


'''simple docstring'''
from typing import Any




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							)    ->  Optional[Any]:
					lowercase						:		Dict   					=		data
					lowercase						:		List[Any]   					=		None


				def __repr__(     self							)    ->  str:
					return F'''Node({self.data})'''




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							)    ->  List[Any]:
					lowercase						:		Optional[Any]   					=		None

				def __iter__(     self							)    ->  Any:
					lowercase						:		Any   					=		self.head
					while node:
						yield node.data
						lowercase						:		int   					=		node.next

				def __len__(     self							)    ->  int:
					return sum(1 for _ in self							)

				def __repr__(     self							)    ->  str:
					return "->".join([str(a_							) for item in self]							)

				def __getitem__(     self							,      a_							)    ->  Any:
					if not 0 <= index < len(self							):
						raise ValueError("list index out of range."							)
					for i, node in enumerate(self							):
						if i == index:
							return node
					return None

				def __setitem__(     self							,      a_							,      a_							)    ->  None:
					if not 0 <= index < len(self							):
						raise ValueError("list index out of range."							)
					lowercase						:		List[str]   					=		self.head
					for _ in range(a_							):
						lowercase						:		Union[str, Any]   					=		current.next
					lowercase						:		Optional[Any]   					=		data

				def 	a__		(     self							,      a_							)    ->  None:
					self.insert_nth(len(self							)							,      a_							)

				def 	a__		(     self							,      a_							)    ->  None:
					self.insert_nth(0							,      a_							)

				def 	a__		(     self							,      a_							,      a_							)    ->  None:
					if not 0 <= index <= len(self							):
						raise IndexError("list index out of range"							)
					lowercase						:		Any   					=		Node(a_							)
					if self.head is None:
						lowercase						:		List[str]   					=		new_node
					elif index == 0:
						lowercase						:		Optional[int]   					=		self.head  # link new_node to head
						lowercase						:		Dict   					=		new_node
					else:
						lowercase						:		Optional[Any]   					=		self.head
						for _ in range(index - 1							):
							lowercase						:		int   					=		temp.next
						lowercase						:		Optional[Any]   					=		temp.next
						lowercase						:		Tuple   					=		new_node

				def 	a__		(     self							)    ->  None:  # print every node data
					print(self							)

				def 	a__		(     self							)    ->  Any:
					return self.delete_nth(0							)

				def 	a__		(     self							)    ->  Any:  # delete from tail
					return self.delete_nth(len(self							) - 1							)

				def 	a__		(     self							,      a_ = 0							)    ->  Any:
					if not 0 <= index <= len(self							) - 1:  # test if index is valid
						raise IndexError("List index out of range."							)
					lowercase						:		Union[str, Any]   					=		self.head  # default first node
					if index == 0:
						lowercase						:		Tuple   					=		self.head.next
					else:
						lowercase						:		int   					=		self.head
						for _ in range(index - 1							):
							lowercase						:		str   					=		temp.next
						lowercase						:		int   					=		temp.next
						lowercase						:		int   					=		temp.next.next
					return delete_node.data

				def 	a__		(     self							)    ->  bool:
					return self.head is None


				def 	a__		(     self							)    ->  None:
					lowercase						:		int   					=		None
					lowercase						:		Union[str, Any]   					=		self.head

					while current:
						# Store the current node's next node.
						lowercase						:		str   					=		current.next
						# Make the current node's next point backwards
						lowercase						:		Dict   					=		prev
						# Make the previous node be the current node
						lowercase						:		int   					=		current
						# Make the current node the next node (to progress iteration)
						lowercase						:		Optional[Any]   					=		next_node
					# Return prev in order to put the head at the end
					lowercase						:		Optional[int]   					=		prev




def 	_A  (       )  ->				None:
	lowercase						:		Any   					=		LinkedList()
	assert linked_list.is_empty() is True
	assert str(A							) == ""

	try:
		linked_list.delete_head()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	try:
		linked_list.delete_tail()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	for i in range(1_0							):
		assert len(A							) == i
		linked_list.insert_nth(A							,i + 1							)
	assert str(A							) == "->".join(str(A							) for i in range(1							,1_1							)							)

	linked_list.insert_head(0							)
	linked_list.insert_tail(1_1							)
	assert str(A							) == "->".join(str(A							) for i in range(0							,1_2							)							)

	assert linked_list.delete_head() == 0
	assert linked_list.delete_nth(9							) == 1_0
	assert linked_list.delete_tail() == 1_1
	assert len(A							) == 9
	assert str(A							) == "->".join(str(A							) for i in range(1							,1_0							)							)

	assert all(linked_list[i] == i + 1 for i in range(0							,9							)							) is True

	for i in range(0							,9							):
		lowercase						:		Tuple   					=		-i
	assert all(linked_list[i] == -i for i in range(0							,9							)							) is True

	linked_list.reverse()
	assert str(A							) == "->".join(str(A							) for i in range(-8							,1							)							)




def 	_A  (       )  ->				None:
	lowercase						:		Union[str, Any]   					=		[
	    -9,
	    1_0_0,
	    Node(7_7_3_4_5_1_1_2							),
	    "dlrow olleH",
	    7,
	    5_5_5_5,
	    0,
	    -192.5_5555,
	    "Hello, world!",
	    77.9,
	    Node(1_0							),
	    None,
	    None,
	    12.20,
	]
	lowercase						:		str   					=		LinkedList()

	for i in test_input:
		linked_list.insert_tail(A							)

