




import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
  import torch_xla.debug.metrics as met







class 						lowercase							(   __a		):

     def __init__(  self		,     *_a		,     _a=None		,     _a=None		,     **_a				)						->							Union[str, Any]:
           super().__init__(*_a		,     **_a				)
           _A							:     Union[str, Any]			=						eval_examples
           _A							:     str			=						post_process_function

     def 					a__	(  self		,     _a=None		,     _a=None		,     _a=None		,     _a = "eval"				)						->							str:
           _A							:     List[str]			=						self.eval_dataset if eval_dataset is None else eval_dataset
           _A							:     Optional[Any]			=						self.get_eval_dataloader(_a				)
           _A							:     List[str]			=						self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           _A							:     Union[str, Any]			=						self.compute_metrics
           _A							:     Optional[int]			=						None
           _A							:     Union[str, Any]			=						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           _A							:     Optional[Any]			=						time.time()
           try:
                 _A							:     int			=						eval_loop(
                     _a		,     description="""Evaluation"""		,     prediction_loss_only=True if compute_metrics is None else None		,     ignore_keys=_a		,     metric_key_prefix=_a		,     )
           finally:
                 _A							:     Union[str, Any]			=						compute_metrics
           _A							:     Tuple			=						self.args.eval_batch_size * self.args.world_size
           if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
                 start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
           output.metrics.update(
               speed_metrics(
                   _a		,     _a		,     num_samples=output.num_samples		,     num_steps=math.ceil(output.num_samples / total_batch_size				)		,     )				)
           if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
                 # Only the main node write the results by default
                 _A							:     Any			=						self.post_process_function(_a		,     _a		,     output.predictions				)
                 _A							:     List[Any]			=						self.compute_metrics(_a				)

                 # Prefix all keys with metric_key_prefix + '_'
                 for key in list(metrics.keys()				):
                       if not key.startswith(F'''{metric_key_prefix}_'''				):
                             _A							:     List[str]			=						metrics.pop(_a				)
                 metrics.update(output.metrics				)
           else:
                 _A							:     Dict			=						output.metrics

           if self.args.should_log:
                 # Only the main node log the results by default
                 self.log(_a				)

           if self.args.tpu_metrics_debug or self.args.debug:
                 # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                 xm.master_print(met.metrics_report()				)

           _A							:     str			=						self.callback_handler.on_evaluate(self.args		,     self.state		,     self.control		,     _a				)
           return metrics



     def 					a__	(  self		,     _a		,     _a		,     _a=None		,     _a = "test"				)						->							int:
           _A							:     List[str]			=						self.get_test_dataloader(_a				)

           # Temporarily disable metric computation, we will do it in the loop here.
           _A							:     Optional[Any]			=						self.compute_metrics
           _A							:     Optional[int]			=						None
           _A							:     Optional[Any]			=						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           _A							:     Dict			=						time.time()
           try:
                 _A							:     int			=						eval_loop(
                     _a		,     description="""Prediction"""		,     prediction_loss_only=True if compute_metrics is None else None		,     ignore_keys=_a		,     metric_key_prefix=_a		,     )
           finally:
                 _A							:     int			=						compute_metrics
           _A							:     Optional[Any]			=						self.args.eval_batch_size * self.args.world_size
           if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
                 start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
           output.metrics.update(
               speed_metrics(
                   _a		,     _a		,     num_samples=output.num_samples		,     num_steps=math.ceil(output.num_samples / total_batch_size				)		,     )				)

           if self.post_process_function is None or self.compute_metrics is None:
                 return output

           _A							:     List[Any]			=						self.post_process_function(_a		,     _a		,     output.predictions		,     """predict"""				)
           _A							:     Optional[Any]			=						self.compute_metrics(_a				)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()				):
                 if not key.startswith(F'''{metric_key_prefix}_'''				):
                       _A							:     List[Any]			=						metrics.pop(_a				)
           metrics.update(output.metrics				)
           return PredictionOutput(predictions=predictions.predictions		,     label_ids=predictions.label_ids		,     metrics=_a				)







'''simple docstring'''







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     =logging.getLogger(__name__)


A     ='Hello world! cécé herlolip'


A     =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 						snake_case_							(_a	:							List[Any]    ,   _a	:							Any     ):
    UpperCAmelCase			       =		BertAbsConfig(
        temp_dir='''.'''    ,   finetune_bert=_a    ,   large=_a    ,   share_emb=_a    ,   use_bert_emb=_a    ,   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    ,   )
    UpperCAmelCase			       =		torch.load(_a    ,   lambda _a    ,   _a     : storage     )
    UpperCAmelCase			       =		AbsSummarizer(_a    ,   torch.device('''cpu'''     )    ,   _a     )
    original.eval()

    UpperCAmelCase			       =		BertAbsSummarizer(_a    ,   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'''     )
    UpperCAmelCase			       =		BertTokenizer.from_pretrained('''bert-base-uncased'''     )

    # prepare the model inputs
    UpperCAmelCase			       =		tokenizer.encode('''This is sample éàalj\'-.'''     )
    encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a     ))     )
    UpperCAmelCase			       =		torch.tensor(_a     ).unsqueeze(0     )
    UpperCAmelCase			       =		tokenizer.encode('''This is sample 3 éàalj\'-.'''     )
    decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a     ))     )
    UpperCAmelCase			       =		torch.tensor(_a     ).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
    UpperCAmelCase			       =		encoder_input_ids
    UpperCAmelCase			       =		decoder_input_ids
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		None
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		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
    UpperCAmelCase			       =		original(_a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a     )[0]
    UpperCAmelCase			       =		original.generator(_a     )

    UpperCAmelCase			       =		new_model(
        _a    ,   _a    ,   _a    ,   _a    ,   _a     )[0]
    UpperCAmelCase			       =		new_model.generator(_a     )

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

    UpperCAmelCase			       =		torch.allclose(_a    ,   _a    ,   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     =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     =parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )




