


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)
a : Optional[Any]		=				logging.getLogger(__name__)


a : int		=				'''Hello world! cécé herlolip'''


a : Optional[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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   BertAbsConfig(
			    temp_dir='''.'''       ,			finetune_bert=_UpperCamelCase       ,			large=_UpperCamelCase       ,			share_emb=_UpperCamelCase       ,			use_bert_emb=_UpperCamelCase       ,			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       ,			)
			__lowercase 					=   torch.load(_UpperCamelCase       ,			lambda _UpperCamelCase       ,			_UpperCamelCase  : storage  )
			__lowercase 					=   AbsSummarizer(_UpperCamelCase       ,			torch.device('''cpu'''  )       ,			_UpperCamelCase  )
			original.eval()

			__lowercase 					=   BertAbsSummarizer(_UpperCamelCase       ,			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'''  )
			__lowercase 					=   BertTokenizer.from_pretrained('''bert-base-uncased'''  )

			# prepare the model inputs
			__lowercase 					=   tokenizer.encode('''This is sample éàalj\'-.'''  )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(_UpperCamelCase  ))  )
			__lowercase 					=   torch.tensor(_UpperCamelCase  ).unsqueeze(0  )
			__lowercase 					=   tokenizer.encode('''This is sample 3 éàalj\'-.'''  )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(_UpperCamelCase  ))  )
			__lowercase 					=   torch.tensor(_UpperCamelCase  ).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
			__lowercase 					=   encoder_input_ids
			__lowercase 					=   decoder_input_ids
			__lowercase 					=   __lowercase 					=   None
			__lowercase 					=   None
			__lowercase 					=   __lowercase 					=   None
			__lowercase 					=   __lowercase 					=   None
			__lowercase 					=   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
			__lowercase 					=   original(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )[0]
			__lowercase 					=   original.generator(_UpperCamelCase  )

			__lowercase 					=   new_model(
			    _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )[0]
			__lowercase 					=   new_model.generator(_UpperCamelCase  )

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

			__lowercase 					=   torch.allclose(_UpperCamelCase       ,			_UpperCamelCase       ,			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__":
	a : 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.''',
	)
	a : int		=				parser.parse_args()

	convert_bertabs_checkpoints(
	    args.bertabs_checkpoint_path,
	    args.pytorch_dump_folder_path,
	)






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)
@dataclass
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				42


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .pipeline_if import IFPipeline
	from .pipeline_if_imgaimg import IFImgaImgPipeline
	from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
	from .pipeline_if_inpainting import IFInpaintingPipeline
	from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
	from .pipeline_if_superresolution import IFSuperResolutionPipeline
	from .safety_checker import IFSafetyChecker
	from .watermark import IFWatermarker


