
import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu

class __snake_case		(				nn.Module					):
    def __init__(  self				):



           '''simple docstring'''







           super().__init__()
           lowercase							:					Optional[Any]	     =				nn.Linear(3		,4				)
           lowercase							:					Optional[int]	     =				nn.BatchNormad(4				)
           lowercase							:					Tuple	     =				nn.Linear(4		,5				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return self.lineara(self.batchnorm(self.lineara(snake_case				)				)				)






class __snake_case		(				lowerCAmelCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return (args[0] + 1,) + args[1:], kwargs






class __snake_case		(				lowerCAmelCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           return output + 1






class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				ModelForTest()
           lowercase							:					Any	     =				ModelHook()

           add_hook_to_module(snake_case		,snake_case				)
           self.assertEqual(test_model._hf_hook		,snake_case				)
           self.assertTrue(hasattr(snake_case		,"""_old_forward"""				)				)

           # Check adding the hook did not change the name or the signature
           self.assertEqual(test_model.forward.__name__		,"""forward"""				)
           self.assertListEqual(list(inspect.signature(test_model.forward				).parameters				)		,["""x"""]				)

           remove_hook_from_module(snake_case				)
           self.assertFalse(hasattr(snake_case		,"""_hf_hook"""				)				)
           self.assertFalse(hasattr(snake_case		,"""_old_forward"""				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				ModelForTest()
           lowercase							:					List[str]	     =				ModelHook()

           add_hook_to_module(snake_case		,snake_case				)
           add_hook_to_module(snake_case		,snake_case		,append=snake_case				)

           self.assertEqual(isinstance(test_model._hf_hook		,snake_case				)		,snake_case				)
           self.assertEqual(len(test_model._hf_hook.hooks				)		,2				)
           self.assertTrue(hasattr(snake_case		,"""_old_forward"""				)				)

           # Check adding the hook did not change the name or the signature
           self.assertEqual(test_model.forward.__name__		,"""forward"""				)
           self.assertListEqual(list(inspect.signature(test_model.forward				).parameters				)		,["""x"""]				)

           remove_hook_from_module(snake_case				)
           self.assertFalse(hasattr(snake_case		,"""_hf_hook"""				)				)
           self.assertFalse(hasattr(snake_case		,"""_old_forward"""				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				ModelForTest()
           lowercase							:					Dict	     =				torch.randn(2		,3				)
           lowercase							:					List[Any]	     =				test_model(x + 1				)
           lowercase							:					Any	     =				test_model(x + 2				)

           lowercase							:					Optional[Any]	     =				PreForwardHook()
           add_hook_to_module(snake_case		,snake_case				)
           lowercase							:					Optional[int]	     =				test_model(snake_case				)
           self.assertTrue(torch.allclose(snake_case		,snake_case		,atol=1e-5				)				)

           # Attaching a hook to a model when it already has one replaces, does not chain
           lowercase							:					Optional[Any]	     =				PreForwardHook()
           add_hook_to_module(snake_case		,snake_case				)
           lowercase							:					Optional[Any]	     =				test_model(snake_case				)
           self.assertTrue(torch.allclose(snake_case		,snake_case		,atol=1e-5				)				)

           # You need to use the sequential hook to chain two or more hooks
           lowercase							:					Union[str, Any]	     =				SequentialHook(PreForwardHook()		,PreForwardHook()				)
           add_hook_to_module(snake_case		,snake_case				)

           lowercase							:					List[str]	     =				test_model(snake_case				)
           assert torch.allclose(snake_case		,snake_case		,atol=1e-5				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				ModelForTest()
           lowercase							:					Dict	     =				torch.randn(2		,3				)
           lowercase							:					Optional[Any]	     =				test_model(snake_case				)

           lowercase							:					Optional[Any]	     =				PostForwardHook()
           add_hook_to_module(snake_case		,snake_case				)
           lowercase							:					List[Any]	     =				test_model(snake_case				)
           self.assertTrue(torch.allclose(snake_case		,output + 1		,atol=1e-5				)				)

           # Attaching a hook to a model when it already has one replaces, does not chain
           lowercase							:					Any	     =				PostForwardHook()
           add_hook_to_module(snake_case		,snake_case				)
           lowercase							:					List[str]	     =				test_model(snake_case				)
           self.assertTrue(torch.allclose(snake_case		,output + 1		,atol=1e-5				)				)

           # You need to use the sequential hook to chain two or more hooks
           lowercase							:					Dict	     =				SequentialHook(PostForwardHook()		,PostForwardHook()				)
           add_hook_to_module(snake_case		,snake_case				)

           lowercase							:					List[Any]	     =				test_model(snake_case				)
           assert torch.allclose(snake_case		,output + 2		,atol=1e-5				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				ModelForTest()
           lowercase							:					Any	     =				torch.randn(2		,3				)
           lowercase							:					List[Any]	     =				test_model(snake_case				)

           lowercase							:					Union[str, Any]	     =				PostForwardHook()
           add_hook_to_module(snake_case		,snake_case				)
           lowercase							:					Any	     =				test_model(snake_case				)
           self.assertTrue(torch.allclose(snake_case		,output + 1				)				)
           self.assertTrue(outputa.requires_grad				)

           lowercase							:					Dict	     =				True
           lowercase							:					List[str]	     =				test_model(snake_case				)
           self.assertFalse(outputa.requires_grad				)
    @require_multi_gpu
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				ModelForTest()
           # Everything is on CPU
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)

           # This will move each submodule on different devices
           add_hook_to_module(model.lineara		,AlignDevicesHook(execution_device=0				)				)
           add_hook_to_module(model.batchnorm		,AlignDevicesHook(execution_device=0				)				)
           add_hook_to_module(model.lineara		,AlignDevicesHook(execution_device=1				)				)

           self.assertEqual(model.lineara.weight.device		,torch.device(0				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device(0				)				)
           self.assertEqual(model.batchnorm.running_mean.device		,torch.device(0				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device(1				)				)

           # We can still make a forward pass. The input does not need to be on any particular device
           lowercase							:					Optional[int]	     =				torch.randn(2		,3				)
           lowercase							:					str	     =				model(snake_case				)
           self.assertEqual(output.device		,torch.device(1				)				)

           # We can add a general hook to put back output on same device as input.
           add_hook_to_module(snake_case		,AlignDevicesHook(io_same_device=snake_case				)				)
           lowercase							:					Any	     =				torch.randn(2		,3				).to(0				)
           lowercase							:					str	     =				model(snake_case				)
           self.assertEqual(output.device		,torch.device(0				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				ModelForTest()

           # Everything is on CPU
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)

           # This will move each submodule on different devices
           lowercase							:					Union[str, Any]	     =				{"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}

           add_hook_to_module(model.lineara		,AlignDevicesHook(**snake_case				)				)
           add_hook_to_module(model.batchnorm		,AlignDevicesHook(**snake_case				)				)
           add_hook_to_module(model.lineara		,AlignDevicesHook(**snake_case				)				)

           # Parameters have been offloaded, so on the meta device
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           # Buffers are not included in the offload by default, so are on the execution device
           lowercase							:					Optional[Any]	     =				torch.device(hook_kwargs["""execution_device"""]				)
           self.assertEqual(model.batchnorm.running_mean.device		,snake_case				)

           lowercase							:					int	     =				torch.randn(2		,3				)
           lowercase							:					Tuple	     =				model(snake_case				)
           self.assertEqual(output.device		,snake_case				)

           # Removing hooks loads back the weights in the model.
           remove_hook_from_module(model.lineara				)
           remove_hook_from_module(model.batchnorm				)
           remove_hook_from_module(model.lineara				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)

           # Now test with buffers included in the offload
           lowercase							:					Dict	     =				{
               """execution_device""": 0 if torch.cuda.is_available() else """cpu""",
               """offload""": True,
               """offload_buffers""": True,
           }

           add_hook_to_module(model.lineara		,AlignDevicesHook(**snake_case				)				)
           add_hook_to_module(model.batchnorm		,AlignDevicesHook(**snake_case				)				)
           add_hook_to_module(model.lineara		,AlignDevicesHook(**snake_case				)				)

           # Parameters have been offloaded, so on the meta device, buffers included
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.batchnorm.running_mean.device		,torch.device("""meta"""				)				)

           lowercase							:					str	     =				torch.randn(2		,3				)
           lowercase							:					Dict	     =				model(snake_case				)
           self.assertEqual(output.device		,snake_case				)

           # Removing hooks loads back the weights in the model.
           remove_hook_from_module(model.lineara				)
           remove_hook_from_module(model.batchnorm				)
           remove_hook_from_module(model.lineara				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				ModelForTest()

           # Everything is on CPU
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)

           # This will move each submodule on different devices
           lowercase							:					List[str]	     =				0 if torch.cuda.is_available() else """cpu"""
           attach_align_device_hook(snake_case		,execution_device=snake_case		,offload=snake_case				)

