



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 			__lowerCAmelCase  :




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



    '''simple docstring'''
    __lowerCamelCase    				=     parent
    __lowerCamelCase    				=     batch_size
    __lowerCamelCase    				=     is_training
    __lowerCamelCase    				=     use_auxiliary_loss
    __lowerCamelCase    				=     num_queries
    __lowerCamelCase    				=     num_channels
    __lowerCamelCase    				=     min_size
    __lowerCamelCase    				=     max_size
    __lowerCamelCase    				=     num_labels
    __lowerCamelCase    				=     mask_feature_size




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
        _a					)

    __lowerCamelCase    				=     torch.ones([self.batch_size, self.min_size, self.max_size]						,					device=_a					)

    __lowerCamelCase    				=     (
        torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,					device=_a					) > 0.5
    ).float()
    __lowerCamelCase    				=     (torch.rand((self.batch_size, self.num_labels)						,					device=_a					) > 0.5).long()

    __lowerCamelCase    				=     self.get_config()
    return config, pixel_values, pixel_mask, mask_labels, class_labels




 def 							lowerCamelCase   (    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 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.prepare_config_and_inputs()
    __lowerCamelCase    				=     {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
    return config, inputs_dict




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     output.encoder_hidden_states
    __lowerCamelCase    				=     output.pixel_decoder_hidden_states
    __lowerCamelCase    				=     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 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase=False					):



    '''simple docstring'''
    with torch.no_grad():
       __lowerCamelCase    				=     MaskFormerModel(config=_a					)
       model.to(_a					)
       model.eval()

       __lowerCamelCase    				=     model(pixel_values=_a						,					pixel_mask=_a					)
       __lowerCamelCase    				=     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 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     MaskFormerForInstanceSegmentation(config=_a					)
    model.to(_a					)
    model.eval()

    def comm_check_on_output(__UpperCAmelCase					):
       # 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():
       __lowerCamelCase    				=     model(pixel_values=_a						,					pixel_mask=_a					)
       __lowerCamelCase    				=     model(_a					)

       comm_check_on_output(_a					)

       __lowerCamelCase    				=     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 			__lowerCAmelCase  (						__lowercase	,						__lowercase	,						unittest.TestCase ):
 lowerCAmelCase__							       =	(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
 lowerCAmelCase__							       =	(
     {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
     if is_torch_available()
     else {}
 )

 lowerCAmelCase__							       =	False
 lowerCAmelCase__							       =	False
 lowerCAmelCase__							       =	False
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     MaskFormerModelTester(self					)
    __lowerCamelCase    				=     ConfigTester(self						,					config_class=_a						,					has_text_modality=_a					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskformer_model(_a						,					**_a						,					output_hidden_states=_a					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     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 							lowerCamelCase   (    self					):



    '''simple docstring'''
    pass




 @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''					)
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    pass




 @unittest.skip(reason='''MaskFormer is not a generative model'''					)
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    pass




 @unittest.skip(reason='''MaskFormer does not use token embeddings'''					)
 def 							lowerCamelCase   (    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 							lowerCamelCase   (    self					):



    '''simple docstring'''
    pass




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



    '''simple docstring'''
    pass




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs_for_common()

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

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




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    for model_name in ["facebook/maskformer-swin-small-coco"]:
       __lowerCamelCase    				=     MaskFormerModel.from_pretrained(_a					)
       self.assertIsNotNone(_a					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     (self.model_tester.min_size,) * 2
    __lowerCamelCase    				=     {
        '''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(),
    }

    __lowerCamelCase    				=     MaskFormerForInstanceSegmentation(MaskFormerConfig()					).to(_a					)
    __lowerCamelCase    				=     model(**_a					)
    self.assertTrue(outputs.loss is not None					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskformer_model(_a						,					**_a						,					output_hidden_states=_a					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowerCamelCase    				=     model_class(_a					).to(_a					)
       __lowerCamelCase    				=     model(**_a						,					output_attentions=_a					)
       self.assertTrue(outputs.attentions is not None					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    if not self.model_tester.is_training:
       return
    # only MaskFormerForInstanceSegmentation has the loss
    __lowerCamelCase    				=     self.all_model_classes[1]
    __lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs()

