






'''simple docstring'''






import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       List[Any]  ,		A  :       List[str]  ,		A  :       Any  ,		A  :       Optional[int]  )    -> Union[str, Any]:







			"""simple docstring"""

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

			# Load in the weights from the checkpoint_path
			__snake_case				:      List[Any]     							= torch.load(A  ,		map_location='cpu'  )

			# Load the entity vocab file
			__snake_case				:      Union[str, Any]     							= load_entity_vocab(A  )

			__snake_case				:      List[Any]     							= RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']  )

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

			__snake_case				:      int     							= LukeTokenizer.from_pretrained(A  )

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

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

			__snake_case				:      Optional[Any]     							= LukeModel(config=A  ).eval()

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

			__snake_case				:      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 .'
			)
			__snake_case				:      List[str]     							= (39, 42)
			__snake_case				:      Dict     							= tokenizer(A  ,		entity_spans=[span]  ,		add_prefix_space=A  ,		return_tensors='pt'  )

			__snake_case				:      Dict     							= model(**A  )

			# Verify word hidden states
			if model_size == "large":
						__snake_case				:      Union[str, Any]     							= torch.Size((1, 42, 10_24)  )
						__snake_case				:      Optional[int]     							= torch.tensor(
						    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]  )
			else:  # base
						__snake_case				:      Union[str, Any]     							= torch.Size((1, 42, 7_68)  )
						__snake_case				:      str     							= torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]  )

			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":
						__snake_case				:      Tuple     							= torch.Size((1, 1, 10_24)  )
						__snake_case				:      List[str]     							= torch.tensor([[0.0466, -0.0106, -0.0179]]  )
			else:  # base
						__snake_case				:      Union[str, Any]     							= torch.Size((1, 1, 7_68)  )
						__snake_case				:      Dict     							= torch.tensor([[0.1457, 0.1044, 0.0174]]  )

			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

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




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  )    -> Union[str, Any]:







			"""simple docstring"""
			__snake_case				:      Optional[int]     							= {}
			with open(A  ,		'r'  ,		encoding='utf-8'  ) as f:
						for index, line in enumerate(A  ):
									__snake_case							,__snake_case				:      Optional[Any]     							= line.rstrip().split('\t'  )
									__snake_case				:      List[str]     							= index

			return entity_vocab


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,
						)







'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A    	=   {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''TimmBackbone''']


if TYPE_CHECKING:
      from .configuration_timm_backbone import TimmBackboneConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_timm_backbone import TimmBackbone

else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)