import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			TransfoXLTokenizer
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   super().setUp()

   __a       =  [
       "<unk>",
       "[CLS]",
       "[SEP]",
       "want",
       "unwanted",
       "wa",
       "un",
       "running",
       ",",
       "low",
       "l",
   ]
   __a       =  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 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							**__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  True
   return TransfoXLTokenizer.from_pretrained(self.tmpdirname     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  "<unk> UNwanted , running"
   __a       =  "<unk> unwanted, running"
   return input_text, output_text

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  TransfoXLTokenizer(vocab_file=self.vocab_file     ,							lower_case=__SCREAMING_SNAKE_CASE       )

   __a       =  tokenizer.tokenize("<unk> UNwanted , running"       )
   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							["<unk>", "unwanted", ",", "running"]       )

   self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )     ,							[0, 4, 8, 7]       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  TransfoXLTokenizer(lower_case=__SCREAMING_SNAKE_CASE       )

   self.assertListEqual(
       tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "       )     ,							["hello", "!", "how", "are", "you", "?"]       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  TransfoXLTokenizer(lower_case=__SCREAMING_SNAKE_CASE       )

   self.assertListEqual(
       tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "       )     ,							["HeLLo", "!", "how", "Are", "yoU", "?"]       )

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  TransfoXLTokenizer(lower_case=__SCREAMING_SNAKE_CASE       )
   __a       =  "Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
   __a       =  [
       "Hello",
       "(",
       "bracket",
       ")",
       "and",
       "side",
       "@-@",
       "scrolled",
       "[",
       "and",
       "]",
       "Henry",
       "'s",
       "$",
       "5",
       "@,@",
       "000",
       "with",
       "3",
       "@.@",
       "34",
       "m",
       ".",
       "What",
       "'s",
       "up",
       "!",
       "?",
   ]

   self.assertListEqual(tokenizer.tokenize(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )

   self.assertEqual(tokenizer.convert_tokens_to_string(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  self.get_tokenizer()
   __a       =  len(__SCREAMING_SNAKE_CASE       )

   tokenizer.add_tokens(["new1", "new2"]       )
   tokenizer.move_added_token("new1"     ,							1       )

   # Check that moved token is not copied (duplicate)
   self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							original_len + 2       )
   # Check that token is moved to specified id
   self.assertEqual(tokenizer.encode("new1"       )     ,							[1]       )
   self.assertEqual(tokenizer.decode([1]       )     ,							"new1"       )



from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE :	Dict							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	List[str]							     =		{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			"""xlm-roberta-xl"""

  def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      int=25_08_80     ,							__SCREAMING_SNAKE_CASE :      Dict=25_60     ,							__SCREAMING_SNAKE_CASE :      List[str]=36     ,							__SCREAMING_SNAKE_CASE :      int=32     ,							__SCREAMING_SNAKE_CASE :      int=1_02_40     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_14     ,							__SCREAMING_SNAKE_CASE :      List[Any]=1     ,							__SCREAMING_SNAKE_CASE :      Any=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=1E-05     ,							__SCREAMING_SNAKE_CASE :      str=1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="absolute"     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      str=None     ,							**__SCREAMING_SNAKE_CASE :      str     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  position_embedding_type
   __a       =  use_cache
   __a       =  classifier_dropout
class    A_ (       a_ ):

  @property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   if self.task == "multiple-choice":
    __a       =  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    __a       =  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ]       )


