

"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__	,    A__   )      ->      Tuple:




   """simple docstring"""
   # Load configuration defined in the metadata file
   with open(A__   ) as metadata_file:
      lowerCamelCase__						=			json.load(A__   )
   lowerCamelCase__						=			LukeConfig(use_entity_aware_attention=A__	,    **metadata["model_config"]   )

   # Load in the weights from the checkpoint_path
   lowerCamelCase__						=			torch.load(A__	,    map_location="cpu"   )["module"]

   # Load the entity vocab file
   lowerCamelCase__						=			load_original_entity_vocab(A__   )
   # add an entry for [MASK2]
   lowerCamelCase__						=			max(entity_vocab.values()   ) + 1
   config.entity_vocab_size += 1

   lowerCamelCase__						=			XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]   )

   # Add special tokens to the token vocabulary for downstream tasks
   lowerCamelCase__						=			AddedToken("<ent>"	,    lstrip=A__	,    rstrip=A__   )
   lowerCamelCase__						=			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__	,    "tokenizer_config.json"   )	,    "r"   ) as f:
      lowerCamelCase__						=			json.load(A__   )
   lowerCamelCase__						=			"MLukeTokenizer"
   with open(os.path.join(A__	,    "tokenizer_config.json"   )	,    "w"   ) as f:
      json.dump(A__	,    A__   )

   with open(os.path.join(A__	,    MLukeTokenizer.vocab_files_names["entity_vocab_file"]   )	,    "w"   ) as f:
      json.dump(A__	,    A__   )

   lowerCamelCase__						=			MLukeTokenizer.from_pretrained(A__   )

   # Initialize the embeddings of the special tokens
   lowerCamelCase__						=			tokenizer.convert_tokens_to_ids(["@"]   )[0]
   lowerCamelCase__						=			tokenizer.convert_tokens_to_ids(["#"]   )[0]

   lowerCamelCase__						=			state_dict["embeddings.word_embeddings.weight"]
   lowerCamelCase__						=			word_emb[ent_init_index].unsqueeze(0   )
   lowerCamelCase__						=			word_emb[enta_init_index].unsqueeze(0   )
   lowerCamelCase__						=			torch.cat([word_emb, ent_emb, enta_emb]   )
   # add special tokens for 'entity_predictions.bias'
   for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
      lowerCamelCase__						=			state_dict[bias_name]
      lowerCamelCase__						=			decoder_bias[ent_init_index].unsqueeze(0   )
      lowerCamelCase__						=			decoder_bias[enta_init_index].unsqueeze(0   )
      lowerCamelCase__						=			torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]   )

   # 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__						=			f'encoder.layer.{layer_index}.attention.self.'
         lowerCamelCase__						=			state_dict[prefix + matrix_name]
         lowerCamelCase__						=			state_dict[prefix + matrix_name]
         lowerCamelCase__						=			state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
   lowerCamelCase__						=			state_dict["entity_embeddings.entity_embeddings.weight"]
   lowerCamelCase__						=			entity_emb[entity_vocab["[MASK]"]].unsqueeze(0   )
   lowerCamelCase__						=			torch.cat([entity_emb, entity_mask_emb]   )
   # add [MASK2] for 'entity_predictions.bias'
   lowerCamelCase__						=			state_dict["entity_predictions.bias"]
   lowerCamelCase__						=			entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0   )
   lowerCamelCase__						=			torch.cat([entity_prediction_bias, entity_mask_bias]   )

   lowerCamelCase__						=			LukeForMaskedLM(config=A__   ).eval()

   state_dict.pop("entity_predictions.decoder.weight"   )
   state_dict.pop("lm_head.decoder.weight"   )
   state_dict.pop("lm_head.decoder.bias"   )
   lowerCamelCase__						=			OrderedDict()
   for key, value in state_dict.items():
      if not (key.startswith("lm_head"   ) or key.startswith("entity_predictions"   )):
         lowerCamelCase__						=			state_dict[key]
      else:
         lowerCamelCase__						=			state_dict[key]

   lowerCamelCase__					,       lowerCamelCase__						=			model.load_state_dict(A__	,    strict=A__   )

   if set(A__   ) != {"luke.embeddings.position_ids"}:
      raise ValueError(f'Unexpected unexpected_keys: {unexpected_keys}'   )
   if set(A__   ) != {
       "lm_head.decoder.weight",
       "lm_head.decoder.bias",
       "entity_predictions.decoder.weight",
   }:
      raise ValueError(f'Unexpected missing_keys: {missing_keys}'   )

   model.tie_weights()
   assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
   assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

   # Check outputs
   lowerCamelCase__						=			MLukeTokenizer.from_pretrained(A__	,    task="entity_classification"   )

   lowerCamelCase__						=			"ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
   lowerCamelCase__						=			(0, 9)
   lowerCamelCase__						=			tokenizer(A__	,    entity_spans=[span]	,    return_tensors="pt"   )

   lowerCamelCase__						=			model(**A__   )

   # Verify word hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      lowerCamelCase__						=			torch.Size((1, 33, 768)   )
      lowerCamelCase__						=			torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]   )

   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":
      raise NotImplementedError
   else:  # base
      lowerCamelCase__						=			torch.Size((1, 1, 768)   )
      lowerCamelCase__						=			torch.tensor([[-0.1482, 0.0609, 0.0322]]   )

   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

   # Verify masked word/entity prediction
   lowerCamelCase__						=			MLukeTokenizer.from_pretrained(A__   )
   lowerCamelCase__						=			"Tokyo is the capital of <mask>."
   lowerCamelCase__						=			(24, 30)
   lowerCamelCase__						=			tokenizer(A__	,    entity_spans=[span]	,    return_tensors="pt"   )

   lowerCamelCase__						=			model(**A__   )

   lowerCamelCase__						=			encoding["input_ids"][0].tolist()
   lowerCamelCase__						=			input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"   )   )
   lowerCamelCase__						=			outputs.logits[0][mask_position_id].argmax(dim=-1   )
   assert "Japan" == tokenizer.decode(A__   )

   lowerCamelCase__						=			outputs.entity_logits[0][0].argmax().item()
   lowerCamelCase__						=			[
       entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
   ]
   assert [e for e in multilingual_predicted_entities if e.startswith("en:"   )][0] == "en:Japan"

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



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			["[MASK]", "[PAD]", "[UNK]"]

   lowerCamelCase__						=			[json.loads(A__   ) for line in open(A__   )]

   lowerCamelCase__						=			{}
   for entry in data:
      lowerCamelCase__						=			entry["id"]
      for entity_name, language in entry["entities"]:
         if entity_name in SPECIAL_TOKENS:
            lowerCamelCase__						=			entity_id
            break
         lowerCamelCase__						=			f'{language}:{entity_name}'
         lowerCamelCase__						=			entity_id
   return new_mapping


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			List[str]	  =			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.'''
     )
     SCREAMING_SNAKE_CASE_     :			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,
     )







"""simple docstring"""




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     import PIL.Image

     from .features import FeatureType


SCREAMING_SNAKE_CASE_     :			Optional[List[str]]	  =			None
SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			'''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
SCREAMING_SNAKE_CASE_     :			int	  =			[
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]


@dataclass
class 		_A       :
      __a								=	True
      __a								=	None
      # Automatically constructed
      __a								=	"PIL.Image.Image"
      __a								=	pa.struct({'bytes': pa.binary(), 'path': pa.string()}					)
      __a								=	field(default='Image'				,       init=__a				,       repr=__a					)





      def __call__(  self		)   ->					Dict:
         return self.pa_type





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					dict:
         if config.PIL_AVAILABLE:
            import PIL.Image
         else:
            raise ImportError("To support encoding images, please install 'Pillow'."		)

         if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			np.array(SCREAMING_SNAKE_CASE__		)

         if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            return {"path": value, "bytes": None}
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            return {"path": None, "bytes": value}
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						np.ndarray		):
            # convert the image array to PNG/TIFF bytes
            return encode_np_array(SCREAMING_SNAKE_CASE__		)
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						PIL.Image.Image		):
            # convert the PIL image to bytes (default format is PNG/TIFF)
            return encode_pil_image(SCREAMING_SNAKE_CASE__		)
         elif value.get("path"		) is not None and os.path.isfile(value["path"]		):
            # we set "bytes": None to not duplicate the data if they're already available locally
            return {"bytes": None, "path": value.get("path"		)}
         elif value.get("bytes"		) is not None or value.get("path"		) is not None:
            # store the image bytes, and path is used to infer the image format using the file extension
            return {"bytes": value.get("bytes"		), "path": value.get("path"		)}
         else:
            raise ValueError(
                f'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None		)   ->					"PIL.Image.Image":
         if not self.decode:
            raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead."		)

         if config.PIL_AVAILABLE:
            import PIL.Image
         else:
            raise ImportError("To support decoding images, please install 'Pillow'."		)

         if token_per_repo_id is None:
            lowerCamelCase__						=			{}

         lowerCamelCase__					,       lowerCamelCase__						=			value["path"], value["bytes"]
         if bytes_ is None:
            if path is None:
               raise ValueError(f'An image should have one of \'path\' or \'bytes\' but both are None in {value}.'		)
            else:
               if is_local_path(SCREAMING_SNAKE_CASE__		):
                  lowerCamelCase__						=			PIL.Image.open(SCREAMING_SNAKE_CASE__		)
               else:
                  lowerCamelCase__						=			path.split("::"		)[-1]
                  try:
                     lowerCamelCase__						=			string_to_dict(SCREAMING_SNAKE_CASE__    ,						config.HUB_DATASETS_URL		)["repo_id"]
                     lowerCamelCase__						=			token_per_repo_id.get(SCREAMING_SNAKE_CASE__		)
                  except ValueError:
                     lowerCamelCase__						=			None
                  with xopen(SCREAMING_SNAKE_CASE__    ,						"rb"    ,						use_auth_token=SCREAMING_SNAKE_CASE__		) as f:
                     lowerCamelCase__						=			BytesIO(f.read()		)
                  lowerCamelCase__						=			PIL.Image.open(bytes_		)
         else:
            lowerCamelCase__						=			PIL.Image.open(BytesIO(bytes_		)		)
         image.load()  # to avoid "Too many open files" errors
         return image





      def 					_lowerCamelCase  (  self		)   ->					Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value("binary"		),
                 "path": Value("string"		),
             }
         )





