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


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


_lowerCamelCase       :				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        a_		(       __lowercase   :	Optional[Any]       ,					__lowercase   :	int ) ->  str:
			_snake_case		=			BertAbsConfig(
			    temp_dir='.'       ,					finetune_bert=__lowercase       ,					large=__lowercase       ,					share_emb=__lowercase       ,					use_bert_emb=__lowercase       ,					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       ,					)
			_snake_case		=			torch.load(__lowercase       ,					lambda __lowercase       ,					__lowercase : storage )
			_snake_case		=			AbsSummarizer(__lowercase       ,					torch.device('cpu' )       ,					__lowercase )
			original.eval()

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

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

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

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

			_snake_case		=			torch.allclose(__lowercase       ,					__lowercase       ,					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__":
			_lowerCamelCase       :				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.''',
			)
			_lowerCamelCase       :				List[str]       =			parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
			from ...onnx.config import PatchingSpec
			from ...tokenization_utils_base import PreTrainedTokenizerBase


_lowerCamelCase       :				List[Any]       =			logging.get_logger(__name__)

_lowerCamelCase       :				Union[str, Any]       =			{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					"longformer"



	def __init__(  self	:    Optional[Any]			,  lowercase	:    Union[List[int], int] = 512			,  lowercase	:    int = 2			,  lowercase	:    int = 1			,  lowercase	:    int = 0			,  lowercase	:    int = 2			,  lowercase	:    int = 30_522			,  lowercase	:    int = 768			,  lowercase	:    int = 12			,  lowercase	:    int = 12			,  lowercase	:    int = 3_072			,  lowercase	:    str = "gelu"			,  lowercase	:    float = 0.1			,  lowercase	:    float = 0.1			,  lowercase	:    int = 512			,  lowercase	:    int = 2			,  lowercase	:    float = 0.02			,  lowercase	:    float = 1E-12			,  lowercase	:    bool = False			,  **lowercase	:    Optional[Any]			,  ):


				'''simple docstring'''


				super().__init__(pad_token_id=lowercase			,  **lowercase	)

				_snake_case		=			attention_window
				_snake_case		=			sep_token_id
				_snake_case		=			bos_token_id
				_snake_case		=			eos_token_id
				_snake_case		=			vocab_size
				_snake_case		=			hidden_size
				_snake_case		=			num_hidden_layers
				_snake_case		=			num_attention_heads
				_snake_case		=			hidden_act
				_snake_case		=			intermediate_size
				_snake_case		=			hidden_dropout_prob
				_snake_case		=			attention_probs_dropout_prob
				_snake_case		=			max_position_embeddings
				_snake_case		=			type_vocab_size
				_snake_case		=			initializer_range
				_snake_case		=			layer_norm_eps
				_snake_case		=			onnx_export




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    int			,  lowercase	:    "PretrainedConfig"			,  lowercase	:    str = "default"			,  lowercase	:    "List[PatchingSpec]" = None	):


				'''simple docstring'''


				super().__init__(lowercase			,  lowercase			,  lowercase	)
				_snake_case		=			True



	@property
	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				if self.task == "multiple-choice":
							_snake_case		=			{0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
							_snake_case		=			{0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				        ('global_attention_mask', dynamic_axis),
				    ]	)



	@property
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			super().outputs
				if self.task == "default":
							_snake_case		=			{0: 'batch'}
				return outputs



	@property
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				return 1E-4



	@property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return max(super().default_onnx_opset			,  14	)





	def  A							(  self	:    str			,  lowercase	:    "PreTrainedTokenizerBase"			,  lowercase	:    int = -1			,  lowercase	:    int = -1			,  lowercase	:    bool = False			,  lowercase	:    Optional[TensorType] = None			,  ):


				'''simple docstring'''


				_snake_case		=			super().generate_dummy_inputs(
				    preprocessor=lowercase			,  batch_size=lowercase			,  seq_length=lowercase			,  is_pair=lowercase			,  framework=lowercase	)
				import torch

				# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
				# makes the export fail randomly
				_snake_case		=			torch.zeros_like(inputs['input_ids']	)
				# make every second token global
				_snake_case		=			1

				return inputs