





'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 		UpperCamelCase							(				_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Any=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 		UpperCamelCase							(				_lowerCamelCase   :   Any		,			_lowerCamelCase   :   Optional[Any]=0			):
    A__							=     []
    for old_item in old_list:
        A__							=     old_item.replace("in_layers.0"		,			"norm1"			)
        A__							=     new_item.replace("in_layers.2"		,			"conv1"			)

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

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

        A__							=     shave_segments(lowerCAmelCase_		,			n_shave_prefix_segments=lowerCAmelCase_			)

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

    return mapping





def 		UpperCamelCase							(				_lowerCamelCase   :   Any		,			_lowerCamelCase   :   Tuple=0			):
    A__							=     []
    for old_item in old_list:
        A__							=     old_item

        A__							=     new_item.replace("norm.weight"		,			"group_norm.weight"			)
        A__							=     new_item.replace("norm.bias"		,			"group_norm.bias"			)

        A__							=     new_item.replace("proj_out.weight"		,			"proj_attn.weight"			)
        A__							=     new_item.replace("proj_out.bias"		,			"proj_attn.bias"			)

        A__							=     shave_segments(lowerCAmelCase_		,			n_shave_prefix_segments=lowerCAmelCase_			)

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

    return mapping





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   int=None		,			_lowerCamelCase   :   List[str]=None		,			_lowerCamelCase   :   Any=None			):
    assert isinstance(lowerCAmelCase_		,			lowerCAmelCase_			), "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():
            A__							=     old_checkpoint[path]
            A__							=     old_tensor.shape[0] // 3

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

            A__							=     old_tensor.shape[0] // config["num_head_channels"] // 3

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

            A__							=     query.reshape(lowerCAmelCase_			)
            A__							=     key.reshape(lowerCAmelCase_			)
            A__							=     value.reshape(lowerCAmelCase_			)

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

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

        # Global renaming happens here
        A__							=     new_path.replace("middle_block.0"		,			"mid_block.resnets.0"			)
        A__							=     new_path.replace("middle_block.1"		,			"mid_block.attentions.0"			)
        A__							=     new_path.replace("middle_block.2"		,			"mid_block.resnets.1"			)

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

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            A__							=     old_checkpoint[path["old"]][:, :, 0]
        else:
            A__							=     old_checkpoint[path["old"]]





def 		UpperCamelCase							(				_lowerCamelCase   :   List[Any]		,			_lowerCamelCase   :   List[Any]			):
    A__							=     {}

    A__							=     checkpoint["time_embed.0.weight"]
    A__							=     checkpoint["time_embed.0.bias"]
    A__							=     checkpoint["time_embed.2.weight"]
    A__							=     checkpoint["time_embed.2.bias"]

    A__							=     checkpoint["input_blocks.0.0.weight"]
    A__							=     checkpoint["input_blocks.0.0.bias"]

    A__							=     checkpoint["out.0.weight"]
    A__							=     checkpoint["out.0.bias"]
    A__							=     checkpoint["out.2.weight"]
    A__							=     checkpoint["out.2.bias"]

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

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

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

    for i in range(1		,			lowerCAmelCase_			):
        A__							=     (i - 1) // (config["num_res_blocks"] + 1)
        A__							=     (i - 1) % (config["num_res_blocks"] + 1)

        A__							=     [key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
        A__							=     [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

        if F"input_blocks.{i}.0.op.weight" in checkpoint:
            A__							=     checkpoint[
                F"input_blocks.{i}.0.op.weight"
            ]
            A__							=     checkpoint[
                F"input_blocks.{i}.0.op.bias"
            ]
            continue

        A__							=     renew_resnet_paths(lowerCAmelCase_			)
        A__							=     {"old": F"input_blocks.{i}.0", "new": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
        A__							=     {"old": "resnets.2.op", "new": "downsamplers.0.op"}
        assign_to_checkpoint(
            lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			additional_replacements=[meta_path, resnet_op]		,			config=lowerCAmelCase_			)

        if len(lowerCAmelCase_			):
            A__							=     renew_attention_paths(lowerCAmelCase_			)
            A__							=     {
                "old": F"input_blocks.{i}.1",
                "new": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
            }
            A__							=     {
                F"input_blocks.{i}.1.qkv.bias": {
                    "key": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                    "query": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                    "value": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                },
                F"input_blocks.{i}.1.qkv.weight": {
                    "key": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                    "query": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                    "value": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                },
            }
            assign_to_checkpoint(
                lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			additional_replacements=[meta_path]		,			attention_paths_to_split=lowerCAmelCase_		,			config=lowerCAmelCase_		,			)

    A__							=     middle_blocks[0]
    A__							=     middle_blocks[1]
    A__							=     middle_blocks[2]

    A__							=     renew_resnet_paths(lowerCAmelCase_			)
    assign_to_checkpoint(lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			config=lowerCAmelCase_			)

    A__							=     renew_resnet_paths(lowerCAmelCase_			)
    assign_to_checkpoint(lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			config=lowerCAmelCase_			)

    A__							=     renew_attention_paths(lowerCAmelCase_			)
    A__							=     {
        "middle_block.1.qkv.bias": {
            "key": "mid_block.attentions.0.key.bias",
            "query": "mid_block.attentions.0.query.bias",
            "value": "mid_block.attentions.0.value.bias",
        },
        "middle_block.1.qkv.weight": {
            "key": "mid_block.attentions.0.key.weight",
            "query": "mid_block.attentions.0.query.weight",
            "value": "mid_block.attentions.0.value.weight",
        },
    }
    assign_to_checkpoint(
        lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			attention_paths_to_split=lowerCAmelCase_		,			config=lowerCAmelCase_			)

    for i in range(lowerCAmelCase_			):
        A__							=     i // (config["num_res_blocks"] + 1)
        A__							=     i % (config["num_res_blocks"] + 1)
        A__							=     [shave_segments(lowerCAmelCase_		,			2			) for name in output_blocks[i]]
        A__							=     {}

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

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

            A__							=     renew_resnet_paths(lowerCAmelCase_			)
            A__							=     renew_resnet_paths(lowerCAmelCase_			)

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

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                A__							=     list(output_block_list.values()			).index(["conv.weight", "conv.bias"]			)
                A__							=     checkpoint[
                    F"output_blocks.{i}.{index}.conv.weight"
                ]
                A__							=     checkpoint[
                    F"output_blocks.{i}.{index}.conv.bias"
                ]

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

            if len(lowerCAmelCase_			):
                A__							=     renew_attention_paths(lowerCAmelCase_			)
                A__							=     {
                    "old": F"output_blocks.{i}.1",
                    "new": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                }
                A__							=     {
                    F"output_blocks.{i}.1.qkv.bias": {
                        "key": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                        "query": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                        "value": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                    },
                    F"output_blocks.{i}.1.qkv.weight": {
                        "key": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                        "query": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                        "value": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                    },
                }
                assign_to_checkpoint(
                    lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			additional_replacements=[meta_path]		,			attention_paths_to_split=to_split if any("qkv" in key for key in attentions			) else None		,			config=lowerCAmelCase_		,			)
        else:
            A__							=     renew_resnet_paths(lowerCAmelCase_		,			n_shave_prefix_segments=1			)
            for path in resnet_0_paths:
                A__							=     ".".join(["output_blocks", str(lowerCAmelCase_			), path["old"]]			)
                A__							=     ".".join(["up_blocks", str(lowerCAmelCase_			), "resnets", str(lowerCAmelCase_			), path["new"]]			)

                A__							=     checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
  __lowerCAmelCase				:      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				:      Optional[Any]						      =parser.parse_args()

  __lowerCAmelCase				:      Tuple						      =torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    __lowerCAmelCase				:      Optional[int]						      =json.loads(f.read())

  __lowerCAmelCase				:      Union[str, Any]						      =convert_ldm_checkpoint(checkpoint, config)

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

  __lowerCAmelCase				:      Tuple						      =UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    __lowerCAmelCase				:      Tuple						      =DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
    __lowerCAmelCase				:      Union[str, Any]						      =VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

    __lowerCAmelCase				:      List[str]						      =LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)







__a	 =      256
# Modulus to hash a string
__a	 =      100_0003
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->bool:
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   if p_len > t_len:
      return False

   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  1

   # Calculating the hash of pattern and substring of text
   for i in range(lowerCAmelCase_ ):
      UpperCAmelCase      					=  (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
      UpperCAmelCase      					=  (ord(text[i] ) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      UpperCAmelCase      					=  (modulus_power * alphabet_size) % modulus

   for i in range(0	,    t_len - p_len + 1 ):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      UpperCAmelCase      					=  (
          (text_hash - ord(text[i] ) * modulus_power) * alphabet_size
          + ord(text[i + p_len] )
      ) % modulus
   return False
def 	_UpperCamelCase					(    )    ->None:
   UpperCAmelCase      					=  """abc1abc12"""
   UpperCAmelCase      					=  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
   UpperCAmelCase      					=  """alskfjaldsk23adsfabcabc"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ ) and not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 2)
   UpperCAmelCase      					=  """ABABX"""
   UpperCAmelCase      					=  """ABABZABABYABABX"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 3)
   UpperCAmelCase      					=  """AAAB"""
   UpperCAmelCase      					=  """ABAAAAAB"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 4)
   UpperCAmelCase      					=  """abcdabcy"""
   UpperCAmelCase      					=  """abcxabcdabxabcdabcdabcy"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 5)
   UpperCAmelCase      					=  """Lü"""
   UpperCAmelCase      					=  """Lüsai"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  """Lue"""
   assert not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )
   print("""Success.""" )


if __name__ == "__main__":
 test_rabin_karp()



