






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  (       snake_case__     :   List[str]	,snake_case__     :   Optional[Any]	,snake_case__     :   List[Any]	,snake_case__     :   List[Any]	,snake_case__     :   List[Any]  ):




     '''simple docstring'''






     with open(snake_case__  ) as metadata_file:
          __snake_case			:str          =						json.load(snake_case__  )
     __snake_case			:List[str]          =						LukeConfig(use_entity_aware_attention=snake_case__	,**metadata["""model_config"""]  )

     # Load in the weights from the checkpoint_path
     __snake_case			:Union[str, Any]          =						torch.load(snake_case__	,map_location="""cpu"""  )["""module"""]

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

     __snake_case			:Dict          =						XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]  )

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

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

     __snake_case			:Tuple          =						MLukeTokenizer.from_pretrained(snake_case__  )

     # Initialize the embeddings of the special tokens
     __snake_case			:Dict          =						tokenizer.convert_tokens_to_ids(["""@"""]  )[0]
     __snake_case			:int          =						tokenizer.convert_tokens_to_ids(["""#"""]  )[0]

     __snake_case			:Dict          =						state_dict["""embeddings.word_embeddings.weight"""]
     __snake_case			:Dict          =						word_emb[ent_init_index].unsqueeze(0  )
     __snake_case			:List[str]          =						word_emb[enta_init_index].unsqueeze(0  )
     __snake_case			: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"]:
          __snake_case			:Union[str, Any]          =						state_dict[bias_name]
          __snake_case			:str          =						decoder_bias[ent_init_index].unsqueeze(0  )
          __snake_case			:Optional[int]          =						decoder_bias[enta_init_index].unsqueeze(0  )
          __snake_case			:Union[str, Any]          =						torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]  )

     # Initialize the query layers of the entity-aware self-attention mechanism
     for layer_index in range(config.num_hidden_layers  ):
          for matrix_name in ["query.weight", "query.bias"]:
               __snake_case			:Optional[int]          =						f'''encoder.layer.{layer_index}.attention.self.'''
               __snake_case			:int          =						state_dict[prefix + matrix_name]
               __snake_case			:Optional[Any]          =						state_dict[prefix + matrix_name]
               __snake_case			:Union[str, Any]          =						state_dict[prefix + matrix_name]

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

     __snake_case			:Union[str, Any]          =						LukeForMaskedLM(config=snake_case__  ).eval()

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

     __snake_case       , __snake_case			:Optional[Any]          =						model.load_state_dict(snake_case__	,strict=snake_case__  )

     if set(snake_case__  ) != {"luke.embeddings.position_ids"}:
          raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}'''  )
     if set(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
     __snake_case			:Tuple          =						MLukeTokenizer.from_pretrained(snake_case__	,task="""entity_classification"""  )

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

     __snake_case			:Optional[int]          =						model(**snake_case__  )

     # Verify word hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          __snake_case			:int          =						torch.Size((1, 33, 768)  )
          __snake_case			:Any          =						torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]  )

     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]	,snake_case__	,atol=1e-4  ):
          raise ValueError

     # Verify entity hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          __snake_case			:int          =						torch.Size((1, 1, 768)  )
          __snake_case			:Optional[Any]          =						torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]  )

     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]	,snake_case__	,atol=1e-4  ):
          raise ValueError

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

     __snake_case			:int          =						model(**snake_case__  )

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

     __snake_case			:Union[str, Any]          =						outputs.entity_logits[0][0].argmax().item()
     __snake_case			:Optional[int]          =						[
         entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
     ]
     assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""  )][0] == "en:Japan"

     # Finally, save our PyTorch model and tokenizer
     print("""Saving PyTorch model to {}""".format(snake_case__  )  )
     model.save_pretrained(snake_case__  )






def   UpperCamelCase  (       snake_case__     :   Dict  ):




     '''simple docstring'''






     __snake_case			:Optional[Any]          =						["""[MASK]""", """[PAD]""", """[UNK]"""]

     __snake_case			:Tuple          =						[json.loads(snake_case__  ) for line in open(snake_case__  )]

     __snake_case			:Optional[int]          =						{}
     for entry in data:
          __snake_case			:List[str]          =						entry["""id"""]
          for entity_name, language in entry["entities"]:
               if entity_name in SPECIAL_TOKENS:
                    __snake_case			:int          =						entity_id
                    break
               __snake_case			:List[str]          =						f'''{language}:{entity_name}'''
               __snake_case			:Tuple          =						entity_id
     return new_mapping


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










import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=7			,						a__=3			,						a__=18			,						a__=30			,						a__=4_00			,						a__=True			,						a__=32			,						a__=True			,						) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						parent
            __snake_case			:Dict          =						batch_size
            __snake_case			:Optional[Any]          =						num_channels
            __snake_case			:Dict          =						image_size
            __snake_case			:Dict          =						min_resolution
            __snake_case			:Dict          =						max_resolution
            __snake_case			:List[Any]          =						do_resize
            __snake_case			:Dict          =						size_divisor
            __snake_case			:Union[str, Any]          =						do_rescale



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return {
                "do_resize": self.do_resize,
                "size_divisor": self.size_divisor,
                "do_rescale": self.do_rescale,
            }




@require_torch
@require_vision
class 			snake_case__   (							lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= GLPNImageProcessor if is_vision_available() else None



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Dict          =						GLPNImageProcessingTester(self    )



       @property
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            return self.image_processor_tester.prepare_image_processor_dict()



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






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(a__			,						"""do_resize"""    )    )
            self.assertTrue(hasattr(a__			,						"""size_divisor"""    )    )
            self.assertTrue(hasattr(a__			,						"""resample"""    )    )
            self.assertTrue(hasattr(a__			,						"""do_rescale"""    )    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            __snake_case			:Optional[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						Image.Image    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:Union[str, Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            __snake_case			:List[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__			,						numpify=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						np.ndarray    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:int          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            __snake_case			:Optional[int]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__			,						torchify=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						torch.Tensor    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:List[Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )


