


import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
       from transformers.models.mobilevitva.modeling_mobilevitva import (
           MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
           make_divisible,
       )


if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							str		 =						self.config_class(**self.inputs_dict    )
       self.parent.assertTrue(hasattr(a						,				'width_multiplier'    )    )



class 	A_ :




      '''simple docstring'''


      def __init__(    self:	Dict						,				a:	str						,				a:	Tuple=13						,				a:	List[Any]=64						,				a:	List[str]=2						,				a:	Any=3						,				a:	Union[str, Any]="swish"						,				a:	Any=3						,				a:	Union[str, Any]=32						,				a:	int=0.1						,				a:	Tuple=0.0_2						,				a:	Optional[Any]=True						,				a:	Any=True						,				a:	int=10						,				a:	int=None						,				a:	Optional[Any]=0.2_5						,				a:	str=0.0						,				a:	Optional[Any]=0.0						,				):
       __lowerCamelCase		:							int		 =						parent
       __lowerCamelCase		:							List[Any]		 =						batch_size
       __lowerCamelCase		:							str		 =						image_size
       __lowerCamelCase		:							List[Any]		 =						patch_size
       __lowerCamelCase		:							int		 =						num_channels
       __lowerCamelCase		:							Dict		 =						make_divisible(512 * width_multiplier						,				divisor=8    )
       __lowerCamelCase		:							Optional[int]		 =						hidden_act
       __lowerCamelCase		:							int		 =						conv_kernel_size
       __lowerCamelCase		:							List[str]		 =						output_stride
       __lowerCamelCase		:							Tuple		 =						classifier_dropout_prob
       __lowerCamelCase		:							Dict		 =						use_labels
       __lowerCamelCase		:							Any		 =						is_training
       __lowerCamelCase		:							List[Any]		 =						num_labels
       __lowerCamelCase		:							Optional[int]		 =						initializer_range
       __lowerCamelCase		:							str		 =						scope
       __lowerCamelCase		:							List[Any]		 =						width_multiplier
       __lowerCamelCase		:							str		 =						ffn_dropout
       __lowerCamelCase		:							int		 =						attn_dropout




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							Optional[Any]		 =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

       __lowerCamelCase		:							List[Any]		 =						None
       __lowerCamelCase		:							List[Any]		 =						None
       if self.use_labels:
        __lowerCamelCase		:							List[str]		 =						ids_tensor([self.batch_size]						,				self.num_labels    )
        __lowerCamelCase		:							int		 =						ids_tensor([self.batch_size, self.image_size, self.image_size]						,				self.num_labels    )

       __lowerCamelCase		:							Any		 =						self.get_config()

       return config, pixel_values, labels, pixel_labels




      def 					_snake_case     (    self:	Dict    ):
       return MobileViTVaConfig(
           image_size=self.image_size						,				patch_size=self.patch_size						,				num_channels=self.num_channels						,				hidden_act=self.hidden_act						,				conv_kernel_size=self.conv_kernel_size						,				output_stride=self.output_stride						,				classifier_dropout_prob=self.classifier_dropout_prob						,				initializer_range=self.initializer_range						,				width_multiplier=self.width_multiplier						,				ffn_dropout=self.ffn_dropout_prob						,				attn_dropout=self.attn_dropout_prob						,				)




      def 					_snake_case     (    self:	List[str]						,				a:	Dict						,				a:	int						,				a:	Any						,				a:	str    ):
       __lowerCamelCase		:							List[str]		 =						MobileViTVaModel(config=a    )
       model.to(a    )
       model.eval()
       __lowerCamelCase		:							Union[str, Any]		 =						model(a    )
       self.parent.assertEqual(
           result.last_hidden_state.shape						,				(
               self.batch_size,
               self.last_hidden_size,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )						,				)




      def 					_snake_case     (    self:	str						,				a:	List[Any]						,				a:	Tuple						,				a:	Union[str, Any]						,				a:	List[str]    ):
       __lowerCamelCase		:							Any		 =						self.num_labels
       __lowerCamelCase		:							Tuple		 =						MobileViTVaForImageClassification(a    )
       model.to(a    )
       model.eval()
       __lowerCamelCase		:							int		 =						model(a						,				labels=a    )
       self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels)    )




      def 					_snake_case     (    self:	Tuple						,				a:	List[Any]						,				a:	Tuple						,				a:	str						,				a:	str    ):
       __lowerCamelCase		:							int		 =						self.num_labels
       __lowerCamelCase		:							Tuple		 =						MobileViTVaForSemanticSegmentation(a    )
       model.to(a    )
       model.eval()
       __lowerCamelCase		:							Tuple		 =						model(a    )
       self.parent.assertEqual(
           result.logits.shape						,				(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )						,				)
       __lowerCamelCase		:							List[str]		 =						model(a						,				labels=a    )
       self.parent.assertEqual(
           result.logits.shape						,				(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )						,				)




      def 					_snake_case     (    self:	int    ):
       __lowerCamelCase		:							List[Any]		 =						self.prepare_config_and_inputs()
       __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						config_and_inputs
       __lowerCamelCase		:							List[str]		 =						{'pixel_values': pixel_values}
       return config, inputs_dict



@require_torch
class 	A_ (	__UpperCamelCase ,				__UpperCamelCase ,				unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       (
          (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )

      __snake_case 	=       (
          {
              """feature-extraction""": MobileViTVaModel,
              """image-classification""": MobileViTVaForImageClassification,
              """image-segmentation""": MobileViTVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      __snake_case 	=       False
      __snake_case 	=       False
      __snake_case 	=       False
      __snake_case 	=       False




      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							List[Any]		 =						MobileViTVaModelTester(self    )
       __lowerCamelCase		:							Any		 =						MobileViTVaConfigTester(self						,				config_class=a						,				has_text_modality=a    )




      def 					_snake_case     (    self:	List[Any]    ):
       self.config_tester.run_common_tests()




      @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'    )
      def 					_snake_case     (    self:	List[str]    ):
       pass




      @unittest.skip(reason='MobileViTV2 does not support input and output embeddings'    )
      def 					_snake_case     (    self:	str    ):
       pass




      @unittest.skip(reason='MobileViTV2 does not output attentions'    )
      def 					_snake_case     (    self:	List[Any]    ):
       pass




      @require_torch_multi_gpu
      @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'    )
      def 					_snake_case     (    self:	str    ):
       pass




      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
      def 					_snake_case     (    self:	Optional[int]    ):
       pass




      def 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowerCamelCase		:							Union[str, Any]		 =						model_class(a    )
        __lowerCamelCase		:							int		 =						inspect.signature(model.forward    )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowerCamelCase		:							Union[str, Any]		 =						[*signature.parameters.keys()]

        __lowerCamelCase		:							Any		 =						['pixel_values']
        self.assertListEqual(arg_names[:1]						,				a    )




      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							Tuple		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       def check_hidden_states_output(a:	Optional[Any]						,				a:	Optional[int]						,				a:	Any    ):
        __lowerCamelCase		:							int		 =						model_class(a    )
        model.to(a    )
        model.eval()

        with torch.no_grad():
         __lowerCamelCase		:							Optional[Any]		 =						model(**self._prepare_for_class(a						,				a    )    )

        __lowerCamelCase		:							str		 =						outputs.hidden_states

        __lowerCamelCase		:							List[Any]		 =						5
        self.assertEqual(len(a    )						,				a    )

        # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
        # with the width and height being successively divided by 2.
        __lowerCamelCase		:							int		 =						2
        for i in range(len(a    )    ):
         self.assertListEqual(
             list(hidden_states[i].shape[-2:]    )						,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]						,				)
         divisor *= 2

        self.assertEqual(self.model_tester.output_stride						,				divisor // 2    )

       __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowerCamelCase		:							Union[str, Any]		 =						True
        check_hidden_states_output(a						,				a						,				a    )

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

        check_hidden_states_output(a						,				a						,				a    )




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							Union[str, Any]		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a    )




      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							Optional[Any]		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*a    )




      @slow
      def 					_snake_case     (    self:	str    ):
       for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCamelCase		:							Optional[Any]		 =						MobileViTVaModel.from_pretrained(a    )
        self.assertIsNotNone(a    )



def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							List[str]		 =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
 return image



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




      '''simple docstring'''


      @cached_property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return (
           MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'    )
           if is_vision_available()
           else None
       )




      @slow
      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							str		 =						MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'    ).to(
           a    )

       __lowerCamelCase		:							List[str]		 =						self.default_image_processor
       __lowerCamelCase		:							Dict		 =						prepare_img()
       __lowerCamelCase		:							Union[str, Any]		 =						image_processor(images=a						,				return_tensors='pt'    ).to(a    )

       # forward pass
       with torch.no_grad():
        __lowerCamelCase		:							Any		 =						model(**a    )

       # verify the logits
       __lowerCamelCase		:							Tuple		 =						torch.Size((1, 1000)    )
       self.assertEqual(outputs.logits.shape						,				a    )

       __lowerCamelCase		:							List[str]		 =						torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]    ).to(a    )

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




      @slow
      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							List[str]		 =						MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )
       __lowerCamelCase		:							Optional[int]		 =						model.to(a    )

       __lowerCamelCase		:							Tuple		 =						MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )

       __lowerCamelCase		:							Tuple		 =						prepare_img()
       __lowerCamelCase		:							Any		 =						image_processor(images=a						,				return_tensors='pt'    ).to(a    )

       # forward pass
       with torch.no_grad():
        __lowerCamelCase		:							Union[str, Any]		 =						model(**a    )
       __lowerCamelCase		:							Union[str, Any]		 =						outputs.logits

       # verify the logits
       __lowerCamelCase		:							Tuple		 =						torch.Size((1, 21, 32, 32)    )
       self.assertEqual(logits.shape						,				a    )

       __lowerCamelCase		:							Optional[Any]		 =						torch.tensor(
           [
               [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
               [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
               [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
           ]						,				device=a						,				)

       self.assertTrue(torch.allclose(logits[0, :3, :3, :3]						,				a						,				atol=1e-4    )    )




