



import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__       =	{
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase__       =	{
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase__       =	{
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase__       =	{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
UpperCAmelCase__       =	{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
UpperCAmelCase__       =	{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


UpperCAmelCase__       =	{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase__       =	{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase__       =	{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					VOCAB_FILES_NAMES
			UpperCamelCase        =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase        =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase        =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase        =					DPRContextEncoderTokenizer







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					VOCAB_FILES_NAMES
			UpperCamelCase        =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase        =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase        =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase        =					DPRQuestionEncoderTokenizer





UpperCAmelCase__       =	collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

UpperCAmelCase__       =	collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


UpperCAmelCase__       =	r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "







@add_start_docstrings(A						)
class       __lowerCAmelCase   :



			def __call__(						self       :  Optional[Any]			,		A       :  Optional[Any]			,		A       :  Optional[str] = None			,		A       :  Optional[str] = None			,		A       :  Union[bool, str] = False			,		A       :  Union[bool, str] = False			,		A       :  Optional[int] = None			,		A       :  Optional[Union[str, TensorType]] = None			,		A       :  Optional[bool] = None			,		**A       :  List[Any]			,		)       ->   BatchEncoding:



						"""simple docstring"""






						if titles is None and texts is None:
									return super().__call__(
									    A			,		padding=A			,		truncation=A			,		max_length=A			,		return_tensors=A			,		return_attention_mask=A			,		**A			,		)
						elif titles is None or texts is None:
									_UpperCAmelCase			=       titles if texts is None else texts
									return super().__call__(
									    A			,		A			,		padding=A			,		truncation=A			,		max_length=A			,		return_tensors=A			,		return_attention_mask=A			,		**A			,		)
						_UpperCAmelCase			=       titles if not isinstance(A			,		A) else [titles]
						_UpperCAmelCase			=       texts if not isinstance(A			,		A) else [texts]
						_UpperCAmelCase			=       len(A)
						_UpperCAmelCase			=       questions if not isinstance(A			,		A) else [questions] * n_passages
						assert len(A) == len(
						    A), F"There should be as many titles than texts but got {len(A)} titles and {len(A)} texts."
						_UpperCAmelCase			=       super().__call__(A			,		A			,		padding=A			,		truncation=A)['input_ids']
						_UpperCAmelCase			=       super().__call__(A			,		add_special_tokens=A			,		padding=A			,		truncation=A)['input_ids']
						_UpperCAmelCase			=       {
						    'input_ids': [
						        (encoded_question_and_title + encoded_text)[:max_length]
						        if max_length is not None and truncation
						        else encoded_question_and_title + encoded_text
						        for encoded_question_and_title, encoded_text in zip(A			,		A)
						    ]
						}
						if return_attention_mask is not False:
									_UpperCAmelCase			=       []
									for input_ids in encoded_inputs["input_ids"]:
												attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
									_UpperCAmelCase			=       attention_mask
						return self.pad(A			,		padding=A			,		max_length=A			,		return_tensors=A)



			def    _lowerCamelCase	(						self       :  str			,		A       :  BatchEncoding			,		A       :  DPRReaderOutput			,		A       :  int = 16			,		A       :  int = 64			,		A       :  int = 4			,		)       ->   List[DPRSpanPrediction]:



						"""simple docstring"""






						_UpperCAmelCase			=       reader_input['input_ids']
						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       reader_output[:3]
						_UpperCAmelCase			=       len(A)
						_UpperCAmelCase			=       sorted(range(A)			,		reverse=A			,		key=relevance_logits.__getitem__)
						_UpperCAmelCase			=       []
						for doc_id in sorted_docs:
									_UpperCAmelCase			=       list(input_ids[doc_id])
									# assuming question & title information is at the beginning of the sequence
									_UpperCAmelCase			=       sequence_ids.index(self.sep_token_id			,		2) + 1  # second sep id
									if sequence_ids[-1] == self.pad_token_id:
												_UpperCAmelCase			=       sequence_ids.index(self.pad_token_id)
									else:
												_UpperCAmelCase			=       len(A)

