'''simple docstring'''

import os
import tempfile
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
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,
      NezhaForMaskedLM,
      NezhaForMultipleChoice,
      NezhaForNextSentencePrediction,
      NezhaForPreTraining,
      NezhaForQuestionAnswering,
      NezhaForSequenceClassification,
      NezhaForTokenClassification,
      NezhaModel,
  )
  from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST



class 			UpperCAmelCase  :





       '''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=128	,   __lowerCAmelCase=32	,   __lowerCAmelCase=16	,   __lowerCAmelCase=2	,   __lowerCAmelCase=0.0_2	,   __lowerCAmelCase=3	,   __lowerCAmelCase=4	,   __lowerCAmelCase=None	,   )  ->  Optional[int]:
        lowercase__				:    Union[str, Any]  =					parent
        lowercase__				:    Any  =					batch_size
        lowercase__				:    Union[str, Any]  =					seq_length
        lowercase__				:    Optional[int]  =					is_training
        lowercase__				:    Union[str, Any]  =					use_input_mask
        lowercase__				:    List[str]  =					use_token_type_ids
        lowercase__				:    str  =					use_labels
        lowercase__				:    List[str]  =					vocab_size
        lowercase__				:    Dict  =					hidden_size
        lowercase__				:    int  =					num_hidden_layers
        lowercase__				:    str  =					num_attention_heads
        lowercase__				:    Dict  =					intermediate_size
        lowercase__				:    List[Any]  =					hidden_act
        lowercase__				:    Dict  =					hidden_dropout_prob
        lowercase__				:    List[str]  =					attention_probs_dropout_prob
        lowercase__				:    int  =					max_position_embeddings
        lowercase__				:    Dict  =					type_vocab_size
        lowercase__				:    List[str]  =					type_sequence_label_size
        lowercase__				:    int  =					initializer_range
        lowercase__				:    Any  =					num_labels
        lowercase__				:    List[Any]  =					num_choices
        lowercase__				:    Optional[Any]  =					scope

       def _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__				:    Optional[Any]  =					ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size )

        lowercase__				:    List[str]  =					None
        if self.use_input_mask:
         lowercase__				:    Optional[int]  =					random_attention_mask([self.batch_size, self.seq_length] )

        lowercase__				:    Optional[int]  =					None
        if self.use_token_type_ids:
         lowercase__				:    Dict  =					ids_tensor([self.batch_size, self.seq_length]	,   self.type_vocab_size )

        lowercase__				:    str  =					None
        lowercase__				:    Optional[int]  =					None
        lowercase__				:    Optional[Any]  =					None
        if self.use_labels:
         lowercase__				:    str  =					ids_tensor([self.batch_size]	,   self.type_sequence_label_size )
         lowercase__				:    str  =					ids_tensor([self.batch_size, self.seq_length]	,   self.num_labels )
         lowercase__				:    Union[str, Any]  =					ids_tensor([self.batch_size]	,   self.num_choices )

        lowercase__				:    Dict  =					self.get_config()

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

       def _lowerCAmelCase(						self )  ->  Dict:
        return NezhaConfig(
            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 )  ->  Optional[Any]:
        (
            (
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							       
        )				:    str  =					self.prepare_config_and_inputs()

