

"""simple docstring"""




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

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


if TYPE_CHECKING:
				from ...onnx.config import PatchingSpec
				from ...tokenization_utils_base import PreTrainedTokenizerBase


_lowercase   :	List[str]												=						logging.get_logger(__name__)

_lowercase   :	Tuple												=						{
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Dict					=					"longformer"



	def __init__(  self     :       List[str]			,    _lowercase     :       Union[List[int], int] = 5_12			,    _lowercase     :       int = 2			,    _lowercase     :       int = 1			,    _lowercase     :       int = 0			,    _lowercase     :       int = 2			,    _lowercase     :       int = 3_05_22			,    _lowercase     :       int = 7_68			,    _lowercase     :       int = 12			,    _lowercase     :       int = 12			,    _lowercase     :       int = 30_72			,    _lowercase     :       str = "gelu"			,    _lowercase     :       float = 0.1			,    _lowercase     :       float = 0.1			,    _lowercase     :       int = 5_12			,    _lowercase     :       int = 2			,    _lowercase     :       float = 0.02			,    _lowercase     :       float = 1E-12			,    _lowercase     :       bool = False			,    **_lowercase     :       List[str]			,    ):
		super().__init__(pad_token_id=_lowercase			,    **_lowercase						)

		__UpperCAmelCase      		= attention_window
		__UpperCAmelCase      		= sep_token_id
		__UpperCAmelCase      		= bos_token_id
		__UpperCAmelCase      		= eos_token_id
		__UpperCAmelCase      		= vocab_size
		__UpperCAmelCase      		= hidden_size
		__UpperCAmelCase      		= num_hidden_layers
		__UpperCAmelCase      		= num_attention_heads
		__UpperCAmelCase      		= hidden_act
		__UpperCAmelCase      		= intermediate_size
		__UpperCAmelCase      		= hidden_dropout_prob
		__UpperCAmelCase      		= attention_probs_dropout_prob
		__UpperCAmelCase      		= max_position_embeddings
		__UpperCAmelCase      		= type_vocab_size
		__UpperCAmelCase      		= initializer_range
		__UpperCAmelCase      		= layer_norm_eps
		__UpperCAmelCase      		= onnx_export






class      _UpperCAmelCase   (							_lowerCAmelCase       ):



	def __init__(  self     :       List[str]			,    _lowercase     :       "PretrainedConfig"			,    _lowercase     :       str = "default"			,    _lowercase     :       "List[PatchingSpec]" = None						):
		super().__init__(_lowercase			,    _lowercase			,    _lowercase						)
		__UpperCAmelCase      		= True



	@property
	def        a  (  self     :       Dict						):
		if self.task == "multiple-choice":
			__UpperCAmelCase      		= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
		else:
			__UpperCAmelCase      		= {0: '''batch''', 1: '''sequence'''}
		return OrderedDict(
		    [
		        ('''input_ids''', dynamic_axis),
		        ('''attention_mask''', dynamic_axis),
		        ('''global_attention_mask''', dynamic_axis),
		    ]						)



	@property
	def        a  (  self     :       int						):
		__UpperCAmelCase      		= super().outputs
		if self.task == "default":
			__UpperCAmelCase      		= {0: '''batch'''}
		return outputs



	@property
	def        a  (  self     :       Any						):
		return 1E-4



	@property
	def        a  (  self     :       Optional[Any]						):
		# needs to be >= 14 to support tril operator
		return max(super().default_onnx_opset			,    14						)



	def        a  (  self     :       List[str]			,    _lowercase     :       "PreTrainedTokenizerBase"			,    _lowercase     :       int = -1			,    _lowercase     :       int = -1			,    _lowercase     :       bool = False			,    _lowercase     :       Optional[TensorType] = None			,    ):
		__UpperCAmelCase      		= super().generate_dummy_inputs(
		    preprocessor=_lowercase			,    batch_size=_lowercase			,    seq_length=_lowercase			,    is_pair=_lowercase			,    framework=_lowercase						)
		import torch

		# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
		# makes the export fail randomly
		__UpperCAmelCase      		= torch.zeros_like(inputs['''input_ids''']						)
		# make every second token global
		__UpperCAmelCase      		= 1

		return inputs

"""simple docstring"""


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 							a					(		lowerCAmelCase_ ):


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,					"""prajjwal1/bert-tiny"""       )
      _UpperCAmelCase    			=   BertTokenizer.from_pretrained("""bert-base-uncased"""       )

      _UpperCAmelCase    			=   bertabert.config.encoder.vocab_size
      _UpperCAmelCase    			=   tokenizer.sep_token_id
      _UpperCAmelCase    			=   tokenizer.cls_token_id
      _UpperCAmelCase    			=   128

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

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

      _UpperCAmelCase    			=   4

      def _map_to_encoder_decoder_inputs(__lowerCAmelCase						:	Dict       ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        _UpperCAmelCase    			=   tokenizer(batch["""article"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=512       )
        _UpperCAmelCase    			=   tokenizer(batch["""highlights"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=128       )
        _UpperCAmelCase    			=   inputs.input_ids
        _UpperCAmelCase    			=   inputs.attention_mask

        _UpperCAmelCase    			=   outputs.input_ids
        _UpperCAmelCase    			=   outputs.input_ids.copy()
        _UpperCAmelCase    			=   [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
        ]
        _UpperCAmelCase    			=   outputs.attention_mask

        assert all(len(__lowerCAmelCase       ) == 512 for x in inputs.input_ids       )
        assert all(len(__lowerCAmelCase       ) == 128 for x in outputs.input_ids       )

        return batch

      def _compute_metrics(__lowerCAmelCase						:	int       ):
        _UpperCAmelCase    			=   pred.label_ids
        _UpperCAmelCase    			=   pred.predictions

        # all unnecessary tokens are removed
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )

        _UpperCAmelCase    			=   sum([int(pred_str[i] == label_str[i]       ) for i in range(len(__lowerCAmelCase       )       )]       ) / len(__lowerCAmelCase       )

        return {"accuracy": accuracy}

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

      _UpperCAmelCase    			=   self.get_auto_remove_tmp_dir()

      _UpperCAmelCase    			=   SeqaSeqTrainingArguments(
          output_dir=__lowerCAmelCase ,					per_device_train_batch_size=__lowerCAmelCase ,					per_device_eval_batch_size=__lowerCAmelCase ,					predict_with_generate=__lowerCAmelCase ,					evaluation_strategy="""steps""" ,					do_train=__lowerCAmelCase ,					do_eval=__lowerCAmelCase ,					warmup_steps=0 ,					eval_steps=2 ,					logging_steps=2 ,					)

      # instantiate trainer
      _UpperCAmelCase    			=   SeqaSeqTrainer(
          model=__lowerCAmelCase ,					args=__lowerCAmelCase ,					compute_metrics=_compute_metrics ,					train_dataset=__lowerCAmelCase ,					eval_dataset=__lowerCAmelCase ,					tokenizer=__lowerCAmelCase ,					)

      # start training
      trainer.train()
