'''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


lowercase_     		= [
    # 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 							UpperCamelCase__			(			a__      ):


      '''simple docstring'''






      for pegasus_name, hf_name in PATTERNS:
            _lowerCAmelCase										=k.replace(a__			,      a__      )
      return k



def 							UpperCamelCase__			(			a__			,      a__      ):


      '''simple docstring'''






      _lowerCAmelCase										=DEFAULTS.copy()
      cfg_kwargs.update(a__      )
      _lowerCAmelCase										=PegasusConfig(**a__      )
      _lowerCAmelCase										=PegasusForConditionalGeneration(a__      )
      _lowerCAmelCase										=torch_model.model.state_dict()
      _lowerCAmelCase										={}
      for k, v in tf_weights.items():
            _lowerCAmelCase										=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:
                  _lowerCAmelCase										=v.T
            _lowerCAmelCase										=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
      _lowerCAmelCase										=torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]      )
      _lowerCAmelCase										=mapping['shared.weight']
      _lowerCAmelCase										=mapping['shared.weight']
      _lowerCAmelCase										={k: torch.zeros_like(a__      ) for k, v in sd.items() if k.endswith('bias'      ) and k not in mapping}
      mapping.update(**a__      )
      _lowerCAmelCase						,				_lowerCAmelCase										=torch_model.model.load_state_dict(a__			,      strict=a__      )
      _lowerCAmelCase										=[
          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 							UpperCamelCase__			(			a__="./ckpt/aeslc/model.ckpt-32000"      ):


      '''simple docstring'''






      _lowerCAmelCase										=tf.train.list_variables(a__      )
      _lowerCAmelCase										={}
      _lowerCAmelCase										=['Adafactor', 'global_step']
      for name, shape in tqdm(a__			,      desc='converting tf checkpoint to dict'      ):
            _lowerCAmelCase										=any(pat in name for pat in ignore_name      )
            if skip_key:
                  continue
            _lowerCAmelCase										=tf.train.load_variable(a__			,      a__      )
            _lowerCAmelCase										=array
      return tf_weights



def 							UpperCamelCase__			(			a__			,      a__      ):


      '''simple docstring'''






      _lowerCAmelCase										=Path(a__      ).parent.name
      _lowerCAmelCase										=task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
      _lowerCAmelCase										=PegasusTokenizer.from_pretrained('sshleifer/pegasus'			,      model_max_length=a__      )
      assert tok.model_max_length == desired_max_model_length
      tok.save_pretrained(a__      )

      # convert model
      _lowerCAmelCase										=get_tf_weights_as_numpy(a__      )
      _lowerCAmelCase										=task_specific_params[F'''summarization_{dataset}''']
      if dataset == "large":
            _lowerCAmelCase										=task_specific_params
      _lowerCAmelCase										=convert_pegasus(a__			,      a__      )
      torch_model.save_pretrained(a__      )
      _lowerCAmelCase										=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__":
       lowercase_     		= 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.''')
       lowercase_     		= parser.parse_args()
       if args.save_dir is None:
              lowercase_     		= Path(args.tf_ckpt_path).parent.name
              lowercase_     		= 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 Any
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A				) ->     None:
								_lowerCAmelCase										=num_of_nodes
								_lowerCAmelCase										=[]
								_lowerCAmelCase										={}






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:
								self.m_edges.append([u_node, v_node, weight]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:

								if self.m_component[u_node] == u_node:
														return u_node
								return self.find_component(self.m_component[u_node]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     None:

								if self.m_component[u_node] != u_node:
														for k in self.m_component:
																				_lowerCAmelCase										=self.find_component(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:

								if component_size[u_node] <= component_size[v_node]:
														_lowerCAmelCase										=v_node
														component_size[v_node] += component_size[u_node]
														self.set_component(__A				)

								elif component_size[u_node] >= component_size[v_node]:
														_lowerCAmelCase										=self.find_component(__A				)
														component_size[u_node] += component_size[v_node]
														self.set_component(__A				)






		def 		UpperCamelCase__       (   self				) ->     None:
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=0

								_lowerCAmelCase										=[-1] * self.m_num_of_nodes

								# A list of components (initialized to all of the nodes)
								for node in range(self.m_num_of_nodes				):
														self.m_component.update({node: node}				)
														component_size.append(1				)

								_lowerCAmelCase										=self.m_num_of_nodes

								while num_of_components > 1:
														for edge in self.m_edges:
																				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																				_lowerCAmelCase										=self.m_component[u]
																				_lowerCAmelCase										=self.m_component[v]

																				if u_component != v_component:

																										for component in (u_component, v_component):
																																if (
																																    minimum_weight_edge[component] == -1
																																    or minimum_weight_edge[component][2] > w
																																):
																																						_lowerCAmelCase										=[u, v, w]

														for edge in minimum_weight_edge:
																				if isinstance(__A		,						__A				):
																										_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																										_lowerCAmelCase										=self.m_component[u]
																										_lowerCAmelCase										=self.m_component[v]

																										if u_component != v_component:
																																mst_weight += w
																																self.union(__A		,						__A		,						__A				)
																																print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''				)
																																num_of_components -= 1

														_lowerCAmelCase										=[-1] * self.m_num_of_nodes
								print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''








if __name__ == "__main__":
							import doctest

							doctest.testmod()





