





'''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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       BertAbsConfig(
       temp_dir="""."""							,	finetune_bert=a_							,	large=a_							,	share_emb=a_							,	use_bert_emb=a_							,	encoder="""bert"""							,	max_pos=512							,	enc_layers=6							,	enc_hidden_size=512							,	enc_heads=8							,	enc_ff_size=512							,	enc_dropout=0.2							,	dec_layers=6							,	dec_hidden_size=768							,	dec_heads=8							,	dec_ff_size=2048							,	dec_dropout=0.2							,	)
   __SCREAMING_SNAKE_CASE      							=       torch.load(a_							,	lambda __UpperCAmelCase							,	__UpperCAmelCase      : storage      )
   __SCREAMING_SNAKE_CASE      							=       AbsSummarizer(a_							,	torch.device("""cpu"""      )							,	a_      )
   original.eval()

   __SCREAMING_SNAKE_CASE      							=       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"""      )
   __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] * (512 - len(a_      ))      )
   __SCREAMING_SNAKE_CASE      							=       torch.tensor(a_      ).unsqueeze(0      )
   __SCREAMING_SNAKE_CASE      							=       tokenizer.encode("""This is sample 3 éàalj\'-."""      )
   decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(a_      ))      )
   __SCREAMING_SNAKE_CASE      							=       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
   __SCREAMING_SNAKE_CASE      							=       encoder_input_ids
   __SCREAMING_SNAKE_CASE      							=       decoder_input_ids
   __SCREAMING_SNAKE_CASE      							=       None
   __SCREAMING_SNAKE_CASE      							=       None
   __SCREAMING_SNAKE_CASE      							=       None
   __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(a_							,	a_							,	a_							,	a_							,	a_							,	a_							,	a_      )[0]
   __SCREAMING_SNAKE_CASE      							=       original.generator(a_      )

   __SCREAMING_SNAKE_CASE      							=       new_model(
       a_							,	a_							,	a_							,	a_							,	a_      )[0]
   __SCREAMING_SNAKE_CASE      							=       new_model.generator(a_      )

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

   __SCREAMING_SNAKE_CASE      							=       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,
  )












'''simple docstring'''

import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a        =						logging.get_logger(__name__)







class __a (   _snake_case			):
   __UpperCamelCase   : int	      =	'linear'
   __UpperCamelCase   : Tuple	      =	'cosine'
   __UpperCamelCase   : Tuple	      =	'cosine_with_restarts'
   __UpperCamelCase   : List[Any]	      =	'polynomial'
   __UpperCamelCase   : Optional[Any]	      =	'constant'
   __UpperCamelCase   : Optional[int]	      =	'constant_with_warmup'
   __UpperCamelCase   : List[Any]	      =	'piecewise_constant'





def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   return LambdaLR(__UpperCAmelCase							,	lambda __UpperCAmelCase      : 1							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					List[Any]:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1.0							,	__UpperCAmelCase      )      )
      return 1.0

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       {}
   __SCREAMING_SNAKE_CASE      							=       step_rules.split(""","""      )
   for rule_str in rule_list[:-1]:
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       rule_str.split(""":"""      )
      __SCREAMING_SNAKE_CASE      							=       int(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       float(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       value
   __SCREAMING_SNAKE_CASE      							=       float(rule_list[-1]      )

   def create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      ):
      def rule_func(__UpperCAmelCase      ) -> float:
         __SCREAMING_SNAKE_CASE      							=       sorted(rules_dict.keys()      )
         for i, sorted_step in enumerate(__UpperCAmelCase      ):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   __SCREAMING_SNAKE_CASE      							=       create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=-1      )						->					int:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      return max(
          0.0							,	float(num_training_steps - current_step      ) / float(max(1							,	num_training_steps - num_warmup_steps      )      )      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0.5							,	__UpperCAmelCase = -1      )						->					Dict:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_SNAKE_CASE      							=       float(current_step - num_warmup_steps      ) / float(max(1							,	num_training_steps - num_warmup_steps      )      )
      return max(0.0							,	0.5 * (1.0 + math.cos(math.pi * float(__UpperCAmelCase      ) * 2.0 * progress      ))      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 1							,	__UpperCAmelCase = -1      )						->					Tuple:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_SNAKE_CASE      							=       float(current_step - num_warmup_steps      ) / float(max(1							,	num_training_steps - num_warmup_steps      )      )
      if progress >= 1.0:
         return 0.0
      return max(0.0							,	0.5 * (1.0 + math.cos(math.pi * ((float(__UpperCAmelCase      ) * progress) % 1.0)      ))      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=1e-7							,	__UpperCAmelCase=1.0							,	__UpperCAmelCase=-1      )						->					Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       optimizer.defaults["""lr"""]
   if not (lr_init > lr_end):
      raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""      )

   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         __SCREAMING_SNAKE_CASE      							=       lr_init - lr_end
         __SCREAMING_SNAKE_CASE      							=       num_training_steps - num_warmup_steps
         __SCREAMING_SNAKE_CASE      							=       1 - (current_step - num_warmup_steps) / decay_steps
         __SCREAMING_SNAKE_CASE      							=       lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )


a        =						{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = None							,	__UpperCAmelCase = None							,	__UpperCAmelCase = None							,	__UpperCAmelCase = 1							,	__UpperCAmelCase = 1.0							,	__UpperCAmelCase = -1							,	)						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       SchedulerType(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(__UpperCAmelCase							,	step_rules=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

   # All other schedulers require `num_warmup_steps`
   if num_warmup_steps is None:
      raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""      )

   if name == SchedulerType.CONSTANT_WITH_WARMUP:
      return schedule_func(__UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

   # All other schedulers require `num_training_steps`
   if num_training_steps is None:
      raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""      )

   if name == SchedulerType.COSINE_WITH_RESTARTS:
      return schedule_func(
          __UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	num_training_steps=__UpperCAmelCase							,	num_cycles=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase							,	)

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          __UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	num_training_steps=__UpperCAmelCase							,	power=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase							,	)

   return schedule_func(
       __UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	num_training_steps=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )





