
import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

  if is_vision_available():
    from transformers import MaskFormerImageProcessor

if is_vision_available():
  from PIL import Image

class __snake_case		:
    def __init__(  self		,snake_case		,snake_case=2		,snake_case=True		,snake_case=False		,snake_case=10		,snake_case=3		,snake_case=32 * 4		,snake_case=32 * 6		,snake_case=4		,snake_case=32		,):



           '''simple docstring'''







           lowercase							:					List[str]	     =				parent
           lowercase							:					List[str]	     =				batch_size
           lowercase							:					Optional[int]	     =				is_training
           lowercase							:					Optional[int]	     =				use_auxiliary_loss
           lowercase							:					List[str]	     =				num_queries
           lowercase							:					Union[str, Any]	     =				num_channels
           lowercase							:					int	     =				min_size
           lowercase							:					List[Any]	     =				max_size
           lowercase							:					Optional[Any]	     =				num_labels
           lowercase							:					List[Any]	     =				mask_feature_size
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
               _a				)

           lowercase							:					str	     =				torch.ones([self.batch_size, self.min_size, self.max_size]		,device=_a				)

           lowercase							:					Any	     =				(
               torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]		,device=_a				) > 0.5
           ).float()
           lowercase							:					Optional[int]	     =				(torch.rand((self.batch_size, self.num_labels)		,device=_a				) > 0.5).long()

           lowercase							:					int	     =				self.get_config()
           return config, pixel_values, pixel_mask, mask_labels, class_labels
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return MaskFormerConfig.from_backbone_and_decoder_configs(
               backbone_config=SwinConfig(
                   depths=[1, 1, 1, 1]		,)		,decoder_config=DetrConfig(
                   decoder_ffn_dim=128		,num_queries=self.num_queries		,decoder_attention_heads=2		,d_model=self.mask_feature_size		,)		,mask_feature_size=self.mask_feature_size		,fpn_feature_size=self.mask_feature_size		,num_channels=self.num_channels		,num_labels=self.num_labels		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.prepare_config_and_inputs()
           lowercase							:					Optional[int]	     =				{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
           return config, inputs_dict
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				output.encoder_hidden_states
           lowercase							:					str	     =				output.pixel_decoder_hidden_states
           lowercase							:					Optional[Any]	     =				output.transformer_decoder_hidden_states

           self.parent.assertTrue(len(_a				)		,len(config.backbone_config.depths				)				)
           self.parent.assertTrue(len(_a				)		,len(config.backbone_config.depths				)				)
           self.parent.assertTrue(len(_a				)		,config.decoder_config.decoder_layers				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case=False				):



           '''simple docstring'''







           with torch.no_grad():
                  lowercase							:					Dict	     =				MaskFormerModel(config=_a				)
                  model.to(_a				)
                  model.eval()

                  lowercase							:					Union[str, Any]	     =				model(pixel_values=_a		,pixel_mask=_a				)
                  lowercase							:					int	     =				model(_a		,output_hidden_states=_a				)
           # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
           # encoder and pixel decoder
           self.parent.assertEqual(
               output.transformer_decoder_last_hidden_state.shape		,(self.batch_size, self.num_queries, self.mask_feature_size)		,)
           # let's ensure the other two hidden state exists
           self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None				)
           self.parent.assertTrue(output.encoder_last_hidden_state is not None				)

           if output_hidden_states:
                  self.check_output_hidden_state(_a		,_a				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				MaskFormerForInstanceSegmentation(config=_a				)
           model.to(_a				)
           model.eval()

           def comm_check_on_output(snake_case				):
                  # let's still check that all the required stuff is there
                  self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None				)
                  self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None				)
                  self.parent.assertTrue(result.encoder_last_hidden_state is not None				)
                  # okay, now we need to check the logits shape
                  # due to the encoder compression, masks have a //4 spatial size
                  self.parent.assertEqual(
                      result.masks_queries_logits.shape		,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)		,)
                  # + 1 for null class
                  self.parent.assertEqual(
                      result.class_queries_logits.shape		,(self.batch_size, self.num_queries, self.num_labels + 1)				)

           with torch.no_grad():
                  lowercase							:					Optional[Any]	     =				model(pixel_values=_a		,pixel_mask=_a				)
                  lowercase							:					str	     =				model(_a				)

                  comm_check_on_output(_a				)

                  lowercase							:					List[Any]	     =				model(
                      pixel_values=_a		,pixel_mask=_a		,mask_labels=_a		,class_labels=_a				)

           comm_check_on_output(_a				)

           self.parent.assertTrue(result.loss is not None				)
           self.parent.assertEqual(result.loss.shape		,torch.Size([1]				)				)






