
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_	:    Optional[int]  					=       logging.get_logger(__name__)

UpperCamelCase_	:    List[str]  					=       {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

UpperCamelCase_	:    str  					=       {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
}

UpperCamelCase_	:    Dict  					=       {
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}



@lru_cache()
def 		UpperCamelCase    (					)      -> int:






						'''simple docstring'''

						_lowercase					:  str    			=      (
						    list(range(ord("!"     )   , ord("~"     ) + 1     )     ) + list(range(ord("¡"     )   , ord("¬"     ) + 1     )     ) + list(range(ord("®"     )   , ord("ÿ"     ) + 1     )     )
						)
						_lowercase					:  int    			=      bs[:]
						_lowercase					:  Optional[int]    			=      0
						for b in range(2**8     ):
												if b not in bs:
																		bs.append(_UpperCAmelCase     )
																		cs.append(2**8 + n     )
																		n += 1
						_lowercase					:  Any    			=      [chr(_UpperCAmelCase     ) for n in cs]
						return dict(zip(_UpperCAmelCase   , _UpperCAmelCase     )     )



def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> Union[str, Any]:






						'''simple docstring'''

						_lowercase					:  str    			=      set()
						_lowercase					:  Any    			=      word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)     )
												_lowercase					:  Any    			=      char
						return pairs




class      __lowercase	(	a__					):
	_A   			=      VOCAB_FILES_NAMES
	_A   			=      PRETRAINED_VOCAB_FILES_MAP
	_A   			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A   			=      ["input_ids", "attention_mask"]






	def __init__(self							:   List[Any]			,  snake_case							:   Any			,  snake_case							:   str			,  snake_case							:   Optional[Any]="replace"			,  snake_case							:   List[str]="<s>"			,  snake_case							:   List[str]="</s>"			,  snake_case							:   List[Any]="</s>"			,  snake_case							:   str="<s>"			,  snake_case							:   Union[str, Any]="<unk>"			,  snake_case							:   int="<pad>"			,  snake_case							:   List[str]="<mask>"			,  snake_case							:   Optional[Any]=False			,  **snake_case							:   Dict			,  )   -> List[Any]:
							_lowercase					:  Tuple    			=      AddedToken(__A			,  lstrip=__A			,  rstrip=__A						) if isinstance(__A			,  __A						) else bos_token
							_lowercase					:  Optional[Any]    			=      AddedToken(__A			,  lstrip=__A			,  rstrip=__A						) if isinstance(__A			,  __A						) else eos_token
							_lowercase					:  int    			=      AddedToken(__A			,  lstrip=__A			,  rstrip=__A						) if isinstance(__A			,  __A						) else sep_token
							_lowercase					:  str    			=      AddedToken(__A			,  lstrip=__A			,  rstrip=__A						) if isinstance(__A			,  __A						) else cls_token
							_lowercase					:  List[Any]    			=      AddedToken(__A			,  lstrip=__A			,  rstrip=__A						) if isinstance(__A			,  __A						) else unk_token
							_lowercase					:  Optional[Any]    			=      AddedToken(__A			,  lstrip=__A			,  rstrip=__A						) if isinstance(__A			,  __A						) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							_lowercase					:  Union[str, Any]    			=      AddedToken(__A			,  lstrip=__A			,  rstrip=__A						) if isinstance(__A			,  __A						) else mask_token

							super().__init__(
							    errors=__A			,  bos_token=__A			,  eos_token=__A			,  unk_token=__A			,  sep_token=__A			,  cls_token=__A			,  pad_token=__A			,  mask_token=__A			,  add_prefix_space=__A			,  **__A			,  )

							with open(__A			,  encoding="utf-8"						) as vocab_handle:
													_lowercase					:  Optional[Any]    			=      json.load(__A						)
							_lowercase					:  List[Any]    			=      {v: k for k, v in self.encoder.items()}
							_lowercase					:  Any    			=      errors  # how to handle errors in decoding
							_lowercase					:  Optional[Any]    			=      bytes_to_unicode()
							_lowercase					:  Optional[int]    			=      {v: k for k, v in self.byte_encoder.items()}
							with open(__A			,  encoding="utf-8"						) as merges_handle:
													_lowercase					:  List[Any]    			=      merges_handle.read().split("\n"						)[1:-1]
							_lowercase					:  Union[str, Any]    			=      [tuple(merge.split()						) for merge in bpe_merges]
							_lowercase					:  Dict    			=      dict(zip(__A			,  range(len(__A						)						)						)						)
							_lowercase					:  Any    			=      {}
							_lowercase					:  List[Any]    			=      add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							_lowercase					:  int    			=      re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"						)






	@property
	def 			_a(self							:   Union[str, Any]						)   -> Optional[Any]:
							return len(self.encoder						)






	def 			_a(self							:   Optional[int]						)   -> Tuple:
							return dict(self.encoder			,  **self.added_tokens_encoder						)






	def 			_a(self							:   Optional[Any]			,  snake_case							:   Dict						)   -> List[Any]:







							if token in self.cache:
													return self.cache[token]
							_lowercase					:  Union[str, Any]    			=      tuple(__A						)
							_lowercase					:  List[str]    			=      get_pairs(__A						)

							if not pairs:
													return token

							while True:
													_lowercase					:  Dict    			=      min(__A			,  key=lambda snake_case						: self.bpe_ranks.get(__A			,  float("inf"						)						)						)
													if bigram not in self.bpe_ranks:
																			break
													_lowercase					:  Tuple    			=      bigram
													_lowercase					:  List[Any]    			=      []
													_lowercase					:  Dict    			=      0
													while i < len(__A						):
																			try:
																									_lowercase					:  List[str]    			=      word.index(__A			,  __A						)
																			except ValueError:
																									new_word.extend(word[i:]						)
																									break
																			else:
																									new_word.extend(word[i:j]						)
																									_lowercase					:  Optional[int]    			=      j

																			if word[i] == first and i < len(__A						) - 1 and word[i + 1] == second:
																									new_word.append(first + second						)
																									i += 2
																			else:
																									new_word.append(word[i]						)
																									i += 1
													_lowercase					:  Union[str, Any]    			=      tuple(__A						)
													_lowercase					:  Optional[Any]    			=      new_word
													if len(__A						) == 1:
																			break
													else:
																			_lowercase					:  Optional[Any]    			=      get_pairs(__A						)
							_lowercase					:  Optional[int]    			=      " ".join(__A						)
							_lowercase					:  Optional[Any]    			=      word
							return word






	def 			_a(self							:   str			,  snake_case							:   Optional[Any]						)   -> List[str]:
							_lowercase					:  Any    			=      []
							for token in re.findall(self.pat			,  __A						):
													_lowercase					:  Optional[int]    			=      "".join(
													    self.byte_encoder[b] for b in token.encode("utf-8"						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__A						).split(" "						)						)
							return bpe_tokens






	def 			_a(self							:   Dict			,  snake_case							:   Dict						)   -> List[str]:
							return self.encoder.get(__A			,  self.encoder.get(self.unk_token						)						)






	def 			_a(self							:   int			,  snake_case							:   Any						)   -> Any:
							return self.decoder.get(__A						)






	def 			_a(self							:   List[Any]			,  snake_case							:   Union[str, Any]						)   -> Union[str, Any]:
							_lowercase					:  List[str]    			=      "".join(__A						)
							_lowercase					:  List[Any]    			=      bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"			,  errors=self.errors						)
							return text






	def 			_a(self							:   Tuple			,  snake_case							:   Union[str, Any]			,  snake_case							:   int = None						)   -> Tuple[str]:







							if not os.path.isdir(__A						):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
													return
							_lowercase					:  Optional[Any]    			=      os.path.join(
							    __A			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
							_lowercase					:  Any    			=      os.path.join(
							    __A			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

							with open(__A			,  "w"			,  encoding="utf-8"						) as f:
													f.write(json.dumps(self.encoder			,  indent=2			,  sort_keys=__A			,  ensure_ascii=__A						) + "\n"						)

