

'''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)
lowerCAmelCase__      		=			logging.getLogger(__name__)


lowerCAmelCase__      		=			'''Hello world! cécé herlolip'''


lowerCAmelCase__      		=			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  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				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  ,       )
 __lowercase													=				torch.load(A__  ,       lambda A__  ,       A__   : storage   )
 __lowercase													=				AbsSummarizer(A__  ,       torch.device('''cpu'''   )  ,       A__   )
 original.eval()

 __lowercase													=				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'''   )
 __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] * (512 - len(A__   ))   )
 __lowercase													=				torch.tensor(A__   ).unsqueeze(0   )
 __lowercase													=				tokenizer.encode('''This is sample 3 éàalj\'-.'''   )
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(A__   ))   )
 __lowercase													=				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
 __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(A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__   )[0]
 __lowercase													=				original.generator(A__   )

 __lowercase													=				new_model(
     A__  ,       A__  ,       A__  ,       A__  ,       A__   )[0]
 __lowercase													=				new_model.generator(A__   )

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

 __lowercase													=				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__":
 lowerCAmelCase__      		=			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.''',
 )
 lowerCAmelCase__      		=			parser.parse_args()

 convert_bertabs_checkpoints(
     args.bertabs_checkpoint_path,
     args.pytorch_dump_folder_path,
 )



'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase__      		=			False
lowerCAmelCase__      		=			True
lowerCAmelCase__      		=			False


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()

 parser.add_argument(
     '''--repo_path''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 lowerCAmelCase__      		=			parser.parse_args()

 lowerCAmelCase__      		=			{
     '''image_size''': '''sample_size''',
     '''num_res_blocks''': '''layers_per_block''',
     '''block_channels''': '''block_out_channels''',
     '''down_blocks''': '''down_block_types''',
     '''up_blocks''': '''up_block_types''',
     '''downscale_freq_shift''': '''freq_shift''',
     '''resnet_num_groups''': '''norm_num_groups''',
     '''resnet_act_fn''': '''act_fn''',
     '''resnet_eps''': '''norm_eps''',
     '''num_head_channels''': '''attention_head_dim''',
 }

 lowerCAmelCase__      		=			{
     '''time_steps''': '''time_proj''',
     '''mid''': '''mid_block''',
     '''downsample_blocks''': '''down_blocks''',
     '''upsample_blocks''': '''up_blocks''',
 }

 lowerCAmelCase__      		=			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

 with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
  lowerCAmelCase__      		=			reader.read()
  lowerCAmelCase__      		=			json.loads(text)

 if do_only_config:
  for key in config_parameters_to_change.keys():
   config.pop(key, None)

 if has_file(args.repo_path, '''config.json'''):
  lowerCAmelCase__      		=			UNetaDModel(**config)
 else:
  lowerCAmelCase__      		=			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
  lowerCAmelCase__      		=			class_name(**config)

 if do_only_config:
  model.save_config(os.path.join(args.repo_path, subfolder))

 lowerCAmelCase__      		=			dict(model.config)

 if do_only_renaming:
  for key, value in config_parameters_to_change.items():
   if key in config:
    lowerCAmelCase__      		=			config[key]
    del config[key]

  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

 if do_only_weights:
  lowerCAmelCase__      		=			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

  lowerCAmelCase__      		=			{}
  for param_key, param_value in state_dict.items():
   if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
    continue
   lowerCAmelCase__      		=			False
   for key, new_key in key_parameters_to_change.items():
    if not has_changed and param_key.split('''.''')[0] == key:
     lowerCAmelCase__      		=			param_value
     lowerCAmelCase__      		=			True
   if not has_changed:
    lowerCAmelCase__      		=			param_value

  model.load_state_dict(new_state_dict)
  model.save_pretrained(os.path.join(args.repo_path, subfolder))

