


"""simple docstring"""







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def 					lowercase      (    A_  ,       A_=1	)->				Dict:
  '''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 					lowercase      (    A_  ,       A_=0	)->				List[str]:
  '''simple docstring'''







  a						:		Optional[Any]											=      []
  for old_item in old_list:
    a						:		Optional[Any]											=      old_item.replace("in_layers.0"  ,       "norm1"	)
    a						:		Any											=      new_item.replace("in_layers.2"  ,       "conv1"	)

    a						:		Tuple											=      new_item.replace("out_layers.0"  ,       "norm2"	)
    a						:		Optional[Any]											=      new_item.replace("out_layers.3"  ,       "conv2"	)

    a						:		Dict											=      new_item.replace("emb_layers.1"  ,       "time_emb_proj"	)
    a						:		str											=      new_item.replace("skip_connection"  ,       "conv_shortcut"	)

    a						:		Any											=      shave_segments(A_  ,       n_shave_prefix_segments=A_	)

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

  return mapping


def 					lowercase      (    A_  ,       A_=0	)->				int:
  '''simple docstring'''







  a						:		Optional[int]											=      []
  for old_item in old_list:
    a						:		Optional[int]											=      old_item

    a						:		List[Any]											=      new_item.replace("norm.weight"  ,       "group_norm.weight"	)
    a						:		List[str]											=      new_item.replace("norm.bias"  ,       "group_norm.bias"	)

    a						:		int											=      new_item.replace("proj_out.weight"  ,       "proj_attn.weight"	)
    a						:		Dict											=      new_item.replace("proj_out.bias"  ,       "proj_attn.bias"	)

    a						:		Optional[Any]											=      shave_segments(A_  ,       n_shave_prefix_segments=A_	)

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

  return mapping


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=None  ,       A_=None  ,       A_=None	)->				List[Any]:
  '''simple docstring'''







  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():
      a						:		Optional[Any]											=      old_checkpoint[path]
      a						:		List[Any]											=      old_tensor.shape[0] // 3

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

      a						:		Optional[int]											=      old_tensor.shape[0] // config["num_head_channels"] // 3

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

      a						:		Optional[Any]											=      query.reshape(A_	)
      a						:		Optional[Any]											=      key.reshape(A_	)
      a						:		Any											=      value.reshape(A_	)

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

    if additional_replacements is not None:
      for replacement in additional_replacements:
        a						:		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:
      a						:		Optional[Any]											=      old_checkpoint[path["old"]][:, :, 0]
    else:
      a						:		Dict											=      old_checkpoint[path["old"]]


def 					lowercase      (    A_  ,       A_	)->				str:
  '''simple docstring'''







  a						:		Optional[Any]											=      {}

  a						:		str											=      checkpoint["time_embed.0.weight"]
  a						:		str											=      checkpoint["time_embed.0.bias"]
  a						:		Dict											=      checkpoint["time_embed.2.weight"]
  a						:		List[str]											=      checkpoint["time_embed.2.bias"]

  a						:		int											=      checkpoint["input_blocks.0.0.weight"]
  a						:		List[str]											=      checkpoint["input_blocks.0.0.bias"]

  a						:		Tuple											=      checkpoint["out.0.weight"]
  a						:		int											=      checkpoint["out.0.bias"]
  a						:		Tuple											=      checkpoint["out.2.weight"]
  a						:		int											=      checkpoint["out.2.bias"]

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

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

  # Retrieves the keys for the output blocks only
  a						:		Union[str, Any]											=      len({".".join(layer.split("."	)[:2]	) for layer in checkpoint if "output_blocks" in layer}	)
  a						:		Tuple											=      {
      layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
      for layer_id in range(A_	)
  }

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

    a						:		Any											=      [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
    a						:		List[Any]											=      [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						:		int											=      checkpoint[
          F'''input_blocks.{i}.0.op.weight'''
      ]
      a						:		Tuple											=      checkpoint[
          F'''input_blocks.{i}.0.op.bias'''
      ]
      continue

    a						:		Tuple											=      renew_resnet_paths(A_	)
    a						:		Any											=      {"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
    a						:		List[str]											=      {"old": "resnets.2.op", "new": "downsamplers.0.op"}
    assign_to_checkpoint(
        A_  ,       A_  ,       A_  ,       additional_replacements=[meta_path, resnet_op]  ,       config=A_	)

    if len(A_	):
      a						:		int											=      renew_attention_paths(A_	)
      a						:		List[Any]											=      {
          "old": F'''input_blocks.{i}.1''',
          "new": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
      }
      a						:		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(
          A_  ,       A_  ,       A_  ,       additional_replacements=[meta_path]  ,       attention_paths_to_split=A_  ,       config=A_  ,       )

  a						:		Union[str, Any]											=      middle_blocks[0]
  a						:		int											=      middle_blocks[1]
  a						:		Dict											=      middle_blocks[2]

  a						:		Dict											=      renew_resnet_paths(A_	)
  assign_to_checkpoint(A_  ,       A_  ,       A_  ,       config=A_	)

  a						:		Any											=      renew_resnet_paths(A_	)
  assign_to_checkpoint(A_  ,       A_  ,       A_  ,       config=A_	)

  a						:		List[str]											=      renew_attention_paths(A_	)
  a						:		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(
      A_  ,       A_  ,       A_  ,       attention_paths_to_split=A_  ,       config=A_	)

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

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

    if len(A_	) > 1:
      a						:		str											=      [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
      a						:		int											=      [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

      a						:		List[Any]											=      renew_resnet_paths(A_	)
      a						:		Optional[Any]											=      renew_resnet_paths(A_	)

      a						:		Optional[int]											=      {"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_	)

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

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

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

        a						:		Any											=      checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
 __lowercase    =				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.""")

 __lowercase    =				parser.parse_args()

 __lowercase    =				torch.load(args.checkpoint_path)

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

 __lowercase    =				convert_ldm_checkpoint(checkpoint, config)

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

 __lowercase    =				UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

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

  __lowercase    =				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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
 from ...onnx.config import PatchingSpec
 from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	"""longformer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[List[int], int] = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 1    ,			__UpperCAmelCase    :   int = 0    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 30522    ,			__UpperCAmelCase    :   int = 768    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 3072    ,			__UpperCAmelCase    :   str = "gelu"    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   int = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   float = 0.02    ,			__UpperCAmelCase    :   float = 1e-12    ,			__UpperCAmelCase    :   bool = False    ,			**__UpperCAmelCase    :   int    ,			):
      super().__init__(pad_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)

      a						:		int											=      attention_window
      a						:		List[str]											=      sep_token_id
      a						:		List[Any]											=      bos_token_id
      a						:		int											=      eos_token_id
      a						:		Union[str, Any]											=      vocab_size
      a						:		Dict											=      hidden_size
      a						:		int											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Optional[int]											=      hidden_act
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Tuple											=      hidden_dropout_prob
      a						:		Union[str, Any]											=      attention_probs_dropout_prob
      a						:		str											=      max_position_embeddings
      a						:		Dict											=      type_vocab_size
      a						:		Optional[Any]											=      initializer_range
      a						:		List[Any]											=      layer_norm_eps
      a						:		List[str]											=      onnx_export






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   "PretrainedConfig"    ,			__UpperCAmelCase    :   str = "default"    ,			__UpperCAmelCase    :   "List[PatchingSpec]" = None):
      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      a						:		Optional[Any]											=      True






    @property
    def      __snake_case							( self    :   int):
      if self.task == "multiple-choice":
        a						:		List[Any]											=      {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a						:		int											=      {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("global_attention_mask", dynamic_axis),
          ])






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      a						:		str											=      super().outputs
      if self.task == "default":
        a						:		Tuple											=      {0: "batch"}
      return outputs






    @property
    def      __snake_case							( self    :   Optional[int]):
      return 1e-4






    @property
    def      __snake_case							( self    :   str):
      # needs to be >= 14 to support tril operator
      return max(super().default_onnx_opset    ,			14)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   "PreTrainedTokenizerBase"    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   bool = False    ,			__UpperCAmelCase    :   Optional[TensorType] = None    ,			):
      a						:		str											=      super().generate_dummy_inputs(
          preprocessor=__UpperCAmelCase    ,			batch_size=__UpperCAmelCase    ,			seq_length=__UpperCAmelCase    ,			is_pair=__UpperCAmelCase    ,			framework=__UpperCAmelCase)
      import torch

      # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
      # makes the export fail randomly
      a						:		Union[str, Any]											=      torch.zeros_like(inputs["input_ids"])
      # make every second token global
      a						:		Dict											=      1

      return inputs






