


"""simple docstring"""



import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
  from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class _lowerCAmelCase    :







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=13						,     UpperCamelCase__=32						,     UpperCamelCase__=3						,     UpperCamelCase__=4						,     UpperCamelCase__=[10, 20, 30, 40]						,     UpperCamelCase__=[2, 2, 3, 2]						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=37						,     UpperCamelCase__="gelu"						,     UpperCamelCase__=10						,     UpperCamelCase__=0.02						,     UpperCamelCase__=["stage2", "stage3", "stage4"]						,     UpperCamelCase__=[2, 3, 4]						,     UpperCamelCase__=None						,     )     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Any				 =   parent
       snake_case   :				Union[str, Any]				 =   batch_size
       snake_case   :				str				 =   image_size
       snake_case   :				List[str]				 =   num_channels
       snake_case   :				Optional[int]				 =   num_stages
       snake_case   :				List[str]				 =   hidden_sizes
       snake_case   :				Tuple				 =   depths
       snake_case   :				Dict				 =   is_training
       snake_case   :				Union[str, Any]				 =   use_labels
       snake_case   :				List[Any]				 =   intermediate_size
       snake_case   :				Union[str, Any]				 =   hidden_act
       snake_case   :				Union[str, Any]				 =   num_labels
       snake_case   :				Optional[int]				 =   initializer_range
       snake_case   :				Optional[Any]				 =   out_features
       snake_case   :				str				 =   out_indices
       snake_case   :				List[str]				 =   scope







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				str				 =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       snake_case   :				Optional[Any]				 =   None
       if self.use_labels:
           snake_case   :				List[Any]				 =   ids_tensor([self.batch_size]						,     self.num_labels						)

       snake_case   :				Tuple				 =   self.get_config()

       return config, pixel_values, labels







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       return ConvNextVaConfig(
           num_channels=self.num_channels						,     hidden_sizes=self.hidden_sizes						,     depths=self.depths						,     num_stages=self.num_stages						,     hidden_act=self.hidden_act						,     is_decoder=UpperCamelCase__						,     initializer_range=self.initializer_range						,     out_features=self.out_features						,     out_indices=self.out_indices						,     num_labels=self.num_labels						,     )







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   ConvNextVaModel(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case   :				Any				 =   model(UpperCamelCase__						)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape						,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,     )







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   ConvNextVaForImageClassification(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case   :				Optional[int]				 =   model(UpperCamelCase__						,     labels=UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels)						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   ConvNextVaBackbone(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case   :				Union[str, Any]				 =   model(UpperCamelCase__						)

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps						)						,     len(config.out_features						)						)
       self.parent.assertListEqual(list(result.feature_maps[0].shape						)						,     [self.batch_size, self.hidden_sizes[1], 4, 4]						)

       # verify channels
       self.parent.assertEqual(len(model.channels						)						,     len(config.out_features						)						)
       self.parent.assertListEqual(model.channels						,     config.hidden_sizes[1:]						)

       # verify backbone works with out_features=None
       snake_case   :				Any				 =   None
       snake_case   :				Optional[int]				 =   ConvNextVaBackbone(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case   :				int				 =   model(UpperCamelCase__						)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps						)						,     1						)
       self.parent.assertListEqual(list(result.feature_maps[0].shape						)						,     [self.batch_size, self.hidden_sizes[-1], 1, 1]						)

       # verify channels
       self.parent.assertEqual(len(model.channels						)						,     1						)
       self.parent.assertListEqual(model.channels						,     [config.hidden_sizes[-1]]						)







   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       snake_case   :				Any				 =   self.prepare_config_and_inputs()
       snake_case ,snake_case ,snake_case   :				List[Any]				 =   config_and_inputs
       snake_case   :				Union[str, Any]				 =   {"pixel_values": pixel_values}
       return config, inputs_dict







