





'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase__		:     Optional[Any]			=							logging.get_logger(__name__)

lowercase__		:     int			=							{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
lowercase__		:     Dict			=							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def    a__							(							lowercase   :	Optional[Any],   lowercase   :	List[str],   lowercase   :	List[str],   lowercase   :	List[str],   lowercase   :	List[str] )						->						str:





     """simple docstring"""

     for attribute in key.split('''.''' ):
          _UpperCamelCase							       =			getattr(lowercase,   lowercase )

     if weight_type is not None:
          _UpperCamelCase							       =			getattr(lowercase,   lowercase ).shape
     else:
          _UpperCamelCase							       =			hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
              F""" {value.shape} for {full_name}""" )

     if weight_type == "weight":
          _UpperCamelCase							       =			value
     elif weight_type == "weight_g":
          _UpperCamelCase							       =			value
     elif weight_type == "weight_v":
          _UpperCamelCase							       =			value
     elif weight_type == "bias":
          _UpperCamelCase							       =			value
     elif weight_type == "running_mean":
          _UpperCamelCase							       =			value
     elif weight_type == "running_var":
          _UpperCamelCase							       =			value
     elif weight_type == "num_batches_tracked":
          _UpperCamelCase							       =			value
     elif weight_type == "inv_freq":
          _UpperCamelCase							       =			value
     else:
          _UpperCamelCase							       =			value

     logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )







def    a__							(							lowercase   :	Any,   lowercase   :	List[str],   lowercase   :	Union[str, Any] )						->						List[str]:





     """simple docstring"""

     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			fairseq_model.state_dict()

     _UpperCamelCase							       =			hf_model.wavaveca_conformer.feature_extractor

     for name, value in fairseq_dict.items():
          _UpperCamelCase							       =			False
          if "conv_layers" in name:
               load_conv_layer(
                   lowercase,   lowercase,   lowercase,   lowercase,   hf_model.config.feat_extract_norm == '''group''',   )
               _UpperCamelCase							       =			True
          else:
               for key, mapped_key in MAPPING.items():
                    _UpperCamelCase							       =			'''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
                         _UpperCamelCase							       =			True
                         if "*" in mapped_key:
                              _UpperCamelCase							       =			name.split(lowercase )[0].split('''.''' )[-2]
                              _UpperCamelCase							       =			mapped_key.replace('''*''',   lowercase )
                         if "pos_bias_u" in name:
                              _UpperCamelCase							       =			None
                         elif "pos_bias_v" in name:
                              _UpperCamelCase							       =			None
                         elif "weight_g" in name:
                              _UpperCamelCase							       =			'''weight_g'''
                         elif "weight_v" in name:
                              _UpperCamelCase							       =			'''weight_v'''
                         elif "bias" in name:
                              _UpperCamelCase							       =			'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _UpperCamelCase							       =			'''weight'''
                         elif "running_mean" in name:
                              _UpperCamelCase							       =			'''running_mean'''
                         elif "inv_freq" in name:
                              _UpperCamelCase							       =			'''inv_freq'''
                         elif "running_var" in name:
                              _UpperCamelCase							       =			'''running_var'''
                         elif "num_batches_tracked" in name:
                              _UpperCamelCase							       =			'''num_batches_tracked'''
                         else:
                              _UpperCamelCase							       =			None
                         set_recursively(lowercase,   lowercase,   lowercase,   lowercase,   lowercase )
                    continue
          if not is_used:
               unused_weights.append(lowercase )

     logger.warning(F"""Unused weights: {unused_weights}""" )







def    a__							(							lowercase   :	int,   lowercase   :	List[str],   lowercase   :	List[str],   lowercase   :	Optional[int],   lowercase   :	Union[str, Any] )						->						str:





     """simple docstring"""

     _UpperCamelCase							       =			full_name.split('''conv_layers.''' )[-1]
     _UpperCamelCase							       =			name.split('''.''' )
     _UpperCamelCase							       =			int(items[0] )
     _UpperCamelCase							       =			int(items[1] )

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        F"""{full_name} has size {value.shape}, but"""
                        F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
               _UpperCamelCase							       =			value
               logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        F"""{full_name} has size {value.shape}, but"""
                        F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
               _UpperCamelCase							       =			value
               logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        F"""{full_name} has size {value.shape}, but"""
                        F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
               _UpperCamelCase							       =			value
               logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        F"""{full_name} has size {value.shape}, but"""
                        F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
               _UpperCamelCase							       =			value
               logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
     else:
          unused_weights.append(lowercase )







@torch.no_grad()
def    a__							(							lowercase   :	Tuple,   lowercase   :	Union[str, Any],   lowercase   :	Union[str, Any]=None,   lowercase   :	Optional[int]=None,   lowercase   :	Optional[int]=True )						->						List[str]:





     """simple docstring"""

     if config_path is not None:
          _UpperCamelCase							       =			WavaVecaConformerConfig.from_pretrained(lowercase,   hidden_act='''swish''' )
     else:
          _UpperCamelCase							       =			WavaVecaConformerConfig()

     if "rope" in checkpoint_path:
          _UpperCamelCase							       =			'''rotary'''

     if is_finetuned:
          if dict_path:
               _UpperCamelCase							       =			Dictionary.load(lowercase )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               _UpperCamelCase							       =			target_dict.pad_index
               _UpperCamelCase							       =			target_dict.bos_index
               _UpperCamelCase							       =			target_dict.eos_index
               _UpperCamelCase							       =			len(target_dict.symbols )
               _UpperCamelCase							       =			os.path.join(lowercase,   '''vocab.json''' )
               if not os.path.isdir(lowercase ):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase ) )
                    return
               os.makedirs(lowercase,   exist_ok=lowercase )
               _UpperCamelCase							       =			target_dict.indices

