






from typing import Callable, Optional, Union

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


__A      =				logging.get_logger(__name__)

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





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"xlm-prophetnet"
							A_						  =					["past_key_values"]
							A_						  =					{
							    "num_attention_heads": "num_encoder_attention_heads",
							}

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

												# parameters for xlmprophetnet
												_A								= ngram
												_A								= num_buckets
												_A								= relative_max_distance
												_A								= disable_ngram_loss
												_A								= eps

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

												_A								= use_cache

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

							@property
							def       __A (  self:   Union[str, Any]   )    ->				int:
												return self.num_encoder_layers + self.num_decoder_layers



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











import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					BlenderbotSmallTokenizer
							A_						  =					False

							def       __A (  self:   List[str]   )    ->				int:
												super().setUp()

												_A								= ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
												_A								= dict(zip(__A		,     range(len(__A   )   )   )   )

												_A								= ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
												_A								= {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

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

							def       __A (  self:   str		,     **__A:   Optional[Any]   )    ->				Dict:
												kwargs.update(self.special_tokens_map   )
												return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname		,     **__A   )

							def       __A (  self:   str		,     __A:   List[str]   )    ->				int:
												_A								= '''adapt act apte'''
												_A								= '''adapt act apte'''
												return input_text, output_text

							def       __A (  self:   Union[str, Any]   )    ->				Any:
												_A								= BlenderbotSmallTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map   )
												_A								= '''adapt act apte'''
												_A								= ['''adapt''', '''act''', '''ap@@''', '''te''']
												_A								= tokenizer.tokenize(__A   )
												self.assertListEqual(__A		,     __A   )

												_A								= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

												_A								= [0, 1, 2, 3, 4, 5]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__A   )		,     __A   )

							def       __A (  self:   Any   )    ->				List[str]:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												assert tok('''sam'''   ).input_ids == [13_84]
												_A								= '''I am a small frog.'''
												_A								= tok([src_text]		,     padding=__A		,     truncation=__A   )['''input_ids''']
												_A								= tok.batch_decode(__A		,     skip_special_tokens=__A		,     clean_up_tokenization_spaces=__A   )[0]
												assert src_text != decoded  # I wish it did!
												assert decoded == "i am a small frog ."



							def       __A (  self:   Any   )    ->				int:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												_A								= '''I am a small frog .'''
												_A								= '''.'''
												_A								= tok(__A   )['''input_ids''']
												_A								= tok(__A   )['''input_ids''']

												assert encoded[-1] == encoded_dot[0]



