





'''simple docstring'''
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        LayoutLMvaConfig,
        LayoutLMvaForQuestionAnswering,
        LayoutLMvaForSequenceClassification,
        LayoutLMvaForTokenClassification,
        LayoutLMvaModel,
    )
    from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import LayoutLMvaImageProcessor

class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							int							,	__A       :							str							,	__A       :							Tuple=2							,	__A       :							Optional[Any]=3							,	__A       :							List[str]=4							,	__A       :							Union[str, Any]=2							,	__A       :							str=7							,	__A       :							Tuple=True							,	__A       :							List[Any]=True							,	__A       :							Tuple=True							,	__A       :							int=True							,	__A       :							List[Any]=9_9							,	__A       :							Any=3_6							,	__A       :							Any=3							,	__A       :							str=4							,	__A       :							Tuple=3_7							,	__A       :							Union[str, Any]="gelu"							,	__A       :							Tuple=0.1							,	__A       :							int=0.1							,	__A       :							Tuple=5_1_2							,	__A       :							List[Any]=1_6							,	__A       :							Any=2							,	__A       :							Optional[int]=0.02							,	__A       :							Any=6							,	__A       :							Optional[Any]=6							,	__A       :							Tuple=3							,	__A       :							Optional[int]=4							,	__A       :							str=None							,	__A       :							Tuple=1_0_0_0							,	):
        __UpperCamelCase       =  parent
        __UpperCamelCase       =  batch_size
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  patch_size
        __UpperCamelCase       =  text_seq_length
        __UpperCamelCase       =  is_training
        __UpperCamelCase       =  use_input_mask
        __UpperCamelCase       =  use_token_type_ids
        __UpperCamelCase       =  use_labels
        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  type_vocab_size
        __UpperCamelCase       =  type_sequence_label_size
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  coordinate_size
        __UpperCamelCase       =  shape_size
        __UpperCamelCase       =  num_labels
        __UpperCamelCase       =  num_choices
        __UpperCamelCase       =  scope
        __UpperCamelCase       =  range_bbox

        # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
        __UpperCamelCase       =  text_seq_length
        __UpperCamelCase       =  (image_size // patch_size) ** 2 + 1
        __UpperCamelCase       =  self.text_seq_length + self.image_seq_length






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  ids_tensor([self.batch_size, self.text_seq_length]							,	self.vocab_size	)

        __UpperCamelCase       =  ids_tensor([self.batch_size, self.text_seq_length, 4]							,	self.range_bbox	)
        # Ensure that bbox is legal
        for i in range(bbox.shape[0]	):
               for j in range(bbox.shape[1]	):
                      if bbox[i, j, 3] < bbox[i, j, 1]:
                             __UpperCamelCase       =  bbox[i, j, 3]
                             __UpperCamelCase       =  bbox[i, j, 1]
                             __UpperCamelCase       =  t
                      if bbox[i, j, 2] < bbox[i, j, 0]:
                             __UpperCamelCase       =  bbox[i, j, 2]
                             __UpperCamelCase       =  bbox[i, j, 0]
                             __UpperCamelCase       =  t

        __UpperCamelCase       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

        __UpperCamelCase       =  None
        if self.use_input_mask:
               __UpperCamelCase       =  random_attention_mask([self.batch_size, self.text_seq_length]	)

        __UpperCamelCase       =  None
        if self.use_token_type_ids:
               __UpperCamelCase       =  ids_tensor([self.batch_size, self.text_seq_length]							,	self.type_vocab_size	)

        __UpperCamelCase       =  None
        __UpperCamelCase       =  None
        if self.use_labels:
               __UpperCamelCase       =  ids_tensor([self.batch_size]							,	self.type_sequence_label_size	)
               __UpperCamelCase       =  ids_tensor([self.batch_size, self.text_seq_length]							,	self.num_labels	)

        __UpperCamelCase       =  LayoutLMvaConfig(
            vocab_size=self.vocab_size							,	hidden_size=self.hidden_size							,	num_hidden_layers=self.num_hidden_layers							,	num_attention_heads=self.num_attention_heads							,	intermediate_size=self.intermediate_size							,	hidden_act=self.hidden_act							,	hidden_dropout_prob=self.hidden_dropout_prob							,	attention_probs_dropout_prob=self.attention_probs_dropout_prob							,	max_position_embeddings=self.max_position_embeddings							,	type_vocab_size=self.type_vocab_size							,	initializer_range=self.initializer_range							,	coordinate_size=self.coordinate_size							,	shape_size=self.shape_size							,	input_size=self.image_size							,	patch_size=self.patch_size							,	)

        return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							int							,	__A       :							str							,	__A       :							Optional[int]							,	__A       :							int							,	__A       :							Tuple							,	__A       :							List[Any]							,	__A       :							List[str]							,	__A       :							List[str]	):
        __UpperCamelCase       =  LayoutLMvaModel(config=__A	)
        model.to(__A	)
        model.eval()

        # text + image
        __UpperCamelCase       =  model(__A							,	pixel_values=__A	)
        __UpperCamelCase       =  model(
            __A							,	bbox=__A							,	pixel_values=__A							,	attention_mask=__A							,	token_type_ids=__A	)
        __UpperCamelCase       =  model(__A							,	bbox=__A							,	pixel_values=__A							,	token_type_ids=__A	)
        __UpperCamelCase       =  model(__A							,	bbox=__A							,	pixel_values=__A	)

        self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)	)

        # text only
        __UpperCamelCase       =  model(__A	)
        self.parent.assertEqual(
            result.last_hidden_state.shape							,	(self.batch_size, self.text_seq_length, self.hidden_size)	)

        # image only
        __UpperCamelCase       =  model(pixel_values=__A	)
        self.parent.assertEqual(
            result.last_hidden_state.shape							,	(self.batch_size, self.image_seq_length, self.hidden_size)	)






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							Union[str, Any]							,	__A       :							List[Any]							,	__A       :							Any							,	__A       :							List[Any]							,	__A       :							Optional[int]							,	__A       :							List[str]							,	__A       :							int							,	__A       :							List[Any]	):
        __UpperCamelCase       =  self.num_labels
        __UpperCamelCase       =  LayoutLMvaForSequenceClassification(__A	)
        model.to(__A	)
        model.eval()
        __UpperCamelCase       =  model(
            __A							,	bbox=__A							,	pixel_values=__A							,	attention_mask=__A							,	token_type_ids=__A							,	labels=__A							,	)
        self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels)	)






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							Dict							,	__A       :							Tuple							,	__A       :							int							,	__A       :							Optional[int]							,	__A       :							List[str]							,	__A       :							str							,	__A       :							Dict							,	__A       :							List[str]	):
        __UpperCamelCase       =  self.num_labels
        __UpperCamelCase       =  LayoutLMvaForTokenClassification(config=__A	)
        model.to(__A	)
        model.eval()
        __UpperCamelCase       =  model(
            __A							,	bbox=__A							,	pixel_values=__A							,	attention_mask=__A							,	token_type_ids=__A							,	labels=__A							,	)
        self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.text_seq_length, self.num_labels)	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							Dict							,	__A       :							Dict							,	__A       :							List[str]							,	__A       :							Tuple							,	__A       :							List[str]							,	__A       :							Dict							,	__A       :							int							,	__A       :							Optional[int]	):
        __UpperCamelCase       =  LayoutLMvaForQuestionAnswering(config=__A	)
        model.to(__A	)
        model.eval()
        __UpperCamelCase       =  model(
            __A							,	bbox=__A							,	pixel_values=__A							,	attention_mask=__A							,	token_type_ids=__A							,	start_positions=__A							,	end_positions=__A							,	)
        self.parent.assertEqual(result.start_logits.shape							,	(self.batch_size, self.seq_length)	)
        self.parent.assertEqual(result.end_logits.shape							,	(self.batch_size, self.seq_length)	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  self.prepare_config_and_inputs()
        (
            (
            __UpperCamelCase
        )       ,						(
            __UpperCamelCase
        )       ,						(
            __UpperCamelCase
        )       ,						(
            __UpperCamelCase
        )       ,						(
            __UpperCamelCase
        )       ,						(
            __UpperCamelCase
        )       ,						(
            __UpperCamelCase
        )       ,						(
            __UpperCamelCase
        )       ,						
        )       =  config_and_inputs
        __UpperCamelCase       =  {
            'input_ids': input_ids,
            'bbox': bbox,
            'pixel_values': pixel_values,
            'token_type_ids': token_type_ids,
            'attention_mask': input_mask,
        }
        return config, inputs_dict






