






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      _lowerCamelCase	(						__A    :							Any		,		__A    :							Tuple )      ->					Any:
    _UpperCAmelCase				:      Union[str, Any]			  =				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=2_048		,		dec_dropout=0.2		,		)
    _UpperCAmelCase				:      Optional[int]			  =				torch.load(__A		,		lambda __A		,		__A : storage )
    _UpperCAmelCase				:      List[Any]			  =				AbsSummarizer(__A		,		torch.device('''cpu''' )		,		__A )
    original.eval()

    _UpperCAmelCase				:      List[str]			  =				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				:      Union[str, Any]			  =				BertTokenizer.from_pretrained('''bert-base-uncased''' )

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

    _UpperCAmelCase				:      Union[str, Any]			  =				new_model(
        __A		,		__A		,		__A		,		__A		,		__A )[0]
    _UpperCAmelCase				:      int			  =				new_model.generator(__A )

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

    _UpperCAmelCase				:      Optional[Any]			  =				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,
  )












import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 							A_    (						__lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			int =					None
 _SCREAMING_SNAKE_CASE							:			str =					BloomTokenizerFast
 _SCREAMING_SNAKE_CASE							:			str =					BloomTokenizerFast
 _SCREAMING_SNAKE_CASE							:			Any =					True
 _SCREAMING_SNAKE_CASE							:			Optional[int] =					False
 _SCREAMING_SNAKE_CASE							:			Optional[int] =					"tokenizer_file"
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     super().setUp()
     _UpperCAmelCase				:      List[Any]			  =				BloomTokenizerFast.from_pretrained('''bigscience/tokenizer''')
     tokenizer.save_pretrained(self.tmpdirname)


 def  snake_case__				(	self							,  **_A) ->		List[Any]:

     """simple docstring"""



     kwargs.update(self.special_tokens_map)
     return BloomTokenizerFast.from_pretrained(self.tmpdirname							,  **_A)


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				self.get_rust_tokenizer()

     _UpperCAmelCase				:      int			  =				['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
     _UpperCAmelCase				:      List[Any]			  =				[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

     _UpperCAmelCase				:      Any			  =				tokenizer.batch_encode_plus(_A)['''input_ids''']
     self.assertListEqual(_A							,  _A)

     _UpperCAmelCase				:      Any			  =				tokenizer.batch_decode(_A)
     self.assertListEqual(_A							,  _A)


 def  snake_case__				(	self							,  _A=6) ->		Optional[Any]:

     """simple docstring"""



     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
             _UpperCAmelCase				:      Optional[int]			  =				self.rust_tokenizer_class.from_pretrained(_A							,  **_A)
             # tokenizer_r.pad_token = None # Hotfixing padding = None
             # Simple input
             _UpperCAmelCase				:      List[Any]			  =				'''This is a simple input'''
             _UpperCAmelCase				:      Any			  =				['''This is a simple input 1''', '''This is a simple input 2''']
             _UpperCAmelCase				:      int			  =				('''This is a simple input''', '''This is a pair''')
             _UpperCAmelCase				:      Optional[int]			  =				[
                 ('''This is a simple input 1''', '''This is a simple input 2'''),
                 ('''This is a simple pair 1''', '''This is a simple pair 2'''),
             ]

             # Simple input tests
             try:
                 tokenizer_r.encode(_A							,  max_length=_A)
                 tokenizer_r.encode_plus(_A							,  max_length=_A)

                 tokenizer_r.batch_encode_plus(_A							,  max_length=_A)
                 tokenizer_r.encode(_A							,  max_length=_A)
                 tokenizer_r.batch_encode_plus(_A							,  max_length=_A)
             except ValueError:
                 self.fail('''Bloom Tokenizer should be able to deal with padding''')

             _UpperCAmelCase				:      Tuple			  =				None  # Hotfixing padding = None
             self.assertRaises(_A							,  tokenizer_r.encode							,  _A							,  max_length=_A							,  padding='''max_length''')

             # Simple input
             self.assertRaises(_A							,  tokenizer_r.encode_plus							,  _A							,  max_length=_A							,  padding='''max_length''')

             # Simple input
             self.assertRaises(
                 _A							,  tokenizer_r.batch_encode_plus							,  _A							,  max_length=_A							,  padding='''max_length'''							,  )

             # Pair input
             self.assertRaises(_A							,  tokenizer_r.encode							,  _A							,  max_length=_A							,  padding='''max_length''')

             # Pair input
             self.assertRaises(_A							,  tokenizer_r.encode_plus							,  _A							,  max_length=_A							,  padding='''max_length''')

             # Pair input
             self.assertRaises(
                 _A							,  tokenizer_r.batch_encode_plus							,  _A							,  max_length=_A							,  padding='''max_length'''							,  )


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.get_rust_tokenizer()
     _UpperCAmelCase				:      int			  =				load_dataset('''xnli'''							,  '''all_languages'''							,  split='''test'''							,  streaming=_A)

     _UpperCAmelCase				:      Tuple			  =				next(iter(_A))['''premise''']  # pick up one data
     _UpperCAmelCase				:      List[Any]			  =				list(sample_data.values())

     _UpperCAmelCase				:      Any			  =				list(map(tokenizer.encode							,  _A))
     _UpperCAmelCase				:      List[str]			  =				[tokenizer.decode(_A							,  clean_up_tokenization_spaces=_A) for x in output_tokens]
     self.assertListEqual(_A							,  _A)


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)							,  1)
     self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])							,  1)




