import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__lowerCamelCase					:	str       =  data_utils.TransfoXLTokenizer
__lowerCamelCase					:	Optional[Any]       =  data_utils.TransfoXLCorpus
__lowerCamelCase					:	Optional[int]       =  data_utils
__lowerCamelCase					:	List[str]       =  data_utils




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   int   , lowerCAmelCase							:   Any   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		if transfo_xl_dataset_file:
				# Convert a pre-processed corpus (see original TensorFlow repo)
				with open(lowerCAmelCase   , "rb"	) as fp:
						SCREAMING_SNAKE_CASE_  :		int				      =    pickle.load(lowerCAmelCase   , encoding="latin1"	)
				# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
				SCREAMING_SNAKE_CASE_  :		str				      =    pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
				print(f'Save vocabulary to {pytorch_vocab_dump_path}'	)
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    corpus.vocab.__dict__
				torch.save(lowerCAmelCase   , lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		int				      =    corpus.__dict__
				corpus_dict_no_vocab.pop("vocab"   , lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    pytorch_dump_folder_path + "/" + CORPUS_NAME
				print(f'Save dataset to {pytorch_dataset_dump_path}'	)
				torch.save(lowerCAmelCase   , lowerCAmelCase	)

		if tf_checkpoint_path:
				# Convert a pre-trained TensorFlow model
				SCREAMING_SNAKE_CASE_  :		Any				      =    os.path.abspath(lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    os.path.abspath(lowerCAmelCase	)

				print(f'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'	)
				# Initialise PyTorch model
				if transfo_xl_config_file == "":
						SCREAMING_SNAKE_CASE_  :		int				      =    TransfoXLConfig()
				else:
						SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TransfoXLConfig.from_json_file(lowerCAmelCase	)
				print(f'Building PyTorch model from configuration: {config}'	)
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    TransfoXLLMHeadModel(lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		List[str]				      =    load_tf_weights_in_transfo_xl(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase	)
				# Save pytorch-model
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    os.path.join(lowerCAmelCase   , lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		int				      =    os.path.join(lowerCAmelCase   , lowerCAmelCase	)
				print(f'Save PyTorch model to {os.path.abspath(lowerCAmelCase	)}'	)
				torch.save(model.state_dict()   , lowerCAmelCase	)
				print(f'Save configuration file to {os.path.abspath(lowerCAmelCase	)}'	)
				with open(lowerCAmelCase   , "w"   , encoding="utf-8"	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
		__lowerCamelCase					:	Optional[Any]       =  argparse.ArgumentParser()
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
		)
		parser.add_argument(
		    '''--tf_checkpoint_path''',
		    default='''''',
		    type=str,
		    help='''An optional path to a TensorFlow checkpoint path to be converted.''',
		)
		parser.add_argument(
		    '''--transfo_xl_config_file''',
		    default='''''',
		    type=str,
		    help=(
		        '''An optional config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--transfo_xl_dataset_file''',
		    default='''''',
		    type=str,
		    help='''An optional dataset file to be converted in a vocabulary.''',
		)
		__lowerCamelCase					:	Optional[Any]       =  parser.parse_args()
		convert_transfo_xl_checkpoint_to_pytorch(
		    args.tf_checkpoint_path,
		    args.transfo_xl_config_file,
		    args.pytorch_dump_folder_path,
		    args.transfo_xl_dataset_file,
		)

from collections.abc import Sequence
from queue import Queue






class 		a__						:

					def __init__(    self      :       int,_A      :       List[Any],_A      :       Optional[Any],_A      :       Optional[int],_A      :       int=None,_A      :       List[str]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    start
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    end
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    val
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    left
							SCREAMING_SNAKE_CASE_  :		str				      =    right






					def __repr__(    self      :       Tuple    ):







							"""simple docstring"""






							return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'







class 		a__						:

					def __init__(    self      :       Any,_A      :       Sequence,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    collection
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    function
							if self.collection:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._build_tree(0,len(_A    ) - 1    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							self._update_tree(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							return self._query_range(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       int    ):







							"""simple docstring"""






							if start == end:
									return SegmentTreeNode(_A,_A,self.collection[start]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._build_tree(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._build_tree(mid + 1,_A    )
							return SegmentTreeNode(_A,_A,self.fn(left.val,right.val    ),_A,_A    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       int,_A      :       Tuple,_A      :       Dict    ):







							"""simple docstring"""






							if node.start == i and node.end == i:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    val
									return
							if i <= node.mid:
									self._update_tree(node.left,_A,_A    )
							else:
									self._update_tree(node.right,_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.fn(node.left.val,node.right.val    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str],_A      :       Optional[int],_A      :       Optional[Any]    ):







							"""simple docstring"""






							if node.start == i and node.end == j:
									return node.val

							if i <= node.mid:
									if j <= node.mid:
											# range in left child tree
											return self._query_range(node.left,_A,_A    )
									else:
											# range in left child tree and right child tree
											return self.fn(
											    self._query_range(node.left,_A,node.mid    ),self._query_range(node.right,node.mid + 1,_A    ),)
							else:
									# range in right child tree
									return self._query_range(node.right,_A,_A    )






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							if self.root is not None:
									SCREAMING_SNAKE_CASE_  :		int				      =    Queue()
									queue.put(self.root    )
									while not queue.empty():
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    queue.get()
											yield node

											if node.left is not None:
													queue.put(node.left    )

											if node.right is not None:
													queue.put(node.right    )


if __name__ == "__main__":
		import operator

		for fn in [operator.add, max, min]:
				print('''*''' * 50)
				__lowerCamelCase					:	int       =  SegmentTree([2, 1, 5, 3, 4], fn)
				for node in arr.traverse():
						print(node)
				print()

				arr.update(1, 5)
				for node in arr.traverse():
						print(node)
				print()

				print(arr.query_range(3, 4))  # 7
				print(arr.query_range(2, 2))  # 5
				print(arr.query_range(1, 3))  # 13
				print()
