





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()
a__: Any							   =						logging.get_logger(__name__)

a__: Optional[Any]							   =						'Hello, World!'
a__: Union[str, Any]							   =						'en_XX'


def       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			str					,	UpperCamelCase__     :			bool    )->Tuple:
       A__		   =		Path('''data_bin'''    )
       A__		   =		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__    )

       A__		   =		xmod.model.encoder.sentence_encoder
       A__		   =		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:
              A__		   =		xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

       print('''Our X-MOD config:'''					,	UpperCamelCase__    )

       A__		   =		XmodForSequenceClassification(UpperCamelCase__    ) if classification_head else XmodForMaskedLM(UpperCamelCase__    )
       model.eval()

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

       A__		   =		xmod_sent_encoder.layernorm_embedding.weight
       A__		   =		xmod_sent_encoder.layernorm_embedding.bias

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

              # self attention
              A__		   =		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.'''    )

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

              # self-attention output
              A__		   =		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.'''    )
              A__		   =		xmod_layer.self_attn.out_proj.weight
              A__		   =		xmod_layer.self_attn.out_proj.bias
              A__		   =		xmod_layer.self_attn_layer_norm.weight
              A__		   =		xmod_layer.self_attn_layer_norm.bias

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

              # output
              A__		   =		layer.output
              if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                     raise AssertionError('''Dimensions of feed-forward weights do not match.'''    )
              A__		   =		xmod_layer.fca.weight
              A__		   =		xmod_layer.fca.bias
              A__		   =		xmod_layer.final_layer_norm.weight
              A__		   =		xmod_layer.final_layer_norm.bias
              if bert_output.adapter_layer_norm is not None:
                     A__		   =		xmod_layer.adapter_layer_norm.weight
                     A__		   =		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():
                     A__		   =		bert_output.adapter_modules[lang_code]
                     A__		   =		xmod_layer.adapter_modules[lang_code]
                     A__		   =		from_adapter.fca.weight
                     A__		   =		from_adapter.fca.bias
                     A__		   =		from_adapter.fca.weight
                     A__		   =		from_adapter.fca.bias

              # end of layer

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

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

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

       A__		   =		model(UpperCamelCase__    )[0]
       if classification_head:
              A__		   =		xmod.model.classification_heads['''mnli'''](xmod.extract_features(UpperCamelCase__    )    )
       else:
              A__		   =		xmod.model(UpperCamelCase__					,	lang_id=[SAMPLE_LANGUAGE]    )[0]
       print(our_output.shape					,	their_output.shape    )
       A__		   =		torch.max(torch.abs(our_output - their_output    )    ).item()
       print(f"max_absolute_diff = {max_absolute_diff}"    )  # ~ 1e-7
       A__		   =		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__":
       a__: str							   =						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.'
       )
       a__: Tuple							   =						parser.parse_args()
       convert_xmod_checkpoint_to_pytorch(
           args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )












from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a__: Tuple							   =						TypeVar('T')


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (position - 1) // 2


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (2 * position) + 1


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (2 * position) + 2




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        A__		   =		[]
        A__		   =		{}
        A__		   =		0



 def __len__(				self					):
        return self.elements



 def __repr__(				self					):
        return str(self.heap					)



 def 							UpperCamelCase     (				self					):
        # Check if the priority queue is empty
        return self.elements == 0



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Add an element with given priority to the queue
        self.heap.append((elem, weight)					)
        A__		   =		self.elements
        self.elements += 1
        self._bubble_up(__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        # Remove and return the element with lowest weight (highest priority)
        if self.elements > 1:
               self._swap_nodes(0,self.elements - 1					)
        A__   ,      A__		   =		self.heap.pop()
        del self.position_map[elem]
        self.elements -= 1
        if self.elements > 0:
               A__   ,      A__		   =		self.heap[0]
               self._bubble_down(__lowerCamelCase					)
        return elem



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Update the weight of the given key
        A__		   =		self.position_map[elem]
        A__		   =		(elem, weight)
        if position > 0:
               A__		   =		get_parent_position(__lowerCamelCase					)
               A__   ,      A__		   =		self.heap[parent_position]
               if parent_weight > weight:
                      self._bubble_up(__lowerCamelCase					)
               else:
                      self._bubble_down(__lowerCamelCase					)
        else:
               self._bubble_down(__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Place a node at the proper position (upward movement) [to be used internally
        # only]
        A__		   =		self.position_map[elem]
        if curr_pos == 0:
               return None
        A__		   =		get_parent_position(__lowerCamelCase					)
        A__   ,      A__		   =		self.heap[curr_pos]
        A__   ,      A__		   =		self.heap[parent_position]
        if parent_weight > weight:
               self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
               return self._bubble_up(__lowerCamelCase					)
        return None



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Place a node at the proper position (downward movement) [to be used
        # internally only]
        A__		   =		self.position_map[elem]
        A__   ,      A__		   =		self.heap[curr_pos]
        A__		   =		get_child_left_position(__lowerCamelCase					)
        A__		   =		get_child_right_position(__lowerCamelCase					)
        if child_left_position < self.elements and child_right_position < self.elements:
               A__   ,      A__		   =		self.heap[child_left_position]
               A__   ,      A__		   =		self.heap[child_right_position]
               if child_right_weight < child_left_weight and child_right_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        if child_left_position < self.elements:
               A__   ,      A__		   =		self.heap[child_left_position]
               if child_left_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        else:
               return None
        if child_right_position < self.elements:
               A__   ,      A__		   =		self.heap[child_right_position]
               if child_right_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        return None




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Swap the nodes at the given positions
        A__		   =		self.heap[nodea_pos][0]
        A__		   =		self.heap[nodea_pos][0]
        A__   ,      A__		   =		(
            self.heap[nodea_pos],
            self.heap[nodea_pos],
        )
        A__		   =		nodea_pos
        A__		   =		nodea_pos




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        A__		   =		{}
        A__		   =		0



 def __repr__(				self					):
        return str(self.connections					)



 def __len__(				self					):
        return self.nodes



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Add a node in the graph if it is not in the graph
        if node not in self.connections:
               A__		   =		{}
               self.nodes += 1




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        # Add an edge between 2 nodes in the graph
        self.add_node(__lowerCamelCase					)
        self.add_node(__lowerCamelCase					)
        A__		   =		weight
        A__		   =		weight


def       UpperCamelCase__(			UpperCamelCase__     :			GraphUndirectedWeighted[T]					,	)->tuple[dict[T, int], dict[T, T | None]]:
       A__		   =		{node: maxsize for node in graph.connections}
       A__		   =		{node: None for node in graph.connections}

       A__		   =		MinPriorityQueue()
       for node, weight in dist.items():
              priority_queue.push(UpperCamelCase__					,	UpperCamelCase__    )

       if priority_queue.is_empty():
              return dist, parent

       # initialization
       A__		   =		priority_queue.extract_min()
       A__		   =		0
       for neighbour in graph.connections[node]:
              if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                     A__		   =		dist[node] + graph.connections[node][neighbour]
                     priority_queue.update_key(UpperCamelCase__					,	dist[neighbour]    )
                     A__		   =		node

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





