
"""simple docstring"""


import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     int=None     ):
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
	A						   =   nn.Parameter(snake_case__     )
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
		A						   =   nn.Parameter(snake_case__     )






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     Optional[int]	,      snake_case__   :     List[str]     ):
	# set torch weights for 1-to-1 comparison
	A						   =   np.asarray(weights[0]     )
	A						   =   np.asarray(weights[1]     )
	A						   =   np.asarray(weights[2]     )

	set_param(
	    torch_layer.self_attention.query_key	,      torch.tensor(snake_case__     ).transpose(1	,      2     ).contiguous().view(-1	,      snake_case__     )	,      )
	set_param(
	    torch_layer.self_attention.value	,      torch.tensor(snake_case__     ).transpose(1	,      2     ).contiguous().view(-1	,      snake_case__     )	,      )
	set_param(
	    torch_layer.output.dense	,      torch.tensor(snake_case__     ).view(-1	,      snake_case__     ).contiguous().transpose(0	,      1     )	,      )






def       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     Optional[int]	,      snake_case__   :     Dict     ):
	# set torch weights for 1-to-1 comparison
	A						   =   np.asarray(weights[0]     )
	A						   =   np.asarray(weights[1]     )
	A						   =   np.asarray(weights[2]     )
	A						   =   np.asarray(weights[3]     )

	set_param(
	    torch_layer.self_attention.query	,      torch.tensor(snake_case__     ).transpose(1	,      2     ).contiguous().view(-1	,      snake_case__     )	,      )
	set_param(
	    torch_layer.self_attention.key	,      torch.tensor(snake_case__     ).transpose(1	,      2     ).contiguous().view(-1	,      snake_case__     )	,      )
	set_param(
	    torch_layer.self_attention.value	,      torch.tensor(snake_case__     ).transpose(1	,      2     ).contiguous().view(-1	,      snake_case__     )	,      )
	set_param(
	    torch_layer.output.dense	,      torch.tensor(snake_case__     ).view(-1	,      snake_case__     ).contiguous().transpose(0	,      1     )	,      )






def       _snake_case				(		snake_case__   :     Union[str, Any]	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     List[Any]     ):
	# layernorm 1
	A						   =   weights[0][0][0]
	A						   =   np.asarray(layer_norm_a[0]     )
	A						   =   np.asarray(layer_norm_a[1]     )
	set_param(
	    torch_block.attention.layer_norm	,      torch.tensor(snake_case__     )	,      torch.tensor(snake_case__     )	,      )

	# lsh weights + output
	A						   =   weights[0][1]
	if len(snake_case__     ) < 4:
		set_layer_weights_in_torch_lsh(snake_case__	,      torch_block.attention	,      snake_case__     )
	else:
		set_layer_weights_in_torch_local(snake_case__	,      torch_block.attention	,      snake_case__     )

	# intermediate weighs
	A						   =   weights[2][0][1][2]

	# Chunked Feed Forward
	if len(snake_case__     ) == 4:
		A						   =   intermediate_weights[2]

	# layernorm 2
	A						   =   np.asarray(intermediate_weights[0][0]     )
	A						   =   np.asarray(intermediate_weights[0][1]     )
	set_param(
	    torch_block.feed_forward.layer_norm	,      torch.tensor(snake_case__     )	,      torch.tensor(snake_case__     )	,      )

	# intermediate dense
	A						   =   np.asarray(intermediate_weights[1][0]     )
	A						   =   np.asarray(intermediate_weights[1][1]     )
	set_param(
	    torch_block.feed_forward.dense.dense	,      torch.tensor(snake_case__     ).transpose(0	,      1     ).contiguous()	,      torch.tensor(snake_case__     )	,      )

	# intermediate out
	A						   =   np.asarray(intermediate_weights[4][0]     )
	A						   =   np.asarray(intermediate_weights[4][1]     )
	set_param(
	    torch_block.feed_forward.output.dense	,      torch.tensor(snake_case__     ).transpose(0	,      1     ).contiguous()	,      torch.tensor(snake_case__     )	,      )






def       _snake_case				(		snake_case__   :     Dict	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     List[Any]     ):
	# reformer model
	A						   =   torch_model.reformer

	# word embeds
	A						   =   np.asarray(weights[1]     )
	set_param(
	    torch_model_reformer.embeddings.word_embeddings	,      torch.tensor(snake_case__     )	,      )

	if isinstance(weights[3]	,      snake_case__     ):
		A						   =   torch_model_reformer.embeddings.position_embeddings
		for emb_idx in range(len(position_embeddings.weights     )     ):
			A						   =   np.asarray(weights[3][emb_idx][0]     )
			assert (
			    position_embeddings.weights[emb_idx].shape == emb_weights.shape
			), F'{position_embeddings[emb_idx]} emb does not match'
			A						   =   nn.Parameter(torch.tensor(snake_case__     )     )

