






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


UpperCamelCase 				=	[
    # 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        __lowerCamelCase			(		snake_case__		)							->  Tuple:






						"""simple docstring"""





						for pegasus_name, hf_name in PATTERNS:
												_SCREAMING_SNAKE_CASE	      =  k.replace(UpperCAmelCase_       ,UpperCAmelCase_		)
						return k

def        __lowerCamelCase			(		snake_case__       ,snake_case__		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  DEFAULTS.copy()
						cfg_kwargs.update(UpperCAmelCase_		)
						_SCREAMING_SNAKE_CASE	      =  PegasusConfig(**UpperCAmelCase_		)
						_SCREAMING_SNAKE_CASE	      =  PegasusForConditionalGeneration(UpperCAmelCase_		)
						_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(UpperCAmelCase_		)
												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(UpperCAmelCase_       ,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(UpperCAmelCase_		) for k, v in sd.items() if k.endswith("""bias"""		) and k not in mapping}
						mapping.update(**UpperCAmelCase_		)
						_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE	      =  torch_model.model.load_state_dict(UpperCAmelCase_       ,strict=UpperCAmelCase_		)
						_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        __lowerCamelCase			(		snake_case__="./ckpt/aeslc/model.ckpt-32000"		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  tf.train.list_variables(UpperCAmelCase_		)
						_SCREAMING_SNAKE_CASE	      =  {}
						_SCREAMING_SNAKE_CASE	      =  ["""Adafactor""", """global_step"""]
						for name, shape in tqdm(UpperCAmelCase_       ,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(UpperCAmelCase_       ,UpperCAmelCase_		)
												_SCREAMING_SNAKE_CASE	      =  array
						return tf_weights

def        __lowerCamelCase			(		snake_case__       ,snake_case__		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  Path(UpperCAmelCase_		).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=UpperCAmelCase_		)
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(UpperCAmelCase_		)

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


if __name__ == "__main__":
		UpperCamelCase 				=	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.''')
		UpperCamelCase 				=	parser.parse_args()
		if args.save_dir is None:
				UpperCamelCase 				=	Path(args.tf_ckpt_path).parent.name
				UpperCamelCase 				=	os.path.join('''pegasus''', dataset)
		convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





'''simple docstring'''

from __future__ import annotations

from typing import Generic, TypeVar

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





class      snake_case    (    Generic[T]			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  data
   lowerCamelCase_      =  self
   lowerCamelCase_      =  0





class      snake_case    (    Generic[T]			):





  """simple docstring"""




  def __init__(    self     ):







   """simple docstring"""
   # map from node name to the node object
   lowerCamelCase_      =  {}






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # create a new set with x as its member
   lowerCamelCase_      =  DisjointSetTreeNode(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # find the set x belongs to (with path-compression)
   lowerCamelCase_      =  self.map[data]
   if elem_ref != elem_ref.parent:
    lowerCamelCase_      =  self.find_set(elem_ref.parent.data     )
   return elem_ref.parent






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   # helper function for union operation
   if nodea.rank > nodea.rank:
    lowerCamelCase_      =  nodea
   else:
    lowerCamelCase_      =  nodea
    if nodea.rank == nodea.rank:
     nodea.rank += 1







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   # merge 2 disjoint sets
   self.link(self.find_set(UpperCamelCase     )			,  self.find_set(UpperCamelCase     )     )





class      snake_case    (    Generic[T]			):





  """simple docstring"""




  def __init__(    self     ):







   """simple docstring"""
   # connections: map from the node to the neighbouring nodes (with weights)
   lowerCamelCase_      =  {}






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # add a node ONLY if its not present in the graph
   if node not in self.connections:
    lowerCamelCase_      =  {}






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   # add an edge with the given weight
   self.add_node(UpperCamelCase     )
   self.add_node(UpperCamelCase     )
   lowerCamelCase_      =  weight
   lowerCamelCase_      =  weight







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  []
   lowerCamelCase_      =  set()
   for start in self.connections:
    for end in self.connections[start]:
     if (start, end) not in seen:
      seen.add((end, start)     )
      edges.append((start, end, self.connections[start][end])     )
   edges.sort(key=lambda UpperCamelCase     : x[2]     )

   # creating the disjoint set
   lowerCamelCase_      =  DisjointSetTree[T]()
   for node in self.connections:
    disjoint_set.make_set(UpperCamelCase     )

   # MST generation
   lowerCamelCase_      =  0
   lowerCamelCase_      =  0
   lowerCamelCase_      =  GraphUndirectedWeighted[T]()
   while num_edges < len(self.connections     ) - 1:
    lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  edges[index]
    index += 1
    lowerCamelCase_      =  disjoint_set.find_set(UpperCamelCase     )
    lowerCamelCase_      =  disjoint_set.find_set(UpperCamelCase     )
    if parent_u != parent_v:
     num_edges += 1
     graph.add_edge(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )
     disjoint_set.union(UpperCamelCase			,  UpperCamelCase     )
   return graph



