'''simple docstring'''

import argparse

import torch

from diffusers import UNetaDConditionModel



def lowerCamelCase__						(       A_       ,			A_       ,			A_       ,			A_=None       ,			A_=None       ,			A_=None       ):
 assert isinstance(A_       ,			A_       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

 # Splits the attention layers into three variables.
 if attention_paths_to_split is not None:
  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["num_head_channels"] // 3

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

   UpperCAmelCase_				       =					query.reshape(A_       )
   UpperCAmelCase_				       =					key.reshape(A_       )
   UpperCAmelCase_				       =					value.reshape(A_       )

 for path in paths:
  UpperCAmelCase_				       =					path["new"]

  # These have already been assigned
  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

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

        # proj_attn.weight has to be converted from conv 1D to linear
  UpperCAmelCase_				       =					old_checkpoint[path["old"]]
  UpperCAmelCase_				       =					["proj_attn.weight"]
  UpperCAmelCase_				       =					["proj_out.weight", "proj_in.weight"]
  if any(k in new_path for k in names       ):
   UpperCAmelCase_				       =					weight[:, :, 0]
  elif any(k in new_path for k in names_a       ) and len(weight.shape       ) > 2 and ".attentions." not in new_path:
   UpperCAmelCase_				       =					weight[:, :, 0]
  else:
   UpperCAmelCase_				       =					weight



def lowerCamelCase__						(       A_       ,			A_=0       ):
 UpperCAmelCase_				       =					[]
 for old_item in old_list:
  UpperCAmelCase_				       =					old_item

  #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
  #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

  #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
  #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

  #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

 return mapping



def lowerCamelCase__						(       A_       ,			A_=1       ):
 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__						(       A_       ,			A_=0       ):
 UpperCAmelCase_				       =					[]
 for old_item in old_list:
  mapping.append({"old": old_item, "new": old_item}       )

 return mapping



def lowerCamelCase__						(       A_       ,			A_=0       ):
 UpperCAmelCase_				       =					[]
 for old_item in old_list:
  UpperCAmelCase_				       =					old_item.replace("in_layers.0"       ,			"norm1"       )
  UpperCAmelCase_				       =					new_item.replace("in_layers.2"       ,			"conv1"       )

  UpperCAmelCase_				       =					new_item.replace("out_layers.0"       ,			"norm2"       )
  UpperCAmelCase_				       =					new_item.replace("out_layers.3"       ,			"conv2"       )

  UpperCAmelCase_				       =					new_item.replace("emb_layers.1"       ,			"time_emb_proj"       )
  UpperCAmelCase_				       =					new_item.replace("skip_connection"       ,			"conv_shortcut"       )

  UpperCAmelCase_				       =					shave_segments(A_       ,			n_shave_prefix_segments=A_       )

  if "temopral_conv" not in old_item:
   mapping.append({"old": old_item, "new": new_item}       )

 return mapping



def lowerCamelCase__						(       A_       ,			A_       ,			A_=None       ,			A_=False       ):
 UpperCAmelCase_				       =					{}
 UpperCAmelCase_				       =					list(checkpoint.keys()       )

 UpperCAmelCase_				       =					"model.diffusion_model."

 # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
 if sum(k.startswith("model_ema"       ) for k in keys       ) > 100 and extract_ema:
  print(F"""Checkpoint {path} has both EMA and non-EMA weights."""       )
  print(
      "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
      " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."       )
  for key in keys:
   if key.startswith("model.diffusion_model"       ):
    UpperCAmelCase_				       =					"model_ema." + "".join(key.split("."       )[1:]       )
    UpperCAmelCase_				       =					checkpoint.pop(A_       )
 else:
  if sum(k.startswith("model_ema"       ) for k in keys       ) > 100:
   print(
       "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
       " weights (usually better for inference), please make sure to add the `--extract_ema` flag."       )

  for key in keys:
   UpperCAmelCase_				       =					checkpoint.pop(A_       )

