




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)
_lowerCAmelCase						:   Optional[int]		=   logging.getLogger(__name__)


_lowerCAmelCase						:   Union[str, Any]		=   "Hello world! cécé herlolip"


_lowerCAmelCase						:   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 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   int	)     ->   Tuple:




 """simple docstring"""







 UpperCAmelCase__							 =						BertAbsConfig(
     temp_dir="."						,							finetune_bert=SCREAMING_SNAKE_CASE_						,							large=SCREAMING_SNAKE_CASE_						,							share_emb=SCREAMING_SNAKE_CASE_						,							use_bert_emb=SCREAMING_SNAKE_CASE_						,							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						,							)
 UpperCAmelCase__							 =						torch.load(SCREAMING_SNAKE_CASE_						,							lambda _lowerCAmelCase						,							_lowerCAmelCase	: storage	)
 UpperCAmelCase__							 =						AbsSummarizer(SCREAMING_SNAKE_CASE_						,							torch.device("cpu"	)						,							SCREAMING_SNAKE_CASE_	)
 original.eval()

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

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

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

 UpperCAmelCase__							 =						torch.allclose(SCREAMING_SNAKE_CASE_						,							SCREAMING_SNAKE_CASE_						,							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__":
  _lowerCAmelCase						:   Tuple		=   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.",
  )
  _lowerCAmelCase						:   Any		=   parser.parse_args()

  convert_bertabs_checkpoints(
      args.bertabs_checkpoint_path,
      args.pytorch_dump_folder_path,
  )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





