



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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@slow
					@require_torch
					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'				,					'prajjwal1/bert-tiny'					)
								A__       =    BertTokenizer.from_pretrained('bert-base-uncased'					)

								A__       =    bertabert.config.encoder.vocab_size
								A__       =    tokenizer.sep_token_id
								A__       =    tokenizer.cls_token_id
								A__       =    1_28

								A__       =    datasets.load_dataset('cnn_dailymail'				,					'3.0.0'				,					split='train[:1%]'					)
								A__       =    datasets.load_dataset('cnn_dailymail'				,					'3.0.0'				,					split='validation[:1%]'					)

								A__       =    train_dataset.select(range(32					)					)
								A__       =    val_dataset.select(range(16					)					)

								A__       =    4

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

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

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

											return batch

								def _compute_metrics(_snake_case					:       Union[str, Any]					):
											A__       =    pred.label_ids
											A__       =    pred.predictions

											# all unnecessary tokens are removed
											A__       =    tokenizer.batch_decode(_snake_case				,					skip_special_tokens=_snake_case					)
											A__       =    tokenizer.batch_decode(_snake_case				,					skip_special_tokens=_snake_case					)

											A__       =    sum([int(pred_str[i] == label_str[i]					) for i in range(len(_snake_case					)					)]					) / len(_snake_case					)

											return {"accuracy": accuracy}

								# map train dataset
								A__       =    train_dataset.map(
								    _map_to_encoder_decoder_inputs				,					batched=_snake_case				,					batch_size=_snake_case				,					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__       =    val_dataset.map(
								    _map_to_encoder_decoder_inputs				,					batched=_snake_case				,					batch_size=_snake_case				,					remove_columns=['article', 'highlights']				,					)
								val_dataset.set_format(
								    type='torch'				,					columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']				,					)

								A__       =    self.get_auto_remove_tmp_dir()

								A__       =    SeqaSeqTrainingArguments(
								    output_dir=_snake_case				,					per_device_train_batch_size=_snake_case				,					per_device_eval_batch_size=_snake_case				,					predict_with_generate=_snake_case				,					evaluation_strategy='steps'				,					do_train=_snake_case				,					do_eval=_snake_case				,					warmup_steps=0				,					eval_steps=2				,					logging_steps=2				,					)

								# instantiate trainer
								A__       =    SeqaSeqTrainer(
								    model=_snake_case				,					args=_snake_case				,					compute_metrics=_compute_metrics				,					train_dataset=_snake_case				,					eval_dataset=_snake_case				,					tokenizer=_snake_case				,					)

								# start training
								trainer.train()









from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[str]			       =    "roberta"



					def __init__(     self					:       List[str]				,					_snake_case					:       Union[str, Any]=5_02_65				,					_snake_case					:       List[Any]=7_68				,					_snake_case					:       List[str]=12				,					_snake_case					:       List[str]=12				,					_snake_case					:       Any=30_72				,					_snake_case					:       Union[str, Any]="gelu"				,					_snake_case					:       int=0.1				,					_snake_case					:       Union[str, Any]=0.1				,					_snake_case					:       Tuple=5_12				,					_snake_case					:       Union[str, Any]=2				,					_snake_case					:       Any=0.02				,					_snake_case					:       Any=1E-12				,					_snake_case					:       List[Any]=1				,					_snake_case					:       int=0				,					_snake_case					:       Any=2				,					_snake_case					:       Optional[Any]="absolute"				,					_snake_case					:       int=True				,					_snake_case					:       Any=None				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								super().__init__(pad_token_id=_snake_case				,					bos_token_id=_snake_case				,					eos_token_id=_snake_case				,					**_snake_case					)

								A__       =    vocab_size
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    hidden_act
								A__       =    intermediate_size
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    max_position_embeddings
								A__       =    type_vocab_size
								A__       =    initializer_range
								A__       =    layer_norm_eps
								A__       =    position_embedding_type
								A__       =    use_cache
								A__       =    classifier_dropout







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@property
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								if self.task == "multiple-choice":
											A__       =    {0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
											A__       =    {0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ]					)




