from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__UpperCamelCase		:       Tuple									=     TypeVar('T')
def       A	(    _lowercase						):
						return (position - 1) // 2
def       A	(    _lowercase						):
						return (2 * position) + 1
def       A	(    _lowercase						):
						return (2 * position) + 2





class 					lowercase__						(   Generic[T]):


						def __init__(       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							[]
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							{}
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							0


						def __len__(       self	:				Optional[int]						):





												'''simple docstring'''





												return self.elements


						def __repr__(       self	:				str						):





												'''simple docstring'''





												return str(self.heap						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												return self.elements == 0


						def        __A       (       self	:				int    ,		UpperCamelCase__	:				T    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												self.heap.append((elem, weight)						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.elements
												self.elements += 1
												self._bubble_up(_lowercase						)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												if self.elements > 1:
																		self._swap_nodes(0    ,		self.elements - 1						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[Any]							=							self.heap.pop()
												del self.position_map[elem]
												self.elements -= 1
												if self.elements > 0:
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.heap[0]
																		self._bubble_down(_lowercase						)
												return elem


						def        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				T    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.position_map[elem]
												SCREAMING_SNAKE_CASE							:    Any							=							(elem, weight)
												if position > 0:
																		SCREAMING_SNAKE_CASE							:    str							=							get_parent_position(_lowercase						)
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Dict							=							self.heap[parent_position]
																		if parent_weight > weight:
																								self._bubble_up(_lowercase						)
																		else:
																								self._bubble_down(_lowercase						)
												else:
																		self._bubble_down(_lowercase						)


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				T						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.position_map[elem]
												if curr_pos == 0:
																		return None
												SCREAMING_SNAKE_CASE							:    int							=							get_parent_position(_lowercase						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Tuple							=							self.heap[curr_pos]
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.heap[parent_position]
												if parent_weight > weight:
																		self._swap_nodes(_lowercase    ,		_lowercase						)
																		return self._bubble_up(_lowercase						)
												return None


						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				T						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							self.position_map[elem]
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Any							=							self.heap[curr_pos]
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							get_child_left_position(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							get_child_right_position(_lowercase						)
												if child_left_position < self.elements and child_right_position < self.elements:
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.heap[child_left_position]
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.heap[child_right_position]
																		if child_right_weight < child_left_weight and child_right_weight < weight:
																								self._swap_nodes(_lowercase    ,		_lowercase						)
																								return self._bubble_down(_lowercase						)
												if child_left_position < self.elements:
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.heap[child_left_position]
																		if child_left_weight < weight:
																								self._swap_nodes(_lowercase    ,		_lowercase						)
																								return self._bubble_down(_lowercase						)
												else:
																		return None
												if child_right_position < self.elements:
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							self.heap[child_right_position]
																		if child_right_weight < weight:
																								self._swap_nodes(_lowercase    ,		_lowercase						)
																								return self._bubble_down(_lowercase						)
												return None



						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.heap[nodea_pos][0]
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.heap[nodea_pos][0]
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Dict							=							(
												    self.heap[nodea_pos],
												    self.heap[nodea_pos],
												)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							nodea_pos
												SCREAMING_SNAKE_CASE							:    Any							=							nodea_pos






class 					lowercase__						(   Generic[T]):


						def __init__(       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							{}
												SCREAMING_SNAKE_CASE							:    int							=							0


						def __repr__(       self	:				Union[str, Any]						):





												'''simple docstring'''





												return str(self.connections						)


						def __len__(       self	:				Dict						):





												'''simple docstring'''





												return self.nodes


						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				T						):





												'''simple docstring'''





												if node not in self.connections:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							{}
																		self.nodes += 1



						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				T    ,		UpperCamelCase__	:				T    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												self.add_node(_lowercase						)
												self.add_node(_lowercase						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							weight
												SCREAMING_SNAKE_CASE							:    Tuple							=							weight






def       A	(    _lowercase		,  ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							{node: maxsize for node in graph.connections}
						SCREAMING_SNAKE_CASE							:    Tuple							=							{node: None for node in graph.connections}

						SCREAMING_SNAKE_CASE							:    str							=							MinPriorityQueue()
						for node, weight in dist.items():
												priority_queue.push(__UpperCamelCase		,  __UpperCamelCase						)

						if priority_queue.is_empty():
												return dist, parent

						# initialization
						SCREAMING_SNAKE_CASE							:    Tuple							=							priority_queue.extract_min()
						SCREAMING_SNAKE_CASE							:    str							=							0
						for neighbour in graph.connections[node]:
												if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																		SCREAMING_SNAKE_CASE							:    int							=							dist[node] + graph.connections[node][neighbour]
																		priority_queue.update_key(__UpperCamelCase		,  dist[neighbour]						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							node