        lowercase__				:    Dict  =					True
        lowercase__				:    List[Any]  =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
        lowercase__				:    List[Any]  =					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 )  ->  Tuple:
        lowercase__				:    Optional[Any]  =					NezhaModel(config=__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    Optional[Any]  =					model(__lowerCAmelCase	,   attention_mask=__lowerCAmelCase	,   token_type_ids=__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					model(__lowerCAmelCase	,   token_type_ids=__lowerCAmelCase )
        lowercase__				:    List[Any]  =					model(__lowerCAmelCase )
        self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size) )
        self.parent.assertEqual(result.pooler_output.shape	,   (self.batch_size, self.hidden_size) )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   )  ->  str:
        lowercase__				:    str  =					True
        lowercase__				:    Dict  =					NezhaModel(__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    Any  =					model(
            __lowerCAmelCase	,   attention_mask=__lowerCAmelCase	,   token_type_ids=__lowerCAmelCase	,   encoder_hidden_states=__lowerCAmelCase	,   encoder_attention_mask=__lowerCAmelCase	,   )
        lowercase__				:    Tuple  =					model(
            __lowerCAmelCase	,   attention_mask=__lowerCAmelCase	,   token_type_ids=__lowerCAmelCase	,   encoder_hidden_states=__lowerCAmelCase	,   )
        lowercase__				:    List[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) )
        self.parent.assertEqual(result.pooler_output.shape	,   (self.batch_size, self.hidden_size) )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  List[Any]:
        lowercase__				:    Dict  =					NezhaForMaskedLM(config=__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    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, self.vocab_size) )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Any:
        lowercase__				:    Dict  =					NezhaForNextSentencePrediction(config=__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    Dict  =					model(
            __lowerCAmelCase	,   attention_mask=__lowerCAmelCase	,   token_type_ids=__lowerCAmelCase	,   labels=__lowerCAmelCase	,   )
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, 2) )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Dict:
        lowercase__				:    Tuple  =					NezhaForPreTraining(config=__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    List[Any]  =					model(
            __lowerCAmelCase	,   attention_mask=__lowerCAmelCase	,   token_type_ids=__lowerCAmelCase	,   labels=__lowerCAmelCase	,   next_sentence_label=__lowerCAmelCase	,   )
        self.parent.assertEqual(result.prediction_logits.shape	,   (self.batch_size, self.seq_length, self.vocab_size) )
        self.parent.assertEqual(result.seq_relationship_logits.shape	,   (self.batch_size, 2) )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    Dict  =					NezhaForQuestionAnswering(config=__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    List[str]  =					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 )  ->  Union[str, Any]:
        lowercase__				:    Optional[int]  =					self.num_labels
        lowercase__				:    Optional[int]  =					NezhaForSequenceClassification(__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    Dict  =					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 )  ->  Any:
        lowercase__				:    Optional[int]  =					self.num_labels
        lowercase__				:    int  =					NezhaForTokenClassification(config=__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    List[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 )  ->  List[Any]:
        lowercase__				:    int  =					self.num_choices
        lowercase__				:    int  =					NezhaForMultipleChoice(config=__lowerCAmelCase )
        model.to(__lowerCAmelCase )
        model.eval()
        lowercase__				:    Optional[Any]  =					input_ids.unsqueeze(1 ).expand(-1	,   self.num_choices	,   -1 ).contiguous()
        lowercase__				:    Union[str, Any]  =					token_type_ids.unsqueeze(1 ).expand(-1	,   self.num_choices	,   -1 ).contiguous()
        lowercase__				:    Optional[Any]  =					input_mask.unsqueeze(1 ).expand(-1	,   self.num_choices	,   -1 ).contiguous()
        lowercase__				:    Tuple  =					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:
        lowercase__				:    Optional[int]  =					self.prepare_config_and_inputs()
        (
            (
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							       
        )				:    List[str]  =					config_and_inputs
        lowercase__				:    List[str]  =					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict



@require_torch
class 			UpperCAmelCase  (						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    (
           (
               NezhaModel,
               NezhaForMaskedLM,
               NezhaForMultipleChoice,
               NezhaForNextSentencePrediction,
               NezhaForPreTraining,
               NezhaForQuestionAnswering,
               NezhaForSequenceClassification,
               NezhaForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE							=    (
           {
               "feature-extraction": NezhaModel,
               "fill-mask": NezhaForMaskedLM,
               "question-answering": NezhaForQuestionAnswering,
               "text-classification": NezhaForSequenceClassification,
               "token-classification": NezhaForTokenClassification,
               "zero-shot": NezhaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE							=    True

