
"""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     :			List[Any]  						=			logging.getLogger(__name__)


_A     :			int  						=			"""Hello world! cécé herlolip"""


_A     :			Dict  						=			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__      ( __snake_case							:			str	,					__snake_case							:			Any    )					->							int:
       lowercase       :							int									=				BertAbsConfig(
           temp_dir="."	,					finetune_bert=__snake_case	,					large=__snake_case	,					share_emb=__snake_case	,					use_bert_emb=__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	,					)
       lowercase       :							int									=				torch.load(__snake_case	,					lambda __snake_case	,					__snake_case    : storage    )
       lowercase       :							int									=				AbsSummarizer(__snake_case	,					torch.device("cpu"    )	,					__snake_case    )
       original.eval()

       lowercase       :							Tuple									=				BertAbsSummarizer(__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"    )
       lowercase       :							str									=				BertTokenizer.from_pretrained("bert-base-uncased"    )

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

       lowercase       :							Dict									=				new_model(
           __snake_case	,					__snake_case	,					__snake_case	,					__snake_case	,					__snake_case    )[0]
       lowercase       :							Dict									=				new_model.generator(__snake_case    )

       lowercase       :							List[str]									=				torch.max(torch.abs(output_converted_model - output_original_model    )    ).item()
       print("Maximum absolute difference beween weights: {:.2f}".format(__snake_case    )    )
       lowercase       :							List[str]									=				torch.max(torch.abs(output_converted_generator - output_original_generator    )    ).item()
       print("Maximum absolute difference beween weights: {:.2f}".format(__snake_case    )    )

       lowercase       :							str									=				torch.allclose(__snake_case	,					__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__":
   _A     :			Any  						=			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     :			Optional[Any]  						=			parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )




"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A     :			Optional[int]  						=			{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			[
       """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MgpstrModel""",
       """MgpstrPreTrainedModel""",
       """MgpstrForSceneTextRecognition""",
   ]

if TYPE_CHECKING:
   from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
   from .processing_mgp_str import MgpstrProcessor
   from .tokenization_mgp_str import MgpstrTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mgp_str import (
          MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
          MgpstrForSceneTextRecognition,
          MgpstrModel,
          MgpstrPreTrainedModel,
      )
else:
   import sys

   _A     :			Tuple  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


