






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()
__lowerCamelCase      :    Tuple   		=   logging.get_logger(__name__)

__lowerCamelCase      :    List[str]   		=   """Hello world! cécé herlolip"""







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str  ,						snake_case_      :							str  ,						snake_case_      :							bool     ):
	snake_case__				:   str     =						FairseqRobertaModel.from_pretrained(snake_case_     )
	roberta.eval()  # disable dropout
	snake_case__				:   Tuple     =						roberta.model.encoder.sentence_encoder
	snake_case__				:   str     =						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:
		snake_case__				:   Optional[int]     =						roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our RoBERTa config:"  ,						snake_case_     )

	snake_case__				:   List[str]     =						XLMRobertaXLForSequenceClassification(snake_case_     ) if classification_head else XLMRobertaXLForMaskedLM(snake_case_     )
	model.eval()

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

	snake_case__				:   Union[str, Any]     =						roberta_sent_encoder.layer_norm.weight
	snake_case__				:   str     =						roberta_sent_encoder.layer_norm.bias

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

		snake_case__				:   RobertaAttention     =						layer.attention
		snake_case__				:   Union[str, Any]     =						roberta_layer.self_attn_layer_norm.weight
		snake_case__				:   int     =						roberta_layer.self_attn_layer_norm.bias

		# self attention
		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)     )
		)

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

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

		# this one is final layer norm
		snake_case__				:   Any     =						roberta_layer.final_layer_norm.weight
		snake_case__				:   List[Any]     =						roberta_layer.final_layer_norm.bias

		# intermediate
		snake_case__				:   BertIntermediate     =						layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		snake_case__				:   Dict     =						roberta_layer.fca.weight
		snake_case__				:   Any     =						roberta_layer.fca.bias

		# output
		snake_case__				:   BertOutput     =						layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		snake_case__				:   Dict     =						roberta_layer.fca.weight
		snake_case__				:   Union[str, Any]     =						roberta_layer.fca.bias
		# end of layer

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

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

	snake_case__				:   Any     =						model(snake_case_     )[0]
	if classification_head:
		snake_case__				:   Tuple     =						roberta.model.classification_heads["mnli"](roberta.extract_features(snake_case_     )     )
	else:
		snake_case__				:   Optional[Any]     =						roberta.model(snake_case_     )[0]
	print(our_output.shape  ,						their_output.shape     )
	snake_case__				:   Dict     =						torch.max(torch.abs(our_output - their_output     )     ).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''     )  # ~ 1e-7
	snake_case__				:   Optional[Any]     =						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__":
	__lowerCamelCase      :    Dict   		=   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."""
	)
	__lowerCamelCase      :    List[Any]   		=   parser.parse_args()
	convert_xlm_roberta_xl_checkpoint_to_pytorch(
	    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)











from __future__ import annotations

import time

__lowerCamelCase      :    str   		=   list[tuple[int, int]]

__lowerCamelCase      :    Optional[int]   		=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__lowerCamelCase      :    Tuple   		=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						Union[str, Any]							,	__A  :						int							,	__A  :						int							,	__A  :						int							,	__A  :						int							,	__A  :						Node | None ):
						snake_case__				:   Optional[int]     =						pos_x
						snake_case__				:   Dict     =						pos_y
						snake_case__				:   int     =						(pos_y, pos_x)
						snake_case__				:   Optional[int]     =						goal_x
						snake_case__				:   Tuple     =						goal_y
						snake_case__				:   str     =						parent



