


import math
import sys


class 	a__						:

       def __init__( self   :  int     ):
            a_     :  Dict				      =     0
            a_     :  Optional[int]				      =     []
            a_     :  List[Any]				      =     {}  # To store the pos of node in array

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            return self.cur_size == 0

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  str     ):
            a_     :  str				      =     self.left(lowerCamelCase_     )
            a_     :  Optional[Any]				      =     self.right(lowerCamelCase_     )
            if lc < self.cur_size and self.array(lowerCamelCase_     )[0] < self.array(lowerCamelCase_     )[0]:
                 a_     :  str				      =     lc
            else:
                 a_     :  Union[str, Any]				      =     idx
            if rc < self.cur_size and self.array(lowerCamelCase_     )[0] < self.array(lowerCamelCase_     )[0]:
                 a_     :  int				      =     rc
            if smallest != idx:
                 self.swap(lowerCamelCase_   ,				lowerCamelCase_     )
                 self.min_heapify(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  Union[str, Any]     ):
            # Inserts a node into the Priority Queue
            a_     :  Optional[int]				      =     self.cur_size
            self.cur_size += 1
            self.array.append((sys.maxsize, tup[1])     )
            self.decrease_key((sys.maxsize, tup[1])   ,				tup[0]     )

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            # Removes and returns the min element at top of priority queue
            a_     :  List[Any]				      =     self.array[0][1]
            a_     :  str				      =     self.array[self.cur_size - 1]
            self.cur_size -= 1
            self.min_heapify(1     )
            del self.pos[min_node]
            return min_node

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  int     ):
            # returns the index of left child
            return 2 * i + 1

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  Any     ):
            # returns the index of right child
            return 2 * i + 2

       def     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  str     ):
            # returns the index of parent
            return math.floor(i / 2     )

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Optional[int]     ):
            # swaps array elements at indices i and j
            # update the pos{}
            a_     :  Optional[Any]				      =     j
            a_     :  Optional[int]				      =     i
            a_     :  Optional[int]				      =     self.array[i]
            a_     :  Dict				      =     self.array[j]
            a_     :  List[Any]				      =     temp

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Optional[Any]     ):
            a_     :  Tuple				      =     self.pos[tup[1]]
            # assuming the new_d is atmost old_d
            a_     :  List[str]				      =     (new_d, tup[1])
            while idx > 0 and self.array[self.par(lowerCamelCase_     )][0] > self.array[idx][0]:
                 self.swap(lowerCamelCase_   ,				self.par(lowerCamelCase_     )     )
                 a_     :  int				      =     self.par(lowerCamelCase_     )


class 	a__						:

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  Union[str, Any]     ):
            a_     :  Dict				      =     {}  # To store graph: u -> (v,w)
            a_     :  List[str]				      =     num  # Number of nodes in graph
            # To store the distance from source vertex
            a_     :  Any				      =     [0] * self.num_nodes
            a_     :  Any				      =     [-1] * self.num_nodes  # To store the path

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  str     ):
            #  Edge going from node u to v and v to u with weight w
            # u (w)-> v, v (w) -> u
            # Check if u already in graph
            if u in self.adjList:
                 self.adjList[u].append((v, w)     )
            else:
                 a_     :  Any				      =     [(v, w)]

            # Assuming undirected graph
            if v in self.adjList:
                 self.adjList[v].append((u, w)     )
            else:
                 a_     :  List[Any]				      =     [(u, w)]

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            # u -> v(w)
            for u in self.adjList:
                 print(lowerCamelCase_   ,				"""->"""   ,				""" -> """.join(str(F'''{v}({w})'''     ) for v, w in self.adjList[u]     )     )

