


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


SCREAMING_SNAKE_CASE :   Any							      =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				"▁"

SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

SCREAMING_SNAKE_CASE :   Optional[int]							      =				{
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

SCREAMING_SNAKE_CASE :   List[Any]							      =				{
    "facebook/m2m100_418M": 1024,
}

# fmt: off
SCREAMING_SNAKE_CASE :   Tuple							      =				{
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}






class lowerCamelCase(		_a     ):
       lowercase_				:			Optional[int]									=    VOCAB_FILES_NAMES
       lowercase_				:			Optional[Any]									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_				:			Any									=    PRETRAINED_VOCAB_FILES_MAP
       lowercase_				:			Optional[int]									=    ["""input_ids""", """attention_mask"""]

       lowercase_				:			List[int]									=    []
       lowercase_				:			List[int]									=    []
       def __init__(      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase="<s>",   lowerCamelCase="</s>",   lowerCamelCase="</s>",   lowerCamelCase="<pad>",   lowerCamelCase="<unk>",   lowerCamelCase="m2m100",   lowerCamelCase = None,   lowerCamelCase=8,   **lowerCamelCase,   ) ->     None:




           """simple docstring"""

           _lowercase :							List[str]   	=	{} if sp_model_kwargs is None else sp_model_kwargs

           _lowercase :							Dict   	=	language_codes
           _lowercase :							Optional[Any]   	=	FAIRSEQ_LANGUAGE_CODES[language_codes]
           _lowercase :							Dict   	=	{lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

           _lowercase :							Tuple   	=	kwargs.get('additional_special_tokens',   [])
           kwargs["additional_special_tokens"] += [
               self.get_lang_token(lowerCamelCase)
               for lang_code in fairseq_language_code
               if self.get_lang_token(lowerCamelCase) not in kwargs["additional_special_tokens"]
           ]

           super().__init__(
               src_lang=lowerCamelCase,   tgt_lang=lowerCamelCase,   bos_token=lowerCamelCase,   eos_token=lowerCamelCase,   sep_token=lowerCamelCase,   unk_token=lowerCamelCase,   pad_token=lowerCamelCase,   language_codes=lowerCamelCase,   sp_model_kwargs=self.sp_model_kwargs,   num_madeup_words=lowerCamelCase,   **lowerCamelCase,   )

           _lowercase :							int   	=	vocab_file
           _lowercase :							int   	=	load_json(lowerCamelCase)
           _lowercase :							str   	=	{v: k for k, v in self.encoder.items()}
           _lowercase :							Union[str, Any]   	=	spm_file
           _lowercase :							int   	=	load_spm(lowerCamelCase,   self.sp_model_kwargs)

           _lowercase :							List[Any]   	=	len(self.encoder)

           _lowercase :							Optional[int]   	=	{
               self.get_lang_token(lowerCamelCase): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase)
           }
           _lowercase :							Dict   	=	{lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase)}
           _lowercase :							Any   	=	{v: k for k, v in self.lang_token_to_id.items()}

           _lowercase :							Any   	=	src_lang if src_lang is not None else 'en'
           _lowercase :							Optional[int]   	=	tgt_lang
           _lowercase :							Union[str, Any]   	=	self.get_lang_id(self._src_lang)
           self.set_src_lang_special_tokens(self._src_lang)

           _lowercase :							Optional[Any]   	=	num_madeup_words
       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           return len(self.encoder) + len(self.lang_token_to_id)
       @property
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           return self._src_lang
       @src_lang.setter
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     None:




           """simple docstring"""

           _lowercase :							Tuple   	=	new_src_lang
           self.set_src_lang_special_tokens(self._src_lang)
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           return self.sp_model.encode(lowerCamelCase,   out_type=lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Dict:




           """simple docstring"""

           if token in self.lang_token_to_id:
               return self.lang_token_to_id[token]
           return self.encoder.get(lowerCamelCase,   self.encoder[self.unk_token])
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     str:




           """simple docstring"""

           if index in self.id_to_lang_token:
               return self.id_to_lang_token[index]
           return self.decoder.get(lowerCamelCase,   self.unk_token)
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							List[str]   	=	[]
           _lowercase :							Union[str, Any]   	=	''
           for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(lowerCamelCase) + token
                   _lowercase :							str   	=	[]
               else:
                   current_sub_tokens.append(lowerCamelCase)
           out_string += self.sp_model.decode(lowerCamelCase)
           return out_string.strip()
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = None,   lowerCamelCase = False) ->     List[int]:




           """simple docstring"""

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

           _lowercase :							Any   	=	[1] * len(self.prefix_tokens)
           _lowercase :							List[Any]   	=	[1] * len(self.suffix_tokens)
           if token_ids_a is None:
               return prefix_ones + ([0] * len(lowerCamelCase)) + suffix_ones
           return prefix_ones + ([0] * len(lowerCamelCase)) + ([0] * len(lowerCamelCase)) + suffix_ones
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = None) ->     List[int]:




           """simple docstring"""

           if token_ids_a is None:
               return self.prefix_tokens + token_ids_a + self.suffix_tokens
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	{self.convert_ids_to_tokens(lowerCamelCase): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab
       def __getstate__(      self) ->     Dict:




           """simple docstring"""

           _lowercase :							List[str]   	=	self.__dict__.copy()
           _lowercase :							Tuple   	=	None
           return state
       def __setstate__(      self,   lowerCamelCase) ->     None:




           """simple docstring"""

           _lowercase :							Tuple   	=	d

           # for backward compatibility
           if not hasattr(self,   'sp_model_kwargs'):
               _lowercase :							Union[str, Any]   	=	{}

           _lowercase :							int   	=	load_spm(self.spm_file,   self.sp_model_kwargs)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = None) ->     Tuple[str]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	Path(lowerCamelCase)
           if not save_dir.is_dir():
               raise OSError(F'''{save_directory} should be a directory''')
           _lowercase :							Tuple   	=	save_dir / (
               (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
           )
           _lowercase :							List[Any]   	=	save_dir / (
               (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
           )

           save_json(self.encoder,   lowerCamelCase)

           if os.path.abspath(self.spm_file) != os.path.abspath(lowerCamelCase) and os.path.isfile(self.spm_file):
               copyfile(self.spm_file,   lowerCamelCase)
           elif not os.path.isfile(self.spm_file):
               with open(lowerCamelCase,   'wb') as fi:
                   _lowercase :							List[str]   	=	self.sp_model.serialized_model_proto()
                   fi.write(lowerCamelCase)

           return (str(lowerCamelCase), str(lowerCamelCase))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = "en",   lowerCamelCase = None,   lowerCamelCase = "ro",   **lowerCamelCase,   ) ->     BatchEncoding:




           """simple docstring"""

           _lowercase :							str   	=	src_lang
           _lowercase :							Optional[Any]   	=	tgt_lang
           self.set_src_lang_special_tokens(self.src_lang)
           return super().prepare_seqaseq_batch(lowerCamelCase,   lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase) ->     Tuple:




           """simple docstring"""

           if src_lang is None or tgt_lang is None:
               raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
           _lowercase :							Dict   	=	src_lang
           _lowercase :							List[str]   	=	self(lowerCamelCase,   add_special_tokens=lowerCamelCase,   **lowerCamelCase)
           _lowercase :							int   	=	self.get_lang_id(lowerCamelCase)
           _lowercase :							int   	=	tgt_lang_id
           return inputs
       def     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

           self.set_src_lang_special_tokens(self.src_lang)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           self.set_tgt_lang_special_tokens(self.tgt_lang)
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     None:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.get_lang_token(lowerCamelCase)
           _lowercase :							Dict   	=	self.lang_token_to_id[lang_token]
           _lowercase :							str   	=	[self.cur_lang_id]
           _lowercase :							List[str]   	=	[self.eos_token_id]
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     None:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.get_lang_token(lowerCamelCase)
           _lowercase :							Optional[int]   	=	self.lang_token_to_id[lang_token]
           _lowercase :							Union[str, Any]   	=	[self.cur_lang_id]
           _lowercase :							Dict   	=	[self.eos_token_id]
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     str:




           """simple docstring"""

           return self.lang_code_to_token[lang]




       def     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.get_lang_token(lowerCamelCase)
           return self.lang_token_to_id[lang_token]



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		sentencepiece.SentencePieceProcessor:
    _lowercase :							Any   	=	sentencepiece.SentencePieceProcessor(**lowerCamelCase_				)
    spm.Load(str(lowerCamelCase_				)				)
    return spm



def        UpperCamelCase_(   lowerCamelCase_				)					->		Union[Dict, List]:
    with open(lowerCamelCase_							,				'r'				) as f:
        return json.load(lowerCamelCase_				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		None:
    with open(lowerCamelCase_							,				'w'				) as f:
        json.dump(lowerCamelCase_							,				lowerCamelCase_							,				indent=2				)






import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaVaForMaskedLM,
     DebertaVaForMultipleChoice,
     DebertaVaForQuestionAnswering,
     DebertaVaForSequenceClassification,
     DebertaVaForTokenClassification,
     DebertaVaModel,
 )
 from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST






class _lowerCamelCase(		_a     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=7,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=99,   lowerCamelCase=32,   lowerCamelCase=5,   lowerCamelCase=4,   lowerCamelCase=37,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=5_12,   lowerCamelCase=16,   lowerCamelCase=2,   lowerCamelCase=0.0_2,   lowerCamelCase=False,   lowerCamelCase=True,   lowerCamelCase="None",   lowerCamelCase=3,   lowerCamelCase=4,   lowerCamelCase=None,   ) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	parent
           _lowercase :							Optional[Any]   	=	batch_size
           _lowercase :							Optional[Any]   	=	seq_length
           _lowercase :							Dict   	=	is_training
           _lowercase :							Optional[Any]   	=	use_input_mask
           _lowercase :							Optional[int]   	=	use_token_type_ids
           _lowercase :							str   	=	use_labels
           _lowercase :							List[Any]   	=	vocab_size
           _lowercase :							Dict   	=	hidden_size
           _lowercase :							Any   	=	num_hidden_layers
           _lowercase :							Union[str, Any]   	=	num_attention_heads
           _lowercase :							int   	=	intermediate_size
           _lowercase :							List[str]   	=	hidden_act
           _lowercase :							Tuple   	=	hidden_dropout_prob
           _lowercase :							Optional[Any]   	=	attention_probs_dropout_prob
           _lowercase :							int   	=	max_position_embeddings
           _lowercase :							Any   	=	type_vocab_size
           _lowercase :							Tuple   	=	type_sequence_label_size
           _lowercase :							List[Any]   	=	initializer_range
           _lowercase :							Optional[Any]   	=	num_labels
           _lowercase :							Tuple   	=	num_choices
           _lowercase :							Dict   	=	relative_attention
           _lowercase :							Optional[int]   	=	position_biased_input
           _lowercase :							str   	=	pos_att_type
           _lowercase :							Optional[Any]   	=	scope
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							int   	=	ids_tensor([self.batch_size, self.seq_length],   self.vocab_size)

           _lowercase :							Union[str, Any]   	=	None
           if self.use_input_mask:
               _lowercase :							int   	=	ids_tensor([self.batch_size, self.seq_length],   vocab_size=2)

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

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

           _lowercase :							Union[str, Any]   	=	self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           return DebertaVaConfig(
               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,   initializer_range=self.initializer_range,   relative_attention=self.relative_attention,   position_biased_input=self.position_biased_input,   pos_att_type=self.pos_att_type,   )
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           self.parent.assertListEqual(list(result.loss.size()),   [])
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	DebertaVaModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							int   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)[0]
           _lowercase :							Optional[int]   	=	model(lowerCamelCase,   token_type_ids=lowerCamelCase)[0]
           _lowercase :							Dict   	=	model(lowerCamelCase)[0]

           self.parent.assertListEqual(list(sequence_output.size()),   [self.batch_size, self.seq_length, self.hidden_size])
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[Any]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	DebertaVaForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							str   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)

           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.num_labels
           _lowercase :							Any   	=	DebertaVaForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Any   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertListEqual(list(result.logits.size()),   [self.batch_size, self.num_labels])
           self.check_loss_output(lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.num_labels
           _lowercase :							Optional[int]   	=	DebertaVaForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	DebertaVaForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Union[str, Any]   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   start_positions=lowerCamelCase,   end_positions=lowerCamelCase,   )
           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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Any   	=	DebertaVaForMultipleChoice(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	input_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							List[Any]   	=	token_type_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							int   	=	input_mask.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							str   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase,   )
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices))




       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Any   	=	self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							List[str]   	=	config_and_inputs
           _lowercase :							int   	=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,				_a,				unittest.TestCase     ):
       lowercase_				:			Any									=    (
           (
               DebertaVaModel,
               DebertaVaForMaskedLM,
               DebertaVaForSequenceClassification,
               DebertaVaForTokenClassification,
               DebertaVaForQuestionAnswering,
               DebertaVaForMultipleChoice,
           )
           if is_torch_available()
           else ()
       )
       lowercase_				:			Any									=    (
           {
               """feature-extraction""": DebertaVaModel,
               """fill-mask""": DebertaVaForMaskedLM,
               """question-answering""": DebertaVaForQuestionAnswering,
               """text-classification""": DebertaVaForSequenceClassification,
               """token-classification""": DebertaVaForTokenClassification,
               """zero-shot""": DebertaVaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       lowercase_				:			int									=    True
       lowercase_				:			str									=    False
       lowercase_				:			str									=    False
       lowercase_				:			str									=    False
       lowercase_				:			List[Any]									=    False
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							List[Any]   	=	DebertaVaModelTester(self)
           _lowercase :							List[Any]   	=	ConfigTester(self,   config_class=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCamelCase)




       @slow
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							Dict   	=	DebertaVaModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)






@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase(		unittest.TestCase     ):
       @unittest.skip(reason='Model not available yet')
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           pass




       @slow
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Dict   	=	DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge')

           _lowercase :							str   	=	torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
           _lowercase :							Any   	=	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
           with torch.no_grad():
               _lowercase :							Tuple   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase)[0]
           # compare the actual values for a slice.
           _lowercase :							int   	=	torch.tensor(
               [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
           self.assertTrue(torch.allclose(output[:, 1:4, 1:4],   lowerCamelCase,   atol=1E-4),   F'''{output[:, 1:4, 1:4]}''')


