






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


UpperCamelCase_	:				Optional[int]										= """Hello world! cécé herlolip"""


UpperCamelCase_	:				List[str]										= 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     __lowercase					(    a :				Union[str, Any]       ,		a :				Optional[int] )					->					str:
      __snake_case      :    Tuple  			=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=2_048       ,		dec_dropout=0.2       ,		)
      __snake_case      :    Optional[Any]  			=torch.load(a       ,		lambda a       ,		a : storage )
      __snake_case      :    Any  			=AbsSummarizer(a       ,		torch.device('''cpu''' )       ,		a )
      original.eval()

      __snake_case      :    List[Any]  			=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''' )
      __snake_case      :    Tuple  			=BertTokenizer.from_pretrained('''bert-base-uncased''' )

      # prepare the model inputs
      __snake_case      :    Dict  			=tokenizer.encode('''This is sample éàalj\'-.''' )
      encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(a )) )
      __snake_case      :    List[str]  			=torch.tensor(a ).unsqueeze(0 )
      __snake_case      :    Any  			=tokenizer.encode('''This is sample 3 éàalj\'-.''' )
      decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(a )) )
      __snake_case      :    Union[str, Any]  			=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
      __snake_case      :    str  			=encoder_input_ids
      __snake_case      :    Tuple  			=decoder_input_ids
      __snake_case      :    int  			=None
      __snake_case      :    List[Any]  			=None
      __snake_case      :    int  			=None
      __snake_case      :    Tuple  			=None
      __snake_case      :    List[str]  			=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
      __snake_case      :    Optional[int]  			=original(a       ,		a       ,		a       ,		a       ,		a       ,		a       ,		a )[0]
      __snake_case      :    Union[str, Any]  			=original.generator(a )

      __snake_case      :    Optional[Any]  			=new_model(
          a       ,		a       ,		a       ,		a       ,		a )[0]
      __snake_case      :    Optional[int]  			=new_model.generator(a )

      __snake_case      :    Union[str, Any]  			=torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
      print('''Maximum absolute difference beween weights: {:.2f}'''.format(a ) )
      __snake_case      :    Union[str, Any]  			=torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
      print('''Maximum absolute difference beween weights: {:.2f}'''.format(a ) )

      __snake_case      :    List[Any]  			=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__":
    UpperCamelCase_	:				Dict										= 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_	:				Any										= parser.parse_args()

    convert_bertabs_checkpoints(
        args.bertabs_checkpoint_path,
        args.pytorch_dump_folder_path,
    )










"""simple docstring"""





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCamelCase_	:				int										= subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
UpperCamelCase_	:				Optional[int]										= (
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

UpperCamelCase_	:				List[Any]										= """|""".join(sys.argv[1:])
UpperCamelCase_	:				List[str]										= re.compile(rF'''^({joined_dirs}).*?\.py$''')

UpperCamelCase_	:				str										= [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")