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  List[Any]     ):
            # Flush old junk values in par[]
            a_     :  int				      =     [-1] * self.num_nodes
            # src is the source node
            a_     :  List[str]				      =     0
            a_     :  int				      =     PriorityQueue()
            q.insert((0, src)     )  # (dist from src, node)
            for u in self.adjList:
                 if u != src:
                      a_     :  List[Any]				      =     sys.maxsize  # Infinity
                      a_     :  Optional[int]				      =     -1

            while not q.is_empty():
                 a_     :  Union[str, Any]				      =     q.extract_min()  # Returns node with the min dist from source
                 # Update the distance of all the neighbours of u and
                 # if their prev dist was INFINITY then push them in Q
                 for v, w in self.adjList[u]:
                      a_     :  Dict				      =     self.dist[u] + w
                      if self.dist[v] > new_dist:
                           if self.dist[v] == sys.maxsize:
                                q.insert((new_dist, v)     )
                           else:
                                q.decrease_key((self.dist[v], v)   ,				lowerCamelCase_     )
                           a_     :  Dict				      =     new_dist
                           a_     :  Dict				      =     u

        # Show the shortest distances from src
            self.show_distances(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  Any     ):
            print(F'''Distance from node: {src}'''     )
            for u in range(self.num_nodes     ):
                 print(F'''Node {u} has distance: {self.dist[u]}'''     )

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  Dict   ,				lowerCamelCase_   :  int     ):
            # To show the shortest path from src to dest
            # WARNING: Use it *after* calling dijkstra
            a_     :  int				      =     []
            a_     :  Optional[int]				      =     0
            a_     :  Any				      =     dest
            # Backtracking from dest to src
            while self.par[temp] != -1:
                 path.append(lowerCamelCase_     )
                 if temp != src:
                      for v, w in self.adjList[temp]:
                           if v == self.par[temp]:
                                cost += w
                                break
                 a_     :  Dict				      =     self.par[temp]
            path.append(lowerCamelCase_     )
            path.reverse()

            print(F'''----Path to reach {dest} from {src}----'''     )
            for u in path:
                 print(F'''{u}'''   ,				end=""" """     )
                 if u != dest:
                      print("""-> """   ,				end=""""""     )

            print("""\nTotal cost of path: """   ,				lowerCamelCase_     )


if __name__ == "__main__":
       __lowerCamelCase   	=  Graph(9)
       graph.add_edge(0, 1, 4)
       graph.add_edge(0, 7, 8)
       graph.add_edge(1, 2, 8)
       graph.add_edge(1, 7, 11)
       graph.add_edge(2, 3, 7)
       graph.add_edge(2, 8, 2)
       graph.add_edge(2, 5, 4)
       graph.add_edge(3, 4, 9)
       graph.add_edge(3, 5, 14)
       graph.add_edge(4, 5, 10)
       graph.add_edge(5, 6, 2)
       graph.add_edge(6, 7, 1)
       graph.add_edge(6, 8, 6)
       graph.add_edge(7, 8, 7)
       graph.show_graph()
       graph.dijkstra(0)
       graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21











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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
_a       =    logging.get_logger(__name__)

_a       =    '''Hello world! cécé herlolip'''


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





						"""simple docstring"""



						_UpperCAmelCase							   =     FairseqRobertaModel.from_pretrained(__lowerCAmelCase      )
						roberta.eval()  # disable dropout
						_UpperCAmelCase							   =     roberta.model.encoder.sentence_encoder
						_UpperCAmelCase							   =     RobertaConfig(
						    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,						hidden_size=roberta.args.encoder_embed_dim					,						num_hidden_layers=roberta.args.encoder_layers					,						num_attention_heads=roberta.args.encoder_attention_heads					,						intermediate_size=roberta.args.encoder_ffn_embed_dim					,						max_position_embeddings=514					,						type_vocab_size=1					,						layer_norm_eps=1E-5					,						)
						if classification_head:
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'					,						__lowerCAmelCase      )

						_UpperCAmelCase							   =     RobertaForSequenceClassification(__lowerCAmelCase      ) if classification_head else RobertaForMaskedLM(__lowerCAmelCase      )
						model.eval()

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

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

												# self attention
												_UpperCAmelCase							   =     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)      )
												)

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

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

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

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

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

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

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

						pathlib.Path(__lowerCAmelCase      ).mkdir(parents=__lowerCAmelCase					,						exist_ok=__lowerCAmelCase      )
						print(F"""Saving model to {pytorch_dump_folder_path}"""      )
						model.save_pretrained(__lowerCAmelCase      )


if __name__ == "__main__":
		_a       =    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.'''
		)
		_a       =    parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)
