_ids': {0: 'batch', 1: 'encoder_sequence'},
        'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
    }
    if self.use_past:
     SCREAMING_SNAKE_CASE  :  Any								=		'past_encoder_sequence + sequence'
     SCREAMING_SNAKE_CASE  :  List[str]								=		{0: 'batch'}
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		{0: 'batch', 1: 'past_decoder_sequence + sequence'}
    else:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{0: 'batch', 1: 'decoder_sequence'}
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{0: 'batch', 1: 'decoder_sequence'}

    if self.use_past:
     self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE    ,direction='inputs'      )

    return common_inputs






   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 					__a			(       self      ):
    return 13






   @property
   def 					__a			(       self      ):
    return 5e-4




'''simple docstring'''

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 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






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

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		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    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

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






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

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


