



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 		a__      (		_UpperCamelCase      :			int			,_UpperCamelCase      :			Dict=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 		a__      (		_UpperCamelCase      :			List[Any]			,_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(__a			,n_shave_prefix_segments=__a				)

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

   return mapping





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			Tuple=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(__a			,n_shave_prefix_segments=__a				)

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

   return mapping





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			List[str]=None			,_UpperCamelCase      :			Any=None			,_UpperCamelCase      :			List[str]=None				):
   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():
         __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    				=     old_tensor.split(channels // num_heads			,dim=1				)

         __lowerCamelCase    				=     query.reshape(__a				)
         __lowerCamelCase    				=     key.reshape(__a				)
         __lowerCamelCase    				=     value.reshape(__a				)

   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 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Dict				):
   __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(__a				)
   }

   # 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(__a				)
   }

   # 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(__a				)
   }

   for i in range(1			,__a				):
      __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(__a				)
      __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(
          __a			,__a			,__a			,additional_replacements=[meta_path, resnet_op]			,config=__a				)

      if len(__a				):
         __lowerCamelCase    				=     renew_attention_paths(__a				)
         __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(
             __a			,__a			,__a			,additional_replacements=[meta_path]			,attention_paths_to_split=__a			,config=__a			,)

   __lowerCamelCase    				=     middle_blocks[0]
   __lowerCamelCase    				=     middle_blocks[1]
   __lowerCamelCase    				=     middle_blocks[2]

   __lowerCamelCase    				=     renew_resnet_paths(__a				)
   assign_to_checkpoint(__a			,__a			,__a			,config=__a				)

   __lowerCamelCase    				=     renew_resnet_paths(__a				)
   assign_to_checkpoint(__a			,__a			,__a			,config=__a				)

   __lowerCamelCase    				=     renew_attention_paths(__a				)
   __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(
       __a			,__a			,__a			,attention_paths_to_split=__a			,config=__a				)

   for i in range(__a				):
      __lowerCamelCase    				=     i // (config['''num_res_blocks'''] + 1)
      __lowerCamelCase    				=     i % (config['''num_res_blocks'''] + 1)
      __lowerCamelCase    				=     [shave_segments(__a			,2				) for name in output_blocks[i]]
      __lowerCamelCase    				=     {}

      for layer in output_block_layers:
         __lowerCamelCase    				=     layer.split('''.'''				)[0], shave_segments(__a			,1				)
         if layer_id in output_block_list:
            output_block_list[layer_id].append(__a				)
         else:
            __lowerCamelCase    				=     [layer_name]

      if len(__a				) > 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(__a				)
         __lowerCamelCase    				=     renew_resnet_paths(__a				)

         __lowerCamelCase    				=     {'''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():
            __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(__a				) == 2:
               __lowerCamelCase    				=     []

         if len(__a				):
            __lowerCamelCase    				=     renew_attention_paths(__a				)
            __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(
                __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:
         __lowerCamelCase    				=     renew_resnet_paths(__a			,n_shave_prefix_segments=1				)
         for path in resnet_0_paths:
            __lowerCamelCase    				=     '''.'''.join(['''output_blocks''', str(__a				), path['''old''']]				)
            __lowerCamelCase    				=     '''.'''.join(['''up_blocks''', str(__a				), '''resnets''', str(__a				), path['''new''']]				)

            __lowerCamelCase    				=     checkpoint[old_path]

   return new_checkpoint


if __name__ == "__main__":
  a_								=       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_								=       parser.parse_args()

  a_								=       torch.load(args.checkpoint_path)

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

  a_								=       convert_ldm_checkpoint(checkpoint, config)

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

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

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

    a_								=       LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)









import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Dict						,__a :     List[str]						,__a :     Optional[Any]						,__a :     Tuple      )     ->	Dict:

					"""simple docstring"""

					with open(__a      ) as metadata_file:
										_a      :       Optional[Any]   =						json.load(__a      )
					_a      :       List[Any]   =						LukeConfig(use_entity_aware_attention=__a						,**metadata['''model_config''']      )

					# Load in the weights from the checkpoint_path
					_a      :       Optional[Any]   =						torch.load(__a						,map_location='''cpu'''      )['''module''']

					# Load the entity vocab file
					_a      :       Any   =						load_original_entity_vocab(__a      )
					# add an entry for [MASK2]
					_a      :       Union[str, Any]   =						max(entity_vocab.values()      ) + 1
					config.entity_vocab_size += 1

					_a      :       Dict   =						XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']      )

					# Add special tokens to the token vocabulary for downstream tasks
					_a      :       Optional[int]   =						AddedToken('''<ent>'''						,lstrip=__a						,rstrip=__a      )
					_a      :       Tuple   =						AddedToken('''<ent2>'''						,lstrip=__a						,rstrip=__a      )
					tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}      )
					config.vocab_size += 2

					print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""      )
					tokenizer.save_pretrained(__a      )
					with open(os.path.join(__a						,'''tokenizer_config.json'''      )						,'''r'''      ) as f:
										_a      :       List[str]   =						json.load(__a      )
					_a      :       Tuple   =						'''MLukeTokenizer'''
					with open(os.path.join(__a						,'''tokenizer_config.json'''      )						,'''w'''      ) as f:
										json.dump(__a						,__a      )

					with open(os.path.join(__a						,MLukeTokenizer.vocab_files_names['''entity_vocab_file''']      )						,'''w'''      ) as f:
										json.dump(__a						,__a      )

					_a      :       Optional[int]   =						MLukeTokenizer.from_pretrained(__a      )

					# Initialize the embeddings of the special tokens
					_a      :       str   =						tokenizer.convert_tokens_to_ids(['''@''']      )[0]
					_a      :       Tuple   =						tokenizer.convert_tokens_to_ids(['''#''']      )[0]

					_a      :       Any   =						state_dict['''embeddings.word_embeddings.weight''']
					_a      :       Optional[int]   =						word_emb[ent_init_index].unsqueeze(0      )
					_a      :       Any   =						word_emb[enta_init_index].unsqueeze(0      )
					_a      :       Union[str, Any]   =						torch.cat([word_emb, ent_emb, enta_emb]      )
					# add special tokens for 'entity_predictions.bias'
					for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
										_a      :       Tuple   =						state_dict[bias_name]
										_a      :       Optional[Any]   =						decoder_bias[ent_init_index].unsqueeze(0      )
										_a      :       Optional[int]   =						decoder_bias[enta_init_index].unsqueeze(0      )
										_a      :       Dict   =						torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]      )

					# Initialize the query layers of the entity-aware self-attention mechanism
					for layer_index in range(config.num_hidden_layers      ):
										for matrix_name in ["query.weight", "query.bias"]:
															_a      :       Tuple   =						F"""encoder.layer.{layer_index}.attention.self."""
															_a      :       List[Any]   =						state_dict[prefix + matrix_name]
															_a      :       Dict   =						state_dict[prefix + matrix_name]
															_a      :       List[Any]   =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					_a      :       Union[str, Any]   =						state_dict['''entity_embeddings.entity_embeddings.weight''']
					_a      :       Optional[int]   =						entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0      )
					_a      :       Any   =						torch.cat([entity_emb, entity_mask_emb]      )
					# add [MASK2] for 'entity_predictions.bias'
					_a      :       int   =						state_dict['''entity_predictions.bias''']
					_a      :       int   =						entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0      )
					_a      :       Optional[Any]   =						torch.cat([entity_prediction_bias, entity_mask_bias]      )

					_a      :       Optional[int]   =						LukeForMaskedLM(config=__a      ).eval()

					state_dict.pop('''entity_predictions.decoder.weight'''      )
					state_dict.pop('''lm_head.decoder.weight'''      )
					state_dict.pop('''lm_head.decoder.bias'''      )
					_a      :       int   =						OrderedDict()
					for key, value in state_dict.items():
										if not (key.startswith('''lm_head'''      ) or key.startswith('''entity_predictions'''      )):
															_a      :       Optional[int]   =						state_dict[key]
										else:
															_a      :       Tuple   =						state_dict[key]

					_a ,   _a      :       int   =						model.load_state_dict(__a						,strict=__a      )

					if set(__a      ) != {"luke.embeddings.position_ids"}:
										raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}"""      )
					if set(__a      ) != {
					    "lm_head.decoder.weight",
					    "lm_head.decoder.bias",
					    "entity_predictions.decoder.weight",
					}:
										raise ValueError(F"""Unexpected missing_keys: {missing_keys}"""      )

					model.tie_weights()
					assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
					assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

					# Check outputs
					_a      :       Optional[int]   =						MLukeTokenizer.from_pretrained(__a						,task='''entity_classification'''      )

					_a      :       int   =						'''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
					_a      :       List[Any]   =						(0, 9)
					_a      :       Tuple   =						tokenizer(__a						,entity_spans=[span]						,return_tensors='''pt'''      )

					_a      :       int   =						model(**__a      )

					# Verify word hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										_a      :       List[str]   =						torch.Size((1, 33, 768)      )
										_a      :       Union[str, Any]   =						torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]      )

					if not (outputs.last_hidden_state.shape == expected_shape):
										raise ValueError(
										    F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""      )
					if not torch.allclose(outputs.last_hidden_state[0, :3, :3]						,__a						,atol=1E-4      ):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										_a      :       str   =						torch.Size((1, 1, 768)      )
										_a      :       List[Any]   =						torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]      )

					if not (outputs.entity_last_hidden_state.shape == expected_shape):
										raise ValueError(
										    F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
										    F""" {expected_shape}"""      )
					if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]						,__a						,atol=1E-4      ):
										raise ValueError

					# Verify masked word/entity prediction
					_a      :       Optional[int]   =						MLukeTokenizer.from_pretrained(__a      )
					_a      :       Dict   =						'''Tokyo is the capital of <mask>.'''
					_a      :       List[str]   =						(24, 30)
					_a      :       Optional[int]   =						tokenizer(__a						,entity_spans=[span]						,return_tensors='''pt'''      )

					_a      :       Optional[Any]   =						model(**__a      )

					_a      :       Any   =						encoding['''input_ids'''][0].tolist()
					_a      :       Optional[Any]   =						input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''      )      )
					_a      :       Any   =						outputs.logits[0][mask_position_id].argmax(dim=-1      )
					assert "Japan" == tokenizer.decode(__a      )

					_a      :       Any   =						outputs.entity_logits[0][0].argmax().item()
					_a      :       Optional[Any]   =						[
					    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
					]
					assert [e for e in multilingual_predicted_entities if e.startswith('''en:'''      )][0] == "en:Japan"

					# Finally, save our PyTorch model and tokenizer
					print('''Saving PyTorch model to {}'''.format(__a      )      )
					model.save_pretrained(__a      )



def   __UpperCAmelCase					(      __a :     List[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						['''[MASK]''', '''[PAD]''', '''[UNK]''']

					_a      :       int   =						[json.loads(__a      ) for line in open(__a      )]

					_a      :       List[Any]   =						{}
					for entry in data:
										_a      :       int   =						entry['''id''']
										for entity_name, language in entry["entities"]:
															if entity_name in SPECIAL_TOKENS:
																				_a      :       List[Any]   =						entity_id
																				break
															_a      :       Dict   =						F"""{language}:{entity_name}"""
															_a      :       int   =						entity_id
					return new_mapping


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
			parser.add_argument(
			    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
			)
			parser.add_argument(
			    '''--entity_vocab_path''',
			    default=None,
			    type=str,
			    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
			)
			a__												=						parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)