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						List[Any]							,	__A  :						tuple[int, int]							,	__A  :						tuple[int, int] ):
						snake_case__				:   Tuple     =						Node(start[1]							,	start[0]							,	goal[1]							,	goal[0]							,	__A )
						snake_case__				:   Tuple     =						Node(goal[1]							,	goal[0]							,	goal[1]							,	goal[0]							,	__A )

						snake_case__				:   int     =						[self.start]
						snake_case__				:   Union[str, Any]     =						False



					def  _lowercase  (      self  :						Dict ):
						while self.node_queue:
							snake_case__				:   Optional[Any]     =						self.node_queue.pop(0 )

							if current_node.pos == self.target.pos:
								snake_case__				:   Optional[Any]     =						True
								return self.retrace_path(__A )

							snake_case__				:   int     =						self.get_successors(__A )

							for node in successors:
								self.node_queue.append(__A )

						if not self.reached:
							return [self.start.pos]
						return None



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Node ):
						snake_case__				:   str     =						[]
						for action in delta:
							snake_case__				:   str     =						parent.pos_x + action[1]
							snake_case__				:   Union[str, Any]     =						parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__A ) - 1):
								continue

							if grid[pos_y][pos_x] != 0:
								continue

							successors.append(
							    Node(__A							,	__A							,	self.target.pos_y							,	self.target.pos_x							,	__A ) )
						return successors






					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Node | None ):
						snake_case__				:   Tuple     =						node
						snake_case__				:   Any     =						[]
						while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x) )
							snake_case__				:   Tuple     =						current_node.parent
						path.reverse()
						return path



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						Dict							,	__A  :						str							,	__A  :						int ):
						snake_case__				:   str     =						BreadthFirstSearch(__A							,	__A )
						snake_case__				:   int     =						BreadthFirstSearch(__A							,	__A )
						snake_case__				:   Tuple     =						False



					def  _lowercase  (      self  :						Optional[Any] ):
						while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							snake_case__				:   Any     =						self.fwd_bfs.node_queue.pop(0 )
							snake_case__				:   List[str]     =						self.bwd_bfs.node_queue.pop(0 )

							if current_bwd_node.pos == current_fwd_node.pos:
								snake_case__				:   List[str]     =						True
								return self.retrace_bidirectional_path(
								    __A							,	__A )

							snake_case__				:   Union[str, Any]     =						current_bwd_node
							snake_case__				:   Dict     =						current_fwd_node

							snake_case__				:   List[Any]     =						{
							    self.fwd_bfs: self.fwd_bfs.get_successors(__A ),
							    self.bwd_bfs: self.bwd_bfs.get_successors(__A ),
							}

							for bfs in [self.fwd_bfs, self.bwd_bfs]:
								for node in successors[bfs]:
									bfs.node_queue.append(__A )

						if not self.reached:
							return [self.fwd_bfs.start.pos]
						return None






					def  _lowercase  (      self  :						Any							,	__A  :						Node							,	__A  :						Node ):
						snake_case__				:   List[str]     =						self.fwd_bfs.retrace_path(__A )
						snake_case__				:   Optional[Any]     =						self.bwd_bfs.retrace_path(__A )
						bwd_path.pop()
						bwd_path.reverse()
						snake_case__				:   List[Any]     =						fwd_path + bwd_path
						return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	import doctest

	doctest.testmod()
	__lowerCamelCase      :    str   		=   (0, 0)
	__lowerCamelCase      :    List[str]   		=   (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	__lowerCamelCase      :    Any   		=   time.time()
	__lowerCamelCase      :    Optional[Any]   		=   BreadthFirstSearch(init, goal)
	__lowerCamelCase      :    str   		=   bfs.search()
	__lowerCamelCase      :    Optional[Any]   		=   time.time() - start_bfs_time

	print("""Unidirectional BFS computation time : """, bfs_time)

	__lowerCamelCase      :    Optional[Any]   		=   time.time()
	__lowerCamelCase      :    Optional[int]   		=   BidirectionalBreadthFirstSearch(init, goal)
	__lowerCamelCase      :    str   		=   bd_bfs.search()
	__lowerCamelCase      :    Optional[Any]   		=   time.time() - start_bd_bfs_time

	print("""Bidirectional BFS computation time : """, bd_bfs_time)



