


"""simple docstring"""



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__snake_case             =   logging.get_logger(__name__)







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]			)     ->						List[int]:


    """simple docstring"""





    if isinstance(SCREAMING_SNAKE_CASE__						,							np.ndarray			):
        return list(tensor.shape			)

    snake_case   :				List[str]				 =   tf.shape(SCREAMING_SNAKE_CASE__			)

    if tensor.shape == tf.TensorShape(SCREAMING_SNAKE_CASE__			):
        return dynamic

    snake_case   :				Optional[int]				 =   tensor.shape.as_list()

    return [dynamic[i] if s is None else s for i, s in enumerate(SCREAMING_SNAKE_CASE__			)]







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  str = None						,							lowercase  :  int = None			)     ->						tf.Tensor:


    """simple docstring"""





    return tf.nn.softmax(logits=logits + 1e-9						,							axis=SCREAMING_SNAKE_CASE__						,							name=SCREAMING_SNAKE_CASE__			)







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  Tuple						,							lowercase  :  Union[str, Any]						,							lowercase  :  str=1e-5						,							lowercase  :  List[Any]=-1			)     ->						str:


    """simple docstring"""





    if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__			):
        raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."			)

    # Get mean and variance on the axis to be normalized
    snake_case   :				Tuple				 =   tf.nn.moments(SCREAMING_SNAKE_CASE__						,							axes=[axis]						,							keepdims=SCREAMING_SNAKE_CASE__			)

    if axis != -1:
        # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
        # on every dimension except axis
        snake_case   :				int				 =   [1] * inputs.shape.rank
        snake_case   :				List[Any]				 =   shape_list(SCREAMING_SNAKE_CASE__			)[axis]
        snake_case   :				Tuple				 =   tf.reshape(SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__			)
        snake_case   :				int				 =   tf.reshape(SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__			)

    # Compute layer normalization using the batch_normalization
    # function.
    snake_case   :				Dict				 =   tf.nn.batch_normalization(
        SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__						,							offset=SCREAMING_SNAKE_CASE__						,							scale=SCREAMING_SNAKE_CASE__						,							variance_epsilon=SCREAMING_SNAKE_CASE__						,							)
    return outputs







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  str=0						,							lowercase  :  Tuple=-1			)     ->						int:


    """simple docstring"""





    if end_dim < 0:
        end_dim += input.shape.rank
    if start_dim < 0:
        start_dim += input.shape.rank

    if start_dim == end_dim:
        return input

    snake_case   :				Optional[int]				 =   tf.shape(SCREAMING_SNAKE_CASE__			)
    snake_case   :				Optional[Any]				 =   tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
    snake_case   :				str				 =   tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,							axis=0			)
    return tf.reshape(SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__			)







def   __lowerCAmelCase						(						lowercase  :  Optional[int]			)     ->						tf.Tensor:


    """simple docstring"""





    if not isinstance(SCREAMING_SNAKE_CASE__						,							tf.Tensor			):
        snake_case   :				Optional[int]				 =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE__			)  # Catches stray NumPy inputs
    if encoder_attention_mask.shape.rank == 3:
        snake_case   :				Union[str, Any]				 =   encoder_attention_mask[:, None, :, :]
    if encoder_attention_mask.shape.rank == 2:
        snake_case   :				Optional[int]				 =   encoder_attention_mask[:, None, None, :]
    # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
    # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
    # /transformer/transformer_layers.py#L270
    # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
    # encoder_extended_attention_mask.transpose(-1, -2))
    snake_case   :				Optional[Any]				 =   (
        tf.cast(1						,							encoder_attention_mask.dtype			) - encoder_extended_attention_mask
    ) * encoder_extended_attention_mask.dtype.min

    return encoder_extended_attention_mask







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  List[Any]						,							lowercase  :  Optional[Any] = "input_ids"			)     ->						None:


    """simple docstring"""





    tf.debugging.assert_less(
        SCREAMING_SNAKE_CASE__						,							tf.cast(SCREAMING_SNAKE_CASE__						,							dtype=tensor.dtype			)						,							message=(
            F'The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_SNAKE_CASE__			)}) must be smaller than the embedding '
            F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
        )						,							)







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  str						,							lowercase  :  List[Any]			)     ->						int:


    """simple docstring"""





    snake_case   :				Union[str, Any]				 =   6_4512
    # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
    # because in that case even chunking the array would not make the saving
    # possible.
    snake_case   :				Optional[Any]				 =   [x for x in data if len(SCREAMING_SNAKE_CASE__			) > HDF5_OBJECT_HEADER_LIMIT]

    # Expecting this to never be true.
    if bad_attributes:
        raise RuntimeError(
            "The following attributes cannot be saved to HDF5 file because "
            F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
            F'bytes: {bad_attributes}'			)

    snake_case   :				str				 =   np.asarray(SCREAMING_SNAKE_CASE__			)

    snake_case   :				Dict				 =   1
    snake_case   :				List[Any]				 =   np.array_split(SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__			)

    # This will never loop forever thanks to the test above.
    while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data			):
        num_chunks += 1
        snake_case   :				List[Any]				 =   np.array_split(SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__			)

    if num_chunks > 1:
        for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE__			):
            snake_case   :				Optional[Any]				 =   chunk_data
    else:
        snake_case   :				str				 =   data







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Tuple			)     ->						List[Any]:


    """simple docstring"""





    if name in group.attrs:
        snake_case   :				str				 =   [n.decode("utf8"			) if hasattr(SCREAMING_SNAKE_CASE__						,							"decode"			) else n for n in group.attrs[name]]
    else:
        snake_case   :				Dict				 =   []
        snake_case   :				Any				 =   0
        while "%s%d" % (name, chunk_id) in group.attrs:
            data.extend(
                [n.decode("utf8"			) if hasattr(SCREAMING_SNAKE_CASE__						,							"decode"			) else n for n in group.attrs["%s%d" % (name, chunk_id)]]			)
            chunk_id += 1
    return data







def   __lowerCAmelCase						(						lowercase  :  int			)     ->						Any:


    """simple docstring"""





    def _expand_single_ad_tensor(lowercase  :  int			):
        if isinstance(SCREAMING_SNAKE_CASE__						,							tf.Tensor			) and t.shape.rank == 1:
            return tf.expand_dims(SCREAMING_SNAKE_CASE__						,							axis=-1			)
        return t

    return tf.nest.map_structure(_expand_single_ad_tensor						,							SCREAMING_SNAKE_CASE__			)









"""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   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Dict						,							lowercase  :  int			)     ->						int:


    """simple docstring"""





    with open(lowercase			) as metadata_file:
        snake_case   :				str				 =   json.load(lowercase			)
    snake_case   :				Optional[Any]				 =   LukeConfig(use_entity_aware_attention=lowercase						,							**metadata["model_config"]			)

    # Load in the weights from the checkpoint_path
    snake_case   :				Tuple				 =   torch.load(lowercase						,							map_location="cpu"			)["module"]

    # Load the entity vocab file
    snake_case   :				Optional[Any]				 =   load_original_entity_vocab(lowercase			)
    # add an entry for [MASK2]
    snake_case   :				Dict				 =   max(entity_vocab.values()			) + 1
    config.entity_vocab_size += 1

    snake_case   :				Union[str, Any]				 =   XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

    # Add special tokens to the token vocabulary for downstream tasks
    snake_case   :				Tuple				 =   AddedToken("<ent>"						,							lstrip=lowercase						,							rstrip=lowercase			)
    snake_case   :				str				 =   AddedToken("<ent2>"						,							lstrip=lowercase						,							rstrip=lowercase			)
    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(lowercase			)
    with open(os.path.join(lowercase						,							"tokenizer_config.json"			)						,							"r"			) as f:
        snake_case   :				str				 =   json.load(lowercase			)
    snake_case   :				List[str]				 =   "MLukeTokenizer"
    with open(os.path.join(lowercase						,							"tokenizer_config.json"			)						,							"w"			) as f:
        json.dump(lowercase						,							lowercase			)

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

    snake_case   :				Dict				 =   MLukeTokenizer.from_pretrained(lowercase			)

    # Initialize the embeddings of the special tokens
    snake_case   :				Tuple				 =   tokenizer.convert_tokens_to_ids(["@"]			)[0]
    snake_case   :				str				 =   tokenizer.convert_tokens_to_ids(["#"]			)[0]

    snake_case   :				Union[str, Any]				 =   state_dict["embeddings.word_embeddings.weight"]
    snake_case   :				str				 =   word_emb[ent_init_index].unsqueeze(0			)
    snake_case   :				Union[str, Any]				 =   word_emb[enta_init_index].unsqueeze(0			)
    snake_case   :				Union[str, Any]				 =   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"]:
        snake_case   :				Tuple				 =   state_dict[bias_name]
        snake_case   :				Optional[Any]				 =   decoder_bias[ent_init_index].unsqueeze(0			)
        snake_case   :				Optional[Any]				 =   decoder_bias[enta_init_index].unsqueeze(0			)
        snake_case   :				Any				 =   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"]:
            snake_case   :				Optional[int]				 =   F'encoder.layer.{layer_index}.attention.self.'
            snake_case   :				Optional[Any]				 =   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   :				List[Any]				 =   state_dict["entity_embeddings.entity_embeddings.weight"]
    snake_case   :				str				 =   entity_emb[entity_vocab["[MASK]"]].unsqueeze(0			)
    snake_case   :				Tuple				 =   torch.cat([entity_emb, entity_mask_emb]			)
    # add [MASK2] for 'entity_predictions.bias'
    snake_case   :				Optional[int]				 =   state_dict["entity_predictions.bias"]
    snake_case   :				Optional[int]				 =   entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0			)
    snake_case   :				Union[str, Any]				 =   torch.cat([entity_prediction_bias, entity_mask_bias]			)

    snake_case   :				Union[str, Any]				 =   LukeForMaskedLM(config=lowercase			).eval()

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

    snake_case ,snake_case   :				Optional[Any]				 =   model.load_state_dict(lowercase						,							strict=lowercase			)

    if set(lowercase			) != {"luke.embeddings.position_ids"}:
        raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}'			)
    if set(lowercase			) != {
        "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
    snake_case   :				Optional[Any]				 =   MLukeTokenizer.from_pretrained(lowercase						,							task="entity_classification"			)

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

    snake_case   :				int				 =   model(**lowercase			)

    # Verify word hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        snake_case   :				int				 =   torch.Size((1, 33, 768)			)
        snake_case   :				str				 =   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]						,							lowercase						,							atol=1e-4			):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        snake_case   :				Any				 =   torch.Size((1, 1, 768)			)
        snake_case   :				List[Any]				 =   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]						,							lowercase						,							atol=1e-4			):
        raise ValueError

    # Verify masked word/entity prediction
    snake_case   :				List[str]				 =   MLukeTokenizer.from_pretrained(lowercase			)
    snake_case   :				List[Any]				 =   "Tokyo is the capital of <mask>."
    snake_case   :				Optional[Any]				 =   (24, 30)
    snake_case   :				List[str]				 =   tokenizer(lowercase						,							entity_spans=[span]						,							return_tensors="pt"			)

    snake_case   :				Any				 =   model(**lowercase			)

    snake_case   :				int				 =   encoding["input_ids"][0].tolist()
    snake_case   :				str				 =   input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"			)			)
    snake_case   :				Tuple				 =   outputs.logits[0][mask_position_id].argmax(dim=-1			)
    assert "Japan" == tokenizer.decode(lowercase			)

    snake_case   :				Tuple				 =   outputs.entity_logits[0][0].argmax().item()
    snake_case   :				Optional[int]				 =   [
        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(lowercase			)			)
    model.save_pretrained(lowercase			)







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]			)     ->						Dict:


    """simple docstring"""





    snake_case   :				Tuple				 =   ["[MASK]", "[PAD]", "[UNK]"]

    snake_case   :				Optional[Any]				 =   [json.loads(lowercase			) for line in open(lowercase			)]

    snake_case   :				Any				 =   {}
    for entry in data:
        snake_case   :				Union[str, Any]				 =   entry["id"]
        for entity_name, language in entry["entities"]:
            if entity_name in SPECIAL_TOKENS:
                snake_case   :				Union[str, Any]				 =   entity_id
                break
            snake_case   :				Dict				 =   F'{language}:{entity_name}'
            snake_case   :				str				 =   entity_id
    return new_mapping


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,
  )





