



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


UpperCamelCase__       :	Any    = '''Hello world! cécé herlolip'''


UpperCamelCase__       :	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 				lowerCAmelCase_   (	_lowerCamelCase:   Union[str, Any]       ,      _lowerCamelCase:   List[Any]					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		BertAbsConfig(
		    temp_dir="""."""       ,      finetune_bert=_lowerCamelCase       ,      large=_lowerCamelCase       ,      share_emb=_lowerCamelCase       ,      use_bert_emb=_lowerCamelCase       ,      encoder="""bert"""       ,      max_pos=5_12       ,      enc_layers=6       ,      enc_hidden_size=5_12       ,      enc_heads=8       ,      enc_ff_size=5_12       ,      enc_dropout=0.2       ,      dec_layers=6       ,      dec_hidden_size=7_68       ,      dec_heads=8       ,      dec_ff_size=20_48       ,      dec_dropout=0.2       ,      )
		__SCREAMING_SNAKE_CASE       :		Dict           =		torch.load(_lowerCamelCase       ,      lambda _lowerCamelCase       ,      _lowerCamelCase					: storage					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		AbsSummarizer(_lowerCamelCase       ,      torch.device("""cpu"""					)       ,      _lowerCamelCase					)
		original.eval()

		__SCREAMING_SNAKE_CASE       :		int           =		BertAbsSummarizer(_lowerCamelCase       ,      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"""					)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		BertTokenizer.from_pretrained("""bert-base-uncased"""					)

		# prepare the model inputs
		__SCREAMING_SNAKE_CASE       :		Any           =		tokenizer.encode("""This is sample éàalj'-."""					)
		encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(_lowerCamelCase					))					)
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		torch.tensor(_lowerCamelCase					).unsqueeze(0					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		tokenizer.encode("""This is sample 3 éàalj'-."""					)
		decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(_lowerCamelCase					))					)
		__SCREAMING_SNAKE_CASE       :		str           =		torch.tensor(_lowerCamelCase					).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
		__SCREAMING_SNAKE_CASE       :		int           =		encoder_input_ids
		__SCREAMING_SNAKE_CASE       :		str           =		decoder_input_ids
		__SCREAMING_SNAKE_CASE       :		Dict           =		None
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		None
		__SCREAMING_SNAKE_CASE       :		str           =		None
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		None
		__SCREAMING_SNAKE_CASE       :		Dict           =		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
		__SCREAMING_SNAKE_CASE       :		Dict           =		original(_lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase					)[0]
		__SCREAMING_SNAKE_CASE       :		str           =		original.generator(_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		int           =		new_model(
		    _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase					)[0]
		__SCREAMING_SNAKE_CASE       :		List[str]           =		new_model.generator(_lowerCamelCase					)

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

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		torch.allclose(_lowerCamelCase       ,      _lowerCamelCase       ,      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__       :	Optional[int]    = 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__       :	Any    = parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)




'''simple docstring'''




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)