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					pa.StructArray:
         if pa.types.is_string(storage.type		):
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.binary()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, storage]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([storage, path_array]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_struct(storage.type		):
            if storage.type.get_field_index("bytes"		) >= 0:
               lowerCamelCase__						=			storage.field("bytes"		)
            else:
               lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.binary()		)
            if storage.type.get_field_index("path"		) >= 0:
               lowerCamelCase__						=			storage.field("path"		)
            else:
               lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_list(storage.type		):
            lowerCamelCase__						=			pa.array(
                [encode_np_array(np.array(SCREAMING_SNAKE_CASE__		)		)["bytes"] if arr is not None else None for arr in storage.to_pylist()]    ,						type=pa.binary()    ,						)
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays(
                [bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=bytes_array.is_null()		)
         return array_cast(SCREAMING_SNAKE_CASE__    ,						self.pa_type		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(SCREAMING_SNAKE_CASE__		):
            with xopen(SCREAMING_SNAKE_CASE__    ,						"rb"		) as f:
               lowerCamelCase__						=			f.read()
            return bytes_

         lowerCamelCase__						=			pa.array(
             [
                 (path_to_bytes(x["path"]		) if x["bytes"] is None else x["bytes"]) if x is not None else None
                 for x in storage.to_pylist()
             ]    ,						type=pa.binary()    ,						)
         lowerCamelCase__						=			pa.array(
             [os.path.basename(SCREAMING_SNAKE_CASE__		) if path is not None else None for path in storage.field("path"		).to_pylist()]    ,						type=pa.string()    ,						)
         lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=bytes_array.is_null()		)
         return array_cast(SCREAMING_SNAKE_CASE__    ,						self.pa_type		)






def 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

   global _IMAGE_COMPRESSION_FORMATS
   if _IMAGE_COMPRESSION_FORMATS is None:
      PIL.Image.init()
      lowerCamelCase__						=			list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
   return _IMAGE_COMPRESSION_FORMATS



def 	UpperCAmelCase__   (				A__   )      ->      bytes:




   """simple docstring"""
   lowerCamelCase__						=			BytesIO()
   if image.format in list_image_compression_formats():
      lowerCamelCase__						=			image.format
   else:
      lowerCamelCase__						=			"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
   image.save(A__	,    format=A__   )
   return buffer.getvalue()



def 	UpperCAmelCase__   (				A__   )      ->      dict:




   """simple docstring"""
   if hasattr(A__	,    "filename"   ) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(A__   )}



def 	UpperCAmelCase__   (				A__   )      ->      dict:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

   lowerCamelCase__						=			array.dtype
   lowerCamelCase__						=			dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
   lowerCamelCase__						=			dtype.kind
   lowerCamelCase__						=			dtype.itemsize

   lowerCamelCase__						=			None

   # Multi-channel array case (only np.dtype("|u1") is allowed)
   if array.shape[2:]:
      lowerCamelCase__						=			np.dtype("|u1"   )
      if dtype_kind not in ["u", "i"]:
         raise TypeError(
             f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'   )
      if dtype is not dest_dtype:
         warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''   )
    # Exact match
   elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
      lowerCamelCase__						=			dtype
   else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
      while dtype_itemsize >= 1:
         lowerCamelCase__						=			dtype_byteorder + dtype_kind + str(A__   )
         lowerCamelCase__						=			np.dtype(A__   )
         if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
            warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''   )
            break
         else:
            dtype_itemsize //= 2

   if dest_dtype is None:
      raise TypeError(
          f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'   )
   lowerCamelCase__						=			PIL.Image.fromarray(array.astype(A__   )   )
   return {"path": None, "bytes": image_to_bytes(A__   )}



def 	UpperCAmelCase__   (				A__   )      ->      List[dict]:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

   if objs:
      lowerCamelCase__					,       lowerCamelCase__						=			first_non_null_value(A__   )
      if isinstance(A__	,    A__   ):
         return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
      if isinstance(A__	,    np.ndarray   ):
         lowerCamelCase__						=			no_op_if_value_is_null(A__   )
         return [obj_to_image_dict_func(A__   ) for obj in objs]
      elif isinstance(A__	,    PIL.Image.Image   ):
         lowerCamelCase__						=			no_op_if_value_is_null(A__   )
         return [obj_to_image_dict_func(A__   ) for obj in objs]
      else:
         return objs
   else:
      return objs




