



'''simple docstring'''






from typing import Callable, Optional, Union

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


lowerCAmelCase: Union[str, Any]							=						logging.get_logger(__name__)

lowerCAmelCase: Tuple							=						{
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """xlm-prophetnet"""
    lowercase__			=   ["""past_key_values"""]
    lowercase__			=   {
        """num_attention_heads""": """num_encoder_attention_heads""",
    }







    def __init__(				self			:  Union[str, Any]						,      __snake_case			:  Optional[float] = 0.1						,      __snake_case			:  Optional[Union[str, Callable]] = "gelu"						,      __snake_case			:  Optional[int] = 3_05_22						,      __snake_case			:  Optional[int] = 10_24						,      __snake_case			:  Optional[int] = 40_96						,      __snake_case			:  Optional[int] = 12						,      __snake_case			:  Optional[int] = 16						,      __snake_case			:  Optional[int] = 40_96						,      __snake_case			:  Optional[int] = 12						,      __snake_case			:  Optional[int] = 16						,      __snake_case			:  Optional[float] = 0.1						,      __snake_case			:  Optional[float] = 0.1						,      __snake_case			:  Optional[int] = 5_12						,      __snake_case			:  Optional[float] = 0.02						,      __snake_case			:  Optional[bool] = True						,      __snake_case			:  Optional[bool] = True						,      __snake_case			:  Optional[int] = 0						,      __snake_case			:  Optional[int] = 2						,      __snake_case			:  Optional[int] = 32						,      __snake_case			:  Optional[int] = 1_28						,      __snake_case			:  Optional[bool] = False						,      __snake_case			:  Optional[float] = 0.0						,      __snake_case			:  Optional[bool] = True						,      __snake_case			:  Optional[int] = 0						,      __snake_case			:  Optional[int] = 1						,      __snake_case			:  Optional[int] = 2						,      **__snake_case			:  int						,      ):
      a						: Dict					       =					vocab_size
      a						: List[str]					       =					hidden_size
      a						: str					       =					encoder_ffn_dim
      a						: Dict					       =					num_encoder_layers
      a						: Dict					       =					num_encoder_attention_heads
      a						: int					       =					decoder_ffn_dim
      a						: List[str]					       =					num_decoder_layers
      a						: Optional[int]					       =					num_decoder_attention_heads
      a						: Optional[Any]					       =					max_position_embeddings
      a						: List[Any]					       =					init_std  # Normal(0, this parameter)
      a						: Optional[Any]					       =					activation_function

      # parameters for xlmprophetnet
      a						: str					       =					ngram
      a						: Dict					       =					num_buckets
      a						: str					       =					relative_max_distance
      a						: Optional[Any]					       =					disable_ngram_loss
      a						: str					       =					eps

      # 3 Types of Dropout
      a						: int					       =					attention_dropout
      a						: Optional[Any]					       =					activation_dropout
      a						: List[str]					       =					dropout

      a						: Tuple					       =					use_cache

      super().__init__(
          pad_token_id=__snake_case						,      bos_token_id=__snake_case						,      eos_token_id=__snake_case						,      is_encoder_decoder=__snake_case						,      add_cross_attention=__snake_case						,      decoder_start_token_id=__snake_case						,      **__snake_case						,      )







    @property
    def       lowercase_	(				self			:  Optional[Any]     ):
      return self.num_encoder_layers + self.num_decoder_layers







    @num_hidden_layers.setter
    def       lowercase_	(				self			:  Tuple						,      __snake_case			:  Dict     ):
      raise NotImplementedError(
          'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
          ' `num_decoder_layers`.'     )




'''simple docstring'''






import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 							a__( lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   CTRLTokenizer
    lowercase__			=   False
    lowercase__			=   False







    def       lowercase_	(				self			:  Dict     ):
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a						: Tuple					       =					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
      a						: Union[str, Any]					       =					dict(zip(__snake_case						,      range(len(__snake_case     )     )     )     )
      a						: Union[str, Any]					       =					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
      a						: Optional[Any]					       =					{'unk_token': '<unk>'}

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







    def       lowercase_	(				self			:  int						,      **__snake_case			:  str     ):
      kwargs.update(self.special_tokens_map     )
      return CTRLTokenizer.from_pretrained(self.tmpdirname						,      **__snake_case     )







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Any     ):
      a						: int					       =					'adapt react readapt apt'
      a						: Any					       =					'adapt react readapt apt'
      return input_text, output_text







    def       lowercase_	(				self			:  Dict     ):
      a						: Dict					       =					CTRLTokenizer(self.vocab_file						,      self.merges_file						,      **self.special_tokens_map     )
      a						: List[str]					       =					'adapt react readapt apt'
      a						: Dict					       =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
      a						: Any					       =					tokenizer.tokenize(__snake_case     )
      self.assertListEqual(__snake_case						,      __snake_case     )

      a						: Dict					       =					tokens + [tokenizer.unk_token]

      a						: Optional[Any]					       =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case     )						,      __snake_case     )