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=False )  ->  int:
        lowercase__				:    Dict  =					super()._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase	,   return_labels=__lowerCAmelCase )

        if return_labels:
         if model_class in get_values(__lowerCAmelCase ):
          lowercase__				:    Optional[Any]  =					torch.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length)	,   dtype=torch.long	,   device=__lowerCAmelCase )
          lowercase__				:    Dict  =					torch.zeros(
              self.model_tester.batch_size	,   dtype=torch.long	,   device=__lowerCAmelCase )
        return inputs_dict

       def _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__				:    str  =					NezhaModelTester(self )
        lowercase__				:    Dict  =					ConfigTester(self	,   config_class=__lowerCAmelCase	,   hidden_size=37 )

       def _lowerCAmelCase(						self )  ->  Any:
        self.config_tester.run_common_tests()

       def _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__				:    List[str]  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    Optional[int]  =					self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  List[Any]:
        # This regression test was failing with PyTorch < 1.3
        (
            (
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							(
            lowercase__       
        ) ,							       
        )				:    List[str]  =					self.model_tester.prepare_config_and_inputs_for_decoder()

        lowercase__				:    Dict  =					None

        self.model_tester.create_and_check_model_as_decoder(
            __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   )

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Any  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    Optional[Any]  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Tuple  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_next_sequence_prediction(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    Any  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Union[str, Any]  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    Optional[int]  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    str  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )

       @slow
       def _lowerCAmelCase(						self )  ->  List[str]:
        for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__				:    Optional[int]  =					NezhaModel.from_pretrained(__lowerCAmelCase )
         self.assertIsNotNone(__lowerCAmelCase )


       @slow
       @require_torch_gpu
       def _lowerCAmelCase(						self )  ->  str:
        lowercase__ ,							lowercase__				:    Optional[Any]  =					self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
         # NezhaForMultipleChoice behaves incorrectly in JIT environments.
         if model_class == NezhaForMultipleChoice:
          return

         lowercase__				:    Union[str, Any]  =					True
         lowercase__				:    Optional[int]  =					model_class(config=__lowerCAmelCase )

         lowercase__				:    Optional[int]  =					self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase )
         lowercase__				:    Tuple  =					torch.jit.trace(
             __lowerCAmelCase	,   (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )

         with tempfile.TemporaryDirectory() as tmp:
          torch.jit.save(__lowerCAmelCase	,   os.path.join(__lowerCAmelCase	,   '''bert.pt''' ) )
          lowercase__				:    Dict  =					torch.jit.load(os.path.join(__lowerCAmelCase	,   '''bert.pt''' )	,   map_location=__lowerCAmelCase )
          loaded(inputs_dict['''input_ids'''].to(__lowerCAmelCase )	,   inputs_dict['''attention_mask'''].to(__lowerCAmelCase ) )



@require_torch
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					NezhaModel.from_pretrained('''sijunhe/nezha-cn-base''' )
        lowercase__				:    Union[str, Any]  =					torch.tensor([[0, 1, 2, 3, 4, 5]] )
        lowercase__				:    str  =					torch.tensor([[0, 1, 1, 1, 1, 1]] )
        with torch.no_grad():
         lowercase__				:    Optional[int]  =					model(__lowerCAmelCase	,   attention_mask=__lowerCAmelCase )[0]
        lowercase__				:    List[Any]  =					torch.Size((1, 6, 768) )
        self.assertEqual(output.shape	,   __lowerCAmelCase )
        lowercase__				:    Optional[int]  =					torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]] )

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


       @slow
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    str  =					NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base''' )
        lowercase__				:    Dict  =					torch.tensor([[0, 1, 2, 3, 4, 5]] )
        lowercase__				:    List[Any]  =					torch.tensor([[1, 1, 1, 1, 1, 1]] )
        with torch.no_grad():
         lowercase__				:    str  =					model(__lowerCAmelCase	,   attention_mask=__lowerCAmelCase )[0]
        lowercase__				:    Any  =					torch.Size((1, 6, 21128) )
        self.assertEqual(output.shape	,   __lowerCAmelCase )
        lowercase__				:    List[Any]  =					torch.tensor(
            [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]] )

