





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_a     :    str         =						logging.getLogger(__name__)
@dataclass
class   a_ :
			A__      :			Tuple       	=  field(
			    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
			A__      :			Union[str, Any]       	=  field(
			    default=__a		,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
			A__      :			Optional[int]       	=  field(
			    default=__a		,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
			A__      :			Any       	=  field(
			    default=__a		,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}		,							)
			A__      :			int       	=  field(default=__a		,							metadata={'help': 'Whether tp freeze the encoder.'}      )
			A__      :			List[str]       	=  field(default=__a		,							metadata={'help': 'Whether  to freeze the embeddings.'}      )
@dataclass
class   a_ :
			A__      :			str       	=  field(
			    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}      )
			A__      :			int       	=  field(
			    default='summarization'		,							metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}		,							)
			A__      :			Optional[int]       	=  field(
			    default=1024		,							metadata={
			        'help': (
			            'The maximum total input sequence length after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }		,							)
			A__      :			List[str]       	=  field(
			    default=128		,							metadata={
			        'help': (
			            'The maximum total sequence length for target text after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }		,							)
			A__      :			Optional[int]       	=  field(
			    default=142		,							metadata={
			        'help': (
			            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded. '
			            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
			            'during ``evaluate`` and ``predict``.'
			        )
			    }		,							)
			A__      :			str       	=  field(
			    default=142		,							metadata={
			        'help': (
			            'The maximum total sequence length for test target text after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }		,							)
			A__      :			int       	=  field(default=-1		,							metadata={'help': '# training examples. -1 means use all.'}      )
			A__      :			List[str]       	=  field(default=-1		,							metadata={'help': '# validation examples. -1 means use all.'}      )
			A__      :			List[Any]       	=  field(default=-1		,							metadata={'help': '# test examples. -1 means use all.'}      )
			A__      :			List[Any]       	=  field(default=__a		,							metadata={'help': 'Source language id for translation.'}      )
			A__      :			List[Any]       	=  field(default=__a		,							metadata={'help': 'Target language id for translation.'}      )
			A__      :			str       	=  field(default=__a		,							metadata={'help': '# num_beams to use for evaluation.'}      )
			A__      :			List[Any]       	=  field(
			    default=__a		,							metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}		,							)
def 							a_	(    __magic_name__		,     __magic_name__		,     __magic_name__   )				->			Union[str, Any]:




				"""simple docstring"""






				logger.info(F"***** {split} metrics *****"   )
				for key in sorted(metrics.keys()   ):
								logger.info(F"  {key} = {metrics[key]}"   )
				save_json(__lowerCAmelCase		,     os.path.join(__lowerCAmelCase		,     F"{split}_results.json"   )   )
def 							a_	(    )				->			Tuple:




				"""simple docstring"""






				snake_case   :					Union[str, Any]    						=				HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

				if len(sys.argv   ) == 2 and sys.argv[1].endswith('''.json'''   ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								snake_case						,						snake_case						,						snake_case   :					int    						=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
				else:
								snake_case						,						snake_case						,						snake_case   :					List[str]    						=				parser.parse_args_into_dataclasses()

				check_output_dir(__lowerCAmelCase   )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,     datefmt='''%m/%d/%Y %H:%M:%S'''		,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,     )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''		,     training_args.local_rank		,     training_args.device		,     training_args.n_gpu		,     bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )		,     training_args.fpaa		,     )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank   ):
								transformers.utils.logging.set_verbosity_info()
				logger.info('''Training/evaluation parameters %s'''		,     __lowerCAmelCase   )

				# Set seed
				set_seed(training_args.seed   )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				snake_case   :					List[Any]    						=				AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,     cache_dir=model_args.cache_dir		,     )

				snake_case   :					str    						=				('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
				for p in extra_model_params:
								if getattr(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase   ):
												assert hasattr(__lowerCAmelCase		,     __lowerCAmelCase   ), F"({config.__class__.__name__}) doesn't have a `{p}` attribute"
												setattr(__lowerCAmelCase		,     __lowerCAmelCase		,     getattr(__lowerCAmelCase		,     __lowerCAmelCase   )   )

				snake_case   :					Tuple    						=				AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,     cache_dir=model_args.cache_dir		,     )
				snake_case   :					int    						=				AutoModelForSeqaSeqLM.from_pretrained(
				    model_args.model_name_or_path		,     from_tf='''.ckpt''' in model_args.model_name_or_path		,     config=__lowerCAmelCase		,     cache_dir=model_args.cache_dir		,     )

