




import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: int=1						):




 """simple docstring"""







 if n_shave_prefix_segments >= 0:
  return ".".join(path.split("."						)[n_shave_prefix_segments:]						)
 else:
  return ".".join(path.split("."						)[:n_shave_prefix_segments]						)



def     lowerCAmelCase		(		_lowerCAmelCase				: Tuple    ,				_lowerCAmelCase				: Optional[Any]=0						):




 """simple docstring"""







 UpperCAmelCase__							=    []
 for old_item in old_list:
  UpperCAmelCase__							=    old_item
  UpperCAmelCase__							=    new_item.replace("block."    ,				"resnets."						)
  UpperCAmelCase__							=    new_item.replace("conv_shorcut"    ,				"conv1"						)
  UpperCAmelCase__							=    new_item.replace("in_shortcut"    ,				"conv_shortcut"						)
  UpperCAmelCase__							=    new_item.replace("temb_proj"    ,				"time_emb_proj"						)

  UpperCAmelCase__							=    shave_segments(SCREAMING_SNAKE_CASE_    ,				n_shave_prefix_segments=SCREAMING_SNAKE_CASE_						)

  mapping.append({"old": old_item, "new": new_item}						)

 return mapping



def     lowerCAmelCase		(		_lowerCAmelCase				: List[Any]    ,				_lowerCAmelCase				: Dict=0    ,				_lowerCAmelCase				: Dict=False						):




 """simple docstring"""







 UpperCAmelCase__							=    []
 for old_item in old_list:
  UpperCAmelCase__							=    old_item

  # In `model.mid`, the layer is called `attn`.
  if not in_mid:
   UpperCAmelCase__							=    new_item.replace("attn"    ,				"attentions"						)
  UpperCAmelCase__							=    new_item.replace(".k."    ,				".key."						)
  UpperCAmelCase__							=    new_item.replace(".v."    ,				".value."						)
  UpperCAmelCase__							=    new_item.replace(".q."    ,				".query."						)

  UpperCAmelCase__							=    new_item.replace("proj_out"    ,				"proj_attn"						)
  UpperCAmelCase__							=    new_item.replace("norm"    ,				"group_norm"						)

  UpperCAmelCase__							=    shave_segments(SCREAMING_SNAKE_CASE_    ,				n_shave_prefix_segments=SCREAMING_SNAKE_CASE_						)
  mapping.append({"old": old_item, "new": new_item}						)

 return mapping



def     lowerCAmelCase		(		_lowerCAmelCase				: Union[str, Any]    ,				_lowerCAmelCase				: Optional[int]    ,				_lowerCAmelCase				: Dict    ,				_lowerCAmelCase				: Tuple=None    ,				_lowerCAmelCase				: Optional[int]=None    ,				_lowerCAmelCase				: Union[str, Any]=None						):




 """simple docstring"""







 assert isinstance(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_						), "Paths should be a list of dicts containing 'old' and 'new' keys."

 if attention_paths_to_split is not None:
  if config is None:
   raise ValueError("Please specify the config if setting \'attention_paths_to_split\' to \'True\'."						)

  for path, path_map in attention_paths_to_split.items():
   UpperCAmelCase__							=    old_checkpoint[path]
   UpperCAmelCase__							=    old_tensor.shape[0] // 3

   UpperCAmelCase__							=    (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

   UpperCAmelCase__							=    old_tensor.shape[0] // config.get("num_head_channels"    ,				1						) // 3

   UpperCAmelCase__							=    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
   UpperCAmelCase__							=    old_tensor.split(channels // num_heads    ,				dim=1						)

   UpperCAmelCase__							=    query.reshape(SCREAMING_SNAKE_CASE_						).squeeze()
   UpperCAmelCase__							=    key.reshape(SCREAMING_SNAKE_CASE_						).squeeze()
   UpperCAmelCase__							=    value.reshape(SCREAMING_SNAKE_CASE_						).squeeze()

 for path in paths:
  UpperCAmelCase__							=    path['''new''']

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  UpperCAmelCase__							=    new_path.replace("down."    ,				"down_blocks."						)
  UpperCAmelCase__							=    new_path.replace("up."    ,				"up_blocks."						)

  if additional_replacements is not None:
   for replacement in additional_replacements:
    UpperCAmelCase__							=    new_path.replace(replacement["old"]    ,				replacement["new"]						)

  if "attentions" in new_path:
   UpperCAmelCase__							=    old_checkpoint[path['''old''']].squeeze()
  else:
   UpperCAmelCase__							=    old_checkpoint[path['''old''']]



