


"""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 				_snake_case      (   __snake_case							:      Dict						, __snake_case							:      List[str]						, __snake_case							:      List[Any]						, __snake_case							:      int						, __snake_case							:      Union[str, Any]   ):




   """simple docstring"""







   with open(__snake_case   ) as metadata_file:
      _lowerCamelCase	:					Optional[int]   =		json.load(__snake_case   )
   _lowerCamelCase	:					Any   =		LukeConfig(use_entity_aware_attention=__snake_case						, **metadata["""model_config"""]   )

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

   # Load the entity vocab file
   _lowerCamelCase	:					List[Any]   =		load_entity_vocab(__snake_case   )

   _lowerCamelCase	:					int   =		RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]   )

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

   _lowerCamelCase	:					Optional[Any]   =		LukeTokenizer.from_pretrained(__snake_case   )

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

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

   _lowerCamelCase	:					List[str]   =		LukeModel(config=__snake_case   ).eval()

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

   _lowerCamelCase	:					str   =		(
       """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 ."""
   )
   _lowerCamelCase	:					Optional[Any]   =		(39, 42)
   _lowerCamelCase	:					str   =		tokenizer(__snake_case						, entity_spans=[span]						, add_prefix_space=__snake_case						, return_tensors="""pt"""   )

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

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

   # Verify entity hidden states
   if model_size == "large":
      _lowerCamelCase	:					List[Any]   =		torch.Size((1, 1, 1024)   )
      _lowerCamelCase	:					int   =		torch.tensor([[0.0466, -0.0106, -0.0179]]   )
   else:  # base
      _lowerCamelCase	:					Union[str, Any]   =		torch.Size((1, 1, 768)   )
      _lowerCamelCase	:					Union[str, Any]   =		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]						, __snake_case						, atol=1E-4   ):
      raise ValueError

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

def 				_snake_case      (   __snake_case							:      Dict   ):




   """simple docstring"""







   _lowerCamelCase	:					Dict   =		{}
   with open(__snake_case						, """r"""						, encoding="""utf-8"""   ) as f:
      for index, line in enumerate(__snake_case   ):
         _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		line.rstrip().split("""\t"""   )
         _lowerCamelCase	:					Any   =		index

   return entity_vocab


if __name__ == "__main__":
 UpperCAmelCase				=						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."""
 )
 UpperCAmelCase				=						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_torch_available,
)


UpperCAmelCase				=						{
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""VisionEncoderDecoderModel"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""TFVisionEncoderDecoderModel"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
 from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
 import sys

 UpperCAmelCase				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