 UpperCAmelCase_				       =					{}

 UpperCAmelCase_				       =					unet_state_dict["time_embed.0.weight"]
 UpperCAmelCase_				       =					unet_state_dict["time_embed.0.bias"]
 UpperCAmelCase_				       =					unet_state_dict["time_embed.2.weight"]
 UpperCAmelCase_				       =					unet_state_dict["time_embed.2.bias"]

 if config["class_embed_type"] is None:
  # No parameters to port
  ...
 elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
  UpperCAmelCase_				       =					unet_state_dict["label_emb.0.0.weight"]
  UpperCAmelCase_				       =					unet_state_dict["label_emb.0.0.bias"]
  UpperCAmelCase_				       =					unet_state_dict["label_emb.0.2.weight"]
  UpperCAmelCase_				       =					unet_state_dict["label_emb.0.2.bias"]
 else:
  raise NotImplementedError(F"""Not implemented `class_embed_type`: {config["class_embed_type"]}"""       )

 UpperCAmelCase_				       =					unet_state_dict["input_blocks.0.0.weight"]
 UpperCAmelCase_				       =					unet_state_dict["input_blocks.0.0.bias"]

 UpperCAmelCase_				       =					[v for v in unet_state_dict if v.startswith("input_blocks.0.1"       )]
 UpperCAmelCase_				       =					renew_attention_paths(A_       )
 UpperCAmelCase_				       =					{"old": "input_blocks.0.1", "new": "transformer_in"}
 assign_to_checkpoint(A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )

 UpperCAmelCase_				       =					unet_state_dict["out.0.weight"]
 UpperCAmelCase_				       =					unet_state_dict["out.0.bias"]
 UpperCAmelCase_				       =					unet_state_dict["out.2.weight"]
 UpperCAmelCase_				       =					unet_state_dict["out.2.bias"]

 # Retrieves the keys for the input blocks only
 UpperCAmelCase_				       =					len({".".join(layer.split("."       )[:2]       ) for layer in unet_state_dict if "input_blocks" in layer}       )
 UpperCAmelCase_				       =					{
     layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
     for layer_id in range(A_       )
 }

 # Retrieves the keys for the middle blocks only
 UpperCAmelCase_				       =					len({".".join(layer.split("."       )[:2]       ) for layer in unet_state_dict if "middle_block" in layer}       )
 UpperCAmelCase_				       =					{
     layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
     for layer_id in range(A_       )
 }

 # Retrieves the keys for the output blocks only
 UpperCAmelCase_				       =					len({".".join(layer.split("."       )[:2]       ) for layer in unet_state_dict if "output_blocks" in layer}       )
 UpperCAmelCase_				       =					{
     layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
     for layer_id in range(A_       )
 }

 for i in range(1       ,			A_       ):
  UpperCAmelCase_				       =					(i - 1) // (config["layers_per_block"] + 1)
  UpperCAmelCase_				       =					(i - 1) % (config["layers_per_block"] + 1)

  UpperCAmelCase_				       =					[
      key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
  ]
  UpperCAmelCase_				       =					[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
  UpperCAmelCase_				       =					[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]

  if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
   UpperCAmelCase_				       =					unet_state_dict.pop(
       F"""input_blocks.{i}.op.weight"""       )
   UpperCAmelCase_				       =					unet_state_dict.pop(
       F"""input_blocks.{i}.op.bias"""       )

  UpperCAmelCase_				       =					renew_resnet_paths(A_       )
  UpperCAmelCase_				       =					{"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  assign_to_checkpoint(
      A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )

  UpperCAmelCase_				       =					[key for key in resnets if "temopral_conv" in key]
  UpperCAmelCase_				       =					renew_temp_conv_paths(A_       )
  UpperCAmelCase_				       =					{
      "old": F"""input_blocks.{i}.0.temopral_conv""",
      "new": F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  }
  assign_to_checkpoint(
      A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )

  if len(A_       ):
   UpperCAmelCase_				       =					renew_attention_paths(A_       )
   UpperCAmelCase_				       =					{"old": F"""input_blocks.{i}.1""", "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
   assign_to_checkpoint(
       A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )

  if len(A_       ):
   UpperCAmelCase_				       =					renew_attention_paths(A_       )
   UpperCAmelCase_				       =					{
       "old": F"""input_blocks.{i}.2""",
       "new": F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
   }
   assign_to_checkpoint(
       A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )

 UpperCAmelCase_				       =					middle_blocks[0]
 UpperCAmelCase_				       =					[key for key in resnet_a if "temopral_conv" in key]
 UpperCAmelCase_				       =					middle_blocks[1]
 UpperCAmelCase_				       =					middle_blocks[2]
 UpperCAmelCase_				       =					middle_blocks[3]
 UpperCAmelCase_				       =					[key for key in resnet_a if "temopral_conv" in key]

 UpperCAmelCase_				       =					renew_resnet_paths(A_       )
 UpperCAmelCase_				       =					{"old": "middle_block.0", "new": "mid_block.resnets.0"}
 assign_to_checkpoint(
     A_       ,			A_       ,			A_       ,			config=A_       ,			additional_replacements=[meta_path]       )

 UpperCAmelCase_				       =					renew_temp_conv_paths(A_       )
 UpperCAmelCase_				       =					{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
 assign_to_checkpoint(
     A_       ,			A_       ,			A_       ,			config=A_       ,			additional_replacements=[meta_path]       )

 UpperCAmelCase_				       =					renew_resnet_paths(A_       )
 UpperCAmelCase_				       =					{"old": "middle_block.3", "new": "mid_block.resnets.1"}
 assign_to_checkpoint(
     A_       ,			A_       ,			A_       ,			config=A_       ,			additional_replacements=[meta_path]       )

 UpperCAmelCase_				       =					renew_temp_conv_paths(A_       )
 UpperCAmelCase_				       =					{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
 assign_to_checkpoint(
     A_       ,			A_       ,			A_       ,			config=A_       ,			additional_replacements=[meta_path]       )

 UpperCAmelCase_				       =					renew_attention_paths(A_       )
 UpperCAmelCase_				       =					{"old": "middle_block.1", "new": "mid_block.attentions.0"}
 assign_to_checkpoint(
     A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )

 UpperCAmelCase_				       =					renew_attention_paths(A_       )
 UpperCAmelCase_				       =					{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
 assign_to_checkpoint(
     A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )

 for i in range(A_       ):
  UpperCAmelCase_				       =					i // (config["layers_per_block"] + 1)
  UpperCAmelCase_				       =					i % (config["layers_per_block"] + 1)
  UpperCAmelCase_				       =					[shave_segments(A_       ,			2       ) for name in output_blocks[i]]
  UpperCAmelCase_				       =					{}

  for layer in output_block_layers:
   UpperCAmelCase_       ,       UpperCAmelCase_				       =					layer.split("."       )[0], shave_segments(A_       ,			1       )
   if layer_id in output_block_list:
    output_block_list[layer_id].append(A_       )
   else:
    UpperCAmelCase_				       =					[layer_name]

  if len(A_       ) > 1:
   UpperCAmelCase_				       =					[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
   UpperCAmelCase_				       =					[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
   UpperCAmelCase_				       =					[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

   UpperCAmelCase_				       =					renew_resnet_paths(A_       )
   UpperCAmelCase_				       =					renew_resnet_paths(A_       )

   UpperCAmelCase_				       =					{"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
   assign_to_checkpoint(
       A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )

   UpperCAmelCase_				       =					[key for key in resnets if "temopral_conv" in key]
   UpperCAmelCase_				       =					renew_temp_conv_paths(A_       )
   UpperCAmelCase_				       =					{
       "old": F"""output_blocks.{i}.0.temopral_conv""",
       "new": F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
   }
   assign_to_checkpoint(
       A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )

   UpperCAmelCase_				       =					{k: sorted(A_       ) for k, v in output_block_list.items()}
   if ["conv.bias", "conv.weight"] in output_block_list.values():
    UpperCAmelCase_				       =					list(output_block_list.values()       ).index(["conv.bias", "conv.weight"]       )
    UpperCAmelCase_				       =					unet_state_dict[
        F"""output_blocks.{i}.{index}.conv.weight"""
    ]
    UpperCAmelCase_				       =					unet_state_dict[
        F"""output_blocks.{i}.{index}.conv.bias"""
    ]

    # Clear attentions as they have been attributed above.
    if len(A_       ) == 2:
     UpperCAmelCase_				       =					[]

   if len(A_       ):
    UpperCAmelCase_				       =					renew_attention_paths(A_       )
    UpperCAmelCase_				       =					{
        "old": F"""output_blocks.{i}.1""",
        "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
    }
    assign_to_checkpoint(
        A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )

   if len(A_       ):
    UpperCAmelCase_				       =					renew_attention_paths(A_       )
    UpperCAmelCase_				       =					{
        "old": F"""output_blocks.{i}.2""",
        "new": F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
    }
    assign_to_checkpoint(
        A_       ,			A_       ,			A_       ,			additional_replacements=[meta_path]       ,			config=A_       )
  else:
   UpperCAmelCase_				       =					renew_resnet_paths(A_       ,			n_shave_prefix_segments=1       )
   for path in resnet_0_paths:
    UpperCAmelCase_				       =					".".join(["output_blocks", str(A_       ), path["old"]]       )
    UpperCAmelCase_				       =					".".join(["up_blocks", str(A_       ), "resnets", str(A_       ), path["new"]]       )
    UpperCAmelCase_				       =					unet_state_dict[old_path]

   UpperCAmelCase_				       =					[l for l in output_block_layers if "temopral_conv" in l]
   for path in temopral_conv_paths:
    UpperCAmelCase_				       =					path.split("temopral_conv."       )[-1]
    UpperCAmelCase_				       =					".".join(["output_blocks", str(A_       ), str(A_       ), "temopral_conv", pruned_path]       )
    UpperCAmelCase_				       =					".".join(["up_blocks", str(A_       ), "temp_convs", str(A_       ), pruned_path]       )
    UpperCAmelCase_				       =					unet_state_dict[old_path]

 return new_checkpoint


if __name__ == "__main__":
      __snake_case       :   Dict  		=    argparse.ArgumentParser()

      parser.add_argument(
          '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
      )
      parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
      __snake_case       :   int  		=    parser.parse_args()

      __snake_case       :   int  		=    torch.load(args.checkpoint_path, map_location='''cpu''')
      __snake_case       :   Dict  		=    UNetaDConditionModel()

      __snake_case       :   Any  		=    convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

      __snake_case       :   str  		=    set(unet.state_dict().keys()) - set(converted_ckpt.keys())
      __snake_case       :   List[str]  		=    set(converted_ckpt.keys()) - set(unet.state_dict().keys())

      assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

      # load state_dict
      unet.load_state_dict(converted_ckpt)

      unet.save_pretrained(args.dump_path)

      # -- finish converting the unet --

'''simple docstring'''

import random
import sys

__snake_case       :   str  		=    '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''



def lowerCamelCase__						(       ):
 UpperCAmelCase_				       =					input("Enter message: "       )
 UpperCAmelCase_				       =					"LFWOAYUISVKMNXPBDCRJTQEGHZ"
 UpperCAmelCase_				       =					input("Encrypt/Decrypt [e/d]: "       )

 check_valid_key(A_       )

 if resp.lower().startswith("e"       ):
  UpperCAmelCase_				       =					"encrypt"
  UpperCAmelCase_				       =					encrypt_message(A_       ,			A_       )
 elif resp.lower().startswith("d"       ):
  UpperCAmelCase_				       =					"decrypt"
  UpperCAmelCase_				       =					decrypt_message(A_       ,			A_       )

 print(F"""\n{mode.title()}ion: \n{translated}"""       )



def lowerCamelCase__						(       A_       ):
 UpperCAmelCase_				       =					list(A_       )
 UpperCAmelCase_				       =					list(A_       )
 key_list.sort()
 letters_list.sort()

 if key_list != letters_list:
  sys.exit("Error in the key or symbol set."       )



def lowerCamelCase__						(       A_       ,			A_       ):
 return translate_message(A_       ,			A_       ,			"encrypt"       )



def lowerCamelCase__						(       A_       ,			A_       ):
 return translate_message(A_       ,			A_       ,			"decrypt"       )



def lowerCamelCase__						(       A_       ,			A_       ,			A_       ):
 UpperCAmelCase_				       =					""
 UpperCAmelCase_				       =					LETTERS
 UpperCAmelCase_				       =					key

 if mode == "decrypt":
  UpperCAmelCase_       ,       UpperCAmelCase_				       =					chars_b, chars_a

 for symbol in message:
  if symbol.upper() in chars_a:
   UpperCAmelCase_				       =					chars_a.find(symbol.upper()       )
   if symbol.isupper():
    translated += chars_b[sym_index].upper()
   else:
    translated += chars_b[sym_index].lower()
  else:
   translated += symbol

 return translated



def lowerCamelCase__						(       ):
 UpperCAmelCase_				       =					list(A_       )
 random.shuffle(A_       )
 return "".join(A_       )


if __name__ == "__main__":
      main()
