


"""simple docstring"""

import argparse
from collections import defaultdict

import yaml


UpperCAmelCase__					="docs/source/en/_toctree.yml"


def lowerCAmelCase_   (					UpperCamelCase__						:			List[Any]			):






 """simple docstring"""







 __lowercase						=						defaultdict(UpperCamelCase__			)
 __lowercase						=						[]
 __lowercase						=						[]
 for doc in doc_list:
  if "local" in doc:
   counts[doc["local"]] += 1

  if doc["title"].lower() == "overview":
   overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]}			)
  else:
   new_doc_list.append(UpperCamelCase__			)

 __lowercase						=						new_doc_list
 __lowercase						=						[key for key, value in counts.items() if value > 1]

 __lowercase						=						[]
 for duplicate_key in duplicates:
  __lowercase						=						list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key}			)
  if len(UpperCamelCase__			) > 1:
   raise ValueError(
       f'''{duplicate_key} is present several times in the documentation table of content at '''
       """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
       """others."""			)
  # Only add this once
  new_doc.append({"""local""": duplicate_key, """title""": titles[0]}			)

 # Add none duplicate-keys
 new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1]			)
 __lowercase						=						sorted(UpperCamelCase__					, key=lambda UpperCamelCase__			: s["title"].lower()			)

 # "overview" gets special treatment and is always first
 if len(UpperCamelCase__			) > 1:
  raise ValueError("""{doc_list} has two 'overview' docs which is not allowed."""			)

 overview_doc.extend(UpperCamelCase__			)

 # Sort
 return overview_doc


def lowerCAmelCase_   (					UpperCamelCase__						:			List[Any]=False			):






 """simple docstring"""







 with open(UpperCamelCase__					, encoding="""utf-8"""			) as f:
  __lowercase						=						yaml.safe_load(f.read()			)

 # Get to the API doc
 __lowercase						=						0
 while content[api_idx]["title"] != "API":
  api_idx += 1
 __lowercase						=						content[api_idx]["""sections"""]

 # Then to the model doc
 __lowercase						=						0
 while api_doc[scheduler_idx]["title"] != "Schedulers":
  scheduler_idx += 1

 __lowercase						=						api_doc[scheduler_idx]["""sections"""]
 __lowercase						=						clean_doc_toc(UpperCamelCase__			)

 __lowercase						=						False
 if new_scheduler_doc != scheduler_doc:
  __lowercase						=						True
  if overwrite:
   __lowercase						=						new_scheduler_doc

 if diff:
  if overwrite:
   __lowercase						=						api_doc
   with open(UpperCamelCase__					, """w"""					, encoding="""utf-8"""			) as f:
    f.write(yaml.dump(UpperCamelCase__					, allow_unicode=UpperCamelCase__			)			)
  else:
   raise ValueError(
       """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""			)


def lowerCAmelCase_   (					UpperCamelCase__						:			Union[str, Any]=False			):






 """simple docstring"""







 with open(UpperCamelCase__					, encoding="""utf-8"""			) as f:
  __lowercase						=						yaml.safe_load(f.read()			)

 # Get to the API doc
 __lowercase						=						0
 while content[api_idx]["title"] != "API":
  api_idx += 1
 __lowercase						=						content[api_idx]["""sections"""]

 # Then to the model doc
 __lowercase						=						0
 while api_doc[pipeline_idx]["title"] != "Pipelines":
  pipeline_idx += 1

 __lowercase						=						False
 __lowercase						=						api_doc[pipeline_idx]["""sections"""]
 __lowercase						=						[]

 # sort sub pipeline docs
 for pipeline_doc in pipeline_docs:
  if "section" in pipeline_doc:
   __lowercase						=						pipeline_doc["""section"""]
   __lowercase						=						clean_doc_toc(UpperCamelCase__			)
   if overwrite:
    __lowercase						=						new_sub_pipeline_doc
  new_pipeline_docs.append(UpperCamelCase__			)

 # sort overall pipeline doc
 __lowercase						=						clean_doc_toc(UpperCamelCase__			)

 if new_pipeline_docs != pipeline_docs:
  __lowercase						=						True
  if overwrite:
   __lowercase						=						new_pipeline_docs

 if diff:
  if overwrite:
   __lowercase						=						api_doc
   with open(UpperCamelCase__					, """w"""					, encoding="""utf-8"""			) as f:
    f.write(yaml.dump(UpperCamelCase__					, allow_unicode=UpperCamelCase__			)			)
  else:
   raise ValueError(
       """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""			)


if __name__ == "__main__":
       UpperCAmelCase__					=argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       UpperCAmelCase__					=parser.parse_args()

       check_scheduler_doc(args.fix_and_overwrite)
       check_pipeline_doc(args.fix_and_overwrite)




"""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)
UpperCAmelCase__					=logging.getLogger(__name__)


UpperCAmelCase__					="Hello world! cécé herlolip"


UpperCAmelCase__					=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_   (					UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







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

 __lowercase						=						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"""			)
 __lowercase						=						BertTokenizer.from_pretrained("""bert-base-uncased"""			)

 # prepare the model inputs
 __lowercase						=						tokenizer.encode("""This is sample éàalj'-."""			)
 encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCamelCase__			))			)
 __lowercase						=						torch.tensor(UpperCamelCase__			).unsqueeze(0			)
 __lowercase						=						tokenizer.encode("""This is sample 3 éàalj'-."""			)
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCamelCase__			))			)
 __lowercase						=						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
 __lowercase						=						encoder_input_ids
 __lowercase						=						decoder_input_ids
 __lowercase						=						__lowercase						=						None
 __lowercase						=						None
 __lowercase						=						__lowercase						=						None
 __lowercase						=						__lowercase						=						None
 __lowercase						=						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
 __lowercase						=						original(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)[0]
 __lowercase						=						original.generator(UpperCamelCase__			)

 __lowercase						=						new_model(
     UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)[0]
 __lowercase						=						new_model.generator(UpperCamelCase__			)

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

 __lowercase						=						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__":
       UpperCAmelCase__					=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.",
       )
       UpperCAmelCase__					=parser.parse_args()

       convert_bertabs_checkpoints(
           args.bertabs_checkpoint_path,
           args.pytorch_dump_folder_path,
       )
