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[Any]     =logging.getLogger(__name__)


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


lowerCAmelCase			:							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 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						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   , )
    _lowerCamelCase     :		Dict 						=						torch.load(__A   , lambda __A   , __A	: storage	)
    _lowerCamelCase     :		List[Any] 						=						AbsSummarizer(__A   , torch.device("""cpu"""	)   , __A	)
    original.eval()

    _lowerCamelCase     :		Optional[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"""	)
    _lowerCamelCase     :		Dict 						=						BertTokenizer.from_pretrained("""bert-base-uncased"""	)

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

    _lowerCamelCase     :		Dict 						=						new_model(
        __A   , __A   , __A   , __A   , __A	)[0]
    _lowerCamelCase     :		int 						=						new_model.generator(__A	)

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

    _lowerCamelCase     :		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__":
    lowerCAmelCase			:							str     =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			:							Optional[int]     =parser.parse_args()

    convert_bertabs_checkpoints(
        args.bertabs_checkpoint_path,
        args.pytorch_dump_folder_path,
    )





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase			:							Any     =logging.get_logger(__name__)

lowerCAmelCase			:							List[Any]     ={
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'swin'

      _snake_case    		=       {
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }



      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				List[str]=224    ,  _UpperCamelCase     :				List[str]=4    ,  _UpperCamelCase     :				List[Any]=3    ,  _UpperCamelCase     :				Dict=96    ,  _UpperCamelCase     :				Any=[2, 2, 6, 2]    ,  _UpperCamelCase     :				Any=[3, 6, 12, 24]    ,  _UpperCamelCase     :				Tuple=7    ,  _UpperCamelCase     :				Tuple=4.0    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				Tuple=0.0    ,  _UpperCamelCase     :				Any=0.0    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				Any="gelu"    ,  _UpperCamelCase     :				str=False    ,  _UpperCamelCase     :				str=0.0_2    ,  _UpperCamelCase     :				Dict=1E-5    ,  _UpperCamelCase     :				List[str]=32    ,  _UpperCamelCase     :				Optional[Any]=None    ,  _UpperCamelCase     :				List[Any]=None    ,  **_UpperCamelCase     :				List[Any]    ,  )				->Tuple:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						image_size
          _lowerCamelCase     :		Tuple 						=						patch_size
          _lowerCamelCase     :		Dict 						=						num_channels
          _lowerCamelCase     :		Union[str, Any] 						=						embed_dim
          _lowerCamelCase     :		str 						=						depths
          _lowerCamelCase     :		str 						=						len(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						num_heads
          _lowerCamelCase     :		Tuple 						=						window_size
          _lowerCamelCase     :		int 						=						mlp_ratio
          _lowerCamelCase     :		Optional[int] 						=						qkv_bias
          _lowerCamelCase     :		List[str] 						=						hidden_dropout_prob
          _lowerCamelCase     :		str 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		Tuple 						=						drop_path_rate
          _lowerCamelCase     :		List[str] 						=						hidden_act
          _lowerCamelCase     :		Dict 						=						use_absolute_embeddings
          _lowerCamelCase     :		int 						=						layer_norm_eps
          _lowerCamelCase     :		str 						=						initializer_range
          _lowerCamelCase     :		Dict 						=						encoder_stride
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          _lowerCamelCase     :		int 						=						int(embed_dim * 2 ** (len(_UpperCamelCase) - 1))
          _lowerCamelCase     :		Dict 						=						["""stem"""] + [F"""stage{idx}""" for idx in range(1    ,  len(_UpperCamelCase) + 1)]
          _lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						get_aligned_output_features_output_indices(
              out_features=_UpperCamelCase    ,  out_indices=_UpperCamelCase    ,  stage_names=self.stage_names)





class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       version.parse('1.11'		)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Mapping[str, Mapping[int, str]]:


          """simple docstring"""


          return OrderedDict(
              [
                  ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
              ])




      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->float:


          """simple docstring"""


          return 1E-4