				# use task specific params
				use_task_specific_params(__lowerCAmelCase		,     data_args.task   )

				# set num_beams for evaluation
				if data_args.eval_beams is None:
								snake_case   :					Tuple    						=				model.config.num_beams

				# set decoder_start_token_id for MBart
				if model.config.decoder_start_token_id is None and isinstance(__lowerCAmelCase		,     (MBartTokenizer, MBartTokenizerFast)   ):
								assert (
								    data_args.tgt_lang is not None and data_args.src_lang is not None
								), "mBart requires --tgt_lang and --src_lang"
								if isinstance(__lowerCAmelCase		,     __lowerCAmelCase   ):
												snake_case   :					List[Any]    						=				tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												snake_case   :					Optional[Any]    						=				tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

				if model_args.freeze_embeds:
								freeze_embeds(__lowerCAmelCase   )
				if model_args.freeze_encoder:
								freeze_params(model.get_encoder()   )
								assert_all_frozen(model.get_encoder()   )

				snake_case   :					Optional[Any]    						=				SeqaSeqDataset

				# Get datasets
				snake_case   :					List[str]    						=				(
				    dataset_class(
				        __lowerCAmelCase		,     type_path='''train'''		,     data_dir=data_args.data_dir		,     n_obs=data_args.n_train		,     max_target_length=data_args.max_target_length		,     max_source_length=data_args.max_source_length		,     prefix=model.config.prefix or ''''''		,     )
				    if training_args.do_train
				    else None
				)
				snake_case   :					Optional[int]    						=				(
				    dataset_class(
				        __lowerCAmelCase		,     type_path='''val'''		,     data_dir=data_args.data_dir		,     n_obs=data_args.n_val		,     max_target_length=data_args.val_max_target_length		,     max_source_length=data_args.max_source_length		,     prefix=model.config.prefix or ''''''		,     )
				    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
				    else None
				)
				snake_case   :					int    						=				(
				    dataset_class(
				        __lowerCAmelCase		,     type_path='''test'''		,     data_dir=data_args.data_dir		,     n_obs=data_args.n_test		,     max_target_length=data_args.test_max_target_length		,     max_source_length=data_args.max_source_length		,     prefix=model.config.prefix or ''''''		,     )
				    if training_args.do_predict
				    else None
				)

				# Initialize our Trainer
				snake_case   :					Dict    						=				(
				    build_compute_metrics_fn(data_args.task		,     __lowerCAmelCase   ) if training_args.predict_with_generate else None
				)
				snake_case   :					List[Any]    						=				SeqaSeqTrainer(
				    model=__lowerCAmelCase		,     args=__lowerCAmelCase		,     data_args=__lowerCAmelCase		,     train_dataset=__lowerCAmelCase		,     eval_dataset=__lowerCAmelCase		,     data_collator=SeqaSeqDataCollator(
				        __lowerCAmelCase		,     __lowerCAmelCase		,     model.config.decoder_start_token_id		,     training_args.tpu_num_cores   )		,     compute_metrics=__lowerCAmelCase		,     tokenizer=__lowerCAmelCase		,     )

				snake_case   :					List[Any]    						=				{}
				# Training
				if training_args.do_train:
								logger.info('''*** Train ***'''   )

								snake_case   :					int    						=				trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
								snake_case   :					List[str]    						=				train_result.metrics
								snake_case   :					Tuple    						=				data_args.n_train

								trainer.save_model()  # this also saves the tokenizer

								if trainer.is_world_process_zero():
												handle_metrics('''train'''		,     __lowerCAmelCase		,     training_args.output_dir   )
												all_metrics.update(__lowerCAmelCase   )

												# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
												trainer.state.save_to_json(os.path.join(training_args.output_dir		,     '''trainer_state.json'''   )   )

												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''   )

								snake_case   :					Dict    						=				trainer.evaluate(metric_key_prefix='''val'''   )
								snake_case   :					List[Any]    						=				data_args.n_val
								snake_case   :					int    						=				round(metrics['''val_loss''']		,     4   )

								if trainer.is_world_process_zero():
												handle_metrics('''val'''		,     __lowerCAmelCase		,     training_args.output_dir   )
												all_metrics.update(__lowerCAmelCase   )

				if training_args.do_predict:
								logger.info('''*** Predict ***'''   )

								snake_case   :					List[Any]    						=				trainer.predict(test_dataset=__lowerCAmelCase		,     metric_key_prefix='''test'''   )
								snake_case   :					Any    						=				test_output.metrics
								snake_case   :					Dict    						=				data_args.n_test

								if trainer.is_world_process_zero():
												snake_case   :					List[Any]    						=				round(metrics['''test_loss''']		,     4   )
												handle_metrics('''test'''		,     __lowerCAmelCase		,     training_args.output_dir   )
												all_metrics.update(__lowerCAmelCase   )

												if training_args.predict_with_generate:
																snake_case   :					List[Any]    						=				tokenizer.batch_decode(
																    test_output.predictions		,     skip_special_tokens=__lowerCAmelCase		,     clean_up_tokenization_spaces=__lowerCAmelCase   )
																snake_case   :					str    						=				lmap(str.strip		,     __lowerCAmelCase   )
																write_txt_file(__lowerCAmelCase		,     os.path.join(training_args.output_dir		,     '''test_generations.txt'''   )   )

				if trainer.is_world_process_zero():
								save_json(__lowerCAmelCase		,     os.path.join(training_args.output_dir		,     '''all_results.json'''   )   )

				return all_metrics
def 							a_	(    __magic_name__   )				->			Optional[int]:




				"""simple docstring"""






				main()


if __name__ == "__main__":
			main()


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							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=7							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=99							,							_UpperCamelCase=32							,							_UpperCamelCase=2							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=16							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=False							,							_UpperCamelCase=True							,							_UpperCamelCase="None"							,							_UpperCamelCase=3							,							_UpperCamelCase=4							,							_UpperCamelCase=None							,							)							-> Tuple:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      seq_length
							lowerCAmelCase_         =      is_training
							lowerCAmelCase_         =      use_input_mask
							lowerCAmelCase_         =      use_token_type_ids
							lowerCAmelCase_         =      use_labels
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      type_sequence_label_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      num_labels
							lowerCAmelCase_         =      num_choices
							lowerCAmelCase_         =      relative_attention
							lowerCAmelCase_         =      position_biased_input
							lowerCAmelCase_         =      pos_att_type
							lowerCAmelCase_         =      scope




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size	)

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

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

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

							lowerCAmelCase_         =      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=_UpperCamelCase							,							)

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaModel(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

							lowerCAmelCase_         =      [input_ids, input_mask]
							lowerCAmelCase_         =      model(_UpperCamelCase	)

							lowerCAmelCase_         =      model(_UpperCamelCase	)

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Any:
							lowerCAmelCase_         =      TFDebertaVaForMaskedLM(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      TFDebertaVaForSequenceClassification(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      TFDebertaVaForTokenClassification(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaForQuestionAnswering(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.start_logits.shape							,							(self.batch_size, self.seq_length)	)
							self.parent.assertEqual(result.end_logits.shape							,							(self.batch_size, self.seq_length)	)




				def 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    
							)         =      config_and_inputs
							lowerCAmelCase_         =      {"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			):
				_lowercase     						=(
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowercase     						=(
				    {
				        '''feature-extraction''': TFDebertaVaModel,
				        '''fill-mask''': TFDebertaVaForMaskedLM,
				        '''question-answering''': TFDebertaVaForQuestionAnswering,
				        '''text-classification''': TFDebertaVaForSequenceClassification,
				        '''token-classification''': TFDebertaVaForTokenClassification,
				        '''zero-shot''': TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      TFDebertaVaModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							hidden_size=37	)




				def 	__a		(   self	)							-> str:
							self.config_tester.run_common_tests()




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase	)




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"	)
							self.assertIsNotNone(_UpperCamelCase	)





@require_tf
class       _lowerCAmelCase   (      unittest.TestCase			):




				@unittest.skip(reason="Model not available yet"	)
				def 	__a		(   self	)							-> List[str]:
							pass




				@slow
				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"	)
							lowerCAmelCase_         =      tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	)
							lowerCAmelCase_         =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase	)[0]

							lowerCAmelCase_         =      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]							,							_UpperCamelCase							,							atol=1e-4	)