	# Check if it's empty or not
	assert linked_list.is_empty() is False
	assert (
	    str(A							) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
	    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the head
	lowercase						:		Optional[int]   					=		linked_list.delete_head()
	assert result == -9
	assert (
	    str(A							) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the tail
	lowercase						:		Any   					=		linked_list.delete_tail()
	assert result == 12.2
	assert (
	    str(A							) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None"
	)

	# Delete a node in specific location in linked list
	lowercase						:		Tuple   					=		linked_list.delete_nth(1_0							)
	assert result is None
	assert (
	    str(A							) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None"
	)

	# Add a Node instance to its head
	linked_list.insert_head(Node("Hello again, world!"							)							)
	assert (
	    str(A							)
	    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
	    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
	)

	# Add None to its tail
	linked_list.insert_tail(A							)
	assert (
	    str(A							)
	    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
	    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
	)

	# Reverse the linked list
	linked_list.reverse()
	assert (
	    str(A							)
	    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
	    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
	)




def 	_A  (       )  ->				int:
	from doctest import testmod

	testmod()

	lowercase						:		Optional[Any]   					=		LinkedList()
	linked_list.insert_head(input("Inserting 1st at head "							).strip()							)
	linked_list.insert_head(input("Inserting 2nd at head "							).strip()							)
	print("\nPrint list:"							)
	linked_list.print_list()
	linked_list.insert_tail(input("\nInserting 1st at tail "							).strip()							)
	linked_list.insert_tail(input("Inserting 2nd at tail "							).strip()							)
	print("\nPrint list:"							)
	linked_list.print_list()
	print("\nDelete head"							)
	linked_list.delete_head()
	print("Delete tail"							)
	linked_list.delete_tail()
	print("\nPrint list:"							)
	linked_list.print_list()
	print("\nReverse linked list"							)
	linked_list.reverse()
	print("\nPrint list:"							)
	linked_list.print_list()
	print("\nString representation of linked list:"							)
	print(A							)
	print("\nReading/changing Node data using indexing:"							)
	print(F'''Element at Position 1: {linked_list[1]}'''							)
	lowercase						:		Dict   					=		input("Enter New Value: "							).strip()
	print("New list:"							)
	print(A							)
	print(F'''length of linked_list is : {len(A							)}'''							)


if __name__ == "__main__":
					main()






'''simple docstring'''
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


lowerCAmelCase   : Any             =				[
    # 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  (       A							)  ->				List[str]:
	for pegasus_name, hf_name in PATTERNS:
		lowercase						:		int   					=		k.replace(A							,A							)
	return k




def 	_A  (       A							,A							)  ->				PegasusForConditionalGeneration:
	lowercase						:		int   					=		DEFAULTS.copy()
	cfg_kwargs.update(A							)
	lowercase						:		Any   					=		PegasusConfig(**A							)
	lowercase						:		Optional[Any]   					=		PegasusForConditionalGeneration(A							)
	lowercase						:		Dict   					=		torch_model.model.state_dict()
	lowercase						:		List[str]   					=		{}
	for k, v in tf_weights.items():
		lowercase						:		Optional[Any]   					=		rename_state_dict_key(A							)
		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:
			lowercase						:		Tuple   					=		v.T
		lowercase						:		str   					=		torch.tensor(A							,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
	lowercase						:		Union[str, Any]   					=		torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]							)
	lowercase						:		Optional[int]   					=		mapping["shared.weight"]
	lowercase						:		int   					=		mapping["shared.weight"]
	lowercase						:		Optional[Any]   					=		{k: torch.zeros_like(A							) for k, v in sd.items() if k.endswith("bias"							) and k not in mapping}
	mapping.update(**A							)
	lowercase					,		lowercase						:		Union[str, Any]   					=		torch_model.model.load_state_dict(A							,strict=A							)
	lowercase						:		List[str]   					=		[
	    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  (       A="./ckpt/aeslc/model.ckpt-32000"							)  ->				Dict:
	lowercase						:		Optional[int]   					=		tf.train.list_variables(A							)
	lowercase						:		List[str]   					=		{}
	lowercase						:		List[str]   					=		["Adafactor", "global_step"]
	for name, shape in tqdm(A							,desc="converting tf checkpoint to dict"							):
		lowercase						:		Any   					=		any(pat in name for pat in ignore_name							)
		if skip_key:
			continue
		lowercase						:		Optional[Any]   					=		tf.train.load_variable(A							,A							)
		lowercase						:		Dict   					=		array
	return tf_weights




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	# save tokenizer first
	lowercase						:		int   					=		Path(A							).parent.name
	lowercase						:		str   					=		task_specific_params[F'''summarization_{dataset}''']["max_position_embeddings"]
	lowercase						:		int   					=		PegasusTokenizer.from_pretrained("sshleifer/pegasus"							,model_max_length=A							)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(A							)

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


if __name__ == "__main__":
					lowerCAmelCase   : Optional[int]             =				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.""")
					lowerCAmelCase   : Dict             =				parser.parse_args()
					if args.save_dir is None:
										lowerCAmelCase   : Dict             =				Path(args.tf_ckpt_path).parent.name
										lowerCAmelCase   : Optional[int]             =				os.path.join("""pegasus""", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