	A						   =   weights[5]
	assert len(torch_model_reformer.encoder.layers     ) * 4 == len(
	    snake_case__     ), "HF and trax model do not have the same number of layers"
	for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers     ):
		A						   =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(snake_case__	,      snake_case__	,      snake_case__     )

	# output layer norm
	A						   =   np.asarray(weights[7][0]     )
	A						   =   np.asarray(weights[7][1]     )
	set_param(
	    torch_model_reformer.encoder.layer_norm	,      torch.tensor(snake_case__     )	,      torch.tensor(snake_case__     )	,      )

	# output embeddings
	A						   =   np.asarray(weights[9][0]     )
	A						   =   np.asarray(weights[9][1]     )
	set_param(
	    torch_model.lm_head.decoder	,      torch.tensor(snake_case__     ).transpose(0	,      1     ).contiguous()	,      torch.tensor(snake_case__     )	,      )






def       _snake_case				(		snake_case__   :     Optional[int]	,      snake_case__   :     List[Any]	,      snake_case__   :     Any     ):
	# Initialise PyTorch model
	A						   =   ReformerConfig.from_json_file(snake_case__     )
	print(F'Building PyTorch model from configuration: {config}'     )
	A						   =   ReformerModelWithLMHead(snake_case__     )

	with open(snake_case__	,      'rb'     ) as f:
		A						   =   pickle.load(snake_case__     )['weights']

	set_model_weights_in_torch(snake_case__	,      snake_case__	,      config.hidden_size     )

	# Save pytorch-model
	print(F'Save PyTorch model to {pytorch_dump_path}'     )
	torch.save(model.state_dict()	,      snake_case__     )


if __name__ == "__main__":
		_lowercase							   =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained Reformer model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		_lowercase							   =				parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)


"""simple docstring"""


from collections import deque
from math import floor
from random import random
from time import time







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Dict      )     ->   int:
							A						   =   {}


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Union[str, Any]  ,A_     :      Any  ,A_     :      Optional[Any]=1      )     ->   int:
							if self.graph.get(A_      ):
								if self.graph[u].count([w, v]      ) == 0:
									self.graph[u].append([w, v]      )
							else:
								A						   =   [[w, v]]
							if not self.graph.get(A_      ):
								A						   =   []


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							return list(self.graph      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Union[str, Any]  ,A_     :      Dict      )     ->   Optional[Any]:
							if self.graph.get(A_      ):
								for _ in self.graph[u]:
									if _[1] == v:
										self.graph[u].remove(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      int=-2  ,A_     :      Dict=-1      )     ->   str:
							if s == d:
								return []
							A						   =   []
							A						   =   []
							if s == -2:
								A						   =   list(self.graph      )[0]
							stack.append(A_      )
							visited.append(A_      )
							A						   =   s

							while True:
								# check if there is any non isolated nodes
								if len(self.graph[s]      ) != 0:
									A						   =   s
									for node in self.graph[s]:
										if visited.count(node[1]      ) < 1:
											if node[1] == d:
												visited.append(A_      )
												return visited
											else:
												stack.append(node[1]      )
												visited.append(node[1]      )
												A						   =   node[1]
												break

            # check if all the children are visited
								if s == ss:
									stack.pop()
									if len(A_      ) != 0:
										A						   =   stack[len(A_      ) - 1]
								else:
									A						   =   ss

								# check if se have reached the starting point
								if len(A_      ) == 0:
									return visited


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Any=-1      )     ->   int:
							if c == -1:
								A						   =   floor(random() * 1_0000      ) + 10
							for i in range(A_      ):
								# every vertex has max 100 edges
								for _ in range(floor(random() * 102      ) + 1      ):
									A						   =   floor(random() * c      ) + 1
									if n != i:
										self.add_pair(A_  ,A_  ,1      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Union[str, Any]=-2      )     ->   Optional[Any]:
							A						   =   deque()
							A						   =   []
							if s == -2:
								A						   =   list(self.graph      )[0]
							d.append(A_      )
							visited.append(A_      )
							while d:
								A						   =   d.popleft()
								if len(self.graph[s]      ) != 0:
									for node in self.graph[s]:
										if visited.count(node[1]      ) < 1:
											d.append(node[1]      )
											visited.append(node[1]      )
							return visited


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Tuple      )     ->   Any:
							A						   =   0
							for x in self.graph:
								for y in self.graph[x]:
									if y[1] == u:
										count += 1
							return count


