


"""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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					with open(lowerCAmelCase_   ) as metadata_file:
										__SCREAMING_SNAKE_CASE						   =				json.load(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				LukeConfig(use_entity_aware_attention=lowerCAmelCase_  ,						**metadata["model_config"]   )

					# Load in the weights from the checkpoint_path
					__SCREAMING_SNAKE_CASE						   =				torch.load(lowerCAmelCase_  ,						map_location="cpu"   )

					# Load the entity vocab file
					__SCREAMING_SNAKE_CASE						   =				load_entity_vocab(lowerCAmelCase_   )

					__SCREAMING_SNAKE_CASE						   =				RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]   )

					# Add special tokens to the token vocabulary for downstream tasks
					__SCREAMING_SNAKE_CASE						   =				AddedToken("<ent>"  ,						lstrip=lowerCAmelCase_  ,						rstrip=lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				AddedToken("<ent2>"  ,						lstrip=lowerCAmelCase_  ,						rstrip=lowerCAmelCase_   )
					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(lowerCAmelCase_   )
					with open(os.path.join(lowerCAmelCase_  ,						LukeTokenizer.vocab_files_names["entity_vocab_file"]   )  ,						"w"   ) as f:
										json.dump(lowerCAmelCase_  ,						lowerCAmelCase_   )

					__SCREAMING_SNAKE_CASE						   =				LukeTokenizer.from_pretrained(lowerCAmelCase_   )

					# Initialize the embeddings of the special tokens
					__SCREAMING_SNAKE_CASE						   =				state_dict["embeddings.word_embeddings.weight"]
					__SCREAMING_SNAKE_CASE						   =				word_emb[tokenizer.convert_tokens_to_ids(["@"]   )[0]].unsqueeze(0   )
					__SCREAMING_SNAKE_CASE						   =				word_emb[tokenizer.convert_tokens_to_ids(["#"]   )[0]].unsqueeze(0   )
					__SCREAMING_SNAKE_CASE						   =				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"]:
															__SCREAMING_SNAKE_CASE						   =				f"""encoder.layer.{layer_index}.attention.self."""
															__SCREAMING_SNAKE_CASE						   =				state_dict[prefix + matrix_name]
															__SCREAMING_SNAKE_CASE						   =				state_dict[prefix + matrix_name]
															__SCREAMING_SNAKE_CASE						   =				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					__SCREAMING_SNAKE_CASE						   =				state_dict["entity_embeddings.entity_embeddings.weight"]
					__SCREAMING_SNAKE_CASE						   =				entity_emb[entity_vocab["[MASK]"]]

					__SCREAMING_SNAKE_CASE						   =				LukeModel(config=lowerCAmelCase_   ).eval()

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

					__SCREAMING_SNAKE_CASE						   =				(
					    "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 ."
					)
					__SCREAMING_SNAKE_CASE						   =				(39, 42)
					__SCREAMING_SNAKE_CASE						   =				tokenizer(lowerCAmelCase_  ,						entity_spans=[span]  ,						add_prefix_space=lowerCAmelCase_  ,						return_tensors="pt"   )

					__SCREAMING_SNAKE_CASE						   =				model(**lowerCAmelCase_   )

					# Verify word hidden states
					if model_size == "large":
										__SCREAMING_SNAKE_CASE						   =				torch.Size((1, 42, 1024)   )
										__SCREAMING_SNAKE_CASE						   =				torch.tensor(
										    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]   )
					else:  # base
										__SCREAMING_SNAKE_CASE						   =				torch.Size((1, 42, 768)   )
										__SCREAMING_SNAKE_CASE						   =				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]  ,						lowerCAmelCase_  ,						atol=1E-4   ):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										__SCREAMING_SNAKE_CASE						   =				torch.Size((1, 1, 1024)   )
										__SCREAMING_SNAKE_CASE						   =				torch.tensor([[0.0466, -0.0106, -0.0179]]   )
					else:  # base
										__SCREAMING_SNAKE_CASE						   =				torch.Size((1, 1, 768)   )
										__SCREAMING_SNAKE_CASE						   =				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]  ,						lowerCAmelCase_  ,						atol=1E-4   ):
										raise ValueError

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




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				{}
					with open(lowerCAmelCase_  ,						"r"  ,						encoding="utf-8"   ) as f:
										for index, line in enumerate(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				line.rstrip().split("\t"   )
															__SCREAMING_SNAKE_CASE						   =				index

					return entity_vocab


if __name__ == "__main__":
		a__				:							Optional[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.'''
		)
		a__				:							int						=     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"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A:   str								=		{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		["DistilBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[Any]								=		[
          "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DistilBertForMaskedLM",
          "DistilBertForMultipleChoice",
          "DistilBertForQuestionAnswering",
          "DistilBertForSequenceClassification",
          "DistilBertForTokenClassification",
          "DistilBertModel",
          "DistilBertPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		[
          "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDistilBertForMaskedLM",
          "TFDistilBertForMultipleChoice",
          "TFDistilBertForQuestionAnswering",
          "TFDistilBertForSequenceClassification",
          "TFDistilBertForTokenClassification",
          "TFDistilBertMainLayer",
          "TFDistilBertModel",
          "TFDistilBertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   int								=		[
          "FlaxDistilBertForMaskedLM",
          "FlaxDistilBertForMultipleChoice",
          "FlaxDistilBertForQuestionAnswering",
          "FlaxDistilBertForSequenceClassification",
          "FlaxDistilBertForTokenClassification",
          "FlaxDistilBertModel",
          "FlaxDistilBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      A:   Optional[int]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
