
'''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)
lowercase     :							Optional[int]       						=     logging.getLogger(__name__)


lowercase     :							Union[str, Any]       						=     'Hello world! cécé herlolip'


lowercase     :							Optional[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 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		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						,							)
    A			:  Any   =		torch.load(snake_case__						,							lambda snake_case__						,							snake_case__		: storage		)
    A			:  List[Any]   =		AbsSummarizer(snake_case__						,							torch.device('''cpu'''		)						,							snake_case__		)
    original.eval()

    A			:  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'''		)
    A			:  Optional[Any]   =		BertTokenizer.from_pretrained('''bert-base-uncased'''		)

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

    A			:  Tuple   =		new_model(
        snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		)[0]
    A			:  Dict   =		new_model.generator(snake_case__		)

    A			:  Tuple   =		torch.max(torch.abs(output_converted_model - output_original_model		)		).item()
    print('''Maximum absolute difference beween weights: {:.2f}'''.format(snake_case__		)		)
    A			:  Optional[int]   =		torch.max(torch.abs(output_converted_generator - output_original_generator		)		).item()
    print('''Maximum absolute difference beween weights: {:.2f}'''.format(snake_case__		)		)

    A			:  List[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__":
      lowercase     :							List[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.',
      )
      lowercase     :							Union[str, Any]       						=     parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							Any       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''timesformer'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE=224		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3072		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="divided_space_time"		,     SCREAMING_SNAKE_CASE=0		,     **SCREAMING_SNAKE_CASE		,     )					->							Tuple:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE      )

         A			:  List[Any]   =		image_size
         A			:  List[str]   =		patch_size
         A			:  List[str]   =		num_channels
         A			:  str   =		num_frames

         A			:  Any   =		hidden_size
         A			:  Dict   =		num_hidden_layers
         A			:  Any   =		num_attention_heads
         A			:  Optional[Any]   =		intermediate_size
         A			:  Union[str, Any]   =		hidden_act
         A			:  Tuple   =		hidden_dropout_prob
         A			:  Optional[int]   =		attention_probs_dropout_prob
         A			:  Tuple   =		initializer_range
         A			:  List[Any]   =		layer_norm_eps
         A			:  Union[str, Any]   =		qkv_bias

         A			:  List[str]   =		attention_type
         A			:  Union[str, Any]   =		drop_path_rate




