from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets



class  SCREAMING_SNAKE_CASE_ (					_UpperCAmelCase			):
  '''simple docstring'''


  @slow
  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Optional[Any]:
         A						:     List[Any]         =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'      ,	'prajjwal1/bert-tiny'     )
         A						:     List[str]         =BertTokenizer.from_pretrained('bert-base-uncased'     )

         A						:     Union[str, Any]         =bertabert.config.encoder.vocab_size
         A						:     str         =tokenizer.sep_token_id
         A						:     str         =tokenizer.cls_token_id
         A						:     Dict         =1_28

         A						:     str         =datasets.load_dataset('cnn_dailymail'      ,	'3.0.0'      ,	split='train[:1%]'     )
         A						:     Union[str, Any]         =datasets.load_dataset('cnn_dailymail'      ,	'3.0.0'      ,	split='validation[:1%]'     )

         A						:     List[str]         =train_dataset.select(range(32     )     )
         A						:     List[str]         =val_dataset.select(range(16     )     )

         A						:     Dict         =4

         def _map_to_encoder_decoder_inputs(SCREAMING_SNAKE_CASE__    :					Optional[int]     ):
                # Tokenizer will automatically set [BOS] <text> [EOS]
                A						:     Union[str, Any]         =tokenizer(batch['article']      ,	padding='max_length'      ,	truncation=A_      ,	max_length=5_12     )
                A						:     List[str]         =tokenizer(batch['highlights']      ,	padding='max_length'      ,	truncation=A_      ,	max_length=1_28     )
                A						:     int         =inputs.input_ids
                A						:     Any         =inputs.attention_mask

                A						:     str         =outputs.input_ids
                A						:     Any         =outputs.input_ids.copy()
                A						:     Any         =[
                    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                ]
                A						:     Any         =outputs.attention_mask

                assert all(len(A_     ) == 5_12 for x in inputs.input_ids     )
                assert all(len(A_     ) == 1_28 for x in outputs.input_ids     )

                return batch

         def _compute_metrics(SCREAMING_SNAKE_CASE__    :					str     ):
                A						:     List[Any]         =pred.label_ids
                A						:     Tuple         =pred.predictions

                # all unnecessary tokens are removed
                A						:     int         =tokenizer.batch_decode(A_      ,	skip_special_tokens=A_     )
                A						:     str         =tokenizer.batch_decode(A_      ,	skip_special_tokens=A_     )

                A						:     Optional[Any]         =sum([int(pred_str[i] == label_str[i]     ) for i in range(len(A_     )     )]     ) / len(A_     )

                return {"accuracy": accuracy}

         # map train dataset
         A						:     Optional[int]         =train_dataset.map(
             _map_to_encoder_decoder_inputs      ,	batched=A_      ,	batch_size=A_      ,	remove_columns=['article', 'highlights']      ,	)
         train_dataset.set_format(
             type='torch'      ,	columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']      ,	)

         # same for validation dataset
         A						:     Dict         =val_dataset.map(
             _map_to_encoder_decoder_inputs      ,	batched=A_      ,	batch_size=A_      ,	remove_columns=['article', 'highlights']      ,	)
         val_dataset.set_format(
             type='torch'      ,	columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']      ,	)

         A						:     int         =self.get_auto_remove_tmp_dir()

         A						:     Any         =SeqaSeqTrainingArguments(
             output_dir=A_      ,	per_device_train_batch_size=A_      ,	per_device_eval_batch_size=A_      ,	predict_with_generate=A_      ,	evaluation_strategy='steps'      ,	do_train=A_      ,	do_eval=A_      ,	warmup_steps=0      ,	eval_steps=2      ,	logging_steps=2      ,	)

         # instantiate trainer
         A						:     Union[str, Any]         =SeqaSeqTrainer(
             model=A_      ,	args=A_      ,	compute_metrics=_compute_metrics      ,	train_dataset=A_      ,	eval_dataset=A_      ,	tokenizer=A_      ,	)

         # start training
         trainer.train()







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_lowercase							:      Optional[int]      							=logging.get_logger(__name__)

_lowercase							:      List[str]      							={
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     int            =				"deberta-v2"







  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					str=12_81_00      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=15_36      ,	SCREAMING_SNAKE_CASE__    :					Dict=24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=61_44      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="gelu"      ,	SCREAMING_SNAKE_CASE__    :					int=0.1      ,	SCREAMING_SNAKE_CASE__    :					Any=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[str]=5_12      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1e-7      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=False      ,	SCREAMING_SNAKE_CASE__    :					Tuple=-1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	SCREAMING_SNAKE_CASE__    :					List[str]=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]="gelu"      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				Dict:
         super().__init__(**SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =hidden_size
         A						:     Optional[Any]         =num_hidden_layers
         A						:     Optional[int]         =num_attention_heads
         A						:     Optional[int]         =intermediate_size
         A						:     Any         =hidden_act
         A						:     Any         =hidden_dropout_prob
         A						:     Union[str, Any]         =attention_probs_dropout_prob
         A						:     Optional[Any]         =max_position_embeddings
         A						:     Tuple         =type_vocab_size
         A						:     Tuple         =initializer_range
         A						:     int         =relative_attention
         A						:     int         =max_relative_positions
         A						:     Optional[Any]         =pad_token_id
         A						:     Union[str, Any]         =position_biased_input

         # Backwards compatibility
         if type(SCREAMING_SNAKE_CASE__     ) == str:
                A						:     Any         =[x.strip() for x in pos_att_type.lower().split('|'     )]

         A						:     Any         =pos_att_type
         A						:     Tuple         =vocab_size
         A						:     Any         =layer_norm_eps

         A						:     Optional[Any]         =kwargs.get('pooler_hidden_size'      ,	SCREAMING_SNAKE_CASE__     )
         A						:     str         =pooler_dropout
         A						:     Any         =pooler_hidden_act







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
                A						:     List[Any]         ={0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
                A						:     int         ={0: 'batch', 1: 'sequence'}
         if self._config.type_vocab_size > 0:
                return OrderedDict(
                    [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]     )
         else:
                return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]     )







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				int:
         return 12







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					Optional["TensorType"] = None      ,	SCREAMING_SNAKE_CASE__    :					int = 3      ,	SCREAMING_SNAKE_CASE__    :					int = 40      ,	SCREAMING_SNAKE_CASE__    :					int = 40      ,	SCREAMING_SNAKE_CASE__    :					"PreTrainedTokenizerBase" = None      ,	)    ->				Mapping[str, Any]:
         A						:     str         =super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE__      ,	framework=SCREAMING_SNAKE_CASE__     )
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                del dummy_inputs["token_type_ids"]
         return dummy_inputs