               # fairseq has the <pad> and <s> switched
               _UpperCamelCase							       =			0
               _UpperCamelCase							       =			1
               with open(lowercase,   '''w''',   encoding='''utf-8''' ) as vocab_handle:
                    json.dump(lowercase,   lowercase )
               _UpperCamelCase							       =			WavaVecaCTCTokenizer(
                   lowercase,   unk_token=target_dict.unk_word,   pad_token=target_dict.pad_word,   bos_token=target_dict.bos_word,   eos_token=target_dict.eos_word,   word_delimiter_token='''|''',   do_lower_case=lowercase,   )
               _UpperCamelCase							       =			True if config.feat_extract_norm == '''layer''' else False
               _UpperCamelCase							       =			WavaVecaFeatureExtractor(
                   feature_size=1,   sampling_rate=16000,   padding_value=0,   do_normalize=lowercase,   return_attention_mask=lowercase,   )
               _UpperCamelCase							       =			WavaVecaProcessor(feature_extractor=lowercase,   tokenizer=lowercase )
               processor.save_pretrained(lowercase )

          _UpperCamelCase							       =			WavaVecaConformerForCTC(lowercase )
     else:
          _UpperCamelCase							       =			WavaVecaConformerForPreTraining(lowercase )

     if is_finetuned:
          _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path],   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
     else:
          _UpperCamelCase							       =			argparse.Namespace(task='''audio_pretraining''' )
          _UpperCamelCase							       =			fairseq.tasks.setup_task(lowercase )

          _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],   task=lowercase )

     _UpperCamelCase							       =			model[0].eval()

     recursively_load_weights(lowercase,   lowercase,   not is_finetuned )

     hf_wavavec.save_pretrained(lowercase )


if __name__ == "__main__":
   lowercase__		:     List[Any]			=							argparse.ArgumentParser()
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
   parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
   parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
   parser.add_argument(
       '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
   )
   lowercase__		:     Dict			=							parser.parse_args()
   convert_wavaveca_conformer_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )






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    _lowercase				:

   def __init__(				self	:    str			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Union[str, Any]=1_3			,						lowerCamelCase__	:    Dict=7			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Tuple=True			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Any=9_9			,						lowerCamelCase__	:    Union[str, Any]=3_2			,						lowerCamelCase__	:    Dict=2			,						lowerCamelCase__	:    Tuple=4			,						lowerCamelCase__	:    int=3_7			,						lowerCamelCase__	:    List[str]="gelu"			,						lowerCamelCase__	:    str=0.1			,						lowerCamelCase__	:    Any=0.1			,						lowerCamelCase__	:    int=5_1_2			,						lowerCamelCase__	:    Optional[Any]=1_6			,						lowerCamelCase__	:    Optional[Any]=2			,						lowerCamelCase__	:    List[str]=0.02			,						lowerCamelCase__	:    Dict=False			,						lowerCamelCase__	:    Union[str, Any]=True			,						lowerCamelCase__	:    List[Any]="None"			,						lowerCamelCase__	:    int=3			,						lowerCamelCase__	:    List[Any]=4			,						lowerCamelCase__	:    List[str]=None			,						) ->			List[str]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    str       ) ->			str:
         """simple docstring"""





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

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

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

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

         A_							       =		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=lowerCamelCase__			,						)

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

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaModel(config=lowerCamelCase__       )
         A_							       =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         A_							       =		[input_ids, input_mask]
         A_							       =		model(lowerCamelCase__       )

         A_							       =		model(lowerCamelCase__       )

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

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[str]       ) ->			str:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





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

         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    str       ) ->			Optional[Any]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Any       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaForQuestionAnswering(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A_							       =		model(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	:    int       ) ->			Optional[int]:
         """simple docstring"""





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



@require_tf
class    _lowercase				(					__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Union[str, Any]  					=    (
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   _lowercase     :       Optional[Any]  					=    (
       {
           'feature-extraction': TFDebertaVaModel,
           'fill-mask': TFDebertaVaForMaskedLM,
           'question-answering': TFDebertaVaForQuestionAnswering,
           'text-classification': TFDebertaVaForSequenceClassification,
           'token-classification': TFDebertaVaForTokenClassification,
           'zero-shot': TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   _lowercase     :       List[str]  					=    False
   _lowercase     :       Any  					=    False

   def     UpperCamelCase					(				self	:    int       ) ->			List[str]:
         """simple docstring"""





         A_							       =		TFDebertaVaModelTester(self       )
         A_							       =		ConfigTester(self			,						config_class=lowerCamelCase__			,						hidden_size=3_7       )

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





         self.config_tester.run_common_tests()

   def     UpperCamelCase					(				self	:    Any       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__       )





   @slow
   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			int:
         """simple docstring"""





         A_							       =		TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''       )
         self.assertIsNotNone(lowerCamelCase__       )



@require_tf
class    _lowercase				(					unittest.TestCase			):

   @unittest.skip(reason='''Model not available yet'''       )
   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         pass





   @slow
   def     UpperCamelCase					(				self	:    Any       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''       )
         A_							       =		tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       )
         A_							       =		tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
         A_							       =		model(lowerCamelCase__			,						attention_mask=lowerCamelCase__       )[0]

         A_							       =		tf.constant(
             [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]       )
         tf.debugging.assert_near(output[:, 1:4, 1:4]			,						lowerCamelCase__			,						atol=1e-4       )





