


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__					:     Tuple       =     [
    # 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__    (		snake_case__					:							Tuple     ):
  for pegasus_name, hf_name in PATTERNS:
    _UpperCAmelCase		:							Optional[int]							=     k.replace(snake_case__						,   snake_case__     )
  return k




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




def    a__    (		snake_case__					:							str						,   snake_case__					:							str     ):
  # save tokenizer first
  _UpperCAmelCase		:							str							=     Path(snake_case__     ).parent.name
  _UpperCAmelCase		:							List[str]							=     task_specific_params[f'''summarization_{dataset}''']["""max_position_embeddings"""]
  _UpperCAmelCase		:							Any							=     PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""						,   model_max_length=snake_case__     )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(snake_case__     )

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     Dict       =     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__					:     Union[str, Any]       =     Path(args.tf_ckpt_path).parent.name
  SCREAMING_SNAKE_CASE__					:     List[str]       =     os.path.join('pegasus', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.




def    a__    (		snake_case__					:							Dict     ):
  _UpperCAmelCase		:							str							=     [False] * len(snake_case__     )
  _UpperCAmelCase		:							str							=     [-1] * len(snake_case__     )

  def dfs(snake_case__					:							Dict						,   snake_case__					:							Optional[Any]     ):
    _UpperCAmelCase		:							str							=     True
    _UpperCAmelCase		:							Optional[Any]							=     c
    for u in graph[v]:
      if not visited[u]:
        dfs(snake_case__						,   1 - c     )

  for i in range(len(snake_case__     )     ):
    if not visited[i]:
      dfs(snake_case__						,   0     )

  for i in range(len(snake_case__     )     ):
    for j in graph[i]:
      if color[i] == color[j]:
        return False

  return True


# Adjacency list of graph
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}





print(check_bipartite_dfs(graph))





