
import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
      )
      from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST






class     a_       :



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase=13				,       _lowerCamelCase=7				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase=99				,       _lowerCamelCase=32				,       _lowerCamelCase=5				,       _lowerCamelCase=4				,       _lowerCamelCase=37				,       _lowerCamelCase="gelu"				,       _lowerCamelCase=0.1				,       _lowerCamelCase=0.1				,       _lowerCamelCase=512				,       _lowerCamelCase=16				,       _lowerCamelCase=2				,       _lowerCamelCase=0.0_2				,       _lowerCamelCase=3				,       _lowerCamelCase=4				,       _lowerCamelCase=None				,       )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = parent
           SCREAMING_SNAKE_CASE				:				List[str]				      = batch_size
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = seq_length
           SCREAMING_SNAKE_CASE				:				Tuple				      = is_training
           SCREAMING_SNAKE_CASE				:				Tuple				      = use_input_mask
           SCREAMING_SNAKE_CASE				:				Dict				      = use_token_type_ids
           SCREAMING_SNAKE_CASE				:				List[Any]				      = use_labels
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = vocab_size
           SCREAMING_SNAKE_CASE				:				Dict				      = hidden_size
           SCREAMING_SNAKE_CASE				:				Any				      = num_hidden_layers
           SCREAMING_SNAKE_CASE				:				int				      = num_attention_heads
           SCREAMING_SNAKE_CASE				:				List[Any]				      = intermediate_size
           SCREAMING_SNAKE_CASE				:				Any				      = hidden_act
           SCREAMING_SNAKE_CASE				:				Tuple				      = hidden_dropout_prob
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = max_position_embeddings
           SCREAMING_SNAKE_CASE				:				int				      = type_vocab_size
           SCREAMING_SNAKE_CASE				:				str				      = type_sequence_label_size
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = initializer_range
           SCREAMING_SNAKE_CASE				:				Any				      = num_labels
           SCREAMING_SNAKE_CASE				:				Any				      = num_choices
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = scope






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size   )

           SCREAMING_SNAKE_CASE				:				int				      = None
           if self.use_input_mask:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = random_attention_mask([self.batch_size, self.seq_length]   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = None
           if self.use_token_type_ids:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = None
           SCREAMING_SNAKE_CASE				:				Any				      = None
           SCREAMING_SNAKE_CASE				:				Dict				      = None
           if self.use_labels:
               SCREAMING_SNAKE_CASE				:				Any				      = ids_tensor([self.batch_size]				,       self.type_sequence_label_size   )
               SCREAMING_SNAKE_CASE				:				Dict				      = ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels   )
               SCREAMING_SNAKE_CASE				:				str				      = ids_tensor([self.batch_size]				,       self.num_choices   )
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ids_tensor([self.batch_size, self.seq_length]				,       1   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.get_config()

           return (
               config,
               input_ids,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               fake_token_labels,
           )






       def 	__lowerCAmelCase     (		self   )					->int:
           return ElectraConfig(
               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				,       is_decoder=_lowerCamelCase				,       initializer_range=self.initializer_range				,       )






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           (
               (
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							
           )				:				List[str]				      = self.prepare_config_and_inputs()

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = True
           SCREAMING_SNAKE_CASE				:				Any				      = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
           SCREAMING_SNAKE_CASE				:				Dict				      = ids_tensor([self.batch_size, self.seq_length]				,       vocab_size=2   )

           return (
               config,
               input_ids,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Dict:
           SCREAMING_SNAKE_CASE				:				int				      = ElectraModel(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = model(_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = True
           SCREAMING_SNAKE_CASE				:				List[Any]				      = ElectraModel(_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				List[str]				      = model(
               _lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       encoder_hidden_states=_lowerCamelCase				,       encoder_attention_mask=_lowerCamelCase				,       )
           SCREAMING_SNAKE_CASE				:				str				      = model(
               _lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       encoder_hidden_states=_lowerCamelCase				,       )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )
           self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Dict:
           SCREAMING_SNAKE_CASE				:				Tuple				      = ElectraForMaskedLM(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Dict				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = ElectraForCausalLM(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->int:
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = ElectraForTokenClassification(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->int:
           SCREAMING_SNAKE_CASE				:				str				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = ElectraForPreTraining(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				Any				      = ElectraForSequenceClassification(_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = ElectraForQuestionAnswering(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = model(
               _lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       start_positions=_lowerCamelCase				,       end_positions=_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 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.num_choices
           SCREAMING_SNAKE_CASE				:				Dict				      = ElectraForMultipleChoice(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = input_ids.unsqueeze(1   ).expand(-1				,       self.num_choices				,       -1   ).contiguous()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = token_type_ids.unsqueeze(1   ).expand(-1				,       self.num_choices				,       -1   ).contiguous()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = input_mask.unsqueeze(1   ).expand(-1				,       self.num_choices				,       -1   ).contiguous()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = model(
               _lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase				,       )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_choices)   )






       def 	__lowerCAmelCase     (		self   )					->Dict:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.prepare_config_and_inputs()
           (
               (
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							
           )				:				Optional[Any]				      = config_and_inputs
           SCREAMING_SNAKE_CASE				:				Dict				      = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict






@require_torch
class     a_       (			a__				,    a__				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					str						=  (
           (
               ElectraModel,
               ElectraForPreTraining,
               ElectraForMaskedLM,
               ElectraForCausalLM,
               ElectraForMultipleChoice,
               ElectraForTokenClassification,
               ElectraForSequenceClassification,
               ElectraForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       __SCREAMING_SNAKE_CASE       :					Union[str, Any]						=  (
           {
               'feature-extraction': ElectraModel,
               'fill-mask': ElectraForMaskedLM,
               'question-answering': ElectraForQuestionAnswering,
               'text-classification': ElectraForSequenceClassification,
               'text-generation': ElectraForCausalLM,
               'token-classification': ElectraForTokenClassification,
               'zero-shot': ElectraForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __SCREAMING_SNAKE_CASE       :					Union[str, Any]						=  True






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase=False   )					->Any:
           SCREAMING_SNAKE_CASE				:				Any				      = super()._prepare_for_class(_lowerCamelCase				,       _lowerCamelCase				,       return_labels=_lowerCamelCase   )

           if return_labels:
               if model_class in get_values(_lowerCamelCase   ):
                   SCREAMING_SNAKE_CASE				:				Any				      = torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length)				,       dtype=torch.long				,       device=_lowerCamelCase   )
           return inputs_dict






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = ElectraModelTester(self   )
           SCREAMING_SNAKE_CASE				:				Dict				      = ConfigTester(self				,       config_class=_lowerCamelCase				,       hidden_size=37   )






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






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				Any				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_model(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Any				      = self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_electra_model_as_decoder(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				str				      = self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               SCREAMING_SNAKE_CASE				:				Tuple				      = type
               self.model_tester.create_and_check_electra_model(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->str:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_masked_lm(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				str				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_token_classification(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				str				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_pretraining(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				int				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_sequence_classification(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->str:
           SCREAMING_SNAKE_CASE				:				Dict				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_question_answering(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				str				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_multiple_choice(*_lowerCamelCase   )






       @slow
       def 	__lowerCAmelCase     (		self   )					->int:
           for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               SCREAMING_SNAKE_CASE				:				Any				      = ElectraModel.from_pretrained(_lowerCamelCase   )
               self.assertIsNotNone(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				str				      = self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_electra_for_causal_lm(*_lowerCamelCase   )






@require_torch
class     a_       (			unittest.TestCase							):



       """simple docstring"""






       @slow
       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = ElectraModel.from_pretrained('''google/electra-small-discriminator'''   )
           SCREAMING_SNAKE_CASE				:				Dict				      = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase   )[0]
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size((1, 11, 256)   )
           self.assertEqual(output.shape				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.tensor(
               [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]]   )

           self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,       _lowerCamelCase				,       atol=1e-4   )   )


import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


a__				:      Any    		=     logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


a__				:      Optional[int]    		=     namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = BertTokenizer.from_pretrained('''bert-base-uncased''' ,						do_lower_case=a__   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''   )
    model.to(args.device   )
    model.eval()

    SCREAMING_SNAKE_CASE				:				int				      = {
        '''BOS''': tokenizer.vocab['''[unused0]'''],
        '''EOS''': tokenizer.vocab['''[unused1]'''],
        '''PAD''': tokenizer.vocab['''[PAD]'''],
    }

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
        SCREAMING_SNAKE_CASE				:				Dict				      = []

        import nltk
        import rouge

        nltk.download('''punkt'''   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = rouge.Rouge(
            metrics=['''rouge-n''', '''rouge-l'''] ,						max_n=2 ,						limit_length=a__ ,						length_limit=args.beam_size ,						length_limit_type='''words''' ,						apply_avg=a__ ,						apply_best=a__ ,						alpha=0.5 ,						weight_factor=1.2 ,						stemming=a__ ,						)

    # these (unused) arguments are defined to keep the compatibility
    # with the legacy code and will be deleted in a next iteration.
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ''''''
    SCREAMING_SNAKE_CASE				:				Tuple				      = ''''''

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = build_data_iterator(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				str				      = build_predictor(a__ ,						a__ ,						a__ ,						a__   )

    logger.info('''***** Running evaluation *****'''   )
    logger.info('''  Number examples = %d''' ,						len(data_iterator.dataset   )   )
    logger.info('''  Batch size = %d''' ,						args.batch_size   )
    logger.info(''''''   )
    logger.info('''***** Beam Search parameters *****'''   )
    logger.info('''  Beam size = %d''' ,						args.beam_size   )
    logger.info('''  Minimum length = %d''' ,						args.min_length   )
    logger.info('''  Maximum length = %d''' ,						args.max_length   )
    logger.info('''  Alpha (length penalty) = %.2f''' ,						args.alpha   )
    logger.info('''  Trigrams %s be blocked''' ,						('''will''' if args.block_trigram else '''will NOT''')   )

    for batch in tqdm(a__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = predictor.translate_batch(a__   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = predictor.from_batch(a__   )
        SCREAMING_SNAKE_CASE				:				List[str]				      = [format_summary(a__   ) for t in translations]
        save_summaries(a__ ,						args.summaries_output_dir ,						batch.document_names   )

        if args.compute_rouge:
            reference_summaries += batch.tgt_str
            generated_summaries += summaries

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = rouge_evaluator.get_scores(a__ ,						a__   )
        SCREAMING_SNAKE_CASE				:				Dict				      = format_rouge_scores(a__   )
        save_rouge_scores(a__   )
        print(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    for summary, document_name in zip(a__ ,						a__   ):
        # Prepare the summary file's name
        if "." in document_name:
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''.'''.join(document_name.split('''.'''   )[:-1]   )
            SCREAMING_SNAKE_CASE				:				int				      = document_name.split('''.'''   )[-1]
            SCREAMING_SNAKE_CASE				:				Tuple				      = bare_document_name + '''_summary.''' + extension
        else:
            SCREAMING_SNAKE_CASE				:				Optional[int]				      = document_name + '''_summary'''

        SCREAMING_SNAKE_CASE				:				List[Any]				      = os.path.join(a__ ,						a__   )
        with open(a__ ,						'''w'''   ) as output:
            output.write(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = translation
    SCREAMING_SNAKE_CASE				:				int				      = (
        raw_summary.replace('''[unused0]''' ,						''''''   )
        .replace('''[unused3]''' ,						''''''   )
        .replace('''[PAD]''' ,						''''''   )
        .replace('''[unused1]''' ,						''''''   )
        .replace(r''' +''' ,						''' '''   )
        .replace(''' [unused2] ''' ,						'''. '''   )
        .replace('''[unused2]''' ,						''''''   )
        .strip()
    )

    return summary







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
        scores['''rouge-1''']['''f'''] ,						scores['''rouge-1''']['''p'''] ,						scores['''rouge-1''']['''r'''] ,						scores['''rouge-2''']['''f'''] ,						scores['''rouge-2''']['''p'''] ,						scores['''rouge-2''']['''r'''] ,						scores['''rouge-l''']['''f'''] ,						scores['''rouge-l''']['''p'''] ,						scores['''rouge-l''']['''r'''] ,						)







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    with open('''rouge_scores.txt''' ,						'''w'''   ) as output:
        output.write(a__   )