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


import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A   :     Optional[int]       =				logging.getLogger(__name__)


A   :     int       =				"Hello world! cécé herlolip"


A   :     List[Any]       =				namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     BertAbsConfig(
          temp_dir="."   ,					finetune_bert=__UpperCamelCase   ,					large=__UpperCamelCase   ,					share_emb=__UpperCamelCase   ,					use_bert_emb=__UpperCamelCase   ,					encoder="bert"   ,					max_pos=5_1_2   ,					enc_layers=6   ,					enc_hidden_size=5_1_2   ,					enc_heads=8   ,					enc_ff_size=5_1_2   ,					enc_dropout=0.2   ,					dec_layers=6   ,					dec_hidden_size=7_6_8   ,					dec_heads=8   ,					dec_ff_size=2_0_4_8   ,					dec_dropout=0.2   ,					)
      SCREAMING_SNAKE_CASE_      =     torch.load(__UpperCamelCase   ,					lambda __UpperCamelCase   ,					__UpperCamelCase : storage )
      SCREAMING_SNAKE_CASE_      =     AbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" )   ,					__UpperCamelCase )
      original.eval()

      SCREAMING_SNAKE_CASE_      =     BertAbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" ) )
      new_model.eval()

      # -------------------
      # Convert the weights
      # -------------------

      logging.info("convert the model" )
      new_model.bert.load_state_dict(original.bert.state_dict() )
      new_model.decoder.load_state_dict(original.decoder.state_dict() )
      new_model.generator.load_state_dict(original.generator.state_dict() )

      # ----------------------------------
      # Make sure the outpus are identical
      # ----------------------------------

      logging.info("Make sure that the models' outputs are identical" )
      SCREAMING_SNAKE_CASE_      =     BertTokenizer.from_pretrained("bert-base-uncased" )

      # prepare the model inputs
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample éàalj'-." )
      encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample 3 éàalj'-." )
      decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )

      # failsafe to make sure the weights reset does not affect the
      # loaded weights.
      assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0

      # forward pass
      SCREAMING_SNAKE_CASE_      =     encoder_input_ids
      SCREAMING_SNAKE_CASE_      =     decoder_input_ids
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     None

      # The original model does not apply the geneator layer immediatly but rather in
      # the beam search (where it combines softmax + linear layer). Since we already
      # apply the softmax in our generation process we only apply the linear layer here.
      # We make sure that the outputs of the full stack are identical
      SCREAMING_SNAKE_CASE_      =     original(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     original.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     new_model(
          __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     new_model.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
      print("Maximum absolute difference beween weights: {:.2f}".format(__UpperCamelCase ) )
      SCREAMING_SNAKE_CASE_      =     torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
      print("Maximum absolute difference beween weights: {:.2f}".format(__UpperCamelCase ) )

      SCREAMING_SNAKE_CASE_      =     torch.allclose(__UpperCamelCase   ,					__UpperCamelCase   ,					atol=1E-3 )
      if are_identical:
            logging.info("all weights are equal up to 1e-3" )
      else:
            raise ValueError("the weights are different. The new model is likely different from the original one." )

      # The model has been saved with torch.save(model) and this is bound to the exact
      # directory structure. We save the state_dict instead.
      logging.info("saving the model's state dictionary" )
      torch.save(
          new_model.state_dict()   ,					"./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin" )


if __name__ == "__main__":
      A   :     Any       =				argparse.ArgumentParser()
      parser.add_argument(
          "--bertabs_checkpoint_path",
          default=None,
          type=str,
          required=True,
          help="Path the official PyTorch dump.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the output PyTorch model.",
      )
      A   :     int       =				parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )


