

'''simple docstring'''



from __future__ import annotations

from dataclasses import dataclass



@dataclass
class 				_snake_case					:
  SCREAMING_SNAKE_CASE			:       float
  SCREAMING_SNAKE_CASE			:       TreeNode | None				=     None
  SCREAMING_SNAKE_CASE			:       TreeNode | None				=     None



def       snake_case   (	snake_case      : TreeNode | None			) ->  bool:


    """simple docstring"""


    def is_valid_tree(snake_case      : TreeNode | None			) -> bool:
        if node is None:
            return True

        if not isinstance(snake_case					,		snake_case			):
            return False

        try:
            float(node.data			)
        except (TypeError, ValueError):
            return False

        return is_valid_tree(node.left			) and is_valid_tree(node.right			)

    if not is_valid_tree(snake_case			):
        raise ValueError(
            'Each node should be type of TreeNode and data should be float.'			)

    def is_binary_search_tree_recursive_check(
        snake_case      : TreeNode | None					,		snake_case      : float					,		snake_case      : float			) -> bool:

        if node is None:
            return True

        return (
            left_bound < node.data < right_bound
            and is_binary_search_tree_recursive_check(node.left					,		snake_case					,		node.data			)
            and is_binary_search_tree_recursive_check(
                node.right					,		node.data					,		snake_case			)
        )

    return is_binary_search_tree_recursive_check(snake_case					,		-float('inf'			)					,		float('inf'			)			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''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[Any]							       =				logging.getLogger(__name__)


_UpperCamelCase						:  List[Any]							       =				"Hello world! cécé herlolip"


_UpperCamelCase						:  int							       =				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       snake_case   (	snake_case      : int					,		snake_case      : Optional[Any]			) ->  Optional[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							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					,		)
    lowerCAmelCase 						=							torch.load(snake_case					,		lambda snake_case					,		snake_case			: storage			)
    lowerCAmelCase 						=							AbsSummarizer(snake_case					,		torch.device('cpu'			)					,		snake_case			)
    original.eval()

    lowerCAmelCase 						=							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'			)
    lowerCAmelCase 						=							BertTokenizer.from_pretrained('bert-base-uncased'			)

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

    lowerCAmelCase 						=							new_model(
        snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)[0]
    lowerCAmelCase 						=							new_model.generator(snake_case			)

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

    lowerCAmelCase 						=							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__":
     _UpperCamelCase						:  List[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.",
     )
     _UpperCamelCase						:  Optional[int]							       =				parser.parse_args()

     convert_bertabs_checkpoints(
         args.bertabs_checkpoint_path,
         args.pytorch_dump_folder_path,
     )





