
"""simple docstring"""




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       List[str]=1  ) ->				List[Any]:
       if n_shave_prefix_segments >= 0:
              return ".".join(path.split("."  )[n_shave_prefix_segments:]  )
       else:
              return ".".join(path.split("."  )[:n_shave_prefix_segments]  )


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       List[Any]=0  ) ->				int:
       lowercase    :				Dict       		=							[]
       for old_item in old_list:
              lowercase    :				Any       		=							old_item.replace("in_layers.0"     , "norm1"  )
              lowercase    :				Union[str, Any]       		=							new_item.replace("in_layers.2"     , "conv1"  )

              lowercase    :				List[str]       		=							new_item.replace("out_layers.0"     , "norm2"  )
              lowercase    :				Optional[Any]       		=							new_item.replace("out_layers.3"     , "conv2"  )

              lowercase    :				int       		=							new_item.replace("emb_layers.1"     , "time_emb_proj"  )
              lowercase    :				List[Any]       		=							new_item.replace("skip_connection"     , "conv_shortcut"  )

              lowercase    :				Optional[Any]       		=							shave_segments(__snake_case     , n_shave_prefix_segments=__snake_case  )

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

       return mapping


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       List[str]=0  ) ->				Optional[Any]:
       lowercase    :				Optional[int]       		=							[]
       for old_item in old_list:
              lowercase    :				Dict       		=							old_item

              lowercase    :				Any       		=							new_item.replace("norm.weight"     , "group_norm.weight"  )
              lowercase    :				Any       		=							new_item.replace("norm.bias"     , "group_norm.bias"  )

              lowercase    :				int       		=							new_item.replace("proj_out.weight"     , "proj_attn.weight"  )
              lowercase    :				List[Any]       		=							new_item.replace("proj_out.bias"     , "proj_attn.bias"  )

              lowercase    :				Dict       		=							shave_segments(__snake_case     , n_shave_prefix_segments=__snake_case  )

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

       return mapping


def   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       Tuple     , __snake_case				:       Tuple     , __snake_case				:       Any=None     , __snake_case				:       str=None     , __snake_case				:       Union[str, Any]=None  ) ->				Tuple:
       assert isinstance(__snake_case     , __snake_case  ), "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():
                     lowercase    :				Any       		=							old_checkpoint[path]
                     lowercase    :				List[str]       		=							old_tensor.shape[0] // 3

                     lowercase    :				str       		=							(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

                     lowercase    :				Dict       		=							old_tensor.shape[0] // config["num_head_channels"] // 3

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

                     lowercase    :				Optional[Any]       		=							query.reshape(__snake_case  )
                     lowercase    :				Optional[Any]       		=							key.reshape(__snake_case  )
                     lowercase    :				Any       		=							value.reshape(__snake_case  )

       for path in paths:
              lowercase    :				Any       		=							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
              lowercase    :				Optional[int]       		=							new_path.replace("middle_block.0"     , "mid_block.resnets.0"  )
              lowercase    :				Union[str, Any]       		=							new_path.replace("middle_block.1"     , "mid_block.attentions.0"  )
              lowercase    :				Dict       		=							new_path.replace("middle_block.2"     , "mid_block.resnets.1"  )

              if additional_replacements is not None:
                     for replacement in additional_replacements:
                            lowercase    :				Union[str, Any]       		=							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:
                     lowercase    :				List[Any]       		=							old_checkpoint[path["old"]][:, :, 0]
              else:
                     lowercase    :				int       		=							old_checkpoint[path["old"]]


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Optional[int]  ) ->				Any:
       lowercase    :				Any       		=							{}

       lowercase    :				Any       		=							checkpoint["time_embed.0.weight"]
       lowercase    :				Dict       		=							checkpoint["time_embed.0.bias"]
       lowercase    :				Union[str, Any]       		=							checkpoint["time_embed.2.weight"]
       lowercase    :				int       		=							checkpoint["time_embed.2.bias"]

       lowercase    :				Dict       		=							checkpoint["input_blocks.0.0.weight"]
       lowercase    :				List[str]       		=							checkpoint["input_blocks.0.0.bias"]

       lowercase    :				Optional[Any]       		=							checkpoint["out.0.weight"]
       lowercase    :				int       		=							checkpoint["out.0.bias"]
       lowercase    :				Optional[Any]       		=							checkpoint["out.2.weight"]
       lowercase    :				List[str]       		=							checkpoint["out.2.bias"]

       # Retrieves the keys for the input blocks only
       lowercase    :				str       		=							len({".".join(layer.split("."  )[:2]  ) for layer in checkpoint if "input_blocks" in layer}  )
       lowercase    :				List[str]       		=							{
           layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
           for layer_id in range(__snake_case  )
       }

       # Retrieves the keys for the middle blocks only
       lowercase    :				Dict       		=							len({".".join(layer.split("."  )[:2]  ) for layer in checkpoint if "middle_block" in layer}  )
       lowercase    :				Optional[int]       		=							{
           layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
           for layer_id in range(__snake_case  )
       }

       # Retrieves the keys for the output blocks only
       lowercase    :				List[Any]       		=							len({".".join(layer.split("."  )[:2]  ) for layer in checkpoint if "output_blocks" in layer}  )
       lowercase    :				Optional[int]       		=							{
           layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
           for layer_id in range(__snake_case  )
       }

       for i in range(1     , __snake_case  ):
              lowercase    :				Optional[Any]       		=							(i - 1) // (config["num_res_blocks"] + 1)
              lowercase    :				Optional[int]       		=							(i - 1) % (config["num_res_blocks"] + 1)

              lowercase    :				Dict       		=							[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
              lowercase    :				Dict       		=							[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:
                     lowercase    :				Optional[int]       		=							checkpoint[
                         f"""input_blocks.{i}.0.op.weight"""
                     ]
                     lowercase    :				Any       		=							checkpoint[
                         f"""input_blocks.{i}.0.op.bias"""
                     ]
                     continue

              lowercase    :				Any       		=							renew_resnet_paths(__snake_case  )
              lowercase    :				Optional[int]       		=							{"old": f"""input_blocks.{i}.0""", "new": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
              lowercase    :				Optional[int]       		=							{"old": "resnets.2.op", "new": "downsamplers.0.op"}
              assign_to_checkpoint(
                  __snake_case     , __snake_case     , __snake_case     , additional_replacements=[meta_path, resnet_op]     , config=__snake_case  )

              if len(__snake_case  ):
                     lowercase    :				str       		=							renew_attention_paths(__snake_case  )
                     lowercase    :				Optional[Any]       		=							{
                         "old": f"""input_blocks.{i}.1""",
                         "new": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
                     }
                     lowercase    :				Optional[Any]       		=							{
                         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(
                         __snake_case     , __snake_case     , __snake_case     , additional_replacements=[meta_path]     , attention_paths_to_split=__snake_case     , config=__snake_case     , )

       lowercase    :				str       		=							middle_blocks[0]
       lowercase    :				Union[str, Any]       		=							middle_blocks[1]
       lowercase    :				str       		=							middle_blocks[2]

       lowercase    :				List[str]       		=							renew_resnet_paths(__snake_case  )
       assign_to_checkpoint(__snake_case     , __snake_case     , __snake_case     , config=__snake_case  )

       lowercase    :				Optional[int]       		=							renew_resnet_paths(__snake_case  )
       assign_to_checkpoint(__snake_case     , __snake_case     , __snake_case     , config=__snake_case  )

       lowercase    :				Dict       		=							renew_attention_paths(__snake_case  )
       lowercase    :				Any       		=							{
           "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(
           __snake_case     , __snake_case     , __snake_case     , attention_paths_to_split=__snake_case     , config=__snake_case  )

       for i in range(__snake_case  ):
              lowercase    :				Optional[int]       		=							i // (config["num_res_blocks"] + 1)
              lowercase    :				Optional[Any]       		=							i % (config["num_res_blocks"] + 1)
              lowercase    :				Optional[int]       		=							[shave_segments(__snake_case     , 2  ) for name in output_blocks[i]]
              lowercase    :				Dict       		=							{}

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

              if len(__snake_case  ) > 1:
                     lowercase    :				Tuple       		=							[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
                     lowercase    :				str       		=							[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

                     lowercase    :				Tuple       		=							renew_resnet_paths(__snake_case  )
                     lowercase    :				Any       		=							renew_resnet_paths(__snake_case  )

                     lowercase    :				Union[str, Any]       		=							{"old": f"""output_blocks.{i}.0""", "new": f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
                     assign_to_checkpoint(__snake_case     , __snake_case     , __snake_case     , additional_replacements=[meta_path]     , config=__snake_case  )

                     if ["conv.weight", "conv.bias"] in output_block_list.values():
                            lowercase    :				List[str]       		=							list(output_block_list.values()  ).index(["conv.weight", "conv.bias"]  )
                            lowercase    :				List[str]       		=							checkpoint[
                                f"""output_blocks.{i}.{index}.conv.weight"""
                            ]
                            lowercase    :				List[str]       		=							checkpoint[
                                f"""output_blocks.{i}.{index}.conv.bias"""
                            ]

                            # Clear attentions as they have been attributed above.
                            if len(__snake_case  ) == 2:
                                   lowercase    :				Optional[Any]       		=							[]

                     if len(__snake_case  ):
                            lowercase    :				List[Any]       		=							renew_attention_paths(__snake_case  )
                            lowercase    :				Dict       		=							{
                                "old": f"""output_blocks.{i}.1""",
                                "new": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                            }
                            lowercase    :				Any       		=							{
                                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(
                                __snake_case     , __snake_case     , __snake_case     , additional_replacements=[meta_path]     , attention_paths_to_split=to_split if any("qkv" in key for key in attentions  ) else None     , config=__snake_case     , )
              else:
                     lowercase    :				List[Any]       		=							renew_resnet_paths(__snake_case     , n_shave_prefix_segments=1  )
                     for path in resnet_0_paths:
                            lowercase    :				int       		=							".".join(["output_blocks", str(__snake_case  ), path["old"]]  )
                            lowercase    :				List[Any]       		=							".".join(["up_blocks", str(__snake_case  ), "resnets", str(__snake_case  ), path["new"]]  )

                            lowercase    :				int       		=							checkpoint[old_path]

       return new_checkpoint


if __name__ == "__main__":
   _A			:       Optional[int]   		=       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.""")

   _A			:       List[Any]   		=       parser.parse_args()

   _A			:       List[Any]   		=       torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      _A			:       Dict   		=       json.loads(f.read())

   _A			:       int   		=       convert_ldm_checkpoint(checkpoint, config)

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

   _A			:       Optional[int]   		=       UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      _A			:       str   		=       DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
      _A			:       int   		=       VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

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




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A			:       Optional[int]   		=       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Union[str, Any]   		=       ["""LlamaTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Dict   		=       ["""LlamaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       int   		=       [
       """LlamaForCausalLM""",
       """LlamaModel""",
       """LlamaPreTrainedModel""",
       """LlamaForSequenceClassification""",
   ]


if TYPE_CHECKING:
   from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama import LlamaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama_fast import LlamaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
   import sys

   _A			:       str   		=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


