


import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


SCREAMING_SNAKE_CASE :   List[Any]							      =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :   Dict							      =				{
    "artists_file": "artists.json",
    "lyrics_file": "lyrics.json",
    "genres_file": "genres.json",
}

SCREAMING_SNAKE_CASE :   int							      =				{
    "artists_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json",
    },
    "genres_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json",
    },
    "lyrics_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json",
    },
}

SCREAMING_SNAKE_CASE :   Tuple							      =				{
    "jukebox": 512,
}






class lowerCamelCase(		__lowerCAmelCase     ):
       lowercase_				:			int									=    VOCAB_FILES_NAMES
       lowercase_				:			Any									=    PRETRAINED_VOCAB_FILES_MAP
       lowercase_				:			Tuple									=    PRETRAINED_LYRIC_TOKENS_SIZES
       lowercase_				:			Tuple									=    ['''input_ids''', '''attention_mask''']
       def __init__(      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase=["v3", "v2", "v2"],   lowerCamelCase=5_12,   lowerCamelCase=5,   lowerCamelCase="<|endoftext|>",   **lowerCamelCase,   ) ->     Tuple:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	AddedToken(lowerCAmelCase_,   lstrip=lowerCAmelCase_,   rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_,   lowerCAmelCase_) else unk_token
           super().__init__(
               unk_token=lowerCAmelCase_,   n_genres=lowerCAmelCase_,   version=lowerCAmelCase_,   max_n_lyric_tokens=lowerCAmelCase_,   **lowerCAmelCase_,   )
           _lowercase :							Any   	=	version
           _lowercase :							Any   	=	max_n_lyric_tokens
           _lowercase :							Any   	=	n_genres

           with open(lowerCAmelCase_,   encoding='utf-8') as vocab_handle:
               _lowercase :							Tuple   	=	json.load(lowerCAmelCase_)

           with open(lowerCAmelCase_,   encoding='utf-8') as vocab_handle:
               _lowercase :							str   	=	json.load(lowerCAmelCase_)

           with open(lowerCAmelCase_,   encoding='utf-8') as vocab_handle:
               _lowercase :							List[str]   	=	json.load(lowerCAmelCase_)

           _lowercase :							Optional[Any]   	=	R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
           # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
           if len(self.lyrics_encoder) == 79:
               _lowercase :							Optional[Any]   	=	oov.replace(R'\-\'',   R'\-+\'')

           _lowercase :							List[str]   	=	regex.compile(lowerCAmelCase_)
           _lowercase :							Optional[int]   	=	{v: k for k, v in self.artists_encoder.items()}
           _lowercase :							List[Any]   	=	{v: k for k, v in self.genres_encoder.items()}
           _lowercase :							Union[str, Any]   	=	{v: k for k, v in self.lyrics_encoder.items()}
       @property
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           return len(self.artists_encoder) + len(self.genres_encoder) + len(self.lyrics_encoder)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           return dict(self.artists_encoder,   self.genres_encoder,   self.lyrics_encoder)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Dict:




           """simple docstring"""

           _lowercase :							Dict   	=	[self.artists_encoder.get(lowerCAmelCase_,   0) for artist in list_artists]
           for genres in range(len(lowerCAmelCase_)):
               _lowercase :							Any   	=	[self.genres_encoder.get(lowerCAmelCase_,   0) for genre in list_genres[genres]]
               _lowercase :							List[Any]   	=	list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]))

           _lowercase :							Union[str, Any]   	=	[[self.lyrics_encoder.get(lowerCAmelCase_,   0) for character in list_lyrics[0]], [], []]
           return artists_id, list_genres, lyric_ids
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           return list(lowerCAmelCase_)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase	,       _lowercase	,       _lowercase :							Union[str, Any]   	=	self.prepare_for_tokenization(lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_)
           _lowercase :							Dict   	=	self._tokenize(lowerCAmelCase_)
           return artist, genre, lyrics
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase = False) ->     Tuple[str, str, str, Dict[str, Any]]:




           """simple docstring"""

           for idx in range(len(self.version)):
               if self.version[idx] == "v3":
                   _lowercase :							Union[str, Any]   	=	artists[idx].lower()
                   _lowercase :							Optional[int]   	=	[genres[idx].lower()]
               else:
                   _lowercase :							Optional[int]   	=	self._normalize(artists[idx]) + '.v2'
                   _lowercase :							Optional[int]   	=	[
                       self._normalize(lowerCAmelCase_) + '.v2' for genre in genres[idx].split('_')
                   ]  # split is for the full dictionary with combined genres

           if self.version[0] == "v2":
               _lowercase :							Dict   	=	regex.compile(R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+')
               _lowercase :							Dict   	=	'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
               _lowercase :							Optional[Any]   	=	{vocab[index]: index + 1 for index in range(len(lowerCAmelCase_))}
               _lowercase :							Dict   	=	0
               _lowercase :							str   	=	len(lowerCAmelCase_) + 1
               _lowercase :							Tuple   	=	self.vocab
               _lowercase :							Any   	=	{v: k for k, v in self.vocab.items()}
               _lowercase :							Tuple   	=	''
           else:
               _lowercase :							Optional[Any]   	=	regex.compile(R'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+')

