



'''simple docstring'''






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_							: Dict						 =	logging.getLogger(__name__)


A_							: Optional[int]						 =	"""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     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						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=2048       ,   dec_dropout=0.2       ,   )
       _UpperCAmelCase  :						Dict			     = torch.load(SCREAMING_SNAKE_CASE_       ,   lambda lowerCAmelCase_       ,   lowerCAmelCase_		: storage		)
       _UpperCAmelCase  :						Dict			     = AbsSummarizer(SCREAMING_SNAKE_CASE_       ,   torch.device("""cpu"""		)       ,   SCREAMING_SNAKE_CASE_		)
       original.eval()

       _UpperCAmelCase  :						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"""		)
       _UpperCAmelCase  :						Dict			     = BertTokenizer.from_pretrained("""bert-base-uncased"""		)

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

       _UpperCAmelCase  :						Union[str, Any]			     = new_model(
           SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_		)[0]
       _UpperCAmelCase  :						List[str]			     = new_model.generator(SCREAMING_SNAKE_CASE_		)

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

       _UpperCAmelCase  :						List[str]			     = 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_							: Any						 =	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_							: Optional[Any]						 =	parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )





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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCamelCase__          =		logging.get_logger(__name__)

lowerCamelCase__          =		{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class      A__							(     __magic_name__	):
				lowercase			    =  'gptj'
				lowercase			    =  {
				    'max_position_embeddings': 'n_positions',
				    'hidden_size': 'n_embd',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}






				def __init__(						self					:      Optional[Any]    ,     a					:      Dict=50_400    ,     a					:      Union[str, Any]=2_048    ,     a					:      List[str]=4_096    ,     a					:      Any=28    ,     a					:      Optional[Any]=16    ,     a					:      Optional[Any]=64    ,     a					:      int=None    ,     a					:      Any="gelu_new"    ,     a					:      Union[str, Any]=0.0    ,     a					:      List[Any]=0.0    ,     a					:      List[Any]=0.0    ,     a					:      Optional[Any]=1E-5    ,     a					:      Optional[int]=0.0_2    ,     a					:      int=True    ,     a					:      str=50_256    ,     a					:      str=50_256    ,     a					:      Any=False    ,     **a					:      Dict    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		vocab_size
							lowerCAmelCase__      :      List[Any]         =		n_positions
							lowerCAmelCase__      :      str         =		n_embd
							lowerCAmelCase__      :      str         =		n_layer
							lowerCAmelCase__      :      str         =		n_head
							lowerCAmelCase__      :      Dict         =		n_inner
							lowerCAmelCase__      :      Union[str, Any]         =		rotary_dim
							lowerCAmelCase__      :      Optional[int]         =		activation_function
							lowerCAmelCase__      :      Any         =		resid_pdrop
							lowerCAmelCase__      :      int         =		embd_pdrop
							lowerCAmelCase__      :      int         =		attn_pdrop
							lowerCAmelCase__      :      List[Any]         =		layer_norm_epsilon
							lowerCAmelCase__      :      str         =		initializer_range
							lowerCAmelCase__      :      Dict         =		use_cache

							lowerCAmelCase__      :      str         =		bos_token_id
							lowerCAmelCase__      :      int         =		eos_token_id

							super().__init__(
							    bos_token_id=a    ,     eos_token_id=a    ,     tie_word_embeddings=a    ,     **a   )







class      A__							(     __magic_name__	):






				def __init__(						self					:      str    ,     a					:      PretrainedConfig    ,     a					:      str = "default"    ,     a					:      List[PatchingSpec] = None    ,     a					:      bool = False    ,     ):






							'''simple docstring'''







							super().__init__(a    ,     task=a    ,     patching_specs=a    ,     use_past=a   )
							if not getattr(self._config    ,     'pad_token_id'    ,     a   ):
										# TODO: how to do that better?
										lowerCAmelCase__      :      int         =		0






				@property
				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
							if self.use_past:
										self.fill_with_past_key_values_(a    ,     direction='inputs'   )
										lowerCAmelCase__      :      Optional[Any]         =		{0: 'batch', 1: 'past_sequence + sequence'}
							else:
										lowerCAmelCase__      :      Tuple         =		{0: 'batch', 1: 'sequence'}

							return common_inputs






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






							'''simple docstring'''







							return self._config.n_layer






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							return self._config.n_head






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      PreTrainedTokenizer    ,     a					:      int = -1    ,     a					:      int = -1    ,     a					:      bool = False    ,     a					:      Optional[TensorType] = None    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		super(a    ,     self   ).generate_dummy_inputs(
							    a    ,     batch_size=a    ,     seq_length=a    ,     is_pair=a    ,     framework=a   )

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase__      :      Optional[int]         =		OrderedDict({'input_ids': common_inputs['input_ids']}   )

							# Need to add the past_keys
							if self.use_past:
										if not is_torch_available():
													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
										else:
													import torch

													lowerCAmelCase__      ,				lowerCAmelCase__      :      int         =		common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													lowerCAmelCase__      :      Optional[int]         =		seqlen + 2
													lowerCAmelCase__      :      Tuple         =		(
													    batch,
													    self.num_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // self.num_attention_heads,
													)
													lowerCAmelCase__      :      Tuple         =		[
													    (torch.zeros(a   ), torch.zeros(a   )) for _ in range(self.num_layers   )
													]

							lowerCAmelCase__      :      Any         =		common_inputs['attention_mask']
							if self.use_past:
										lowerCAmelCase__      :      List[str]         =		ordered_inputs['attention_mask'].dtype
										lowerCAmelCase__      :      Optional[Any]         =		torch.cat(
										    [ordered_inputs['attention_mask'], torch.ones(a    ,     a    ,     dtype=a   )]    ,     dim=1   )

							return ordered_inputs






				@property
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							return 13