





class 	UpperCamelCase_							:


							def __init__(				self     :Optional[int]				,					__A     :str = ""				,					__A     :bool = False		) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   {}

									# A node will be a leaf if the tree contains its word
									SCREAMING_SNAKE_CASE__								=   is_leaf

									SCREAMING_SNAKE_CASE__								=   prefix


							def   _snake_case	(				self     :Optional[int]				,					__A     :str		) -> tuple[str, str, str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   0
									for q, w in zip(self.prefix				,					__A		):
											if q != w:
													break

											x += 1

									return self.prefix[:x], self.prefix[x:], word[x:]


							def   _snake_case	(				self     :Any				,					__A     :list[str]		) -> None:



									"""simple docstring"""



									for word in words:
											self.insert(__A		)


							def   _snake_case	(				self     :Tuple				,					__A     :str		) -> None:



									"""simple docstring"""



									if self.prefix == word:
											SCREAMING_SNAKE_CASE__								=   True

									# Case 2: The node has no edges that have a prefix to the word
									# Solution: We create an edge from the current node to a new one
									# containing the word
									elif word[0] not in self.nodes:
											SCREAMING_SNAKE_CASE__								=   RadixNode(prefix=__A				,					is_leaf=__A		)

									else:
											SCREAMING_SNAKE_CASE__								=   self.nodes[word[0]]
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   incoming_node.match(
											    __A		)

											# Case 3: The node prefix is equal to the matching
											# Solution: We insert remaining word on the next node
											if remaining_prefix == "":
													self.nodes[matching_string[0]].insert(__A		)

											# Case 4: The word is greater equal to the matching
											# Solution: Create a node in between both nodes, change
											# prefixes and add the new node for the remaining word
											else:
													SCREAMING_SNAKE_CASE__								=   remaining_prefix

													SCREAMING_SNAKE_CASE__								=   self.nodes[matching_string[0]]
													SCREAMING_SNAKE_CASE__								=   RadixNode(__A				,					__A		)
													SCREAMING_SNAKE_CASE__								=   aux_node

													if remaining_word == "":
															SCREAMING_SNAKE_CASE__								=   True
													else:
															self.nodes[matching_string[0]].insert(__A		)


							def   _snake_case	(				self     :Optional[int]				,					__A     :str		) -> bool:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.nodes.get(word[0]				,					__A		)
									if not incoming_node:
											return False
									else:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   incoming_node.match(
											    __A		)
											# If there is remaining prefix, the word can't be on the tree
											if remaining_prefix != "":
													return False
											# This applies when the word and the prefix are equal
											elif remaining_word == "":
													return incoming_node.is_leaf
											# We have word remaining so we check the next node
											else:
													return incoming_node.find(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :str		) -> bool:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.nodes.get(word[0]				,					__A		)
									if not incoming_node:
											return False
									else:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   incoming_node.match(
											    __A		)
											# If there is remaining prefix, the word can't be on the tree
											if remaining_prefix != "":
													return False
											# We have word remaining so we check the next node
											elif remaining_word != "":
													return incoming_node.delete(__A		)
											else:
													# If it is not a leaf, we don't have to delete
													if not incoming_node.is_leaf:
															return False
													else:
															# We delete the nodes if no edges go from it
															if len(incoming_node.nodes		) == 0:
																	del self.nodes[word[0]]
																	# We merge the current node with its only child
																	if len(self.nodes		) == 1 and not self.is_leaf:
																			SCREAMING_SNAKE_CASE__								=   list(self.nodes.values()		)[0]
																			SCREAMING_SNAKE_CASE__								=   merging_node.is_leaf
																			self.prefix += merging_node.prefix
																			SCREAMING_SNAKE_CASE__								=   merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
															elif len(incoming_node.nodes		) > 1:
																	SCREAMING_SNAKE_CASE__								=   False
															# If there is 1 edge, we merge it with its child
															else:
																	SCREAMING_SNAKE_CASE__								=   list(incoming_node.nodes.values()		)[0]
																	SCREAMING_SNAKE_CASE__								=   merging_node.is_leaf
																	incoming_node.prefix += merging_node.prefix
																	SCREAMING_SNAKE_CASE__								=   merging_node.nodes

															return True


							def   _snake_case	(				self     :Optional[Any]				,					__A     :int = 0		) -> None:



									"""simple docstring"""



									if self.prefix != "":
											print("""-""" * height				,					self.prefix				,					"""  (leaf)""" if self.is_leaf else """"""		)

									for value in self.nodes.values():
											value.print_tree(height + 1		)