												# running prim's algorithm
						while not priority_queue.is_empty():
												SCREAMING_SNAKE_CASE							:    int							=							priority_queue.extract_min()
												for neighbour in graph.connections[node]:
																		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																								SCREAMING_SNAKE_CASE							:    Dict							=							dist[node] + graph.connections[node][neighbour]
																								priority_queue.update_key(__UpperCamelCase		,  dist[neighbour]						)
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							node
						return dist, parent






import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
   raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
   raise Exception('''requires fairseq < v2''')

logging.set_verbosity_info()
__lowerCamelCase			: Dict							   =		logging.get_logger(__name__)

__lowerCamelCase			: Dict							   =		'''Hello, World!'''
__lowerCamelCase			: Optional[Any]							   =		'''en_XX'''



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       bool	)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Path("""data_bin"""	)
  SCREAMING_SNAKE_CASE__										=      FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(__UpperCamelCase	).parent	) ,     checkpoint_file=Path(__UpperCamelCase	).name ,     _name="""xmod_base""" ,     arch="""xmod_base""" ,     task="""multilingual_masked_lm""" ,     data_name_or_path=str(__UpperCamelCase	) ,     bpe="""sentencepiece""" ,     sentencepiece_model=str(Path(__UpperCamelCase	).parent / """sentencepiece.bpe.model"""	) ,     src_dict=str(data_dir / """dict.txt"""	) ,     )
  xmod.eval()  # disable dropout
  print(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.sentence_encoder
  SCREAMING_SNAKE_CASE__										=      XmodConfig(
      vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,     hidden_size=xmod.cfg.model.encoder_embed_dim ,     num_hidden_layers=xmod.cfg.model.encoder_layers ,     num_attention_heads=xmod.cfg.model.encoder_attention_heads ,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,     max_position_embeddings=5_14 ,     type_vocab_size=1 ,     layer_norm_eps=1E-5 ,     pre_norm=xmod.cfg.model.encoder_normalize_before ,     adapter_reduction_factor=getattr(xmod.cfg.model ,     """bottleneck""" ,     2	) ,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,     ln_before_adapter=xmod.cfg.model.ln_before_adapter ,     languages=xmod.cfg.model.languages ,     )
  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

  print("""Our X-MOD config:""" ,     __UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      XmodForSequenceClassification(__UpperCamelCase	) if classification_head else XmodForMaskedLM(__UpperCamelCase	)
  model.eval()

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

  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layernorm_embedding.weight
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    SCREAMING_SNAKE_CASE__										=      layer.attention.self
    if not (
        xmod_layer.self_attn.k_proj.weight.data.shape
        == xmod_layer.self_attn.q_proj.weight.data.shape
        == xmod_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)	)
    ):
      raise AssertionError("""Dimensions of self-attention weights do not match."""	)

    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.q_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.q_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.k_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.k_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.v_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.v_proj.bias

    # self-attention output
    SCREAMING_SNAKE_CASE__										=      layer.attention.output
    if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
      raise AssertionError("""Dimensions of self-attention output weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.out_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.out_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn_layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn_layer_norm.bias

    # intermediate
    SCREAMING_SNAKE_CASE__										=      layer.intermediate
    if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of intermediate weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.bias

    # output
    SCREAMING_SNAKE_CASE__										=      layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of feed-forward weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.final_layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_layer_norm.weight
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_layer_norm.bias

    if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
      raise AssertionError("""Lists of language adapters do not match."""	)
    for lang_code, adapter in xmod_layer.adapter_modules.items():
      SCREAMING_SNAKE_CASE__										=      bert_output.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.weight
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.bias
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.weight
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layer_norm.bias

  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].dense.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].dense.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.dense.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.dense.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.layer_norm.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  SCREAMING_SNAKE_CASE__										=      xmod.encode(__UpperCamelCase	).unsqueeze(0	)  # batch of size 1
  model.roberta.set_default_language(__UpperCamelCase	)

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

  Path(__UpperCamelCase	).mkdir(parents=__UpperCamelCase ,     exist_ok=__UpperCamelCase	)
  print(f"""Saving model to {pytorch_dump_folder_path}"""	)
  model.save_pretrained(__UpperCamelCase	)


if __name__ == "__main__":
   __lowerCamelCase			: Dict							   =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--xmod_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.'''
   )
   __lowerCamelCase			: str							   =		parser.parse_args()
   convert_xmod_checkpoint_to_pytorch(
       args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )
