




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=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,			lowerCamelCase=0   ):
  lowercase    :Tuple      	=						[]
  for old_item in old_list:
    lowercase    :List[Any]      	=						old_item.replace("in_layers.0",			"norm1"   )
    lowercase    :Any      	=						new_item.replace("in_layers.2",			"conv1"   )

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

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

    lowercase    :Union[str, Any]      	=						shave_segments(_lowerCamelCase,			n_shave_prefix_segments=_lowerCamelCase   )

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

  return mapping



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=0   ):
  lowercase    :Optional[int]      	=						[]
  for old_item in old_list:
    lowercase    :Union[str, Any]      	=						old_item

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

    lowercase    :Optional[Any]      	=						new_item.replace("proj_out.weight",			"proj_attn.weight"   )
    lowercase    :Union[str, Any]      	=						new_item.replace("proj_out.bias",			"proj_attn.bias"   )

    lowercase    :int      	=						shave_segments(_lowerCamelCase,			n_shave_prefix_segments=_lowerCamelCase   )

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

  return mapping



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=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():
      lowercase    :Tuple      	=						old_checkpoint[path]
      lowercase    :int      	=						old_tensor.shape[0] // 3

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

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

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

      lowercase    :Tuple      	=						query.reshape(_lowerCamelCase   )
      lowercase    :int      	=						key.reshape(_lowerCamelCase   )
      lowercase    :str      	=						value.reshape(_lowerCamelCase   )

  for path in paths:
    lowercase    :Dict      	=						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[Any]      	=						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[str]      	=						old_checkpoint[path["old"]][:, :, 0]
    else:
      lowercase    :Dict      	=						old_checkpoint[path["old"]]



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  lowercase    :Dict      	=						{}

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

  lowercase    :Optional[Any]      	=						checkpoint["input_blocks.0.0.weight"]
  lowercase    :Optional[Any]      	=						checkpoint["input_blocks.0.0.bias"]

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

  # Retrieves the keys for the input blocks only
  lowercase    :Any      	=						len({".".join(layer.split("."   )[:2]   ) for layer in checkpoint if "input_blocks" in layer}   )
  lowercase    :Union[str, Any]      	=						{
      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
  lowercase    :Optional[int]      	=						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(_lowerCamelCase   )
  }

  # Retrieves the keys for the output blocks only
  lowercase    :List[str]      	=						len({".".join(layer.split("."   )[:2]   ) for layer in checkpoint if "output_blocks" in layer}   )
  lowercase    :List[str]      	=						{
      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   ):
    lowercase    :str      	=						(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    :str      	=						[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    :List[Any]      	=						checkpoint[
          F"input_blocks.{i}.0.op.weight"
      ]
      lowercase    :Any      	=						checkpoint[
          F"input_blocks.{i}.0.op.bias"
      ]
      continue

    lowercase    :Any      	=						renew_resnet_paths(_lowerCamelCase   )
    lowercase    :List[str]      	=						{"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(
        _lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			additional_replacements=[meta_path, resnet_op],			config=_lowerCamelCase   )

    if len(_lowerCamelCase   ):
      lowercase    :List[Any]      	=						renew_attention_paths(_lowerCamelCase   )
      lowercase    :List[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(
          _lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			additional_replacements=[meta_path],			attention_paths_to_split=_lowerCamelCase,			config=_lowerCamelCase,			)

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

  lowercase    :Any      	=						renew_resnet_paths(_lowerCamelCase   )
  assign_to_checkpoint(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			config=_lowerCamelCase   )

  lowercase    :Optional[Any]      	=						renew_resnet_paths(_lowerCamelCase   )
  assign_to_checkpoint(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			config=_lowerCamelCase   )

  lowercase    :List[str]      	=						renew_attention_paths(_lowerCamelCase   )
  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(
      _lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			attention_paths_to_split=_lowerCamelCase,			config=_lowerCamelCase   )

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

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

    if len(_lowerCamelCase   ) > 1:
      lowercase    :str      	=						[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
      lowercase    :Optional[Any]      	=						[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

      lowercase    :Union[str, Any]      	=						renew_resnet_paths(_lowerCamelCase   )
      lowercase    :int      	=						renew_resnet_paths(_lowerCamelCase   )

      lowercase    :List[str]      	=						{"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():
        lowercase    :Optional[Any]      	=						list(output_block_list.values()   ).index(["conv.weight", "conv.bias"]   )
        lowercase    :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(_lowerCamelCase   ) == 2:
          lowercase    :Any      	=						[]

      if len(_lowerCamelCase   ):
        lowercase    :str      	=						renew_attention_paths(_lowerCamelCase   )
        lowercase    :List[str]      	=						{
            "old": F"output_blocks.{i}.1",
            "new": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
        }
        lowercase    :List[str]      	=						{
            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:
      lowercase    :Any      	=						renew_resnet_paths(_lowerCamelCase,			n_shave_prefix_segments=1   )
      for path in resnet_0_paths:
        lowercase    :List[Any]      	=						".".join(["output_blocks", str(_lowerCamelCase   ), path["old"]]   )
        lowercase    :Any      	=						".".join(["up_blocks", str(_lowerCamelCase   ), "resnets", str(_lowerCamelCase   ), path["new"]]   )

        lowercase    :List[str]      	=						checkpoint[old_path]

  return new_checkpoint


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

     _UpperCAmelCase	:						Dict			=     parser.parse_args()

