





"""simple docstring"""


from __future__ import annotations

from collections.abc import Sequence
from typing import Literal




def        lowercase__     (       lowercase_		,lowercase_      )       ->		str | Literal[False]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							list(lowercase_      )
      _UpperCamelCase   :						Union[str, Any]    =							list(lowercase_      )
      _UpperCamelCase   :						Tuple    =							0
      for i in range(len(lowercase_      )      ):
            if lista[i] != lista[i]:
                  count += 1
                  _UpperCamelCase   :						Optional[int]    =							"_"
      if count > 1:
            return False
      else:
            return "".join(lowercase_      )




def        lowercase__     (       lowercase_      )       ->		list[str]:




      """simple docstring"""

      _UpperCamelCase   :						Dict    =							[]
      while True:
            _UpperCamelCase   :						Optional[int]    =							["$"] * len(lowercase_      )
            _UpperCamelCase   :						List[str]    =							[]
            for i in range(len(lowercase_      )      ):
                  for j in range(i + 1		,len(lowercase_      )      ):
                        _UpperCamelCase   :						Optional[int]    =							compare_string(binary[i]		,binary[j]      )
                        if k is False:
                              _UpperCamelCase   :						int    =							"*"
                              _UpperCamelCase   :						Any    =							"*"
                              temp.append("X"      )
            for i in range(len(lowercase_      )      ):
                  if checka[i] == "$":
                        pi.append(binary[i]      )
            if len(lowercase_      ) == 0:
                  return pi
            _UpperCamelCase   :						int    =							list(set(lowercase_      )      )




def        lowercase__     (       lowercase_		,lowercase_      )       ->		list[str]:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							[]
      for minterm in minterms:
            _UpperCamelCase   :						Union[str, Any]    =							""
            for _ in range(lowercase_      ):
                  _UpperCamelCase   :						Any    =							str(minterm % 2      ) + string
                  minterm //= 2
            temp.append(lowercase_      )
      return temp




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		bool:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							list(lowercase_      )
      _UpperCamelCase   :						Tuple    =							list(lowercase_      )
      _UpperCamelCase   :						List[str]    =							0
      for i in range(len(lowercase_      )      ):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count




def        lowercase__     (       lowercase_		,lowercase_      )       ->		list[str]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							[]
      _UpperCamelCase   :						Dict    =							[0] * len(lowercase_      )
      for i in range(len(chart[0]      )      ):
            _UpperCamelCase   :						Union[str, Any]    =							0
            _UpperCamelCase   :						Optional[Any]    =							-1
            for j in range(len(lowercase_      )      ):
                  if chart[j][i] == 1:
                        count += 1
                        _UpperCamelCase   :						Dict    =							j
            if count == 1:
                  _UpperCamelCase   :						int    =							1
      for i in range(len(lowercase_      )      ):
            if select[i] == 1:
                  for j in range(len(chart[0]      )      ):
                        if chart[i][j] == 1:
                              for k in range(len(lowercase_      )      ):
                                    _UpperCamelCase   :						List[Any]    =							0
                  temp.append(prime_implicants[i]      )
      while True:
            _UpperCamelCase   :						Union[str, Any]    =							0
            _UpperCamelCase   :						List[Any]    =							-1
            _UpperCamelCase   :						Tuple    =							0
            for i in range(len(lowercase_      )      ):
                  _UpperCamelCase   :						str    =							chart[i].count(1      )
                  if count_n > max_n:
                        _UpperCamelCase   :						Any    =							count_n
                        _UpperCamelCase   :						Dict    =							i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]      )

            for i in range(len(chart[0]      )      ):
                  if chart[rem][i] == 1:
                        for j in range(len(lowercase_      )      ):
                              _UpperCamelCase   :						int    =							0




def        lowercase__     (       lowercase_		,lowercase_      )       ->		list[list[int]]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[[0 for x in range(len(lowercase_      )      )] for x in range(len(lowercase_      )      )]
      for i in range(len(lowercase_      )      ):
            _UpperCamelCase   :						Tuple    =							prime_implicants[i].count("_"      )
            for j in range(len(lowercase_      )      ):
                  if is_for_table(prime_implicants[i]		,binary[j]		,lowercase_      ):
                        _UpperCamelCase   :						str    =							1

      return chart




def        lowercase__     (       )       ->		None:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							int(input("Enter the no. of variables\n"      )      )
      _UpperCamelCase   :						List[Any]    =							[
          float(lowercase_      )
          for x in input(
              "Enter the decimal representation of Minterms 'Spaces Separated'\n"      ).split()
      ]
      _UpperCamelCase   :						Dict    =							decimal_to_binary(lowercase_		,lowercase_      )

      _UpperCamelCase   :						List[str]    =							check(lowercase_      )
      print("Prime Implicants are:"      )
      print(lowercase_      )
      _UpperCamelCase   :						Optional[int]    =							prime_implicant_chart(lowercase_		,lowercase_      )

      _UpperCamelCase   :						Dict    =							selection(lowercase_		,lowercase_      )
      print("Essential Prime Implicants are:"      )
      print(lowercase_      )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()







"""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))