      @slow
      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							List[Any]		 =						MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )
       __lowerCamelCase		:							Optional[int]		 =						model.to(a    )

       __lowerCamelCase		:							Optional[Any]		 =						MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )

       __lowerCamelCase		:							Union[str, Any]		 =						prepare_img()
       __lowerCamelCase		:							List[Any]		 =						image_processor(images=a						,				return_tensors='pt'    ).to(a    )

       # forward pass
       with torch.no_grad():
        __lowerCamelCase		:							int		 =						model(**a    )

       __lowerCamelCase		:							Union[str, Any]		 =						outputs.logits.detach().cpu()

       __lowerCamelCase		:							Tuple		 =						image_processor.post_process_semantic_segmentation(outputs=a						,				target_sizes=[(50, 60)]    )
       __lowerCamelCase		:							Optional[Any]		 =						torch.Size((50, 60)    )
       self.assertEqual(segmentation[0].shape						,				a    )

       __lowerCamelCase		:							List[str]		 =						image_processor.post_process_semantic_segmentation(outputs=a    )
       __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((32, 32)    )
       self.assertEqual(segmentation[0].shape						,				a    )





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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 # Load configuration defined in the metadata file
 with open(SCREAMING_SNAKE_CASE__						) as metadata_file:
  __lowerCamelCase		:							List[str]		 =						json.load(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__      ,	**metadata['model_config']						)

 # Load in the weights from the checkpoint_path
 __lowerCamelCase		:							Union[str, Any]		 =						torch.load(SCREAMING_SNAKE_CASE__      ,	map_location='cpu'						)

 # Load the entity vocab file
 __lowerCamelCase		:							List[Any]		 =						load_entity_vocab(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Optional[int]		 =						RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']						)

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

 __lowerCamelCase		:							str		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						)

 # Initialize the embeddings of the special tokens
 __lowerCamelCase		:							Union[str, Any]		 =						state_dict['embeddings.word_embeddings.weight']
 __lowerCamelCase		:							Tuple		 =						word_emb[tokenizer.convert_tokens_to_ids(['@']						)[0]].unsqueeze(0						)
 __lowerCamelCase		:							Any		 =						word_emb[tokenizer.convert_tokens_to_ids(['#']						)[0]].unsqueeze(0						)
 __lowerCamelCase		:							List[str]		 =						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"]:
   __lowerCamelCase		:							Optional[int]		 =						f'encoder.layer.{layer_index}.attention.self.'
   __lowerCamelCase		:							Dict		 =						state_dict[prefix + matrix_name]
   __lowerCamelCase		:							List[Any]		 =						state_dict[prefix + matrix_name]
   __lowerCamelCase		:							Union[str, Any]		 =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 __lowerCamelCase		:							Optional[int]		 =						state_dict['entity_embeddings.entity_embeddings.weight']
 __lowerCamelCase		:							Union[str, Any]		 =						entity_emb[entity_vocab['[MASK]']]

 __lowerCamelCase		:							Optional[Any]		 =						LukeModel(config=SCREAMING_SNAKE_CASE__						).eval()

 __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						model.load_state_dict(SCREAMING_SNAKE_CASE__      ,	strict=SCREAMING_SNAKE_CASE__						)
 if not (len(SCREAMING_SNAKE_CASE__						) == 1 and missing_keys[0] == "embeddings.position_ids"):
  raise ValueError(f'Missing keys {", ".join(SCREAMING_SNAKE_CASE__						)}. 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
 __lowerCamelCase		:							Optional[Any]		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__      ,	task='entity_classification'						)

 __lowerCamelCase		:							Dict		 =						(
     '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 .'
 )
 __lowerCamelCase		:							Union[str, Any]		 =						(39, 42)
 __lowerCamelCase		:							Optional[Any]		 =						tokenizer(SCREAMING_SNAKE_CASE__      ,	entity_spans=[span]      ,	add_prefix_space=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'						)

 __lowerCamelCase		:							List[str]		 =						model(**SCREAMING_SNAKE_CASE__						)

 # Verify word hidden states
 if model_size == "large":
  __lowerCamelCase		:							Dict		 =						torch.Size((1, 42, 1_024)						)
  __lowerCamelCase		:							int		 =						torch.tensor(
      [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]						)
 else:  # base
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 42, 768)						)
  __lowerCamelCase		:							Tuple		 =						torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]						)

 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":
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 1, 1_024)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.0_466, -0.0_106, -0.0_179]]						)
 else:  # base
  __lowerCamelCase		:							int		 =						torch.Size((1, 1, 768)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.1_457, 0.1_044, 0.0_174]]						)

 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

 # Finally, save our PyTorch model and tokenizer
 print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__						)						)
 model.save_pretrained(SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						{}
 with open(SCREAMING_SNAKE_CASE__      ,	'r'      ,	encoding='utf-8'						) as f:
  for index, line in enumerate(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						line.rstrip().split('\t'						)
   __lowerCamelCase		:							Any		 =						index

 return entity_vocab


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

