





"""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        lowercase__     (       lowercase_		,lowercase_      )       ->		Dict:




      """simple docstring"""

      _UpperCamelCase   :						int    =							BertAbsConfig(
          temp_dir="."		,finetune_bert=lowercase_		,large=lowercase_		,share_emb=lowercase_		,use_bert_emb=lowercase_		,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   :						Union[str, Any]    =							torch.load(lowercase_		,lambda lowercase_		,lowercase_      : storage      )
      _UpperCamelCase   :						List[Any]    =							AbsSummarizer(lowercase_		,torch.device("cpu"      )		,lowercase_      )
      original.eval()

      _UpperCamelCase   :						List[Any]    =							BertAbsSummarizer(lowercase_		,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   :						Tuple    =							BertTokenizer.from_pretrained("bert-base-uncased"      )

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

      _UpperCamelCase   :						Optional[int]    =							new_model(
          lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )[0]
      _UpperCamelCase   :						List[str]    =							new_model.generator(lowercase_      )

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

      _UpperCamelCase   :						List[Any]    =							torch.allclose(lowercase_		,lowercase_		,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))