    __lowerCamelCase    				=     model_class(_a					)
    model.to(_a					)
    model.train()

    __lowerCamelCase    				=     model(_a						,					mask_labels=_a						,					class_labels=_a					).loss
    loss.backward()




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # only MaskFormerForInstanceSegmentation has the loss
    __lowerCamelCase    				=     self.all_model_classes[1]
    __lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs()
    __lowerCamelCase    				=     True
    __lowerCamelCase    				=     True

    __lowerCamelCase    				=     model_class(_a					)
    model.to(_a					)
    model.train()

    __lowerCamelCase    				=     model(_a						,					mask_labels=_a						,					class_labels=_a					)

    __lowerCamelCase    				=     outputs.encoder_hidden_states[0]
    encoder_hidden_states.retain_grad()

    __lowerCamelCase    				=     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
    __lowerCamelCase    				=     outputs.transformer_decoder_hidden_states[0]
    transformer_decoder_hidden_states.retain_grad()

    __lowerCamelCase    				=     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					)



a_								=       1E-4





def 		a__      (		):
   __lowerCamelCase    				=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
   return image





@require_vision
@slow
class 			__lowerCAmelCase  (						unittest.TestCase ):




 @cached_property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return (
        MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
        if is_vision_available()
        else None
    )




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''					).to(_a					)
    __lowerCamelCase    				=     self.default_image_processor
    __lowerCamelCase    				=     prepare_img()
    __lowerCamelCase    				=     image_processor(_a						,					return_tensors='''pt'''					).to(_a					)
    __lowerCamelCase    				=     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():
       __lowerCamelCase    				=     model(**_a					)

    __lowerCamelCase    				=     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					)					)

    __lowerCamelCase    				=     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					)					)

    __lowerCamelCase    				=     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 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     (
        MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
        .to(_a					)
        .eval()
    )
    __lowerCamelCase    				=     self.default_image_processor
    __lowerCamelCase    				=     prepare_img()
    __lowerCamelCase    				=     image_processor(_a						,					return_tensors='''pt'''					).to(_a					)
    __lowerCamelCase    				=     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():
       __lowerCamelCase    				=     model(**_a					)
    # masks_queries_logits
    __lowerCamelCase    				=     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)						,					)
    __lowerCamelCase    				=     [
        [-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],
    ]
    __lowerCamelCase    				=     torch.tensor(_a					).to(_a					)
    self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,					_a						,					atol=_a					)					)
    # class_queries_logits
    __lowerCamelCase    				=     outputs.class_queries_logits
    self.assertEqual(
        class_queries_logits.shape						,					(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
    __lowerCamelCase    				=     torch.tensor(
        [
            [1.6_5_1_2E0_0, -5.2_5_7_2E0_0, -3.3_5_1_9E0_0],
            [3.6_1_6_9E-0_2, -5.9_0_2_5E0_0, -2.9_3_1_3E0_0],
            [1.0_7_6_6E-0_4, -7.7_6_3_0E0_0, -5.1_2_6_3E0_0],
        ]					).to(_a					)
    self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,					_a						,					atol=_a					)					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     (
        MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''					)
        .to(_a					)
        .eval()
    )
    __lowerCamelCase    				=     self.default_image_processor
    __lowerCamelCase    				=     prepare_img()
    __lowerCamelCase    				=     image_processor(_a						,					return_tensors='''pt'''					).to(_a					)
    __lowerCamelCase    				=     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():
       __lowerCamelCase    				=     model(**_a					)
    # masks_queries_logits
    __lowerCamelCase    				=     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)						,					)
    __lowerCamelCase    				=     [[-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]]
    __lowerCamelCase    				=     torch.tensor(_a					).to(_a					)
    self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,					_a						,					atol=_a					)					)
    # class_queries_logits
    __lowerCamelCase    				=     outputs.class_queries_logits
    self.assertEqual(
        class_queries_logits.shape						,					(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
    __lowerCamelCase    				=     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 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     (
        MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
        .to(_a					)
        .eval()
    )
    __lowerCamelCase    				=     self.default_image_processor

    __lowerCamelCase    				=     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'''						,					)

    __lowerCamelCase    				=     inputs['''pixel_values'''].to(_a					)
    __lowerCamelCase    				=     [el.to(_a					) for el in inputs['''mask_labels''']]
    __lowerCamelCase    				=     [el.to(_a					) for el in inputs['''class_labels''']]

    with torch.no_grad():
       __lowerCamelCase    				=     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   __UpperCAmelCase					(      __a :     Tuple						,__a :     Dict						,__a :     List[str]						,__a :     Optional[Any]						,__a :     Tuple      )     ->	Dict:

					"""simple docstring"""

					with open(__a      ) as metadata_file:
										_a      :       Optional[Any]   =						json.load(__a      )
					_a      :       List[Any]   =						LukeConfig(use_entity_aware_attention=__a						,**metadata['''model_config''']      )

					# Load in the weights from the checkpoint_path
					_a      :       Optional[Any]   =						torch.load(__a						,map_location='''cpu'''      )['''module''']

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

					_a      :       Dict   =						XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']      )

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

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

					_a      :       Optional[int]   =						MLukeTokenizer.from_pretrained(__a      )

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

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

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

					_a      :       Optional[int]   =						LukeForMaskedLM(config=__a      ).eval()

					state_dict.pop('''entity_predictions.decoder.weight'''      )
					state_dict.pop('''lm_head.decoder.weight'''      )
					state_dict.pop('''lm_head.decoder.bias'''      )
					_a      :       int   =						OrderedDict()
					for key, value in state_dict.items():
										if not (key.startswith('''lm_head'''      ) or key.startswith('''entity_predictions'''      )):
															_a      :       Optional[int]   =						state_dict[key]
										else:
															_a      :       Tuple   =						state_dict[key]

					_a ,   _a      :       int   =						model.load_state_dict(__a						,strict=__a      )

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

					_a      :       int   =						'''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
					_a      :       List[Any]   =						(0, 9)
					_a      :       Tuple   =						tokenizer(__a						,entity_spans=[span]						,return_tensors='''pt'''      )

					_a      :       int   =						model(**__a      )

					# Verify word hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										_a      :       List[str]   =						torch.Size((1, 33, 768)      )
										_a      :       Union[str, Any]   =						torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]      )

					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]						,__a						,atol=1E-4      ):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										_a      :       str   =						torch.Size((1, 1, 768)      )
										_a      :       List[Any]   =						torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]      )

					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]						,__a						,atol=1E-4      ):
										raise ValueError

					# Verify masked word/entity prediction
					_a      :       Optional[int]   =						MLukeTokenizer.from_pretrained(__a      )
					_a      :       Dict   =						'''Tokyo is the capital of <mask>.'''
					_a      :       List[str]   =						(24, 30)
					_a      :       Optional[int]   =						tokenizer(__a						,entity_spans=[span]						,return_tensors='''pt'''      )

					_a      :       Optional[Any]   =						model(**__a      )

					_a      :       Any   =						encoding['''input_ids'''][0].tolist()
					_a      :       Optional[Any]   =						input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''      )      )
					_a      :       Any   =						outputs.logits[0][mask_position_id].argmax(dim=-1      )
					assert "Japan" == tokenizer.decode(__a      )

					_a      :       Any   =						outputs.entity_logits[0][0].argmax().item()
					_a      :       Optional[Any]   =						[
					    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(__a      )      )
					model.save_pretrained(__a      )



def   __UpperCAmelCase					(      __a :     List[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						['''[MASK]''', '''[PAD]''', '''[UNK]''']

					_a      :       int   =						[json.loads(__a      ) for line in open(__a      )]

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


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