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Union[str, Any]      )     ->   str:
							return len(self.graph[u]      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Union[str, Any]=-2      )     ->   Any:
							A						   =   []
							A						   =   []
							if s == -2:
								A						   =   list(self.graph      )[0]
							stack.append(A_      )
							visited.append(A_      )
							A						   =   s
							A						   =   []

							while True:
								# check if there is any non isolated nodes
								if len(self.graph[s]      ) != 0:
									A						   =   s
									for node in self.graph[s]:
										if visited.count(node[1]      ) < 1:
											stack.append(node[1]      )
											visited.append(node[1]      )
											A						   =   node[1]
											break

            # check if all the children are visited
								if s == ss:
									sorted_nodes.append(stack.pop()      )
									if len(A_      ) != 0:
										A						   =   stack[len(A_      ) - 1]
								else:
									A						   =   ss

								# check if se have reached the starting point
								if len(A_      ) == 0:
									return sorted_nodes


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Tuple:
							A						   =   []
							A						   =   []
							A						   =   list(self.graph      )[0]
							stack.append(A_      )
							visited.append(A_      )
							A						   =   -2
							A						   =   []
							A						   =   s
							A						   =   False
							A						   =   set()

							while True:
								# check if there is any non isolated nodes
								if len(self.graph[s]      ) != 0:
									A						   =   s
									for node in self.graph[s]:
										if (
										    visited.count(node[1]      ) > 0
										    and node[1] != parent
										    and indirect_parents.count(node[1]      ) > 0
										    and not on_the_way_back
										):
											A						   =   len(A_      ) - 1
											while len_stack >= 0:
												if stack[len_stack] == node[1]:
													anticipating_nodes.add(node[1]      )
													break
												else:
													anticipating_nodes.add(stack[len_stack]      )
													len_stack -= 1
										if visited.count(node[1]      ) < 1:
											stack.append(node[1]      )
											visited.append(node[1]      )
											A						   =   node[1]
											break

            # check if all the children are visited
								if s == ss:
									stack.pop()
									A						   =   True
									if len(A_      ) != 0:
										A						   =   stack[len(A_      ) - 1]
								else:
									A						   =   False
									indirect_parents.append(A_      )
									A						   =   s
									A						   =   ss

								# check if se have reached the starting point
								if len(A_      ) == 0:
									return list(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[int]:
							A						   =   []
							A						   =   []
							A						   =   list(self.graph      )[0]
							stack.append(A_      )
							visited.append(A_      )
							A						   =   -2
							A						   =   []
							A						   =   s
							A						   =   False
							A						   =   set()

							while True:
								# check if there is any non isolated nodes
								if len(self.graph[s]      ) != 0:
									A						   =   s
									for node in self.graph[s]:
										if (
										    visited.count(node[1]      ) > 0
										    and node[1] != parent
										    and indirect_parents.count(node[1]      ) > 0
										    and not on_the_way_back
										):
											A						   =   len(A_      ) - 1
											while len_stack_minus_one >= 0:
												if stack[len_stack_minus_one] == node[1]:
													anticipating_nodes.add(node[1]      )
													break
												else:
													return True
										if visited.count(node[1]      ) < 1:
											stack.append(node[1]      )
											visited.append(node[1]      )
											A						   =   node[1]
											break

            # check if all the children are visited
								if s == ss:
									stack.pop()
									A						   =   True
									if len(A_      ) != 0:
										A						   =   stack[len(A_      ) - 1]
								else:
									A						   =   False
									indirect_parents.append(A_      )
									A						   =   s
									A						   =   ss

								# check if se have reached the starting point
								if len(A_      ) == 0:
									return False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Tuple=-2  ,A_     :      List[str]=-1      )     ->   str:
							A						   =   time()
							self.dfs(A_  ,A_      )
							A						   =   time()
							return end - begin




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Union[str, Any]=-2      )     ->   Dict:
							A						   =   time()
							self.bfs(A_      )
							A						   =   time()
							return end - begin







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      List[Any]      )     ->   Tuple:
							A						   =   {}


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      List[str]=1      )     ->   Dict:
							# check if the u exists
							if self.graph.get(A_      ):
								# if there already is a edge
								if self.graph[u].count([w, v]      ) == 0:
									self.graph[u].append([w, v]      )
							else:
								# if u does not exist
								A						   =   [[w, v]]
							# add the other way
							if self.graph.get(A_      ):
								# if there already is a edge
								if self.graph[v].count([w, u]      ) == 0:
									self.graph[v].append([w, u]      )
							else:
								# if u does not exist
								A						   =   [[w, u]]