@require_torch
class __snake_case		(				UpperCamelCase_ ,     UpperCamelCase_ ,     unittest.TestCase					):
    _a		:						Tuple=    (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
    _a		:						Union[str, Any]=    (
        {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
        if is_torch_available()
        else {}
    )

    _a		:						int=    False
    _a		:						Any=    False
    _a		:						Any=    False
    _a		:						Any=    False
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				MaskFormerModelTester(self				)
           lowercase							:					Tuple	     =				ConfigTester(self		,config_class=_a		,has_text_modality=_a				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskformer_model(_a		,**_a		,output_hidden_states=_a				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_a				)
    @unittest.skip(reason="""MaskFormer does not use inputs_embeds"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    @unittest.skip(reason="""MaskFormer is not a generative model"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    @unittest.skip(reason="""MaskFormer does not use token embeddings"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    @require_torch_multi_gpu
    @unittest.skip(
        reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:					Optional[int]	     =				model_class(_a				)
                  lowercase							:					Any	     =				inspect.signature(model.forward				)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  lowercase							:					int	     =				[*signature.parameters.keys()]

                  lowercase							:					Optional[Any]	     =				["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,_a				)
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           for model_name in ["facebook/maskformer-swin-small-coco"]:
                  lowercase							:					Optional[int]	     =				MaskFormerModel.from_pretrained(_a				)
                  self.assertIsNotNone(_a				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				(self.model_tester.min_size,) * 2
           lowercase							:					List[str]	     =				{
               """pixel_values""": torch.randn((2, 3, *size)		,device=_a				),
               """mask_labels""": torch.randn((2, 10, *size)		,device=_a				),
               """class_labels""": torch.zeros(2		,10		,device=_a				).long(),
           }

           lowercase							:					Dict	     =				MaskFormerForInstanceSegmentation(MaskFormerConfig()				).to(_a				)
           lowercase							:					Optional[int]	     =				model(**_a				)
           self.assertTrue(outputs.loss is not None				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskformer_model(_a		,**_a		,output_hidden_states=_a				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:					Optional[Any]	     =				model_class(_a				).to(_a				)
                  lowercase							:					Dict	     =				model(**_a		,output_attentions=_a				)
                  self.assertTrue(outputs.attentions is not None				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if not self.model_tester.is_training:
                  return
           # only MaskFormerForInstanceSegmentation has the loss
           lowercase							:					List[str]	     =				self.all_model_classes[1]
           lowercase							:					Union[str, Any]	     =				self.model_tester.prepare_config_and_inputs()

           lowercase							:					Optional[Any]	     =				model_class(_a				)
           model.to(_a				)
           model.train()

           lowercase							:					List[str]	     =				model(_a		,mask_labels=_a		,class_labels=_a				).loss
           loss.backward()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.all_model_classes[1]
           lowercase							:					Optional[Any]	     =				self.model_tester.prepare_config_and_inputs()
           lowercase							:					Optional[int]	     =				True
           lowercase							:					Any	     =				True

           lowercase							:					List[str]	     =				model_class(_a				)
           model.to(_a				)
           model.train()

           lowercase							:					Dict	     =				model(_a		,mask_labels=_a		,class_labels=_a				)

           lowercase							:					Dict	     =				outputs.encoder_hidden_states[0]
           encoder_hidden_states.retain_grad()

           lowercase							:					str	     =				outputs.pixel_decoder_hidden_states[0]
           pixel_decoder_hidden_states.retain_grad()
           # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
           lowercase							:					List[Any]	     =				outputs.transformer_decoder_hidden_states[0]
           transformer_decoder_hidden_states.retain_grad()

           lowercase							:					Dict	     =				outputs.attentions[0]
           attentions.retain_grad()

           outputs.loss.backward(retain_graph=_a				)

           self.assertIsNotNone(encoder_hidden_states.grad				)
           self.assertIsNotNone(pixel_decoder_hidden_states.grad				)
           self.assertIsNotNone(transformer_decoder_hidden_states.grad				)
           self.assertIsNotNone(attentions.grad				)






lowercase  : Union[str, Any]     =							1e-4







def 						_snake_case(  )			->   Optional[Any]:
       lowercase							:					Union[str, Any]	     =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
       return image




@require_vision
@slow
class __snake_case		(				unittest.TestCase					):
    @cached_property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return (
               MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""				)
               if is_vision_available()
               else None
           )
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""				).to(_a				)
           lowercase							:					int	     =				self.default_image_processor
           lowercase							:					List[str]	     =				prepare_img()
           lowercase							:					Union[str, Any]	     =				image_processor(_a		,return_tensors="""pt"""				).to(_a				)
           lowercase							:					Dict	     =				inputs["""pixel_values"""].shape
           # check size is divisible by 32
           self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
           # check size
           self.assertEqual(_a		,(1, 3, 800, 1088)				)

           with torch.no_grad():
                  lowercase							:					str	     =				model(**_a				)

           lowercase							:					Any	     =				torch.tensor(
               [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]				).to(_a				)
           self.assertTrue(
               torch.allclose(
                   outputs.encoder_last_hidden_state[0, 0, :3, :3]		,_a		,atol=_a				)				)

           lowercase							:					Any	     =				torch.tensor(
               [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]				).to(_a				)
           self.assertTrue(
               torch.allclose(
                   outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,_a		,atol=_a				)				)

           lowercase							:					Union[str, Any]	     =				torch.tensor(
               [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]				).to(_a				)
           self.assertTrue(
               torch.allclose(
                   outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,_a		,atol=_a				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				(
               MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""				)
               .to(_a				)
               .eval()
           )
           lowercase							:					List[str]	     =				self.default_image_processor
           lowercase							:					Optional[int]	     =				prepare_img()
           lowercase							:					List[Any]	     =				image_processor(_a		,return_tensors="""pt"""				).to(_a				)
           lowercase							:					Tuple	     =				inputs["""pixel_values"""].shape
           # check size is divisible by 32
           self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
           # check size
           self.assertEqual(_a		,(1, 3, 800, 1088)				)

           with torch.no_grad():
                  lowercase							:					str	     =				model(**_a				)
           # masks_queries_logits
           lowercase							:					Any	     =				outputs.masks_queries_logits
           self.assertEqual(
               masks_queries_logits.shape		,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		,)
           lowercase							:					str	     =				[
               [-1.3_737_124, -1.7_724_937, -1.9_364_233],
               [-1.5_977_281, -1.9_867_939, -2.1_523_695],
               [-1.5_795_398, -1.9_269_832, -2.093_942],
           ]
           lowercase							:					Optional[int]	     =				torch.tensor(_a				).to(_a				)
           self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,_a		,atol=_a				)				)
           # class_queries_logits
           lowercase							:					Tuple	     =				outputs.class_queries_logits
           self.assertEqual(
               class_queries_logits.shape		,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)				)
           lowercase							:					List[str]	     =				torch.tensor(
               [
                   [1.6512e00, -5.2572e00, -3.3519e00],
                   [3.6169e-02, -5.9025e00, -2.9313e00],
                   [1.0766e-04, -7.7630e00, -5.1263e00],
               ]				).to(_a				)
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,_a		,atol=_a				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				(
               MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""				)
               .to(_a				)
               .eval()
           )
           lowercase							:					Any	     =				self.default_image_processor
           lowercase							:					Tuple	     =				prepare_img()
           lowercase							:					int	     =				image_processor(_a		,return_tensors="""pt"""				).to(_a				)
           lowercase							:					Any	     =				inputs["""pixel_values"""].shape
           # check size is divisible by 32
           self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
           # check size
           self.assertEqual(_a		,(1, 3, 800, 1088)				)

           with torch.no_grad():
                  lowercase							:					str	     =				model(**_a				)
           # masks_queries_logits
           lowercase							:					Union[str, Any]	     =				outputs.masks_queries_logits
           self.assertEqual(
               masks_queries_logits.shape		,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		,)
           lowercase							:					str	     =				[[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
           lowercase							:					Tuple	     =				torch.tensor(_a				).to(_a				)
           self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,_a		,atol=_a				)				)
           # class_queries_logits
           lowercase							:					Optional[int]	     =				outputs.class_queries_logits
           self.assertEqual(
               class_queries_logits.shape		,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)				)
           lowercase							:					Dict	     =				torch.tensor(
               [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]				).to(_a				)
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,_a		,atol=_a				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				(
               MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""				)
               .to(_a				)
               .eval()
           )
           lowercase							:					List[Any]	     =				self.default_image_processor

           lowercase							:					Optional[Any]	     =				image_processor(
               [np.zeros((3, 800, 1333)				), np.zeros((3, 800, 1333)				)]		,segmentation_maps=[np.zeros((384, 384)				).astype(np.floataa				), np.zeros((384, 384)				).astype(np.floataa				)]		,return_tensors="""pt"""		,)

           lowercase							:					Union[str, Any]	     =				inputs["""pixel_values"""].to(_a				)
           lowercase							:					Tuple	     =				[el.to(_a				) for el in inputs["""mask_labels"""]]
           lowercase							:					Tuple	     =				[el.to(_a				) for el in inputs["""class_labels"""]]

           with torch.no_grad():
                  lowercase							:					Optional[int]	     =				model(**_a				)

           self.assertTrue(outputs.loss is not None				)


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,
  )
