"""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 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						):







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

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

  # Load the entity vocab file
  _UpperCAmelCase         =					load_entity_vocab(_snake_case						)

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

  # Add special tokens to the token vocabulary for downstream tasks
  _UpperCAmelCase         =					AddedToken("""<ent>"""      ,lstrip=_snake_case      ,rstrip=_snake_case						)
  _UpperCAmelCase         =					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						)

  _UpperCAmelCase         =					LukeTokenizer.from_pretrained(_snake_case						)

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

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

  _UpperCAmelCase         =					LukeModel(config=_snake_case						).eval()

  _UpperCAmelCase         =					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
  _UpperCAmelCase         =					LukeTokenizer.from_pretrained(_snake_case      ,task="""entity_classification"""						)

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

  _UpperCAmelCase         =					model(**_snake_case						)

  # Verify word hidden states
  if model_size == "large":
    _UpperCAmelCase         =					torch.Size((1, 42, 10_24)						)
    _UpperCAmelCase         =					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
    _UpperCAmelCase         =					torch.Size((1, 42, 7_68)						)
    _UpperCAmelCase         =					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]      ,_snake_case      ,atol=1E-4						):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    _UpperCAmelCase         =					torch.Size((1, 1, 10_24)						)
    _UpperCAmelCase         =					torch.tensor([[0.04_66, -0.01_06, -0.01_79]]						)
  else:  # base
    _UpperCAmelCase         =					torch.Size((1, 1, 7_68)						)
    _UpperCAmelCase         =					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]      ,_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 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					{}
  with open(_snake_case      ,"""r"""      ,encoding="""utf-8"""						) as f:
    for index, line in enumerate(_snake_case						):
      _UpperCAmelCase         =					line.rstrip().split("""\t"""						)
      _UpperCAmelCase         =					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"""




import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
					raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
UpperCAmelCase__      :    Union[str, Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    List[str]    							= 'Hello world! cécé herlolip'

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   FairseqRobertaModel.from_pretrained(_snake_case						)
							roberta.eval()  # disable dropout
							SCREAMING_SNAKE_CASE__						:       Any           =   roberta.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE__						:       Any           =   XLMRobertaConfig(
							    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,hidden_size=roberta.cfg.model.encoder_embed_dim   ,num_hidden_layers=roberta.cfg.model.encoder_layers   ,num_attention_heads=roberta.cfg.model.encoder_attention_heads   ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim   ,max_position_embeddings=514   ,type_vocab_size=1   ,layer_norm_eps=1E-5   ,)
							if classification_head:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

							print("""Our RoBERTa config:"""   ,_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   XLMRobertaXLForSequenceClassification(_snake_case						) if classification_head else XLMRobertaXLForMaskedLM(_snake_case						)
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_sent_encoder.embed_tokens.weight
							SCREAMING_SNAKE_CASE__						:       int           =   roberta_sent_encoder.embed_positions.weight
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c RoBERTa doesn't use them.

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_sent_encoder.layer_norm.weight
							SCREAMING_SNAKE_CASE__						:       Dict           =   roberta_sent_encoder.layer_norm.bias

							for i in range(config.num_hidden_layers						):
														# Encoder: start of layer
														SCREAMING_SNAKE_CASE__						:       BertLayer           =   model.roberta.encoder.layer[i]
														SCREAMING_SNAKE_CASE__						:       TransformerSentenceEncoderLayer           =   roberta_sent_encoder.layers[i]

														SCREAMING_SNAKE_CASE__						:       RobertaAttention           =   layer.attention
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.self_attn_layer_norm.bias

														# self attention
														SCREAMING_SNAKE_CASE__						:       BertSelfAttention           =   layer.attention.self
														assert (
														    roberta_layer.self_attn.k_proj.weight.data.shape
														    == roberta_layer.self_attn.q_proj.weight.data.shape
														    == roberta_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)						)
														)

														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.q_proj.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.self_attn.q_proj.bias
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.self_attn.k_proj.weight
														SCREAMING_SNAKE_CASE__						:       int           =   roberta_layer.self_attn.k_proj.bias
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.v_proj.weight
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta_layer.self_attn.v_proj.bias

														# self-attention output
														SCREAMING_SNAKE_CASE__						:       BertSelfOutput           =   layer.attention.output
														assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta_layer.self_attn.out_proj.weight
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.out_proj.bias

														# this one is final layer norm
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_layer.final_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE__						:       BertIntermediate           =   layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.fca.bias

														# output
														SCREAMING_SNAKE_CASE__						:       BertOutput           =   layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_layer.fca.bias
														# end of layer

							if classification_head:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta.model.classification_heads["""mnli"""].dense.weight
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   roberta.model.classification_heads["""mnli"""].dense.bias
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   roberta.model.classification_heads["""mnli"""].out_proj.weight
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta.model.classification_heads["""mnli"""].out_proj.bias
							else:
														# LM Head
														SCREAMING_SNAKE_CASE__						:       str           =   roberta.model.encoder.lm_head.dense.weight
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta.model.encoder.lm_head.dense.bias
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta.model.encoder.lm_head.layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.encoder.lm_head.layer_norm.bias
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta.model.encoder.lm_head.weight
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							SCREAMING_SNAKE_CASE__						:       torch.Tensor           =   roberta.encode(_snake_case						).unsqueeze(0						)  # batch of size 1

							SCREAMING_SNAKE_CASE__						:       Tuple           =   model(_snake_case						)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""](roberta.extract_features(_snake_case						)						)
							else:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta.model(_snake_case						)[0]
							print(our_output.shape   ,their_output.shape						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.max(torch.abs(our_output - their_output						)						).item()
							print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
							SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.allclose(_snake_case   ,_snake_case   ,atol=1E-3						)
							print("""Do both models output the same tensors?"""   ,"""🔥""" if success else """💩"""						)
							if not success:
														raise Exception("""Something went wRoNg"""						)

							pathlib.Path(_snake_case						).mkdir(parents=_snake_case   ,exist_ok=_snake_case						)
							print(f'''Saving model to {pytorch_dump_folder_path}'''						)
							model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__      :    Union[str, Any]    							= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					parser.add_argument(
					    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
					)
					UpperCAmelCase__      :    Any    							= parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)




