'''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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Dict:
  # Load configuration defined in the metadata file
  with open(SCREAMING_SNAKE_CASE_						) as metadata_file:
    lowercase_     =       json.load(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE_ ,	**metadata["""model_config"""]						)

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

  # Load the entity vocab file
  lowercase_     =       load_entity_vocab(SCREAMING_SNAKE_CASE_						)

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

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

  lowercase_     =       LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_						)

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

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

  lowercase_     =       LukeModel(config=SCREAMING_SNAKE_CASE_						).eval()

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

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

  lowercase_     =       model(**SCREAMING_SNAKE_CASE_						)

  # Verify word hidden states
  if model_size == "large":
    lowercase_     =       torch.Size((1, 42, 10_24)						)
    lowercase_     =       torch.tensor(
        [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]						)
  else:  # base
    lowercase_     =       torch.Size((1, 42, 7_68)						)
    lowercase_     =       torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]						)

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

  # Verify entity hidden states
  if model_size == "large":
    lowercase_     =       torch.Size((1, 1, 10_24)						)
    lowercase_     =       torch.tensor([[0.04_66, -0.01_06, -0.01_79]]						)
  else:  # base
    lowercase_     =       torch.Size((1, 1, 7_68)						)
    lowercase_     =       torch.tensor([[0.14_57, 0.10_44, 0.01_74]]						)

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

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

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       {}
  with open(SCREAMING_SNAKE_CASE_ ,	"""r""" ,	encoding="""utf-8"""						) as f:
    for index, line in enumerate(SCREAMING_SNAKE_CASE_						):
      lowercase_     ,	lowercase_     =       line.rstrip().split("""\t"""						)
      lowercase_     =       index

  return entity_vocab


if __name__ == "__main__":
       __snake_case						  =		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."""
       )
       __snake_case						  =		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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						  =		{"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """FocalNetForImageClassification""",
           """FocalNetForMaskedImageModeling""",
           """FocalNetBackbone""",
           """FocalNetModel""",
           """FocalNetPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

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