									_UpperCAmelCase			=       self._get_best_spans(
									    start_logits=start_logits[doc_id][passage_offset:sequence_len]			,		end_logits=end_logits[doc_id][passage_offset:sequence_len]			,		max_answer_length=A			,		top_spans=A			,		)
									for start_index, end_index in best_spans:
												start_index += passage_offset
												end_index += passage_offset
												nbest_spans_predictions.append(
												    DPRSpanPrediction(
												        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]			,		relevance_score=relevance_logits[doc_id]			,		doc_id=A			,		start_index=A			,		end_index=A			,		text=self.decode(sequence_ids[start_index : end_index + 1])			,		))
									if len(A) >= num_spans:
												break
						return nbest_spans_predictions[:num_spans]



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  List[int]			,		A       :  List[int]			,		A       :  int			,		A       :  int			,		)       ->   List[DPRSpanPrediction]:



						"""simple docstring"""






						_UpperCAmelCase			=       []
						for start_index, start_score in enumerate(A):
									for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
												scores.append(((start_index, start_index + answer_length), start_score + end_score))
						_UpperCAmelCase			=       sorted(A			,		key=lambda A: x[1]			,		reverse=A)
						_UpperCAmelCase			=       []
						for (start_index, end_index), score in scores:
									assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
									_UpperCAmelCase			=       end_index - start_index + 1
									assert length <= max_answer_length, F"Span is too long: {length} > {max_answer_length}"
									if any(
									    start_index <= prev_start_index <= prev_end_index <= end_index
									    or prev_start_index <= start_index <= end_index <= prev_end_index
									    for (prev_start_index, prev_end_index) in chosen_span_intervals):
												continue
									chosen_span_intervals.append((start_index, end_index))

									if len(A) == top_spans:
												break
						return chosen_span_intervals







@add_end_docstrings(A						)
class       __lowerCAmelCase   (      A    ,						A						):
			UpperCamelCase        =					VOCAB_FILES_NAMES
			UpperCamelCase        =					READER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase        =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase        =					READER_PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase        =					['''input_ids''', '''attention_mask''']
			UpperCamelCase        =					DPRReaderTokenizer





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
							    TFDebertaVaForMaskedLM,
							    TFDebertaVaForQuestionAnswering,
							    TFDebertaVaForSequenceClassification,
							    TFDebertaVaForTokenClassification,
							    TFDebertaVaModel,
							)







class       __lowerCAmelCase   :



			def __init__(						self       :  str			,		A       :  str			,		A       :  Dict=13			,		A       :  int=7			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  Any=True			,		A       :  Dict=True			,		A       :  Dict=99			,		A       :  Tuple=32			,		A       :  Any=2			,		A       :  Any=4			,		A       :  Any=37			,		A       :  Optional[Any]="gelu"			,		A       :  List[Any]=0.1			,		A       :  Tuple=0.1			,		A       :  Optional[Any]=5_12			,		A       :  Tuple=16			,		A       :  int=2			,		A       :  List[str]=0.0_2			,		A       :  int=False			,		A       :  List[Any]=True			,		A       :  Optional[Any]="None"			,		A       :  Union[str, Any]=3			,		A       :  List[str]=4			,		A       :  List[Any]=None			,		)       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       seq_length
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       use_input_mask
						_UpperCAmelCase			=       use_token_type_ids
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       type_vocab_size
						_UpperCAmelCase			=       type_sequence_label_size
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       num_labels
						_UpperCAmelCase			=       num_choices
						_UpperCAmelCase			=       relative_attention
						_UpperCAmelCase			=       position_biased_input
						_UpperCAmelCase			=       pos_att_type
						_UpperCAmelCase			=       scope



			def    _lowerCamelCase	(						self       :  Any)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size)

