import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  s.rsplit(lowerCamelCase_     ,      lowerCamelCase_				)
      return new.join(lowerCamelCase_				)


def      a__ (		__UpperCamelCase				):
      return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()				)


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  {}

      SCREAMING_SNAKE_CASE_          =  ["""group_1""", """group_2""", """group_3""", """group_4"""]
      for key, value in state_dict.items():
            for group_key in group_keys:
                  if group_key in key:
                        SCREAMING_SNAKE_CASE_          =  key.replace(F'''{group_key}.'''     ,      F'''{group_key}.group.'''				)

            if "res_path" in key:
                  SCREAMING_SNAKE_CASE_          =  key.replace("res_path."     ,      "res_path.path."				)

            if key.endswith(".w"				):
                  SCREAMING_SNAKE_CASE_          =  rreplace(lowerCamelCase_     ,      ".w"     ,      ".weight"     ,      1				)
            if key.endswith(".b"				):
                  SCREAMING_SNAKE_CASE_          =  rreplace(lowerCamelCase_     ,      ".b"     ,      ".bias"     ,      1				)

            SCREAMING_SNAKE_CASE_          =  value.float()

      return upgrade


@torch.no_grad()
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase=None     ,      __UpperCamelCase=True				):
      from dall_e import Encoder

      SCREAMING_SNAKE_CASE_          =  Encoder()
      if os.path.exists(lowerCamelCase_				):
            SCREAMING_SNAKE_CASE_          =  torch.load(lowerCamelCase_				)
      else:
            SCREAMING_SNAKE_CASE_          =  torch.hub.load_state_dict_from_url(lowerCamelCase_				)

      if isinstance(lowerCamelCase_     ,      lowerCamelCase_				):
            SCREAMING_SNAKE_CASE_          =  ckpt.state_dict()
      encoder.load_state_dict(lowerCamelCase_				)

      if config_path is not None:
            SCREAMING_SNAKE_CASE_          =  FlavaImageCodebookConfig.from_pretrained(lowerCamelCase_				)
      else:
            SCREAMING_SNAKE_CASE_          =  FlavaImageCodebookConfig()

      SCREAMING_SNAKE_CASE_          =  FlavaImageCodebook(lowerCamelCase_				).eval()
      SCREAMING_SNAKE_CASE_          =  encoder.state_dict()

      SCREAMING_SNAKE_CASE_          =  upgrade_state_dict(lowerCamelCase_				)
      hf_model.load_state_dict(lowerCamelCase_				)
      SCREAMING_SNAKE_CASE_          =  hf_model.state_dict()
      SCREAMING_SNAKE_CASE_          =  count_parameters(lowerCamelCase_				)
      SCREAMING_SNAKE_CASE_          =  count_parameters(lowerCamelCase_				)

      assert torch.allclose(lowerCamelCase_     ,      lowerCamelCase_     ,      atol=1E-3				)

      if save_checkpoint:
            hf_model.save_pretrained(lowerCamelCase_				)
      else:
            return hf_state_dict


if __name__ == "__main__":
      A     :  Optional[Any]					     =       argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      A     :  Tuple					     =       parser.parse_args()

      convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

A     :  Dict					     =       TypeVar("T")


class   lowerCamelCase					(Generic[T]					):
  """simple docstring"""







  def __init__(							self :       Optional[int]  ,     __magic_name__ :       bool = True   )  ->	None:
        SCREAMING_SNAKE_CASE_          =  {}  # dictionary of lists
        SCREAMING_SNAKE_CASE_          =  directed







  def    __A     (							self :       Dict  ,     __magic_name__ :       T  ,     __magic_name__ :       T   )  ->	GraphAdjacencyList[T]:

        if not self.directed:  # For undirected graphs
              # if both source vertex and destination vertex are both present in the
              # adjacency list, add destination vertex to source vertex list of adjacent
              # vertices and add source vertex to destination vertex list of adjacent
              # vertices.
              if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    self.adj_list[destination_vertex].append(__magic_name__   )
              # if only source vertex is present in adjacency list, add destination vertex
              # to source vertex list of adjacent vertices, then create a new vertex with
              # destination vertex as key and assign a list containing the source vertex
              # as it's first adjacent vertex.
              elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  [source_vertex]
              # if only destination vertex is present in adjacency list, add source vertex
              # to destination vertex list of adjacent vertices, then create a new vertex
              # with source vertex as key and assign a list containing the source vertex
              # as it's first adjacent vertex.
              elif destination_vertex in self.adj_list:
                    self.adj_list[destination_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
              # if both source vertex and destination vertex are not present in adjacency
              # list, create a new vertex with source vertex as key and assign a list
              # containing the destination vertex as it's first adjacent vertex also
              # create a new vertex with destination vertex as key and assign a list
              # containing the source vertex as it's first adjacent vertex.
              else:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
                    SCREAMING_SNAKE_CASE_          =  [source_vertex]
        else:  # For directed graphs
              # if both source vertex and destination vertex are present in adjacency
              # list, add destination vertex to source vertex list of adjacent vertices.
              if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
              # if only source vertex is present in adjacency list, add destination
              # vertex to source vertex list of adjacent vertices and create a new vertex
              # with destination vertex as key, which has no adjacent vertex
              elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  []
              # if only destination vertex is present in adjacency list, create a new
              # vertex with source vertex as key and assign a list containing destination
              # vertex as first adjacent vertex
              elif destination_vertex in self.adj_list:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
              # if both source vertex and destination vertex are not present in adjacency
              # list, create a new vertex with source vertex as key and a list containing
              # destination vertex as it's first adjacent vertex. Then create a new vertex
              # with destination vertex as key, which has no adjacent vertex
              else:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
                    SCREAMING_SNAKE_CASE_          =  []

        return self







  def __repr__(							self :       int   )  ->	str:
        return pformat(self.adj_list   )


