
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						:      int									= logging.getLogger(__name__)


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


lowercase						:      Union[str, Any]									= 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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Optional[Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      BertAbsConfig(
							    temp_dir="."		,						finetune_bert=_lowerCamelCase		,						large=_lowerCamelCase		,						share_emb=_lowerCamelCase		,						use_bert_emb=_lowerCamelCase		,						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		,						)
							__UpperCamelCase			:     Tuple         =      torch.load(_lowerCamelCase		,						lambda _lowerCamelCase		,						_lowerCamelCase: storage)
							__UpperCamelCase			:     int         =      AbsSummarizer(_lowerCamelCase		,						torch.device("cpu")		,						_lowerCamelCase)
							original.eval()

							__UpperCamelCase			:     Any         =      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")
							__UpperCamelCase			:     Any         =      BertTokenizer.from_pretrained("bert-base-uncased")

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

							__UpperCamelCase			:     str         =      new_model(
							    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)[0]
							__UpperCamelCase			:     Optional[int]         =      new_model.generator(_lowerCamelCase)

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

							__UpperCamelCase			:     Any         =      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__":
		lowercase						:      Union[str, 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						:      Optional[Any]									= parser.parse_args()

		convert_bertabs_checkpoints(
		    args.bertabs_checkpoint_path,
		    args.pytorch_dump_folder_path,
		)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Tuple									= {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Union[str, Any]									= [
		    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Swinv2ForImageClassification',
		    'Swinv2ForMaskedImageModeling',
		    'Swinv2Model',
		    'Swinv2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_swinva import (
				    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwinvaForImageClassification,
				    SwinvaForMaskedImageModeling,
				    SwinvaModel,
				    SwinvaPreTrainedModel,
				)


else:
		import sys

		lowercase						:      Optional[Any]									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)