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = load_and_cache_examples(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = SequentialSampler(a__   )

    def collate_fn(a__   ):
        return collate(a__ ,						a__ ,						block_size=512 ,						device=args.device   )

    SCREAMING_SNAKE_CASE				:				Dict				      = DataLoader(
        a__ ,						sampler=a__ ,						batch_size=args.batch_size ,						collate_fn=a__ ,						)

    return iterator







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = CNNDMDataset(args.documents_dir   )
    return dataset







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = [x for x in data if not len(x[1]   ) == 0]  # remove empty_files
    SCREAMING_SNAKE_CASE				:				int				      = [name for name, _, _ in data]
    SCREAMING_SNAKE_CASE				:				List[str]				      = [''' '''.join(a__   ) for _, _, summary_list in data]

    SCREAMING_SNAKE_CASE				:				Dict				      = [encode_for_summarization(a__ ,						a__ ,						a__   ) for _, story, summary in data]
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor(
        [truncate_or_pad(a__ ,						a__ ,						tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = compute_token_type_ids(a__ ,						tokenizer.cls_token_id   )
    SCREAMING_SNAKE_CASE				:				int				      = build_mask(a__ ,						tokenizer.pad_token_id   )

    SCREAMING_SNAKE_CASE				:				Optional[int]				      = Batch(
        document_names=a__ ,						batch_size=len(a__   ) ,						src=encoded_stories.to(a__   ) ,						segs=encoder_token_type_ids.to(a__   ) ,						mask_src=encoder_mask.to(a__   ) ,						tgt_str=a__ ,						)

    return batch







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = summary_tokens.to('''cpu'''   ).numpy()
    SCREAMING_SNAKE_CASE				:				str				      = tokenizer.decode(a__   )
    SCREAMING_SNAKE_CASE				:				int				      = summary.split('''.'''   )
    SCREAMING_SNAKE_CASE				:				Dict				      = [s + '''.''' for s in sentences]
    return sentences







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = argparse.ArgumentParser()
    parser.add_argument(
        '''--documents_dir''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''The folder where the documents to summarize are located.''' ,						)
    parser.add_argument(
        '''--summaries_output_dir''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are''' ,						)
    parser.add_argument(
        '''--compute_rouge''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.''' ,						)
    # EVALUATION options
    parser.add_argument(
        '''--no_cuda''' ,						default=a__ ,						type=a__ ,						help='''Whether to force the execution on CPU.''' ,						)
    parser.add_argument(
        '''--batch_size''' ,						default=4 ,						type=a__ ,						help='''Batch size per GPU/CPU for training.''' ,						)
    # BEAM SEARCH arguments
    parser.add_argument(
        '''--min_length''' ,						default=50 ,						type=a__ ,						help='''Minimum number of tokens for the summaries.''' ,						)
    parser.add_argument(
        '''--max_length''' ,						default=200 ,						type=a__ ,						help='''Maixmum number of tokens for the summaries.''' ,						)
    parser.add_argument(
        '''--beam_size''' ,						default=5 ,						type=a__ ,						help='''The number of beams to start with for each example.''' ,						)
    parser.add_argument(
        '''--alpha''' ,						default=0.95 ,						type=a__ ,						help='''The value of alpha for the length penalty in the beam search.''' ,						)
    parser.add_argument(
        '''--block_trigram''' ,						default=a__ ,						type=a__ ,						help='''Whether to block the existence of repeating trigrams in the text generated by beam search.''' ,						)
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = parser.parse_args()

    # Select device (distibuted not available)
    SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''   )

    # Check the existence of directories
    if not args.summaries_output_dir:
        SCREAMING_SNAKE_CASE				:				Tuple				      = args.documents_dir

    if not documents_dir_is_valid(args.documents_dir   ):
        raise FileNotFoundError(
            '''We could not find the directory you specified for the documents to summarize, or it was empty. Please'''
            ''' specify a valid path.'''   )
    os.makedirs(args.summaries_output_dir ,						exist_ok=a__   )

    evaluate(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    if not os.path.exists(a__   ):
        return False

    SCREAMING_SNAKE_CASE				:				List[str]				      = os.listdir(a__   )
    if len(a__   ) == 0:
        return False

    return True


if __name__ == "__main__":
      main()
