
'''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					:    Union[str, 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						:		Dict):
							for pegasus_name, hf_name in PATTERNS:
														UpperCamelCase   			=     k.replace(A		,     A)
							return k







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







def 		A_(     A						:		str		,     A						:		str):
							# save tokenizer first
							UpperCamelCase   			=     Path(A).parent.name
							UpperCamelCase   			=     task_specific_params[f'''summarization_{dataset}''']['max_position_embeddings']
							UpperCamelCase   			=     PegasusTokenizer.from_pretrained('sshleifer/pegasus'		,     model_max_length=A)
							assert tok.model_max_length == desired_max_model_length
							tok.save_pretrained(A)

							# convert model
							UpperCamelCase   			=     get_tf_weights_as_numpy(A)
							UpperCamelCase   			=     task_specific_params[f'''summarization_{dataset}''']
							if dataset == "large":
														UpperCamelCase   			=     task_specific_params
							UpperCamelCase   			=     convert_pegasus(A		,     A)
							torch_model.save_pretrained(A)
							UpperCamelCase   			=     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					:    List[str]           = 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					:    Any           = parser.parse_args()
						if args.save_dir is None:
												lowerCAmelCase					:    Optional[int]           = Path(args.tf_ckpt_path).parent.name
												lowerCAmelCase					:    List[Any]           = os.path.join('pegasus', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)












from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

snake_case__   :						List[str]													=   TypeVar("""T""")






def      _snake_case     (__lowercase):
   return (position - 1) // 2






def      _snake_case     (__lowercase):
   return (2 * position) + 1






def      _snake_case     (__lowercase):
   return (2 * position) + 2


class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  []
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __len__(  self			)      ->			int:
     return self.elements




  def __repr__(  self			)      ->			str:
     return str(self.heap			)




  def  _UpperCAmelCase  (  self			)      ->			bool:
     # Check if the priority queue is empty
     return self.elements == 0




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an element with given priority to the queue
     self.heap.append((elem, weight)			)
     UpperCamelCase_						=  self.elements
     self.elements += 1
     self._bubble_up(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			T:
     # Remove and return the element with lowest weight (highest priority)
     if self.elements > 1:
        self._swap_nodes(0		,		self.elements - 1			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap.pop()
     del self.position_map[elem]
     self.elements -= 1
     if self.elements > 0:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[0]
        self._bubble_down(_UpperCAmelCase			)
     return elem




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Update the weight of the given key
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_						=  (elem, weight)
     if position > 0:
        UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
        if parent_weight > weight:
           self._bubble_up(_UpperCAmelCase			)
        else:
           self._bubble_down(_UpperCAmelCase			)
     else:
        self._bubble_down(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (upward movement) [to be used internally
     # only]
     UpperCamelCase_						=  self.position_map[elem]
     if curr_pos == 0:
        return None
     UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
     if parent_weight > weight:
        self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
        return self._bubble_up(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (downward movement) [to be used
     # internally only]
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_						=  get_child_left_position(_UpperCAmelCase			)
     UpperCamelCase_						=  get_child_right_position(_UpperCAmelCase			)
     if child_left_position < self.elements and child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     if child_left_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        if child_left_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     else:
        return None
     if child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Swap the nodes at the given positions
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_		,   UpperCamelCase_						=  (
         self.heap[nodea_pos],
         self.heap[nodea_pos],
     )
     UpperCamelCase_						=  nodea_pos
     UpperCamelCase_						=  nodea_pos




class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __repr__(  self			)      ->			str:
     return str(self.connections			)




  def __len__(  self			)      ->			int:
     return self.nodes




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Add a node in the graph if it is not in the graph
     if node not in self.connections:
        UpperCamelCase_						=  {}
        self.nodes += 1




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an edge between 2 nodes in the graph
     self.add_node(_UpperCAmelCase			)
     self.add_node(_UpperCAmelCase			)
     UpperCamelCase_						=  weight
     UpperCamelCase_						=  weight






def      _snake_case     (__lowercase     ,   ):
   UpperCamelCase_						=  {node: maxsize for node in graph.connections}
   UpperCamelCase_						=  {node: None for node in graph.connections}

   UpperCamelCase_						=  MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(__lowercase     ,   __lowercase)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCamelCase_						=  priority_queue.extract_min()
   UpperCamelCase_						=  0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(__lowercase     ,   dist[neighbour])
         UpperCamelCase_						=  node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCamelCase_						=  priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(__lowercase     ,   dist[neighbour])
            UpperCamelCase_						=  node
   return dist, parent