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   """banana bananas bandana band apple all beast""".split()
		SCREAMING_SNAKE_CASE__								=   RadixNode()
		root.insert_many(UpperCamelCase__     )

		assert all(root.find(UpperCamelCase__     ) for word in words     )
		assert not root.find("""bandanas"""     )
		assert not root.find("""apps"""     )
		root.delete("""all"""     )
		assert not root.find("""all"""     )
		root.delete("""banana"""     )
		assert not root.find("""banana"""     )
		assert root.find("""bananas"""     )

		return True






def 					SCREAMING_SNAKE_CASE__				(							):
		assert test_trie()






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   RadixNode()
		SCREAMING_SNAKE_CASE__								=   """banana bananas bandanas bandana band apple all beast""".split()
		root.insert_many(UpperCamelCase__     )

		print("""Words:"""       , UpperCamelCase__     )
		print("""Tree:"""     )
		root.print_tree()


if __name__ == "__main__":
	main()





import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


SCREAMING_SNAKE_CASE:     Optional[int]									= [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]


def 					_a							(					lowerCAmelCase )->	str:
		for pegasus_name, hf_name in PATTERNS:
				SCREAMING_SNAKE_CASE_					      =							k.replace(lowerCAmelCase    ,						lowerCAmelCase )
		return k


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	PegasusForConditionalGeneration:
		SCREAMING_SNAKE_CASE_					      =							DEFAULTS.copy()
		cfg_kwargs.update(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							PegasusConfig(**lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							PegasusForConditionalGeneration(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							torch_model.model.state_dict()
		SCREAMING_SNAKE_CASE_					      =							{}
		for k, v in tf_weights.items():
				SCREAMING_SNAKE_CASE_					      =							rename_state_dict_key(lowerCAmelCase )
				if new_k not in sd:
						raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )

				if "dense" in k or "proj" in new_k:
						SCREAMING_SNAKE_CASE_					      =							v.T
				SCREAMING_SNAKE_CASE_					      =							torch.tensor(lowerCAmelCase    ,						dtype=sd[new_k].dtype )
				assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
		# make sure embedding.padding_idx is respected
		SCREAMING_SNAKE_CASE_					      =							torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
		SCREAMING_SNAKE_CASE_					      =							mapping['shared.weight']
		SCREAMING_SNAKE_CASE_					      =							mapping['shared.weight']
		SCREAMING_SNAKE_CASE_					      =							{k: torch.zeros_like(lowerCAmelCase ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
		mapping.update(**lowerCAmelCase )
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							torch_model.model.load_state_dict(lowerCAmelCase    ,						strict=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							[
		    k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
		]
		assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
		assert extra == [], F'''no matches found for the following tf keys {extra}'''
		return torch_model


def 					_a							(					lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" )->	Dict:
		SCREAMING_SNAKE_CASE_					      =							tf.train.list_variables(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							{}
		SCREAMING_SNAKE_CASE_					      =							['Adafactor', 'global_step']
		for name, shape in tqdm(lowerCAmelCase    ,						desc='converting tf checkpoint to dict' ):
				SCREAMING_SNAKE_CASE_					      =							any(pat in name for pat in ignore_name )
				if skip_key:
						continue
				SCREAMING_SNAKE_CASE_					      =							tf.train.load_variable(lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							array
		return tf_weights


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Optional[Any]:
		# save tokenizer first
		SCREAMING_SNAKE_CASE_					      =							Path(lowerCAmelCase ).parent.name
		SCREAMING_SNAKE_CASE_					      =							task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
		SCREAMING_SNAKE_CASE_					      =							PegasusTokenizer.from_pretrained('sshleifer/pegasus'    ,						model_max_length=lowerCAmelCase )
		assert tok.model_max_length == desired_max_model_length
		tok.save_pretrained(lowerCAmelCase )

		# convert model
		SCREAMING_SNAKE_CASE_					      =							get_tf_weights_as_numpy(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							task_specific_params[F'''summarization_{dataset}''']
		if dataset == "large":
				SCREAMING_SNAKE_CASE_					      =							task_specific_params
		SCREAMING_SNAKE_CASE_					      =							convert_pegasus(lowerCAmelCase    ,						lowerCAmelCase )
		torch_model.save_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							torch_model.state_dict()
		sd.pop('model.decoder.embed_positions.weight' )
		sd.pop('model.encoder.embed_positions.weight' )
		torch.save(lowerCAmelCase    ,						Path(lowerCAmelCase ) / 'pytorch_model.bin' )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
						parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
						SCREAMING_SNAKE_CASE:     int									= parser.parse_args()
						if args.save_dir is None:
												SCREAMING_SNAKE_CASE:     List[Any]									= Path(args.tf_ckpt_path).parent.name
												SCREAMING_SNAKE_CASE:     Tuple									= os.path.join('''pegasus''', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)