
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		: List[str]								=     logging.getLogger(__name__)


A		: str								=     '''Hello world! cécé herlolip'''


A		: 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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	Optional[int]       )  ->     List[str]:
						_lowercase					       =					BertAbsConfig(
						    temp_dir="""."""		,   finetune_bert=SCREAMING_SNAKE_CASE_		,   large=SCREAMING_SNAKE_CASE_		,   share_emb=SCREAMING_SNAKE_CASE_		,   use_bert_emb=SCREAMING_SNAKE_CASE_		,   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(SCREAMING_SNAKE_CASE_		,   lambda SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       : storage       )
						_lowercase					       =					AbsSummarizer(SCREAMING_SNAKE_CASE_		,   torch.device("""cpu"""       )		,   SCREAMING_SNAKE_CASE_       )
						original.eval()

						_lowercase					       =					BertAbsSummarizer(SCREAMING_SNAKE_CASE_		,   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(SCREAMING_SNAKE_CASE_       ))       )
						_lowercase					       =					torch.tensor(SCREAMING_SNAKE_CASE_       ).unsqueeze(0       )
						_lowercase					       =					tokenizer.encode("""This is sample 3 éàalj'-."""       )
						decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(SCREAMING_SNAKE_CASE_       ))       )
						_lowercase					       =					torch.tensor(SCREAMING_SNAKE_CASE_       ).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(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )[0]
						_lowercase					       =					original.generator(SCREAMING_SNAKE_CASE_       )

						_lowercase					       =					new_model(
						    SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )[0]
						_lowercase					       =					new_model.generator(SCREAMING_SNAKE_CASE_       )

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

						_lowercase					       =					torch.allclose(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   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		: List[str]								=     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		: Tuple								=     parser.parse_args()

		convert_bertabs_checkpoints(
		    args.bertabs_checkpoint_path,
		    args.pytorch_dump_folder_path,
		)

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Optional[int]:
						return {key.lstrip("""-"""       ): value for key, value in zip(unknown_args[::2]		,   unknown_args[1::2]       )}




def 	UpperCamelCase__  (     )  ->     Dict:
						_lowercase					       =					ArgumentParser(
						    """HuggingFace Datasets CLI tool"""		,   usage="""datasets-cli <command> [<args>]"""		,   allow_abbrev=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					parser.add_subparsers(help="""datasets-cli command helpers"""       )
						set_verbosity_info()

						# Register commands
						ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						TestCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						RunBeamCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						DummyDataCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )

						# Parse args
						_lowercase       ,						_lowercase					       =					parser.parse_known_args()
						if not hasattr(SCREAMING_SNAKE_CASE_		,   """func"""       ):
												parser.print_help()
												exit(1       )
						_lowercase					       =					parse_unknown_args(SCREAMING_SNAKE_CASE_       )

						# Run
						_lowercase					       =					args.func(SCREAMING_SNAKE_CASE_		,   **SCREAMING_SNAKE_CASE_       )
						service.run()


if __name__ == "__main__":
		main()