



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)
SCREAMING_SNAKE_CASE     				=						logging.getLogger(__name__)


SCREAMING_SNAKE_CASE     				=						"Hello world! cécé herlolip"


SCREAMING_SNAKE_CASE     				=						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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Dict:
	UpperCAmelCase_						=				BertAbsConfig(
	    temp_dir="."			,     finetune_bert=__A			,     large=__A			,     share_emb=__A			,     use_bert_emb=__A			,     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			,     )
	UpperCAmelCase_						=				torch.load(__A			,     lambda __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE : storage )
	UpperCAmelCase_						=				AbsSummarizer(__A			,     torch.device("cpu" )			,     __A )
	original.eval()

	UpperCAmelCase_						=				BertAbsSummarizer(__A			,     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_						=				BertTokenizer.from_pretrained("bert-base-uncased" )

	# prepare the model inputs
	UpperCAmelCase_						=				tokenizer.encode("This is sample éàalj'-." )
	encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__A )) )
	UpperCAmelCase_						=				torch.tensor(__A ).unsqueeze(0 )
	UpperCAmelCase_						=				tokenizer.encode("This is sample 3 éàalj'-." )
	decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__A )) )
	UpperCAmelCase_						=				torch.tensor(__A ).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_						=				encoder_input_ids
	UpperCAmelCase_						=				decoder_input_ids
	UpperCAmelCase_						=				None
	UpperCAmelCase_						=				None
	UpperCAmelCase_						=				None
	UpperCAmelCase_						=				None
	UpperCAmelCase_						=				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_						=				original(__A			,     __A			,     __A			,     __A			,     __A			,     __A			,     __A )[0]
	UpperCAmelCase_						=				original.generator(__A )

	UpperCAmelCase_						=				new_model(
	    __A			,     __A			,     __A			,     __A			,     __A )[0]
	UpperCAmelCase_						=				new_model.generator(__A )

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

	UpperCAmelCase_						=				torch.allclose(__A			,     __A			,     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__":
			SCREAMING_SNAKE_CASE     				=						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.",
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  'xlm-roberta'



					def __init__(			self    ,	lowerCAmelCase=3_0522    ,	lowerCAmelCase=768    ,	lowerCAmelCase=12    ,	lowerCAmelCase=12    ,	lowerCAmelCase=3072    ,	lowerCAmelCase="gelu"    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=512    ,	lowerCAmelCase=2    ,	lowerCAmelCase=0.02    ,	lowerCAmelCase=1e-1_2    ,	lowerCAmelCase=1    ,	lowerCAmelCase=0    ,	lowerCAmelCase=2    ,	lowerCAmelCase="absolute"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(pad_token_id=lowerCAmelCase    ,	bos_token_id=lowerCAmelCase    ,	eos_token_id=lowerCAmelCase    ,	**lowerCAmelCase     )

							UpperCAmelCase_						=				vocab_size
							UpperCAmelCase_						=				hidden_size
							UpperCAmelCase_						=				num_hidden_layers
							UpperCAmelCase_						=				num_attention_heads
							UpperCAmelCase_						=				hidden_act
							UpperCAmelCase_						=				intermediate_size
							UpperCAmelCase_						=				hidden_dropout_prob
							UpperCAmelCase_						=				attention_probs_dropout_prob
							UpperCAmelCase_						=				max_position_embeddings
							UpperCAmelCase_						=				type_vocab_size
							UpperCAmelCase_						=				initializer_range
							UpperCAmelCase_						=				layer_norm_eps
							UpperCAmelCase_						=				position_embedding_type
							UpperCAmelCase_						=				use_cache
							UpperCAmelCase_						=				classifier_dropout





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					@property
					def    A__		(			self     ):
							if self.task == "multiple-choice":
									UpperCAmelCase_						=				{0: "batch", 1: "choice", 2: "sequence"}
							else:
									UpperCAmelCase_						=				{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]     )

