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


__lowercase							:   Tuple					=     '''Hello world! cécé herlolip'''


__lowercase							:   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 lowercase_	(   _lowercase			,       _lowercase				)     ->							Dict:
					'''simple docstring'''

					lowerCamelCase_					:      Union[str, 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=2_048			,       dec_dropout=0.2			,       )
					lowerCamelCase_					:      Any     =		torch.load(__snake_case			,       lambda _lowercase			,       _lowercase				: storage				)
					lowerCamelCase_					:      Tuple     =		AbsSummarizer(__snake_case			,       torch.device('''cpu'''				)			,       __snake_case				)
					original.eval()

					lowerCamelCase_					:      Any     =		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_					:      str     =		BertTokenizer.from_pretrained('''bert-base-uncased'''				)

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

					lowerCamelCase_					:      Union[str, Any]     =		new_model(
					    __snake_case			,       __snake_case			,       __snake_case			,       __snake_case			,       __snake_case				)[0]
					lowerCamelCase_					:      Tuple     =		new_model.generator(__snake_case				)

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

					lowerCamelCase_					:      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[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.''',
					)
					__lowercase							:   Dict					=     parser.parse_args()

					convert_bertabs_checkpoints(
					    args.bertabs_checkpoint_path,
					    args.pytorch_dump_folder_path,
					)




'''simple docstring'''


from collections import namedtuple

__lowercase							:   List[Any]					=     namedtuple('''from_to''', '''from_ to''')

__lowercase							:   Optional[Any]					=     {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 2_64.1_72),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 42_26.75),
}




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							float:
					'''simple docstring'''

					if from_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					if to_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


