






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


__A      =				logging.get_logger(__name__)

__A      =				{
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"transfo-xl"
							A_						  =					["mems"]
							A_						  =					{
							    "n_token": "vocab_size",
							    "hidden_size": "d_model",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}

							def __init__(  self:   int		,     __A:   str=26_77_35		,     __A:   Tuple=[2_00_00, 4_00_00, 20_00_00]		,     __A:   Any=10_24		,     __A:   int=10_24		,     __A:   Optional[int]=16		,     __A:   List[str]=64		,     __A:   List[Any]=40_96		,     __A:   Optional[int]=4		,     __A:   Optional[int]=False		,     __A:   str=18		,     __A:   str=16_00		,     __A:   str=10_00		,     __A:   Optional[Any]=True		,     __A:   Optional[int]=True		,     __A:   Optional[Any]=0		,     __A:   Optional[int]=-1		,     __A:   int=True		,     __A:   Optional[int]=0.1		,     __A:   Dict=0.0		,     __A:   Optional[Any]=True		,     __A:   int="normal"		,     __A:   Any=0.01		,     __A:   Optional[Any]=0.01		,     __A:   Optional[int]=0.02		,     __A:   Dict=1e-5		,     __A:   List[Any]=0		,     **__A:   Tuple		,     )    ->				Dict:
												_A								= vocab_size
												_A								= []
												self.cutoffs.extend(__A   )
												if proj_share_all_but_first:
																	_A								= [False] + [True] * len(self.cutoffs   )
												else:
																	_A								= [False] + [False] * len(self.cutoffs   )
												_A								= d_model
												_A								= d_embed
												_A								= d_head
												_A								= d_inner
												_A								= div_val
												_A								= pre_lnorm
												_A								= n_layer
												_A								= n_head
												_A								= mem_len
												_A								= same_length
												_A								= attn_type
												_A								= clamp_len
												_A								= sample_softmax
												_A								= adaptive
												_A								= dropout
												_A								= dropatt
												_A								= untie_r
												_A								= init
												_A								= init_range
												_A								= proj_init_std
												_A								= init_std
												_A								= layer_norm_epsilon
												super().__init__(eos_token_id=__A		,     **__A   )

							@property
							def       __A (  self:   Any   )    ->				Optional[int]:
												# Message copied from Transformer-XL documentation
												logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""   )
												return -1



							@max_position_embeddings.setter
							def       __A (  self:   Union[str, Any]		,     __A:   Tuple   )    ->				Optional[Any]:
												# Message copied from Transformer-XL documentation
												raise NotImplementedError(
												    f"""The model {self.model_type} is one of the few models that has no sequence length limit."""   )











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]



