


'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def __snake_case					(      UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						List[str]=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 __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						List[str]=0		):
 lowerCamelCase_      =  []
 for old_item in old_list:
  lowerCamelCase_      =  old_item.replace("in_layers.0"					,				"norm1"		)
  lowerCamelCase_      =  new_item.replace("in_layers.2"					,				"conv1"		)

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

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

  lowerCamelCase_      =  shave_segments(UpperCAmelCase_					,				n_shave_prefix_segments=UpperCAmelCase_		)

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

 return mapping





def __snake_case					(      UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						List[Any]=0		):
 lowerCamelCase_      =  []
 for old_item in old_list:
  lowerCamelCase_      =  old_item

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

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

  lowerCamelCase_      =  shave_segments(UpperCAmelCase_					,				n_shave_prefix_segments=UpperCAmelCase_		)

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

 return mapping





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Union[str, Any]=None					,				UpperCAmelCase_						:						Optional[int]=None					,				UpperCAmelCase_						:						Union[str, Any]=None		):
 assert isinstance(UpperCAmelCase_					,				UpperCAmelCase_		), "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():
   lowerCamelCase_      =  old_checkpoint[path]
   lowerCamelCase_      =  old_tensor.shape[0] // 3

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

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

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

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

 for path in paths:
  lowerCamelCase_      =  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
  lowerCamelCase_      =  new_path.replace("middle_block.0"					,				"mid_block.resnets.0"		)
  lowerCamelCase_      =  new_path.replace("middle_block.1"					,				"mid_block.attentions.0"		)
  lowerCamelCase_      =  new_path.replace("middle_block.2"					,				"mid_block.resnets.1"		)

  if additional_replacements is not None:
   for replacement in additional_replacements:
    lowerCamelCase_      =  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:
   lowerCamelCase_      =  old_checkpoint[path["old"]][:, :, 0]
  else:
   lowerCamelCase_      =  old_checkpoint[path["old"]]





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_      =  {}

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

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

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

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

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

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

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

  lowerCamelCase_      =  [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
  lowerCamelCase_      =  [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:
   lowerCamelCase_      =  checkpoint[
       F'''input_blocks.{i}.0.op.weight'''
   ]
   lowerCamelCase_      =  checkpoint[
       F'''input_blocks.{i}.0.op.bias'''
   ]
   continue

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

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

 lowerCamelCase_      =  middle_blocks[0]
 lowerCamelCase_      =  middle_blocks[1]
 lowerCamelCase_      =  middle_blocks[2]

 lowerCamelCase_      =  renew_resnet_paths(UpperCAmelCase_		)
 assign_to_checkpoint(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				config=UpperCAmelCase_		)

 lowerCamelCase_      =  renew_resnet_paths(UpperCAmelCase_		)
 assign_to_checkpoint(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				config=UpperCAmelCase_		)

 lowerCamelCase_      =  renew_attention_paths(UpperCAmelCase_		)
 lowerCamelCase_      =  {
     "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(
     UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				attention_paths_to_split=UpperCAmelCase_					,				config=UpperCAmelCase_		)

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

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

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

   lowerCamelCase_      =  renew_resnet_paths(UpperCAmelCase_		)
   lowerCamelCase_      =  renew_resnet_paths(UpperCAmelCase_		)

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

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

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

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

    lowerCamelCase_      =  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_  :				Union[str, Any]						= parser.parse_args()

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

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

     a_  :				int						= convert_ldm_checkpoint(checkpoint, config)

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

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

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

          a_  :				Union[str, 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'''

import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_A			:      str					      =				logging.get_logger(__name__)

_A			:      str					      =				[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     Optional[int]:






 '''simple docstring'''

 if k == "embeddings.weight":
  return "shared.weight"

 for parlai_name, hf_name in PATTERNS:
  __lowerCAmelCase											=						k.replace(snake_case_						,		snake_case_      )

 if k.startswith("""encoder"""      ):
  __lowerCAmelCase											=						k.replace(""".attn"""						,		""".self_attn"""      )
  __lowerCAmelCase											=						k.replace("""norm1"""						,		"""self_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm2"""						,		"""final_layer_norm"""      )
 elif k.startswith("""decoder"""      ):
  __lowerCAmelCase											=						k.replace("""norm1"""						,		"""self_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm2"""						,		"""encoder_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm3"""						,		"""final_layer_norm"""      )
 return k







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """model.encoder.layernorm_embedding.weight""",
     """model.encoder.layernorm_embedding.bias""",
     """model.decoder.layernorm_embedding.weight""",
     """model.decoder.layernorm_embedding.bias""",
 ]
 for k in keys:
  __lowerCAmelCase											=						sd.pop(snake_case_      )
  __lowerCAmelCase											=						k.replace("""layernorm_embedding"""						,		"""layer_norm"""      )
  assert new_k not in sd
  __lowerCAmelCase											=						v


_A			:      int					      =				['''START''']







@torch.no_grad()
def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Any						,		snake_case_       :       Optional[int]      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						model["""model"""]
 __lowerCAmelCase											=						BlenderbotConfig.from_json_file(snake_case_      )
 __lowerCAmelCase											=						BlenderbotForConditionalGeneration(snake_case_      )
 __lowerCAmelCase											=						m.model.state_dict().keys()
 __lowerCAmelCase											=						[]
 __lowerCAmelCase											=						{}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

  __lowerCAmelCase											=						rename_state_dict_key(snake_case_      )
  if new_k not in valid_keys:
   failures.append([k, new_k]      )
  else:
   __lowerCAmelCase											=						v
 if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
  rename_layernorm_keys(snake_case_      )
 m.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 m.half()
 m.save_pretrained(snake_case_      )


if __name__ == "__main__":
 _A			:      Optional[Any]					      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
 parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
 parser.add_argument(
     '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
 )
 _A			:      Tuple					      =				parser.parse_args()
 convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






