



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        lowercase_							(A : List[Any]		,					A : str		,					A : int		,					A : int		,					A : Optional[Any]      ):
       # Load configuration defined in the metadata file
       with open(A      ) as metadata_file:
              snake_case__							:	List[str]   	=	json.load(A      )
       snake_case__							:	Optional[Any]   	=	LukeConfig(use_entity_aware_attention=A		,					**metadata['model_config']      )

       # Load in the weights from the checkpoint_path
       snake_case__							:	int   	=	torch.load(A		,					map_location='cpu'      )

       # Load the entity vocab file
       snake_case__							:	int   	=	load_entity_vocab(A      )

       snake_case__							:	Dict   	=	RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']      )

       # Add special tokens to the token vocabulary for downstream tasks
       snake_case__							:	Any   	=	AddedToken('<ent>'		,					lstrip=A		,					rstrip=A      )
       snake_case__							:	Dict   	=	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__							:	Any   	=	LukeTokenizer.from_pretrained(A      )

       # Initialize the embeddings of the special tokens
       snake_case__							:	Optional[Any]   	=	state_dict['embeddings.word_embeddings.weight']
       snake_case__							:	Dict   	=	word_emb[tokenizer.convert_tokens_to_ids(['@']      )[0]].unsqueeze(0      )
       snake_case__							:	Tuple   	=	word_emb[tokenizer.convert_tokens_to_ids(['#']      )[0]].unsqueeze(0      )
       snake_case__							:	Union[str, Any]   	=	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__							:	Tuple   	=	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__							:	Any   	=	state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       snake_case__							:	Union[str, Any]   	=	state_dict['entity_embeddings.entity_embeddings.weight']
       snake_case__							:	Optional[int]   	=	entity_emb[entity_vocab['[MASK]']]

       snake_case__							:	Tuple   	=	LukeModel(config=A      ).eval()

       snake_case__	,				snake_case__							:	Optional[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__							:	Any   	=	LukeTokenizer.from_pretrained(A		,					task='entity_classification'      )

       snake_case__							:	int   	=	(
           '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__							:	str   	=	(3_9, 4_2)
       snake_case__							:	int   	=	tokenizer(A		,					entity_spans=[span]		,					add_prefix_space=A		,					return_tensors='pt'      )

       snake_case__							:	Any   	=	model(**A      )

       # Verify word hidden states
       if model_size == "large":
              snake_case__							:	Optional[int]   	=	torch.Size((1, 4_2, 1_0_2_4)      )
              snake_case__							:	Any   	=	torch.tensor(
                  [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]      )
       else:  # base
              snake_case__							:	str   	=	torch.Size((1, 4_2, 7_6_8)      )
              snake_case__							:	Any   	=	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, 1_0_2_4)      )
              snake_case__							:	List[str]   	=	torch.tensor([[0.0466, -0.0106, -0.0179]]      )
       else:  # base
              snake_case__							:	Tuple   	=	torch.Size((1, 1, 7_6_8)      )
              snake_case__							:	Tuple   	=	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        lowercase_							(A : Optional[int]      ):
       snake_case__							:	Union[str, Any]   	=	{}
       with open(A		,					'r'		,					encoding='utf-8'      ) as f:
              for index, line in enumerate(A      ):
                     snake_case__	,				snake_case__							:	Optional[int]   	=	line.rstrip().split('\t'      )
                     snake_case__							:	Union[str, Any]   	=	index

       return entity_vocab


if __name__ == "__main__":
 a_   :Tuple        =      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_   :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,
 )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


a_   :int        =      {
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongT5EncoderModel",
     "LongT5ForConditionalGeneration",
     "LongT5Model",
     "LongT5PreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      [
     "FlaxLongT5ForConditionalGeneration",
     "FlaxLongT5Model",
     "FlaxLongT5PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longta import (
      LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongTaEncoderModel,
      LongTaForConditionalGeneration,
      LongTaModel,
      LongTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_longta import (
      FlaxLongTaForConditionalGeneration,
      FlaxLongTaModel,
      FlaxLongTaPreTrainedModel,
  )


else:
 import sys

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