           _lowercase :							List[str]   	=	self._run_strip_accents(lowerCAmelCase_)
           _lowercase :							str   	=	lyrics.replace('\\',   '\n')
           _lowercase :							List[str]   	=	self.out_of_vocab.sub('',   lowerCAmelCase_), [], []
           return artists, genres, lyrics
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							List[Any]   	=	unicodedata.normalize('NFD',   lowerCAmelCase_)
           _lowercase :							Tuple   	=	[]
           for char in text:
               _lowercase :							List[Any]   	=	unicodedata.category(lowerCAmelCase_)
               if cat == "Mn":
                   continue
               output.append(lowerCAmelCase_)
           return "".join(lowerCAmelCase_)
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	(
               [chr(lowerCAmelCase_) for i in range(ord('a'),   ord('z') + 1)]
               + [chr(lowerCAmelCase_) for i in range(ord('A'),   ord('Z') + 1)]
               + [chr(lowerCAmelCase_) for i in range(ord('0'),   ord('9') + 1)]
               + ['.']
           )
           _lowercase :							str   	=	frozenset(lowerCAmelCase_)
           _lowercase :							Dict   	=	re.compile(R'_+')
           _lowercase :							int   	=	''.join([c if c in accepted else '_' for c in text.lower()])
           _lowercase :							Dict   	=	pattern.sub('_',   lowerCAmelCase_).strip('_')
           return text
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     str:




           """simple docstring"""

           return " ".join(lowerCAmelCase_)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = None,   lowerCamelCase = False) ->     Union[str, Any]:




           """simple docstring"""

           if not isinstance(lowerCAmelCase_,   lowerCAmelCase_):
               _lowercase :							List[str]   	=	TensorType(lowerCAmelCase_)

           # Get a function reference for the correct framework
           if tensor_type == TensorType.TENSORFLOW:
               if not is_tf_available():
                   raise ImportError(
                       'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.')
               import tensorflow as tf

               _lowercase :							List[str]   	=	tf.constant
               _lowercase :							str   	=	tf.is_tensor
           elif tensor_type == TensorType.PYTORCH:
               if not is_torch_available():
                   raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.')
               import torch

               _lowercase :							Optional[Any]   	=	torch.tensor
               _lowercase :							Tuple   	=	torch.is_tensor
           elif tensor_type == TensorType.JAX:
               if not is_flax_available():
                   raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.')
               import jax.numpy as jnp  # noqa: F811

               _lowercase :							str   	=	jnp.array
               _lowercase :							List[str]   	=	_is_jax
           else:
               _lowercase :							Optional[int]   	=	np.asarray
               _lowercase :							Tuple   	=	_is_numpy

           # Do the tensor conversion in batch

           try:
               if prepend_batch_axis:
                   _lowercase :							Optional[Any]   	=	[inputs]

               if not is_tensor(lowerCAmelCase_):
                   _lowercase :							Any   	=	as_tensor(lowerCAmelCase_)
           except:  # noqa E722
               raise ValueError(
                   'Unable to create tensor, you should probably activate truncation and/or padding '
                   'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.')

           return inputs
       def __call__(      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase="",   lowerCamelCase="pt") ->     BatchEncoding:




           """simple docstring"""

           _lowercase :							List[Any]   	=	[0, 0, 0]
           _lowercase :							List[str]   	=	[artist] * len(self.version)
           _lowercase :							Tuple   	=	[genres] * len(self.version)

           _lowercase	,       _lowercase	,       _lowercase :							int   	=	self.tokenize(lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_)
           _lowercase	,       _lowercase	,       _lowercase :							Any   	=	self._convert_token_to_id(lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_)

           _lowercase :							Any   	=	[-INFINITY] * len(full_tokens[-1])
           _lowercase :							int   	=	[
               self.convert_to_tensors(
                   [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]],   tensor_type=lowerCAmelCase_)
               for i in range(len(self.version))
           ]
           return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks})
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = None) ->     Tuple[str]:




           """simple docstring"""

           if not os.path.isdir(lowerCAmelCase_):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
               return

           _lowercase :							List[Any]   	=	os.path.join(
               lowerCAmelCase_,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file'])
           with open(lowerCAmelCase_,   'w',   encoding='utf-8') as f:
               f.write(json.dumps(self.artists_encoder,   ensure_ascii=lowerCAmelCase_))

           _lowercase :							str   	=	os.path.join(
               lowerCAmelCase_,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file'])
           with open(lowerCAmelCase_,   'w',   encoding='utf-8') as f:
               f.write(json.dumps(self.genres_encoder,   ensure_ascii=lowerCAmelCase_))

           _lowercase :							str   	=	os.path.join(
               lowerCAmelCase_,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file'])
           with open(lowerCAmelCase_,   'w',   encoding='utf-8') as f:
               f.write(json.dumps(self.lyrics_encoder,   ensure_ascii=lowerCAmelCase_))

           return (artists_file, genres_file, lyrics_file)




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




           """simple docstring"""

           _lowercase :							Any   	=	self.artists_decoder.get(lowerCAmelCase_)
           _lowercase :							str   	=	[self.genres_decoder.get(lowerCAmelCase_) for genre in genres_index]
           _lowercase :							Dict   	=	[self.lyrics_decoder.get(lowerCAmelCase_) for character in lyric_index]
           return artist, genres, lyrics






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]}''')


