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


__UpperCamelCase	: List[Any]    		=  'Hello world! cécé herlolip'


__UpperCamelCase	: 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	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							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=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	,						)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.load(SCREAMING_SNAKE_CASE_	,						lambda _lowercase	,						_lowercase		: storage		)
						SCREAMING_SNAKE_CASE							:					int           =							AbsSummarizer(SCREAMING_SNAKE_CASE_	,						torch.device('''cpu'''		)	,						SCREAMING_SNAKE_CASE_		)
						original.eval()

						SCREAMING_SNAKE_CASE							:					str           =							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'''		)
						SCREAMING_SNAKE_CASE							:					Dict           =							BertTokenizer.from_pretrained('''bert-base-uncased'''		)

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

						SCREAMING_SNAKE_CASE							:					Dict           =							new_model(
						    SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_		)[0]
						SCREAMING_SNAKE_CASE							:					str           =							new_model.generator(SCREAMING_SNAKE_CASE_		)

						SCREAMING_SNAKE_CASE							:					Any           =							torch.max(torch.abs(output_converted_model - output_original_model		)		).item()
						print('''Maximum absolute difference beween weights: {:.2f}'''.format(SCREAMING_SNAKE_CASE_		)		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.max(torch.abs(output_converted_generator - output_original_generator		)		).item()
						print('''Maximum absolute difference beween weights: {:.2f}'''.format(SCREAMING_SNAKE_CASE_		)		)

						SCREAMING_SNAKE_CASE							:					Any           =							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__":
					__UpperCamelCase	: 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.',
					)
					__UpperCamelCase	: Dict    		=  parser.parse_args()

					convert_bertabs_checkpoints(
					    args.bertabs_checkpoint_path,
					    args.pytorch_dump_folder_path,
					)






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	FunnelTokenizer
						UpperCamelCase_  =	FunnelTokenizerFast
						UpperCamelCase_  =	True
						UpperCamelCase_  =	True


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[
												    '''<unk>''',
												    '''<cls>''',
												    '''<sep>''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												SCREAMING_SNAKE_CASE							:					List[str]           =							os.path.join(self.tmpdirname					,	VOCAB_FILES_NAMES['''vocab_file''']		)
												with open(self.vocab_file					,	'''w'''					,	encoding='''utf-8'''		) as vocab_writer:
																		vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizer.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizerFast.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''UNwant\u00E9d,running'''
												SCREAMING_SNAKE_CASE							:					int           =							'''unwanted, running'''
												return input_text, output_text


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.tokenizer_class(self.vocab_file		)

												SCREAMING_SNAKE_CASE							:					int           =							tokenizer.tokenize('''UNwant\u00E9d,running'''		)
												self.assertListEqual(UpperCamelCase__					,	['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']		)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)					,	[7, 4, 5, 10, 8, 9]		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		SCREAMING_SNAKE_CASE							:					int           =							tokenizer('''UNwant\u00E9d,running'''		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(inputs['''input_ids''']		) - 1
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len		)

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer('''UNwant\u00E9d,running'''					,	'''UNwant\u00E9d,running'''		)
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len + [1] * sentence_len		)