						_UpperCAmelCase			=       None
						if self.use_input_mask:
									_UpperCAmelCase			=       random_attention_mask([self.batch_size, self.seq_length])

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

						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None
						if self.use_labels:
									_UpperCAmelCase			=       ids_tensor([self.batch_size]			,		self.type_sequence_label_size)
									_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.num_labels)

						_UpperCAmelCase			=       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			,		relative_attention=self.relative_attention			,		position_biased_input=self.position_biased_input			,		initializer_range=self.initializer_range			,		return_dict=A			,		)

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



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  Optional[int]			,		A       :  Tuple			,		A       :  int			,		A       :  Any			,		A       :  List[str]			,		A       :  List[str]			,		A       :  int)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModel(config=A)
						_UpperCAmelCase			=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

						_UpperCAmelCase			=       [input_ids, input_mask]
						_UpperCAmelCase			=       model(A)

						_UpperCAmelCase			=       model(A)

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



			def    _lowerCamelCase	(						self       :  str			,		A       :  Tuple			,		A       :  Tuple			,		A       :  Optional[int]			,		A       :  List[str]			,		A       :  Any			,		A       :  List[str]			,		A       :  List[str])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaForMaskedLM(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.vocab_size))



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Tuple			,		A       :  Tuple			,		A       :  Optional[int]			,		A       :  Optional[int]			,		A       :  List[Any]			,		A       :  Any			,		A       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       TFDebertaVaForSequenceClassification(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}

						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels))



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  List[Any]			,		A       :  List[Any]			,		A       :  List[str]			,		A       :  Optional[Any]			,		A       :  int			,		A       :  Any			,		A       :  int)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       TFDebertaVaForTokenClassification(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.num_labels))



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  List[Any]			,		A       :  List[str]			,		A       :  Dict			,		A       :  Dict			,		A       :  Any			,		A       :  Tuple			,		A       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaForQuestionAnswering(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}

						_UpperCAmelCase			=       model(A)
						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    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()
						(
						    (
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					
						)			=       config_and_inputs
						_UpperCAmelCase			=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
						return config, inputs_dict







@require_tf
class       __lowerCAmelCase   (      A    ,						A    ,						unittest.TestCase						):
			UpperCamelCase        =					(
			    (
			        TFDebertaVaModel,
			        TFDebertaVaForMaskedLM,
			        TFDebertaVaForQuestionAnswering,
			        TFDebertaVaForSequenceClassification,
			        TFDebertaVaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCamelCase        =					(
			    {
			        '''feature-extraction''': TFDebertaVaModel,
			        '''fill-mask''': TFDebertaVaForMaskedLM,
			        '''question-answering''': TFDebertaVaForQuestionAnswering,
			        '''text-classification''': TFDebertaVaForSequenceClassification,
			        '''token-classification''': TFDebertaVaForTokenClassification,
			        '''zero-shot''': TFDebertaVaForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			UpperCamelCase        =					False
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  int)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModelTester(self)
						_UpperCAmelCase			=       ConfigTester(self			,		config_class=A			,		hidden_size=37)



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[str]:



						"""simple docstring"""






						self.config_tester.run_common_tests()



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*A)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*A)



			def    _lowerCamelCase	(						self       :  int)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*A)



			def    _lowerCamelCase	(						self       :  Dict)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*A)



			@slow
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
						self.assertIsNotNone(A)







@require_tf
class       __lowerCAmelCase   (      unittest.TestCase						):



			@unittest.skip(reason='Model not available yet')
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Dict:



						"""simple docstring"""






						pass



			@slow
			def    _lowerCamelCase	(						self       :  List[str])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
						_UpperCAmelCase			=       tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
						_UpperCAmelCase			=       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
						_UpperCAmelCase			=       model(A			,		attention_mask=A)[0]

						_UpperCAmelCase			=       tf.constant(
						    [[[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]]])
						tf.debugging.assert_near(output[:, 1:4, 1:4]			,		A			,		atol=1E-4)