           # Parameters have been offloaded, so on the meta device
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           # Buffers are not included in the offload by default, so are on the execution device
           lowercase							:					List[str]	     =				torch.device(snake_case				)
           self.assertEqual(model.batchnorm.running_mean.device		,snake_case				)

           lowercase							:					Union[str, Any]	     =				torch.randn(2		,3				)
           lowercase							:					Optional[Any]	     =				model(snake_case				)
           self.assertEqual(output.device		,snake_case				)

           # Removing hooks loads back the weights in the model.
           remove_hook_from_submodules(snake_case				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)

           # Now test with buffers included in the offload
           attach_align_device_hook(snake_case		,execution_device=snake_case		,offload=snake_case		,offload_buffers=snake_case				)

           # Parameters have been offloaded, so on the meta device, buffers included
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.batchnorm.running_mean.device		,torch.device("""meta"""				)				)

           lowercase							:					str	     =				torch.randn(2		,3				)
           lowercase							:					Any	     =				model(snake_case				)
           self.assertEqual(output.device		,snake_case				)

           # Removing hooks loads back the weights in the model.
           remove_hook_from_submodules(snake_case				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				ModelForTest()

           # Everything is on CPU
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)

           # This will move each submodule on different devices
           lowercase							:					Union[str, Any]	     =				0 if torch.cuda.is_available() else """cpu"""
           attach_align_device_hook(
               snake_case		,execution_device=snake_case		,offload=snake_case		,weights_map=model.state_dict()				)

           # Parameters have been offloaded, so on the meta device
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           # Buffers are not included in the offload by default, so are on the execution device
           lowercase							:					Optional[int]	     =				torch.device(snake_case				)
           self.assertEqual(model.batchnorm.running_mean.device		,snake_case				)

           lowercase							:					str	     =				torch.randn(2		,3				)
           lowercase							:					Dict	     =				model(snake_case				)
           self.assertEqual(output.device		,snake_case				)

           # Removing hooks loads back the weights in the model.
           remove_hook_from_submodules(snake_case				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)

           # Now test with buffers included in the offload
           attach_align_device_hook(
               snake_case		,execution_device=snake_case		,offload=snake_case		,weights_map=model.state_dict()		,offload_buffers=snake_case		,)

           # Parameters have been offloaded, so on the meta device, buffers included
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""meta"""				)				)
           self.assertEqual(model.batchnorm.running_mean.device		,torch.device("""meta"""				)				)

           lowercase							:					int	     =				torch.randn(2		,3				)
           lowercase							:					Optional[Any]	     =				model(snake_case				)
           self.assertEqual(output.device		,snake_case				)

           # Removing hooks loads back the weights in the model.
           remove_hook_from_submodules(snake_case				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.batchnorm.weight.device		,torch.device("""cpu"""				)				)
           self.assertEqual(model.lineara.weight.device		,torch.device("""cpu"""				)				)


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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       # Load configuration defined in the metadata file
       with open(SCREAMING_SNAKE_CASE__    ) as metadata_file:
              lowercase							:					Union[str, Any]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__ ,     **metadata["""model_config"""]    )

       # Load in the weights from the checkpoint_path
       lowercase							:					Tuple	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )["""module"""]

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

       lowercase							:					Dict	     =				XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]    )

       # Add special tokens to the token vocabulary for downstream tasks
       lowercase							:					List[Any]	     =				AddedToken("""<ent>""" ,     lstrip=SCREAMING_SNAKE_CASE__ ,     rstrip=SCREAMING_SNAKE_CASE__    )
       lowercase							:					int	     =				AddedToken("""<ent2>""" ,     lstrip=SCREAMING_SNAKE_CASE__ ,     rstrip=SCREAMING_SNAKE_CASE__    )
       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(SCREAMING_SNAKE_CASE__    )
       with open(os.path.join(SCREAMING_SNAKE_CASE__ ,     """tokenizer_config.json"""    ) ,     """r"""    ) as f:
              lowercase							:					List[str]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[Any]	     =				"""MLukeTokenizer"""
       with open(os.path.join(SCREAMING_SNAKE_CASE__ ,     """tokenizer_config.json"""    ) ,     """w"""    ) as f:
              json.dump(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

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

       lowercase							:					int	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )

       # Initialize the embeddings of the special tokens
       lowercase							:					Dict	     =				tokenizer.convert_tokens_to_ids(["""@"""]    )[0]
       lowercase							:					Dict	     =				tokenizer.convert_tokens_to_ids(["""#"""]    )[0]

       lowercase							:					int	     =				state_dict["""embeddings.word_embeddings.weight"""]
       lowercase							:					Union[str, Any]	     =				word_emb[ent_init_index].unsqueeze(0    )
       lowercase							:					List[str]	     =				word_emb[enta_init_index].unsqueeze(0    )
       lowercase							:					str	     =				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"]:
              lowercase							:					List[Any]	     =				state_dict[bias_name]
              lowercase							:					Any	     =				decoder_bias[ent_init_index].unsqueeze(0    )
              lowercase							:					Tuple	     =				decoder_bias[enta_init_index].unsqueeze(0    )
              lowercase							:					int	     =				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"]:
                     lowercase							:					Union[str, Any]	     =				f"encoder.layer.{layer_index}.attention.self."
                     lowercase							:					List[str]	     =				state_dict[prefix + matrix_name]
                     lowercase							:					Any	     =				state_dict[prefix + matrix_name]
                     lowercase							:					str	     =				state_dict[prefix + matrix_name]

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

       lowercase							:					List[str]	     =				LukeForMaskedLM(config=SCREAMING_SNAKE_CASE__    ).eval()

       state_dict.pop("""entity_predictions.decoder.weight"""    )
       state_dict.pop("""lm_head.decoder.weight"""    )
       state_dict.pop("""lm_head.decoder.bias"""    )
       lowercase							:					List[str]	     =				OrderedDict()
       for key, value in state_dict.items():
              if not (key.startswith("""lm_head"""    ) or key.startswith("""entity_predictions"""    )):
                     lowercase							:					List[Any]	     =				state_dict[key]
              else:
                     lowercase							:					Union[str, Any]	     =				state_dict[key]

       lowercase     ,      lowercase							:					int	     =				model.load_state_dict(SCREAMING_SNAKE_CASE__ ,     strict=SCREAMING_SNAKE_CASE__    )

       if set(SCREAMING_SNAKE_CASE__    ) != {"luke.embeddings.position_ids"}:
              raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}"    )
       if set(SCREAMING_SNAKE_CASE__    ) != {
           "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
       lowercase							:					str	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ,     task="""entity_classification"""    )

       lowercase							:					str	     =				"""ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
       lowercase							:					str	     =				(0, 9)
       lowercase							:					Dict	     =				tokenizer(SCREAMING_SNAKE_CASE__ ,     entity_spans=[span] ,     return_tensors="""pt"""    )

       lowercase							:					Any	     =				model(**SCREAMING_SNAKE_CASE__    )

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              lowercase							:					List[Any]	     =				torch.Size((1, 33, 768)    )
              lowercase							:					str	     =				torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]    )

       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] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    ):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              lowercase							:					Optional[int]	     =				torch.Size((1, 1, 768)    )
              lowercase							:					List[Any]	     =				torch.tensor([[-0.1482, 0.0609, 0.0322]]    )

       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] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    ):
              raise ValueError

       # Verify masked word/entity prediction
       lowercase							:					Any	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[Any]	     =				"""Tokyo is the capital of <mask>."""
       lowercase							:					List[Any]	     =				(24, 30)
       lowercase							:					int	     =				tokenizer(SCREAMING_SNAKE_CASE__ ,     entity_spans=[span] ,     return_tensors="""pt"""    )

       lowercase							:					Dict	     =				model(**SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				encoding["""input_ids"""][0].tolist()
       lowercase							:					List[Any]	     =				input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""    )    )
       lowercase							:					Dict	     =				outputs.logits[0][mask_position_id].argmax(dim=-1    )
       assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				outputs.entity_logits[0][0].argmax().item()
       lowercase							:					int	     =				[
           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(SCREAMING_SNAKE_CASE__    )    )
       model.save_pretrained(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Dict:
       lowercase							:					Optional[int]	     =				["""[MASK]""", """[PAD]""", """[UNK]"""]

       lowercase							:					List[str]	     =				[json.loads(SCREAMING_SNAKE_CASE__    ) for line in open(SCREAMING_SNAKE_CASE__    )]

       lowercase							:					int	     =				{}
       for entry in data:
              lowercase							:					Optional[Any]	     =				entry["""id"""]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            lowercase							:					Optional[Any]	     =				entity_id
                            break
                     lowercase							:					List[Any]	     =				f"{language}:{entity_name}"
                     lowercase							:					Union[str, Any]	     =				entity_id
       return new_mapping


if __name__ == "__main__":
  lowercase  : Any						   =							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."""
  )
  lowercase  : str						   =							parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )
