



from heapq import heappop, heappush

import numpy as np
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				)->tuple[float | int, list[tuple[int, int]]]:
       _lowerCAmelCase			,   _lowerCAmelCase 			=  grid.shape
       _lowerCAmelCase 			=  [-1, 1, 0, 0]
       _lowerCAmelCase 			=  [0, 0, -1, 1]
       if allow_diagonal:
              dx += [-1, -1, 1, 1]
              dy += [-1, 1, -1, 1]

       _lowerCAmelCase			,   _lowerCAmelCase 			=  [(0, source)], set()
       _lowerCAmelCase 			=  np.full((rows, cols)     ,				np.inf				)
       _lowerCAmelCase 			=  0
       _lowerCAmelCase 			=  np.empty((rows, cols)     ,				dtype=_SCREAMING_SNAKE_CASE				)
       _lowerCAmelCase 			=  None

       while queue:
              ((_lowerCAmelCase)			,   (_lowerCAmelCase)) 			=  heappop(_SCREAMING_SNAKE_CASE				)
              if (x, y) in visited:
                     continue
              visited.add((x, y)				)

              if (x, y) == destination:
                     _lowerCAmelCase 			=  []
                     while (x, y) != source:
                            path.append((x, y)				)
                            _lowerCAmelCase			,   _lowerCAmelCase 			=  predecessors[x, y]
                     path.append(_SCREAMING_SNAKE_CASE				)  # add the source manually
                     path.reverse()
                     return matrix[destination], path

              for i in range(len(_SCREAMING_SNAKE_CASE				)				):
                     _lowerCAmelCase			,   _lowerCAmelCase 			=  x + dx[i], y + dy[i]
                     if 0 <= nx < rows and 0 <= ny < cols:
                            _lowerCAmelCase 			=  grid[nx][ny]
                            if next_node == 1 and matrix[nx, ny] > dist + 1:
                                   heappush(_SCREAMING_SNAKE_CASE     ,				(dist + 1, (nx, ny))				)
                                   _lowerCAmelCase 			=  dist + 1
                                   _lowerCAmelCase 			=  (x, y)

       return np.inf, []


if __name__ == "__main__":
    import doctest

    doctest.testmod()




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_									=							logging.get_logger(__name__)

UpperCAmelCase_									=							"Hello world! cécé herlolip"
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	bool				)->List[Any]:
      _lowerCAmelCase 			=  FairseqRobertaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
      roberta.eval()  # disable dropout
      _lowerCAmelCase 			=  roberta.model.encoder.sentence_encoder
      _lowerCAmelCase 			=  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=5_1_4     ,				type_vocab_size=1     ,				layer_norm_eps=1e-5     ,				)
      if classification_head:
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

      print('''Our RoBERTa config:'''     ,				_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  XLMRobertaXLForSequenceClassification(_SCREAMING_SNAKE_CASE				) if classification_head else XLMRobertaXLForMaskedLM(_SCREAMING_SNAKE_CASE				)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      _lowerCAmelCase 			=  roberta_sent_encoder.embed_tokens.weight
      _lowerCAmelCase 			=  roberta_sent_encoder.embed_positions.weight
      _lowerCAmelCase 			=  torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c RoBERTa doesn't use them.

      _lowerCAmelCase 			=  roberta_sent_encoder.layer_norm.weight
      _lowerCAmelCase 			=  roberta_sent_encoder.layer_norm.bias

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

            _lowerCAmelCase 			=  layer.attention
            _lowerCAmelCase 			=  roberta_layer.self_attn_layer_norm.weight
            _lowerCAmelCase 			=  roberta_layer.self_attn_layer_norm.bias

            # self attention
            _lowerCAmelCase 			=  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)				)
            )

            _lowerCAmelCase 			=  roberta_layer.self_attn.q_proj.weight
            _lowerCAmelCase 			=  roberta_layer.self_attn.q_proj.bias
            _lowerCAmelCase 			=  roberta_layer.self_attn.k_proj.weight
            _lowerCAmelCase 			=  roberta_layer.self_attn.k_proj.bias
            _lowerCAmelCase 			=  roberta_layer.self_attn.v_proj.weight
            _lowerCAmelCase 			=  roberta_layer.self_attn.v_proj.bias

            # self-attention output
            _lowerCAmelCase 			=  layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            _lowerCAmelCase 			=  roberta_layer.self_attn.out_proj.weight
            _lowerCAmelCase 			=  roberta_layer.self_attn.out_proj.bias

            # this one is final layer norm
            _lowerCAmelCase 			=  roberta_layer.final_layer_norm.weight
            _lowerCAmelCase 			=  roberta_layer.final_layer_norm.bias

            # intermediate
            _lowerCAmelCase 			=  layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            _lowerCAmelCase 			=  roberta_layer.fca.weight
            _lowerCAmelCase 			=  roberta_layer.fca.bias

            # output
            _lowerCAmelCase 			=  layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            _lowerCAmelCase 			=  roberta_layer.fca.weight
            _lowerCAmelCase 			=  roberta_layer.fca.bias
            # end of layer

      if classification_head:
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].dense.weight
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].dense.bias
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].out_proj.weight
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].out_proj.bias
      else:
            # LM Head
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.dense.weight
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.dense.bias
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.layer_norm.weight
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.layer_norm.bias
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.weight
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      _lowerCAmelCase 			=  roberta.encode(_SCREAMING_SNAKE_CASE				).unsqueeze(0				)  # batch of size 1

      _lowerCAmelCase 			=  model(_SCREAMING_SNAKE_CASE				)[0]
      if classification_head:
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''](roberta.extract_features(_SCREAMING_SNAKE_CASE				)				)
      else:
            _lowerCAmelCase 			=  roberta.model(_SCREAMING_SNAKE_CASE				)[0]
      print(our_output.shape     ,				their_output.shape				)
      _lowerCAmelCase 			=  torch.max(torch.abs(our_output - their_output				)				).item()
      print(f'''max_absolute_diff = {max_absolute_diff}'''				)  # ~ 1e-7
      _lowerCAmelCase 			=  torch.allclose(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_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(_SCREAMING_SNAKE_CASE				).mkdir(parents=_SCREAMING_SNAKE_CASE     ,				exist_ok=_SCREAMING_SNAKE_CASE				)
      print(f'''Saving model to {pytorch_dump_folder_path}'''				)
      model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
    UpperCAmelCase_									=							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_									=							parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )