





'''simple docstring'''






import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


A_		:  str		 =						logging.get_logger(__name__)
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''AutoTokenizer'''
   lowerCamelCase__    							=      ['''tokenizer''']

   lowerCamelCase__    							=      {
       '''semantic_prompt''': 1,
       '''coarse_prompt''': 2,
       '''fine_prompt''': 2,
   }

   def __init__(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE=None       ):
    super().__init__(__SCREAMING_SNAKE_CASE       )

    snake_case__     :					int    =  speaker_embeddings

   @classmethod
   def 		__UpperCamelCase						(							cls	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"	,		**__SCREAMING_SNAKE_CASE       ):

    if speaker_embeddings_dict_path is not None:
     snake_case__     :					Dict    =  get_file_from_repo(
         __SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		subfolder=kwargs.pop("""subfolder"""	,		__SCREAMING_SNAKE_CASE       )	,		cache_dir=kwargs.pop("""cache_dir"""	,		__SCREAMING_SNAKE_CASE       )	,		force_download=kwargs.pop("""force_download"""	,		__SCREAMING_SNAKE_CASE       )	,		proxies=kwargs.pop("""proxies"""	,		__SCREAMING_SNAKE_CASE       )	,		resume_download=kwargs.pop("""resume_download"""	,		__SCREAMING_SNAKE_CASE       )	,		local_files_only=kwargs.pop("""local_files_only"""	,		__SCREAMING_SNAKE_CASE       )	,		use_auth_token=kwargs.pop("""use_auth_token"""	,		__SCREAMING_SNAKE_CASE       )	,		revision=kwargs.pop("""revision"""	,		__SCREAMING_SNAKE_CASE       )	,		)
     if speaker_embeddings_path is None:
      logger.warning(
          f"`{os.path.join(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."       )
      snake_case__     :					str    =  None
     else:
      with open(__SCREAMING_SNAKE_CASE       ) as speaker_embeddings_json:
       snake_case__     :					Optional[int]    =  json.load(__SCREAMING_SNAKE_CASE       )
    else:
     snake_case__     :					Tuple    =  None

    snake_case__     :					List[str]    =  AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

    return cls(tokenizer=__SCREAMING_SNAKE_CASE	,		speaker_embeddings=__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"	,		__SCREAMING_SNAKE_CASE="speaker_embeddings"	,		__SCREAMING_SNAKE_CASE = False	,		**__SCREAMING_SNAKE_CASE	,		):
    if self.speaker_embeddings is not None:
     os.makedirs(os.path.join(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		"""v2"""       )	,		exist_ok=__SCREAMING_SNAKE_CASE       )

     snake_case__     :					str    =  {}

     snake_case__     :					Tuple    =  save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       snake_case__     :					Dict    =  self._load_voice_preset(__SCREAMING_SNAKE_CASE       )

       snake_case__     :					Optional[Any]    =  {}
       for key in self.speaker_embeddings[prompt_key]:
        np.save(
            os.path.join(
                embeddings_dict["""repo_or_path"""]	,		__SCREAMING_SNAKE_CASE	,		f"{prompt_key}_{key}"       )	,		voice_preset[key]	,		allow_pickle=__SCREAMING_SNAKE_CASE	,		)
        snake_case__     :					Optional[Any]    =  os.path.join(__SCREAMING_SNAKE_CASE	,		f"{prompt_key}_{key}.npy"       )

       snake_case__     :					Any    =  tmp_dict

     with open(os.path.join(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )	,		"""w"""       ) as fp:
      json.dump(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

    super().save_pretrained(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE = None	,		**__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Dict    =  self.speaker_embeddings[voice_preset]

    snake_case__     :					str    =  {}
    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset_paths:
      raise ValueError(
          f"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."       )

     snake_case__     :					Tuple    =  get_file_from_repo(
         self.speaker_embeddings.get("""repo_or_path"""	,		"""/"""       )	,		voice_preset_paths[key]	,		subfolder=kwargs.pop("""subfolder"""	,		__SCREAMING_SNAKE_CASE       )	,		cache_dir=kwargs.pop("""cache_dir"""	,		__SCREAMING_SNAKE_CASE       )	,		force_download=kwargs.pop("""force_download"""	,		__SCREAMING_SNAKE_CASE       )	,		proxies=kwargs.pop("""proxies"""	,		__SCREAMING_SNAKE_CASE       )	,		resume_download=kwargs.pop("""resume_download"""	,		__SCREAMING_SNAKE_CASE       )	,		local_files_only=kwargs.pop("""local_files_only"""	,		__SCREAMING_SNAKE_CASE       )	,		use_auth_token=kwargs.pop("""use_auth_token"""	,		__SCREAMING_SNAKE_CASE       )	,		revision=kwargs.pop("""revision"""	,		__SCREAMING_SNAKE_CASE       )	,		)
     if path is None:
      raise ValueError(
          f"`{os.path.join(self.speaker_embeddings.get('repo_or_path'	,		'/'       )	,		voice_preset_paths[key]       )}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."       )

     snake_case__     :					Dict    =  np.load(__SCREAMING_SNAKE_CASE       )

    return voice_preset_dict

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE = None       ):
    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset:
      raise ValueError(f"Voice preset unrecognized, missing {key} as a key."       )

     if not isinstance(voice_preset[key]	,		np.ndarray       ):
      raise ValueError(f"{key} voice preset must be a {str(self.preset_shape[key]       )}D ndarray."       )

     if len(voice_preset[key].shape       ) != self.preset_shape[key]:
      raise ValueError(f"{key} voice preset must be a {str(self.preset_shape[key]       )}D ndarray."       )


   def __call__(							self	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE="pt"	,		__SCREAMING_SNAKE_CASE=2_5_6	,		__SCREAMING_SNAKE_CASE=False	,		__SCREAMING_SNAKE_CASE=True	,		__SCREAMING_SNAKE_CASE=False	,		**__SCREAMING_SNAKE_CASE	,		):
    if voice_preset is not None and not isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
     if (
         isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )
         and self.speaker_embeddings is not None
         and voice_preset in self.speaker_embeddings
     ):
      snake_case__     :					List[Any]    =  self._load_voice_preset(__SCREAMING_SNAKE_CASE       )

     else:
      if isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ) and not voice_preset.endswith(""".npz"""       ):
       snake_case__     :					Optional[Any]    =  voice_preset + """.npz"""

      snake_case__     :					Any    =  np.load(__SCREAMING_SNAKE_CASE       )

    if voice_preset is not None:
     self._validate_voice_preset_dict(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
     snake_case__     :					Tuple    =  BatchFeature(data=__SCREAMING_SNAKE_CASE	,		tensor_type=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Tuple    =  self.tokenizer(
        __SCREAMING_SNAKE_CASE	,		return_tensors=__SCREAMING_SNAKE_CASE	,		padding="""max_length"""	,		max_length=__SCREAMING_SNAKE_CASE	,		return_attention_mask=__SCREAMING_SNAKE_CASE	,		return_token_type_ids=__SCREAMING_SNAKE_CASE	,		add_special_tokens=__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE	,		)

    if voice_preset is not None:
     snake_case__     :					Any    =  voice_preset

    return encoded_text







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_      =     logging.getLogger(__name__)


a_      =     'Hello world! cécé herlolip'


a_      =     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				,      _a):
	SCREAMING_SNAKE_CASE :   List[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=2048				,      dec_dropout=0.2				,      )
	SCREAMING_SNAKE_CASE :   Dict					  =							torch.load(_a				,      lambda _a				,      _a: storage)
	SCREAMING_SNAKE_CASE :   str					  =							AbsSummarizer(_a				,      torch.device("cpu")				,      _a)
	original.eval()

	SCREAMING_SNAKE_CASE :   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")
	SCREAMING_SNAKE_CASE :   List[str]					  =							BertTokenizer.from_pretrained("bert-base-uncased")

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

	SCREAMING_SNAKE_CASE :   Any					  =							new_model(
	    _a				,      _a				,      _a				,      _a				,      _a)[0]
	SCREAMING_SNAKE_CASE :   Tuple					  =							new_model.generator(_a)

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

	SCREAMING_SNAKE_CASE :   int					  =							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__":
			a_      =     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_      =     parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)