@require_torch
class      snake_case     (    __lowerCamelCase			,       __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					=False
 SCREAMING_SNAKE_CASE_	:					str					=False
 SCREAMING_SNAKE_CASE_	:					int					=False

 SCREAMING_SNAKE_CASE_	:					Any					=(
     (
         LayoutLMvaModel,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_	:					int					=(
     {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
     if is_torch_available()
     else {}
 )






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							str							,	__A       :							Dict							,	__A       :							Optional[int]							,	__A       :							Union[str, Any]							,	__A       :							Union[str, Any]	):
        # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
        # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
        # the sequence dimension of the text embedding only.
        # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
        return True






 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  LayoutLMvaModelTester(self	)
        __UpperCamelCase       =  ConfigTester(self							,	config_class=__A							,	hidden_size=3_7	)






 def        _lowerCamelCase					(       self       :							str							,	__A       :							Dict							,	__A       :							Optional[int]							,	__A       :							Tuple=False	):
        __UpperCamelCase       =  copy.deepcopy(__A	)
        if model_class in get_values(__A	):
               __UpperCamelCase       =  {
                   k: v.unsqueeze(1	).expand(-1							,	self.model_tester.num_choices							,	-1	).contiguous()
                   if isinstance(__A							,	torch.Tensor	) and v.ndim > 1
                   else v
                   for k, v in inputs_dict.items()
               }
        if return_labels:
               if model_class in get_values(__A	):
                      __UpperCamelCase       =  torch.ones(self.model_tester.batch_size							,	dtype=torch.long							,	device=__A	)
               elif model_class in get_values(__A	):
                      __UpperCamelCase       =  torch.zeros(
                          self.model_tester.batch_size							,	dtype=torch.long							,	device=__A	)
                      __UpperCamelCase       =  torch.zeros(
                          self.model_tester.batch_size							,	dtype=torch.long							,	device=__A	)
               elif model_class in [
                   *get_values(__A	),
               ]:
                      __UpperCamelCase       =  torch.zeros(
                          self.model_tester.batch_size							,	dtype=torch.long							,	device=__A	)
               elif model_class in [
                   *get_values(__A	),
               ]:
                      __UpperCamelCase       =  torch.zeros(
                          (self.model_tester.batch_size, self.model_tester.text_seq_length)							,	dtype=torch.long							,	device=__A							,	)

        return inputs_dict






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        self.config_tester.run_common_tests()






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A	)






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
               __UpperCamelCase       =  type
               self.model_tester.create_and_check_model(*__A	)






 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__A	)






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__A	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__A	)






 @slow
 def        _lowerCamelCase					(       self       :							List[str]	):
        for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCamelCase       =  LayoutLMvaModel.from_pretrained(__A	)
               self.assertIsNotNone(__A	)






def 				lowercase__   (   ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image


@require_torch
class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 @cached_property
 def        _lowerCamelCase					(       self       :							List[str]	):
        return LayoutLMvaImageProcessor(apply_ocr=__A	) if is_vision_available() else None






 @slow
 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'	).to(__A	)

        __UpperCamelCase       =  self.default_image_processor
        __UpperCamelCase       =  prepare_img()
        __UpperCamelCase       =  image_processor(images=__A							,	return_tensors='pt'	).pixel_values.to(__A	)

        __UpperCamelCase       =  torch.tensor([[1, 2]]	)
        __UpperCamelCase       =  torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]	).unsqueeze(0	)

        # forward pass
        __UpperCamelCase       =  model(
            input_ids=input_ids.to(__A	)							,	bbox=bbox.to(__A	)							,	pixel_values=pixel_values.to(__A	)							,	)

        # verify the logits
        __UpperCamelCase       =  torch.Size((1, 1_9_9, 7_6_8)	)
        self.assertEqual(outputs.last_hidden_state.shape							,	__A	)

        __UpperCamelCase       =  torch.tensor(
            [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]	).to(__A	)

        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]							,	__A							,	atol=1e-4	)	)







'''simple docstring'''
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Any     ) ->      Optional[Any]:



       """simple docstring"""






       with open(__lowercase     ) as metadata_file:
              __UpperCamelCase       =  json.load(__lowercase     )
       __UpperCamelCase       =  LukeConfig(use_entity_aware_attention=__lowercase				,  **metadata['model_config']     )

       # Load in the weights from the checkpoint_path
       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )

       # Load the entity vocab file
       __UpperCamelCase       =  load_entity_vocab(__lowercase     )

       __UpperCamelCase       =  RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']     )

       # Add special tokens to the token vocabulary for downstream tasks
       __UpperCamelCase       =  AddedToken('<ent>'				,  lstrip=__lowercase				,  rstrip=__lowercase     )
       __UpperCamelCase       =  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				,  LukeTokenizer.vocab_files_names['entity_vocab_file']     )				,  'w'     ) as f:
              json.dump(__lowercase				,  __lowercase     )

       __UpperCamelCase       =  LukeTokenizer.from_pretrained(__lowercase     )

       # Initialize the embeddings of the special tokens
       __UpperCamelCase       =  state_dict['embeddings.word_embeddings.weight']
       __UpperCamelCase       =  word_emb[tokenizer.convert_tokens_to_ids(['@']     )[0]].unsqueeze(0     )
       __UpperCamelCase       =  word_emb[tokenizer.convert_tokens_to_ids(['#']     )[0]].unsqueeze(0     )
       __UpperCamelCase       =  torch.cat([word_emb, ent_emb, enta_emb]     )

       # 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"]:
                     __UpperCamelCase       =  F'''encoder.layer.{layer_index}.attention.self.'''
                     __UpperCamelCase       =  state_dict[prefix + matrix_name]
                     __UpperCamelCase       =  state_dict[prefix + matrix_name]
                     __UpperCamelCase       =  state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       __UpperCamelCase       =  state_dict['entity_embeddings.entity_embeddings.weight']
       __UpperCamelCase       =  entity_emb[entity_vocab['[MASK]']]

       __UpperCamelCase       =  LukeModel(config=__lowercase     ).eval()

       __UpperCamelCase       ,						__UpperCamelCase       =  model.load_state_dict(__lowercase				,  strict=__lowercase     )
       if not (len(__lowercase     ) == 1 and missing_keys[0] == "embeddings.position_ids"):
              raise ValueError(F'''Missing keys {', '.join(__lowercase     )}. Expected only missing embeddings.position_ids'''     )
       if not (all(key.startswith('entity_predictions'     ) or key.startswith('lm_head'     ) for key in unexpected_keys     )):
              raise ValueError(
                  'Unexpected keys'
                  F''' {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'     ) or key.startswith('lm_head'     ))]     )}'''     )

       # Check outputs
       __UpperCamelCase       =  LukeTokenizer.from_pretrained(__lowercase				,  task='entity_classification'     )

       __UpperCamelCase       =  (
           'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
           ' new world number one avoid a humiliating second- round exit at Wimbledon .'
       )
       __UpperCamelCase       =  (39, 42)
       __UpperCamelCase       =  tokenizer(__lowercase				,  entity_spans=[span]				,  add_prefix_space=__lowercase				,  return_tensors='pt'     )

       __UpperCamelCase       =  model(**__lowercase     )

       # Verify word hidden states
       if model_size == "large":
              __UpperCamelCase       =  torch.Size((1, 42, 1024)     )
              __UpperCamelCase       =  torch.tensor(
                  [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]     )
       else:  # base
              __UpperCamelCase       =  torch.Size((1, 42, 768)     )
              __UpperCamelCase       =  torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]     )

       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":
              __UpperCamelCase       =  torch.Size((1, 1, 1024)     )
              __UpperCamelCase       =  torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]     )
       else:  # base
              __UpperCamelCase       =  torch.Size((1, 1, 768)     )
              __UpperCamelCase       =  torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]     )

       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

       # Finally, save our PyTorch model and tokenizer
       print('Saving PyTorch model to {}'.format(__lowercase     )     )
       model.save_pretrained(__lowercase     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  {}
       with open(__lowercase				,  'r'				,  encoding='utf-8'     ) as f:
              for index, line in enumerate(__lowercase     ):
                     __UpperCamelCase       ,						__UpperCamelCase       =  line.rstrip().split('\t'     )
                     __UpperCamelCase       =  index

       return entity_vocab


if __name__ == "__main__":
    a__							:							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.'''
    )
    a__							:							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,
    )