   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       snake_case   :				Dict				 =   self.prepare_config_and_inputs()
       snake_case ,snake_case ,snake_case   :				Tuple				 =   config_and_inputs
       snake_case   :				Optional[int]				 =   {"pixel_values": pixel_values, "labels": labels}
       return config, inputs_dict







@require_torch
class _lowerCAmelCase    (				snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	List[str]							  =  (
       (
           ConvNextVaModel,
           ConvNextVaForImageClassification,
           ConvNextVaBackbone,
       )
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase			:	Tuple							  =  (
       {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
       if is_torch_available()
       else {}
   )

   __UpperCAmelCase			:	Any							  =  False
   __UpperCAmelCase			:	Any							  =  False
   __UpperCAmelCase			:	int							  =  False
   __UpperCAmelCase			:	List[Any]							  =  False
   __UpperCAmelCase			:	List[str]							  =  False







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				str				 =   ConvNextVaModelTester(self						)
       snake_case   :				Union[str, Any]				 =   ConfigTester(self						,     config_class=UpperCamelCase__						,     has_text_modality=UpperCamelCase__						,     hidden_size=37						)







   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()







   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       return







   @unittest.skip(reason="ConvNextV2 does not use inputs_embeds"						)
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       pass







   @unittest.skip(reason="ConvNextV2 does not support input and output embeddings"						)
   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       pass







   @unittest.skip(reason="ConvNextV2 does not use feedforward chunking"						)
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       pass







   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       if not self.model_tester.is_training:
           return

       for model_class in self.all_model_classes:
           snake_case ,snake_case   :				Dict				 =   self.model_tester.prepare_config_and_inputs_with_labels()
           snake_case   :				Tuple				 =   True

           if model_class.__name__ in [
               *get_values(UpperCamelCase__						),
               *get_values(UpperCamelCase__						),
           ]:
               continue

           snake_case   :				Optional[int]				 =   model_class(UpperCamelCase__						)
           model.to(UpperCamelCase__						)
           model.train()
           snake_case   :				Tuple				 =   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__						,     return_labels=UpperCamelCase__						)
           snake_case   :				Optional[Any]				 =   model(**UpperCamelCase__						).loss
           loss.backward()







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       if not self.model_tester.is_training:
           return

       for model_class in self.all_model_classes:
           snake_case ,snake_case   :				List[str]				 =   self.model_tester.prepare_config_and_inputs_with_labels()
           snake_case   :				List[str]				 =   False
           snake_case   :				Any				 =   True

           if (
               model_class.__name__
               in [*get_values(UpperCamelCase__						), *get_values(UpperCamelCase__						)]
               or not model_class.supports_gradient_checkpointing
           ):
               continue

           snake_case   :				Dict				 =   model_class(UpperCamelCase__						)
           model.to(UpperCamelCase__						)
           model.gradient_checkpointing_enable()
           model.train()
           snake_case   :				Union[str, Any]				 =   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__						,     return_labels=UpperCamelCase__						)
           snake_case   :				List[Any]				 =   model(**UpperCamelCase__						).loss
           loss.backward()







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case ,snake_case   :				Union[str, Any]				 =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case   :				List[str]				 =   model_class(UpperCamelCase__						)
           snake_case   :				Tuple				 =   inspect.signature(model.forward						)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           snake_case   :				int				 =   [*signature.parameters.keys()]

           snake_case   :				Optional[Any]				 =   ["pixel_values"]
           self.assertListEqual(arg_names[:1]						,     UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       def check_hidden_states_output(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						):
           snake_case   :				Tuple				 =   model_class(UpperCamelCase__						)
           model.to(UpperCamelCase__						)
           model.eval()

           with torch.no_grad():
               snake_case   :				Any				 =   model(**self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__						)						)

           snake_case   :				str				 =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           snake_case   :				str				 =   self.model_tester.num_stages
           self.assertEqual(len(UpperCamelCase__						)						,     expected_num_stages + 1						)

           # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]						)						,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,     )

       snake_case ,snake_case   :				List[Any]				 =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case   :				List[Any]				 =   True
           check_hidden_states_output(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           snake_case   :				str				 =   True

           check_hidden_states_output(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__						)







   @slow
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case   :				Union[str, Any]				 =   ConvNextVaModel.from_pretrained(UpperCamelCase__						)
           self.assertIsNotNone(UpperCamelCase__						)







def   __lowerCAmelCase						(						)     ->						int:


    """simple docstring"""





    snake_case   :				Optional[int]				 =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
    return image







@require_torch
@require_vision
class _lowerCAmelCase    (				unittest.TestCase     ):







   @cached_property
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224"						) if is_vision_available() else None







   @slow
   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"						).to(UpperCamelCase__						)

       snake_case   :				str				 =   self.default_image_processor
       snake_case   :				str				 =   prepare_img()
       snake_case   :				Dict				 =   preprocessor(images=UpperCamelCase__						,     return_tensors="pt"						).to(UpperCamelCase__						)

       # forward pass
       with torch.no_grad():
           snake_case   :				Optional[int]				 =   model(**UpperCamelCase__						)

       # verify the logits
       snake_case   :				List[str]				 =   torch.Size((1, 1000)						)
       self.assertEqual(outputs.logits.shape						,     UpperCamelCase__						)

       snake_case   :				Optional[int]				 =   torch.tensor([0.9996, 0.1966, -0.4386]						).to(UpperCamelCase__						)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]						,     UpperCamelCase__						,     atol=1e-4						)						)









"""simple docstring"""



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   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Dict						,							lowercase  :  int			)     ->						int:


    """simple docstring"""





    with open(lowercase			) as metadata_file:
        snake_case   :				str				 =   json.load(lowercase			)
    snake_case   :				Optional[Any]				 =   LukeConfig(use_entity_aware_attention=lowercase						,							**metadata["model_config"]			)

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

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

    snake_case   :				Union[str, Any]				 =   XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

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

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

    snake_case   :				Dict				 =   MLukeTokenizer.from_pretrained(lowercase			)

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

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

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

    snake_case   :				Union[str, Any]				 =   LukeForMaskedLM(config=lowercase			).eval()

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

    snake_case ,snake_case   :				Optional[Any]				 =   model.load_state_dict(lowercase						,							strict=lowercase			)

    if set(lowercase			) != {"luke.embeddings.position_ids"}:
        raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}'			)
    if set(lowercase			) != {
        "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
    snake_case   :				Optional[Any]				 =   MLukeTokenizer.from_pretrained(lowercase						,							task="entity_classification"			)

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

    snake_case   :				int				 =   model(**lowercase			)

    # Verify word hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        snake_case   :				int				 =   torch.Size((1, 33, 768)			)
        snake_case   :				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]						,							lowercase						,							atol=1e-4			):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        snake_case   :				Any				 =   torch.Size((1, 1, 768)			)
        snake_case   :				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]						,							lowercase						,							atol=1e-4			):
        raise ValueError

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

    snake_case   :				Any				 =   model(**lowercase			)

    snake_case   :				int				 =   encoding["input_ids"][0].tolist()
    snake_case   :				str				 =   input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"			)			)
    snake_case   :				Tuple				 =   outputs.logits[0][mask_position_id].argmax(dim=-1			)
    assert "Japan" == tokenizer.decode(lowercase			)

    snake_case   :				Tuple				 =   outputs.entity_logits[0][0].argmax().item()
    snake_case   :				Optional[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(lowercase			)			)
    model.save_pretrained(lowercase			)







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]			)     ->						Dict:


    """simple docstring"""





    snake_case   :				Tuple				 =   ["[MASK]", "[PAD]", "[UNK]"]

    snake_case   :				Optional[Any]				 =   [json.loads(lowercase			) for line in open(lowercase			)]

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


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