							_lowercase					:  Tuple    			=      0
							with open(__A			,  "w"			,  encoding="utf-8"						) as writer:
													writer.write("#version: 0.2\n"						)
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,  key=lambda snake_case						: kv[1]						):
																			if index != token_index:
																									logger.warning(
																									    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																									    " Please check that the tokenizer is not corrupted!"						)
																									_lowercase					:  Optional[Any]    			=      token_index
																			writer.write(" ".join(__A						) + "\n"						)
																			index += 1

							return vocab_file, merge_file






	def 			_a(self							:   List[Any]			,  snake_case							:   int			,  snake_case							:   List[str] = None						)   -> List[int]:







							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_lowercase					:  List[str]    			=      [self.cls_token_id]
							_lowercase					:  Any    			=      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






	def 			_a(self							:   List[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   int = None			,  snake_case							:   List[Any] = False						)   -> List[int]:







							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__A			,  token_ids_a=__A			,  already_has_special_tokens=__A						)

							if token_ids_a is None:
													return [1] + ([0] * len(__A						)) + [1]
							return [1] + ([0] * len(__A						)) + [1, 1] + ([0] * len(__A						)) + [1]






	def 			_a(self							:   List[Any]			,  snake_case							:   Any			,  snake_case							:   Any = None						)   -> List[int]:
							_lowercase					:  Tuple    			=      [self.sep_token_id]
							_lowercase					:  Union[str, Any]    			=      [self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






	def 			_a(self							:   Optional[Any]			,  snake_case							:   Any			,  snake_case							:   str=False			,  **snake_case							:   Tuple						)   -> Optional[Any]:
							_lowercase					:  Optional[Any]    			=      kwargs.pop("add_prefix_space"			,  self.add_prefix_space						)
							if (is_split_into_words or add_prefix_space) and (len(__A						) > 0 and not text[0].isspace()):
													_lowercase					:  Optional[Any]    			=      " " + text
							return (text, kwargs)






from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TFDebertaForMaskedLM,
							    TFDebertaForQuestionAnswering,
							    TFDebertaForSequenceClassification,
							    TFDebertaForTokenClassification,
							    TFDebertaModel,
							)




class      __lowercase	:






	def __init__(self							:   Tuple			,  snake_case							:   Union[str, Any]			,  snake_case							:   List[Any]=13			,  snake_case							:   Union[str, Any]=7			,  snake_case							:   Union[str, Any]=True			,  snake_case							:   int=True			,  snake_case							:   Dict=True			,  snake_case							:   Union[str, Any]=True			,  snake_case							:   str=99			,  snake_case							:   str=32			,  snake_case							:   List[Any]=2			,  snake_case							:   Optional[int]=4			,  snake_case							:   Union[str, Any]=37			,  snake_case							:   int="gelu"			,  snake_case							:   Any=0.1			,  snake_case							:   str=0.1			,  snake_case							:   Dict=512			,  snake_case							:   Any=16			,  snake_case							:   Tuple=2			,  snake_case							:   Dict=0.02			,  snake_case							:   Tuple=3			,  snake_case							:   Dict=4			,  snake_case							:   Optional[Any]=None			,  )   -> str:
							_lowercase					:  Optional[int]    			=      parent
							_lowercase					:  Union[str, Any]    			=      13
							_lowercase					:  Dict    			=      7
							_lowercase					:  Any    			=      True
							_lowercase					:  Tuple    			=      True
							_lowercase					:  Optional[Any]    			=      True
							_lowercase					:  Optional[Any]    			=      True
							_lowercase					:  str    			=      99
							_lowercase					:  Optional[int]    			=      32
							_lowercase					:  int    			=      2
							_lowercase					:  Optional[int]    			=      4
							_lowercase					:  List[str]    			=      37
							_lowercase					:  Dict    			=      "gelu"
							_lowercase					:  str    			=      0.1
							_lowercase					:  Optional[int]    			=      0.1
							_lowercase					:  int    			=      512
							_lowercase					:  str    			=      16
							_lowercase					:  List[Any]    			=      False
							_lowercase					:  Dict    			=      -1
							_lowercase					:  Optional[Any]    			=      True
							_lowercase					:  Dict    			=      2
							_lowercase					:  Union[str, Any]    			=      0.02
							_lowercase					:  int    			=      3
							_lowercase					:  Tuple    			=      4
							_lowercase					:  Optional[Any]    			=      None






	def 			_a(self							:   List[str]						)   -> List[Any]:
							_lowercase					:  Optional[Any]    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size						)

							_lowercase					:  Optional[Any]    			=      None
							if self.use_input_mask:
													_lowercase					:  int    			=      random_attention_mask([self.batch_size, self.seq_length]						)

							_lowercase					:  Dict    			=      None
							if self.use_token_type_ids:
													_lowercase					:  Any    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.type_vocab_size						)

							_lowercase					:  Optional[Any]    			=      None
							_lowercase					:  Tuple    			=      None
							_lowercase					:  List[str]    			=      None
							if self.use_labels:
													_lowercase					:  List[Any]    			=      ids_tensor([self.batch_size]			,  self.type_sequence_label_size						)
													_lowercase					:  Union[str, Any]    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.num_labels						)

							_lowercase					:  Union[str, Any]    			=      DebertaConfig(
							    vocab_size=self.vocab_size			,  hidden_size=self.hidden_size			,  num_hidden_layers=self.num_hidden_layers			,  num_attention_heads=self.num_attention_heads			,  intermediate_size=self.intermediate_size			,  hidden_act=self.hidden_act			,  hidden_dropout_prob=self.hidden_dropout_prob			,  attention_probs_dropout_prob=self.attention_probs_dropout_prob			,  max_position_embeddings=self.max_position_embeddings			,  type_vocab_size=self.type_vocab_size			,  relative_attention=self.relative_attention			,  max_relative_positions=self.max_relative_positions			,  position_biased_input=self.position_biased_input			,  initializer_range=self.initializer_range			,  return_dict=snake_case			,  )

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






	def 			_a(self							:   Dict			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   List[Any]			,  snake_case							:   str			,  snake_case							:   Optional[int]			,  snake_case							:   List[Any]			,  snake_case							:   Union[str, Any]						)   -> str:
							_lowercase					:  Tuple    			=      TFDebertaModel(config=snake_case						)
							_lowercase					:  Any    			=      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

							_lowercase					:  Tuple    			=      [input_ids, input_mask]
							_lowercase					:  Any    			=      model(snake_case						)

							_lowercase					:  Any    			=      model(snake_case						)

							self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)						)






	def 			_a(self							:   int			,  snake_case							:   int			,  snake_case							:   Union[str, Any]			,  snake_case							:   Tuple			,  snake_case							:   List[Any]			,  snake_case							:   Any			,  snake_case							:   Union[str, Any]			,  snake_case							:   int						)   -> int:
							_lowercase					:  int    			=      TFDebertaForMaskedLM(config=snake_case						)
							_lowercase					:  List[Any]    			=      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							_lowercase					:  int    			=      model(snake_case						)
							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)						)






	def 			_a(self							:   str			,  snake_case							:   Dict			,  snake_case							:   Optional[int]			,  snake_case							:   int			,  snake_case							:   Tuple			,  snake_case							:   int			,  snake_case							:   Any			,  snake_case							:   Any						)   -> List[str]:
							_lowercase					:  Dict    			=      self.num_labels
							_lowercase					:  Tuple    			=      TFDebertaForSequenceClassification(config=snake_case						)
							_lowercase					:  Optional[Any]    			=      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							_lowercase					:  Dict    			=      model(snake_case						)
							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)						)






	def 			_a(self							:   Optional[Any]			,  snake_case							:   int			,  snake_case							:   Dict			,  snake_case							:   str			,  snake_case							:   str			,  snake_case							:   Optional[int]			,  snake_case							:   int			,  snake_case							:   List[str]						)   -> Dict:
							_lowercase					:  Union[str, Any]    			=      self.num_labels
							_lowercase					:  List[Any]    			=      TFDebertaForTokenClassification(config=snake_case						)
							_lowercase					:  Union[str, Any]    			=      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							_lowercase					:  str    			=      model(snake_case						)
							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)						)






	def 			_a(self							:   Dict			,  snake_case							:   List[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[int]			,  snake_case							:   Union[str, Any]			,  snake_case							:   Dict			,  snake_case							:   str			,  snake_case							:   Optional[Any]						)   -> Optional[Any]:
							_lowercase					:  str    			=      TFDebertaForQuestionAnswering(config=snake_case						)
							_lowercase					:  Optional[Any]    			=      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							_lowercase					:  Tuple    			=      model(snake_case						)
							self.parent.assertEqual(result.start_logits.shape			,  (self.batch_size, self.seq_length)						)
							self.parent.assertEqual(result.end_logits.shape			,  (self.batch_size, self.seq_length)						)






	def 			_a(self							:   List[Any]						)   -> Optional[Any]:
							_lowercase					:  int    			=      self.prepare_config_and_inputs()
							(
							    (
							    _lowercase
							)			,						(
							    _lowercase
							)			,						(
							    _lowercase
							)			,						(
							    _lowercase
							)			,						(
							    _lowercase
							)			,						(
							    _lowercase
							)			,						(
							    _lowercase
							)			,						
							)					:  Optional[Any]    			=      config_and_inputs
							_lowercase					:  str    			=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_tf
class      __lowercase	(	__snake_case      ,   __snake_case      ,   unittest.TestCase					):
	_A   			=      (
	    (
	        TFDebertaModel,
	        TFDebertaForMaskedLM,
	        TFDebertaForQuestionAnswering,
	        TFDebertaForSequenceClassification,
	        TFDebertaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	_A   			=      (
	    {
	        "feature-extraction": TFDebertaModel,
	        "fill-mask": TFDebertaForMaskedLM,
	        "question-answering": TFDebertaForQuestionAnswering,
	        "text-classification": TFDebertaForSequenceClassification,
	        "token-classification": TFDebertaForTokenClassification,
	        "zero-shot": TFDebertaForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	_A   			=      False
	_A   			=      False






	def 			_a(self							:   int						)   -> List[Any]:
							_lowercase					:  List[str]    			=      TFDebertaModelTester(self						)
							_lowercase					:  Optional[int]    			=      ConfigTester(self			,  config_class=snake_case			,  hidden_size=37						)






	def 			_a(self							:   Any						)   -> Tuple:
							self.config_tester.run_common_tests()






	def 			_a(self							:   Optional[Any]						)   -> Dict:
							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case						)






	def 			_a(self							:   Optional[Any]						)   -> Any:
							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*snake_case						)






	def 			_a(self							:   str						)   -> int:
							_lowercase					:  int    			=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*snake_case						)






	def 			_a(self							:   Any						)   -> List[Any]:
							_lowercase					:  Any    			=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*snake_case						)






	def 			_a(self							:   Tuple						)   -> str:
							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*snake_case						)






	@slow
	def 			_a(self							:   Optional[Any]						)   -> int:
							_lowercase					:  Optional[int]    			=      TFDebertaModel.from_pretrained("kamalkraj/deberta-base"						)
							self.assertIsNotNone(snake_case						)




@require_tf
class      __lowercase	(	unittest.TestCase					):






	@unittest.skip(reason="Model not available yet"						)
	def 			_a(self							:   Optional[int]						)   -> int:
							pass






	@slow
	def 			_a(self							:   Optional[int]						)   -> Optional[int]:
							_lowercase					:  Optional[Any]    			=      TFDebertaModel.from_pretrained("kamalkraj/deberta-base"						)
							_lowercase					:  List[Any]    			=      tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						)
							_lowercase					:  Union[str, Any]    			=      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
							_lowercase					:  List[str]    			=      model(snake_case			,  attention_mask=snake_case						)[0]

							_lowercase					:  List[str]    			=      tf.constant(
							    [
							        [
							            [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
							            [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
							            [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
							        ]
							    ]						)
							tf.debugging.assert_near(output[:, 1:4, 1:4]			,  snake_case			,  atol=1e-4						)