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[Any]    ,				_lowerCAmelCase				: Optional[int]						):




 """simple docstring"""







 UpperCAmelCase__							=    {}

 UpperCAmelCase__							=    checkpoint['''temb.dense.0.weight''']
 UpperCAmelCase__							=    checkpoint['''temb.dense.0.bias''']
 UpperCAmelCase__							=    checkpoint['''temb.dense.1.weight''']
 UpperCAmelCase__							=    checkpoint['''temb.dense.1.bias''']

 UpperCAmelCase__							=    checkpoint['''norm_out.weight''']
 UpperCAmelCase__							=    checkpoint['''norm_out.bias''']

 UpperCAmelCase__							=    checkpoint['''conv_in.weight''']
 UpperCAmelCase__							=    checkpoint['''conv_in.bias''']
 UpperCAmelCase__							=    checkpoint['''conv_out.weight''']
 UpperCAmelCase__							=    checkpoint['''conv_out.bias''']

 UpperCAmelCase__							=    len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "down" in layer}						)
 UpperCAmelCase__							=    {
     layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_						)
 }

 UpperCAmelCase__							=    len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "up" in layer}						)
 UpperCAmelCase__							=    {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_						)}

 for i in range(SCREAMING_SNAKE_CASE_						):
  UpperCAmelCase__							=    (i - 1) // (config['''layers_per_block'''] + 1)

  if any("downsample" in layer for layer in down_blocks[i]						):
   UpperCAmelCase__							=    checkpoint[
       F'''down.{i}.downsample.op.weight'''
   ]
   UpperCAmelCase__							=    checkpoint[F'''down.{i}.downsample.op.bias''']
  #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
  #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

  if any("block" in layer for layer in down_blocks[i]						):
   UpperCAmelCase__							=    len(
       {".".join(shave_segments(SCREAMING_SNAKE_CASE_    ,				2						).split("."						)[:2]						) for layer in down_blocks[i] if "block" in layer}						)
   UpperCAmelCase__							=    {
       layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE_						)
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"]						):
     UpperCAmelCase__							=    renew_resnet_paths(blocks[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_						)

  if any("attn" in layer for layer in down_blocks[i]						):
   UpperCAmelCase__							=    len(
       {".".join(shave_segments(SCREAMING_SNAKE_CASE_    ,				2						).split("."						)[:2]						) for layer in down_blocks[i] if "attn" in layer}						)
   UpperCAmelCase__							=    {
       layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE_						)
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"]						):
     UpperCAmelCase__							=    renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				config=SCREAMING_SNAKE_CASE_						)

 UpperCAmelCase__							=    [key for key in checkpoint if '''mid.block_1''' in key]
 UpperCAmelCase__							=    [key for key in checkpoint if '''mid.block_2''' in key]
 UpperCAmelCase__							=    [key for key in checkpoint if '''mid.attn_1''' in key]

 # Mid new 2
 UpperCAmelCase__							=    renew_resnet_paths(SCREAMING_SNAKE_CASE_						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]    ,				)

 UpperCAmelCase__							=    renew_resnet_paths(SCREAMING_SNAKE_CASE_						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]    ,				)

 UpperCAmelCase__							=    renew_attention_paths(SCREAMING_SNAKE_CASE_    ,				in_mid=SCREAMING_SNAKE_CASE_						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]    ,				)

 for i in range(SCREAMING_SNAKE_CASE_						):
  UpperCAmelCase__							=    num_up_blocks - 1 - i

  if any("upsample" in layer for layer in up_blocks[i]						):
   UpperCAmelCase__							=    checkpoint[
       F'''up.{i}.upsample.conv.weight'''
   ]
   UpperCAmelCase__							=    checkpoint[F'''up.{i}.upsample.conv.bias''']

  if any("block" in layer for layer in up_blocks[i]						):
   UpperCAmelCase__							=    len(
       {".".join(shave_segments(SCREAMING_SNAKE_CASE_    ,				2						).split("."						)[:2]						) for layer in up_blocks[i] if "block" in layer}						)
   UpperCAmelCase__							=    {
       layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_						)
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"] + 1						):
     UpperCAmelCase__							=    {'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
     UpperCAmelCase__							=    renew_resnet_paths(blocks[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				additional_replacements=[replace_indices]						)

  if any("attn" in layer for layer in up_blocks[i]						):
   UpperCAmelCase__							=    len(
       {".".join(shave_segments(SCREAMING_SNAKE_CASE_    ,				2						).split("."						)[:2]						) for layer in up_blocks[i] if "attn" in layer}						)
   UpperCAmelCase__							=    {
       layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_						)
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"] + 1						):
     UpperCAmelCase__							=    {'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
     UpperCAmelCase__							=    renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				additional_replacements=[replace_indices]						)

 UpperCAmelCase__							=    {k.replace("mid_new_2"    ,				"mid_block"						): v for k, v in new_checkpoint.items()}
 return new_checkpoint



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: Tuple						):




 """simple docstring"""







 UpperCAmelCase__							=    {}

 UpperCAmelCase__							=    checkpoint['''encoder.norm_out.weight''']
 UpperCAmelCase__							=    checkpoint['''encoder.norm_out.bias''']

 UpperCAmelCase__							=    checkpoint['''encoder.conv_in.weight''']
 UpperCAmelCase__							=    checkpoint['''encoder.conv_in.bias''']
 UpperCAmelCase__							=    checkpoint['''encoder.conv_out.weight''']
 UpperCAmelCase__							=    checkpoint['''encoder.conv_out.bias''']

 UpperCAmelCase__							=    checkpoint['''decoder.norm_out.weight''']
 UpperCAmelCase__							=    checkpoint['''decoder.norm_out.bias''']

 UpperCAmelCase__							=    checkpoint['''decoder.conv_in.weight''']
 UpperCAmelCase__							=    checkpoint['''decoder.conv_in.bias''']
 UpperCAmelCase__							=    checkpoint['''decoder.conv_out.weight''']
 UpperCAmelCase__							=    checkpoint['''decoder.conv_out.bias''']

 UpperCAmelCase__							=    len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "down" in layer}						)
 UpperCAmelCase__							=    {
     layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_						)
 }

 UpperCAmelCase__							=    len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "up" in layer}						)
 UpperCAmelCase__							=    {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_						)}

 for i in range(SCREAMING_SNAKE_CASE_						):
  UpperCAmelCase__							=    (i - 1) // (config['''layers_per_block'''] + 1)

  if any("downsample" in layer for layer in down_blocks[i]						):
   UpperCAmelCase__							=    checkpoint[
       F'''encoder.down.{i}.downsample.conv.weight'''
   ]
   UpperCAmelCase__							=    checkpoint[
       F'''encoder.down.{i}.downsample.conv.bias'''
   ]

  if any("block" in layer for layer in down_blocks[i]						):
   UpperCAmelCase__							=    len(
       {".".join(shave_segments(SCREAMING_SNAKE_CASE_    ,				3						).split("."						)[:3]						) for layer in down_blocks[i] if "block" in layer}						)
   UpperCAmelCase__							=    {
       layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE_						)
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"]						):
     UpperCAmelCase__							=    renew_resnet_paths(blocks[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_						)

  if any("attn" in layer for layer in down_blocks[i]						):
   UpperCAmelCase__							=    len(
       {".".join(shave_segments(SCREAMING_SNAKE_CASE_    ,				3						).split("."						)[:3]						) for layer in down_blocks[i] if "attn" in layer}						)
   UpperCAmelCase__							=    {
       layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE_						)
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"]						):
     UpperCAmelCase__							=    renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				config=SCREAMING_SNAKE_CASE_						)

 UpperCAmelCase__							=    [key for key in checkpoint if '''mid.block_1''' in key]
 UpperCAmelCase__							=    [key for key in checkpoint if '''mid.block_2''' in key]
 UpperCAmelCase__							=    [key for key in checkpoint if '''mid.attn_1''' in key]

 # Mid new 2
 UpperCAmelCase__							=    renew_resnet_paths(SCREAMING_SNAKE_CASE_						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]    ,				)

 UpperCAmelCase__							=    renew_resnet_paths(SCREAMING_SNAKE_CASE_						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]    ,				)

 UpperCAmelCase__							=    renew_attention_paths(SCREAMING_SNAKE_CASE_    ,				in_mid=SCREAMING_SNAKE_CASE_						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]    ,				)

 for i in range(SCREAMING_SNAKE_CASE_						):
  UpperCAmelCase__							=    num_up_blocks - 1 - i

  if any("upsample" in layer for layer in up_blocks[i]						):
   UpperCAmelCase__							=    checkpoint[
       F'''decoder.up.{i}.upsample.conv.weight'''
   ]
   UpperCAmelCase__							=    checkpoint[
       F'''decoder.up.{i}.upsample.conv.bias'''
   ]

  if any("block" in layer for layer in up_blocks[i]						):
   UpperCAmelCase__							=    len(
       {".".join(shave_segments(SCREAMING_SNAKE_CASE_    ,				3						).split("."						)[:3]						) for layer in up_blocks[i] if "block" in layer}						)
   UpperCAmelCase__							=    {
       layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_						)
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"] + 1						):
     UpperCAmelCase__							=    {'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
     UpperCAmelCase__							=    renew_resnet_paths(blocks[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				additional_replacements=[replace_indices]						)

  if any("attn" in layer for layer in up_blocks[i]						):
   UpperCAmelCase__							=    len(
       {".".join(shave_segments(SCREAMING_SNAKE_CASE_    ,				3						).split("."						)[:3]						) for layer in up_blocks[i] if "attn" in layer}						)
   UpperCAmelCase__							=    {
       layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_						)
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"] + 1						):
     UpperCAmelCase__							=    {'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
     UpperCAmelCase__							=    renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				additional_replacements=[replace_indices]						)

 UpperCAmelCase__							=    {k.replace("mid_new_2"    ,				"mid_block"						): v for k, v in new_checkpoint.items()}
 UpperCAmelCase__							=    checkpoint['''quant_conv.weight''']
 UpperCAmelCase__							=    checkpoint['''quant_conv.bias''']
 if "quantize.embedding.weight" in checkpoint:
  UpperCAmelCase__							=    checkpoint['''quantize.embedding.weight''']
 UpperCAmelCase__							=    checkpoint['''post_quant_conv.weight''']
 UpperCAmelCase__							=    checkpoint['''post_quant_conv.bias''']

 return new_checkpoint


if __name__ == "__main__":
  _lowerCAmelCase    : Union[str, Any]		= argparse.ArgumentParser()

  parser.add_argument(
      "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
  )

  parser.add_argument(
      "--config_file",
      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    : List[Any]		= parser.parse_args()
  _lowerCAmelCase    : Optional[Any]		= torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    _lowerCAmelCase    : Any		= json.loads(f.read())

  # unet case
  _lowerCAmelCase    : str		= {key.split(".")[0] for key in checkpoint.keys()}
  if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    _lowerCAmelCase    : Optional[Any]		= convert_vq_autoenc_checkpoint(checkpoint, config)
  else:
    _lowerCAmelCase    : List[Any]		= convert_ddpm_checkpoint(checkpoint, config)

  if "ddpm" in config:
    del config["ddpm"]

  if config["_class_name"] == "VQModel":
    _lowerCAmelCase    : Union[str, Any]		= VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  elif config["_class_name"] == "AutoencoderKL":
    _lowerCAmelCase    : List[str]		= AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  else:
    _lowerCAmelCase    : List[Any]		= UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    _lowerCAmelCase    : List[str]		= DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

    _lowerCAmelCase    : Optional[Any]		= DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)












import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		# Initialise PyTorch model
		lowerCamelCase :			Dict    		=FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		print(F"Building PyTorch model from configuration: {config}"		)
		lowerCamelCase :			Tuple    		=FNetForPreTraining(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[Any]    		=restore_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[str]    		=checkpoint_dict['''target''']

		# Embeddings
		# Position IDs
		lowerCamelCase :			Optional[Any]    		=fnet_pretraining_model.state_dict()

		lowerCamelCase :			Tuple    		=state_dict['''fnet.embeddings.position_ids''']
		lowerCamelCase :			str    		={'''fnet.embeddings.position_ids''': position_ids}
		# Embedding Layers
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']		).T
		lowerCamelCase :			List[Any]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']		)
		lowerCamelCase :			List[str]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']		)

		# Encoder Layers
		for layer in range(config.num_hidden_layers		):
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''scale''']		)
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''kernel''']		).T
				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''bias''']		)

				lowerCamelCase :			str    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''kernel''']		).T
				lowerCamelCase :			Tuple    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''scale''']		)
				lowerCamelCase :			Optional[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''bias''']		)

		# Pooler Layers
		lowerCamelCase :			Any    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']		)

		# Masked LM Layers
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''bias''']		)
		lowerCamelCase :			Optional[Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''scale''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''bias''']		)
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_output''']['''output_bias''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']		)

		# Seq Relationship Layers
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_kernel''']		)
		lowerCamelCase :			Any    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_bias''']		)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE_		)

		# Save PreTrained
		print(F"Saving pretrained model to {save_path}"		)
		fnet_pretraining_model.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				snake_case_				=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--fnet_config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained FNet model. \n'''
				        '''This specifies the model architecture.'''
				    ),
				)
				parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
				snake_case_				=      parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

