





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 					A__       (				_UpperCamelCase      ):




   """simple docstring"""



   __A  :							List[str]			    =    ["image_processor", "tokenizer"]
   __A  :							List[str]			    =    "BridgeTowerImageProcessor"
   __A  :							int			    =    ("RobertaTokenizer", "RobertaTokenizerFast")

   def __init__(   self   ,    lowercase   ,    lowercase)     ->		int:







          '''simple docstring'''

          super().__init__(__a   ,    __a)

   def __call__(   self   ,    lowercase   ,    lowercase = None   ,    lowercase = True   ,    lowercase = False   ,    lowercase = None   ,    lowercase = None   ,    lowercase = 0   ,    lowercase = None   ,    lowercase = None   ,    lowercase = None   ,    lowercase = False   ,    lowercase = False   ,    lowercase = False   ,    lowercase = False   ,    lowercase = True   ,    lowercase = None   ,    **lowercase   ,    )     ->		BatchEncoding:







          '''simple docstring'''

          a__		:      Optional[Any]					=							self.tokenizer(
              text=__a   ,    add_special_tokens=__a   ,    padding=__a   ,    truncation=__a   ,    max_length=__a   ,    stride=__a   ,    pad_to_multiple_of=__a   ,    return_token_type_ids=__a   ,    return_attention_mask=__a   ,    return_overflowing_tokens=__a   ,    return_special_tokens_mask=__a   ,    return_offsets_mapping=__a   ,    return_length=__a   ,    verbose=__a   ,    return_tensors=__a   ,    **__a   ,    )
          # add pixel_values + pixel_mask
          a__		:      int					=							self.image_processor(
              __a   ,    return_tensors=__a   ,    do_normalize=__a   ,    do_center_crop=__a   ,    **__a)
          encoding.update(__a)

          return encoding

   def 				__lowercase						(   self   ,    *lowercase   ,    **lowercase)     ->		Any:







          '''simple docstring'''

          return self.tokenizer.batch_decode(*__a   ,    **__a)

   def 				__lowercase						(   self   ,    *lowercase   ,    **lowercase)     ->		Tuple:







          '''simple docstring'''

          return self.tokenizer.decode(*__a   ,    **__a)

   @property
   def 				__lowercase						(   self)     ->		List[str]:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							self.tokenizer.model_input_names
          a__		:      Dict					=							self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))









"""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))