     _UpperCAmelCase	:						int			=     torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          _UpperCAmelCase	:						List[str]			=     json.loads(f.read())

     _UpperCAmelCase	:						Tuple			=     convert_ldm_checkpoint(checkpoint, config)

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

     _UpperCAmelCase	:						Union[str, Any]			=     UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

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

          _UpperCAmelCase	:						Optional[int]			=     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase	:						Optional[int]			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[Any]			=     {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						'''gptj'''
       _a 					=						{
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }






       def __init__(   self:					List[str]       ,					_lowerCAmelCase:					Union[str, Any]=5_04_00       ,					_lowerCAmelCase:					Optional[int]=20_48       ,					_lowerCAmelCase:					Dict=40_96       ,					_lowerCAmelCase:					str=28       ,					_lowerCAmelCase:					Dict=16       ,					_lowerCAmelCase:					str=64       ,					_lowerCAmelCase:					Dict=None       ,					_lowerCAmelCase:					Optional[int]="gelu_new"       ,					_lowerCAmelCase:					Optional[int]=0.0       ,					_lowerCAmelCase:					List[str]=0.0       ,					_lowerCAmelCase:					str=0.0       ,					_lowerCAmelCase:					Any=1e-5       ,					_lowerCAmelCase:					str=0.02       ,					_lowerCAmelCase:					str=True       ,					_lowerCAmelCase:					Any=5_02_56       ,					_lowerCAmelCase:					Union[str, Any]=5_02_56       ,					_lowerCAmelCase:					Optional[int]=False       ,					**_lowerCAmelCase:					int       ,					):
         lowercase    :str      	=						vocab_size
         lowercase    :Union[str, Any]      	=						n_positions
         lowercase    :Optional[int]      	=						n_embd
         lowercase    :int      	=						n_layer
         lowercase    :Tuple      	=						n_head
         lowercase    :Optional[Any]      	=						n_inner
         lowercase    :List[Any]      	=						rotary_dim
         lowercase    :List[Any]      	=						activation_function
         lowercase    :Tuple      	=						resid_pdrop
         lowercase    :Optional[int]      	=						embd_pdrop
         lowercase    :Any      	=						attn_pdrop
         lowercase    :Optional[int]      	=						layer_norm_epsilon
         lowercase    :str      	=						initializer_range
         lowercase    :Optional[int]      	=						use_cache

         lowercase    :Dict      	=						bos_token_id
         lowercase    :List[Any]      	=						eos_token_id

         super().__init__(
             bos_token_id=_lowerCAmelCase       ,					eos_token_id=_lowerCAmelCase       ,					tie_word_embeddings=_lowerCAmelCase       ,					**_lowerCAmelCase				)







class    __lowerCAmelCase       (							lowerCAmelCase):






       def __init__(   self:					Tuple       ,					_lowerCAmelCase:					PretrainedConfig       ,					_lowerCAmelCase:					str = "default"       ,					_lowerCAmelCase:					List[PatchingSpec] = None       ,					_lowerCAmelCase:					bool = False       ,					):
         super().__init__(_lowerCAmelCase       ,					task=_lowerCAmelCase       ,					patching_specs=_lowerCAmelCase       ,					use_past=_lowerCAmelCase				)
         if not getattr(self._config       ,					"pad_token_id"       ,					_lowerCAmelCase				):
           # TODO: how to do that better?
           lowercase    :int      	=						0






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Optional[int]      	=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
         if self.use_past:
           self.fill_with_past_key_values_(_lowerCAmelCase       ,					direction="inputs"				)
           lowercase    :Any      	=						{0: "batch", 1: "past_sequence + sequence"}
         else:
           lowercase    :Optional[int]      	=						{0: "batch", 1: "sequence"}

         return common_inputs






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         return self._config.n_layer






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         return self._config.n_head






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					PreTrainedTokenizer       ,					_lowerCAmelCase:					int = -1       ,					_lowerCAmelCase:					int = -1       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					Optional[TensorType] = None       ,					):
         lowercase    :Union[str, Any]      	=						super(_lowerCAmelCase       ,					self				).generate_dummy_inputs(
             _lowerCAmelCase       ,					batch_size=_lowerCAmelCase       ,					seq_length=_lowerCAmelCase       ,					is_pair=_lowerCAmelCase       ,					framework=_lowerCAmelCase				)

         # We need to order the input in the way they appears in the forward()
         lowercase    :Optional[Any]      	=						OrderedDict({"input_ids": common_inputs["input_ids"]}				)

         # Need to add the past_keys
         if self.use_past:
           if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
           else:
             import torch

             lowercase	,    lowercase    :Optional[int]      	=						common_inputs["input_ids"].shape
             # Not using the same length for past_key_values
             lowercase    :Union[str, Any]      	=						seqlen + 2
             lowercase    :Optional[Any]      	=						(
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             lowercase    :Optional[int]      	=						[
                 (torch.zeros(_lowerCAmelCase				), torch.zeros(_lowerCAmelCase				)) for _ in range(self.num_layers				)
             ]

         lowercase    :List[Any]      	=						common_inputs["attention_mask"]
         if self.use_past:
           lowercase    :Tuple      	=						ordered_inputs["attention_mask"].dtype
           lowercase    :List[Any]      	=						torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(_lowerCAmelCase       ,					_lowerCAmelCase       ,					dtype=_lowerCAmelCase				)]       ,					dim=1				)

         return ordered_inputs






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         return 13



