

import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				{'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
					__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase       =				MODELS[model_size]  # set model hyperparameters
					__lowerCAmelCase       =				ImageGPTConfig(n_embd=UpperCAmelCase__		,       n_layer=UpperCAmelCase__		,       n_head=UpperCAmelCase__   )
					__lowerCAmelCase       =				ImageGPTForCausalLM(UpperCAmelCase__   )

					# Load weights from numpy
					load_tf_weights_in_imagegpt(UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__   )

					# Save pytorch-model
					__lowerCAmelCase       =				pytorch_dump_folder_path + '/' + WEIGHTS_NAME
					__lowerCAmelCase       =				pytorch_dump_folder_path + '/' + CONFIG_NAME
					print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""   )
					torch.save(model.state_dict()		,       UpperCAmelCase__   )
					print(F"""Save configuration file to {pytorch_config_dump_path}"""   )
					with open(UpperCAmelCase__		,       'w'		,       encoding='utf-8'   ) as f:
										f.write(config.to_json_string()   )


if __name__ == "__main__":
		lowerCamelCase   					=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--imagegpt_checkpoint_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the TensorFlow checkpoint path.''',
		)
		parser.add_argument(
		    '''--model_size''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Size of the model (can be either \'small\', \'medium\' or \'large\').''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowerCamelCase   					=  parser.parse_args()
		convert_imagegpt_checkpoint_to_pytorch(
		    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
		)









import math
import sys





class      snake_case_      :


							"""simple docstring"""





							def __init__(			self				):
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				[]
												__lowerCAmelCase       =				{}  # To store the pos of node in array




							def 							A__	(			self				):
												return self.cur_size == 0




							def 							A__	(			self       ,    _A				):
												__lowerCAmelCase       =				self.left(_A				)
												__lowerCAmelCase       =				self.right(_A				)
												if lc < self.cur_size and self.array(_A				)[0] < self.array(_A				)[0]:
																	__lowerCAmelCase       =				lc
												else:
																	__lowerCAmelCase       =				idx
												if rc < self.cur_size and self.array(_A				)[0] < self.array(_A				)[0]:
																	__lowerCAmelCase       =				rc
												if smallest != idx:
																	self.swap(_A       ,    _A				)
																	self.min_heapify(_A				)




							def 							A__	(			self       ,    _A				):
												# Inserts a node into the Priority Queue
												__lowerCAmelCase       =				self.cur_size
												self.cur_size += 1
												self.array.append((sys.maxsize, tup[1])				)
												self.decrease_key((sys.maxsize, tup[1])       ,    tup[0]				)




							def 							A__	(			self				):
												# Removes and returns the min element at top of priority queue
												__lowerCAmelCase       =				self.array[0][1]
												__lowerCAmelCase       =				self.array[self.cur_size - 1]
												self.cur_size -= 1
												self.min_heapify(1				)
												del self.pos[min_node]
												return min_node




							def 							A__	(			self       ,    _A				):
												# returns the index of left child
												return 2 * i + 1




							def 							A__	(			self       ,    _A				):
												# returns the index of right child
												return 2 * i + 2




							def 							A__	(			self       ,    _A				):
												# returns the index of parent
												return math.floor(i / 2				)




							def 							A__	(			self       ,    _A       ,    _A				):
												# swaps array elements at indices i and j
												# update the pos{}
												__lowerCAmelCase       =				j
												__lowerCAmelCase       =				i
												__lowerCAmelCase       =				self.array[i]
												__lowerCAmelCase       =				self.array[j]
												__lowerCAmelCase       =				temp




							def 							A__	(			self       ,    _A       ,    _A				):
												__lowerCAmelCase       =				self.pos[tup[1]]
												# assuming the new_d is atmost old_d
												__lowerCAmelCase       =				(new_d, tup[1])
												while idx > 0 and self.array[self.par(_A				)][0] > self.array[idx][0]:
																	self.swap(_A       ,    self.par(_A				)				)
																	__lowerCAmelCase       =				self.par(_A				)





class      snake_case_      :


							"""simple docstring"""





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




							def 							A__	(			self       ,    _A       ,    _A       ,    _A				):
												#  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:
																	__lowerCAmelCase       =				[(v, w)]

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




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




							def 							A__	(			self       ,    _A				):
												# Flush old junk values in par[]
												__lowerCAmelCase       =				[-1] * self.num_nodes
												# src is the source node
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				PriorityQueue()
												q.insert((0, src)				)  # (dist from src, node)
												for u in self.adjList:
																	if u != src:
																						__lowerCAmelCase       =				sys.maxsize  # Infinity
																						__lowerCAmelCase       =				-1

												while not q.is_empty():
																	__lowerCAmelCase       =				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]:
																						__lowerCAmelCase       =				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)       ,    _A				)
																											__lowerCAmelCase       =				new_dist
																											__lowerCAmelCase       =				u

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




							def 							A__	(			self       ,    _A				):
												print(F"""Distance from node: {src}"""				)
												for u in range(self.num_nodes				):
																	print(F"""Node {u} has distance: {self.dist[u]}"""				)




							def 							A__	(			self       ,    _A       ,    _A				):
												# To show the shortest path from src to dest
												# WARNING: Use it *after* calling dijkstra
												__lowerCAmelCase       =				[]
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				dest
												# Backtracking from dest to src
												while self.par[temp] != -1:
																	path.append(_A				)
																	if temp != src:
																						for v, w in self.adjList[temp]:
																											if v == self.par[temp]:
																																cost += w
																																break
																	__lowerCAmelCase       =				self.par[temp]
												path.append(_A				)
												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: '       ,    _A				)


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, 1_1)
		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, 1_4)
		graph.add_edge(4, 5, 1_0)
		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