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      List[Any]  ,A_     :      List[str]      )     ->   List[Any]:
							if self.graph.get(A_      ):
								for _ in self.graph[u]:
									if _[1] == v:
										self.graph[u].remove(A_      )
        # the other way round
							if self.graph.get(A_      ):
								for _ in self.graph[v]:
									if _[1] == u:
										self.graph[v].remove(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      List[str]=-2  ,A_     :      List[Any]=-1      )     ->   int:
							if s == d:
								return []
							A						   =   []
							A						   =   []
							if s == -2:
								A						   =   list(self.graph      )[0]
							stack.append(A_      )
							visited.append(A_      )
							A						   =   s

							while True:
								# check if there is any non isolated nodes
								if len(self.graph[s]      ) != 0:
									A						   =   s
									for node in self.graph[s]:
										if visited.count(node[1]      ) < 1:
											if node[1] == d:
												visited.append(A_      )
												return visited
											else:
												stack.append(node[1]      )
												visited.append(node[1]      )
												A						   =   node[1]
												break

            # check if all the children are visited
								if s == ss:
									stack.pop()
									if len(A_      ) != 0:
										A						   =   stack[len(A_      ) - 1]
								else:
									A						   =   ss

								# check if se have reached the starting point
								if len(A_      ) == 0:
									return visited


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Optional[int]=-1      )     ->   List[Any]:
							if c == -1:
								A						   =   floor(random() * 1_0000      ) + 10
							for i in range(A_      ):
								# every vertex has max 100 edges
								for _ in range(floor(random() * 102      ) + 1      ):
									A						   =   floor(random() * c      ) + 1
									if n != i:
										self.add_pair(A_  ,A_  ,1      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Dict=-2      )     ->   List[Any]:
							A						   =   deque()
							A						   =   []
							if s == -2:
								A						   =   list(self.graph      )[0]
							d.append(A_      )
							visited.append(A_      )
							while d:
								A						   =   d.popleft()
								if len(self.graph[s]      ) != 0:
									for node in self.graph[s]:
										if visited.count(node[1]      ) < 1:
											d.append(node[1]      )
											visited.append(node[1]      )
							return visited


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Optional[Any]      )     ->   List[Any]:
							return len(self.graph[u]      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							A						   =   []
							A						   =   []
							A						   =   list(self.graph      )[0]
							stack.append(A_      )
							visited.append(A_      )
							A						   =   -2
							A						   =   []
							A						   =   s
							A						   =   False
							A						   =   set()

							while True:
								# check if there is any non isolated nodes
								if len(self.graph[s]      ) != 0:
									A						   =   s
									for node in self.graph[s]:
										if (
										    visited.count(node[1]      ) > 0
										    and node[1] != parent
										    and indirect_parents.count(node[1]      ) > 0
										    and not on_the_way_back
										):
											A						   =   len(A_      ) - 1
											while len_stack >= 0:
												if stack[len_stack] == node[1]:
													anticipating_nodes.add(node[1]      )
													break
												else:
													anticipating_nodes.add(stack[len_stack]      )
													len_stack -= 1
										if visited.count(node[1]      ) < 1:
											stack.append(node[1]      )
											visited.append(node[1]      )
											A						   =   node[1]
											break

            # check if all the children are visited
								if s == ss:
									stack.pop()
									A						   =   True
									if len(A_      ) != 0:
										A						   =   stack[len(A_      ) - 1]
								else:
									A						   =   False
									indirect_parents.append(A_      )
									A						   =   s
									A						   =   ss

								# check if se have reached the starting point
								if len(A_      ) == 0:
									return list(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Any:
							A						   =   []
							A						   =   []
							A						   =   list(self.graph      )[0]
							stack.append(A_      )
							visited.append(A_      )
							A						   =   -2
							A						   =   []
							A						   =   s
							A						   =   False
							A						   =   set()

							while True:
								# check if there is any non isolated nodes
								if len(self.graph[s]      ) != 0:
									A						   =   s
									for node in self.graph[s]:
										if (
										    visited.count(node[1]      ) > 0
										    and node[1] != parent
										    and indirect_parents.count(node[1]      ) > 0
										    and not on_the_way_back
										):
											A						   =   len(A_      ) - 1
											while len_stack_minus_one >= 0:
												if stack[len_stack_minus_one] == node[1]:
													anticipating_nodes.add(node[1]      )
													break
												else:
													return True
										if visited.count(node[1]      ) < 1:
											stack.append(node[1]      )
											visited.append(node[1]      )
											A						   =   node[1]
											break

            # check if all the children are visited
								if s == ss:
									stack.pop()
									A						   =   True
									if len(A_      ) != 0:
										A						   =   stack[len(A_      ) - 1]
								else:
									A						   =   False
									indirect_parents.append(A_      )
									A						   =   s
									A						   =   ss

								# check if se have reached the starting point
								if len(A_      ) == 0:
									return False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Any:
							return list(self.graph      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]=-2  ,A_     :      List[str]=-1      )     ->   Any:
							A						   =   time()
							self.dfs(A_  ,A_      )
							A						   =   time()
							return end - begin




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      List[Any]=-2      )     ->   Union[str, Any]:
							A						   =   time()
							self.bfs(A_      )
							A						   =   time()
							return end - begin