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__  =					[
    # 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__       (			a__ )      ->List[str]:




  '''simple docstring'''

  for pegasus_name, hf_name in PATTERNS:
    _UpperCamelCase           =    k.replace(snake_case__    ,     snake_case__ )
  return k


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->int:




  '''simple docstring'''

  _UpperCamelCase           =    DEFAULTS.copy()
  cfg_kwargs.update(snake_case__ )
  _UpperCamelCase           =    PegasusConfig(**snake_case__ )
  _UpperCamelCase           =    PegasusForConditionalGeneration(snake_case__ )
  _UpperCamelCase           =    torch_model.model.state_dict()
  _UpperCamelCase           =    {}
  for k, v in tf_weights.items():
    _UpperCamelCase           =    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           =    v.T
    _UpperCamelCase           =    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           =    torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1] )
  _UpperCamelCase           =    mapping["""shared.weight"""]
  _UpperCamelCase           =    mapping["""shared.weight"""]
  _UpperCamelCase           =    {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           =    torch_model.model.load_state_dict(snake_case__    ,     strict=snake_case__ )
  _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 				lowerCAmelCase__       (			a__="./ckpt/aeslc/model.ckpt-32000" )      ->Optional[Any]:




  '''simple docstring'''

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


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Tuple:




  '''simple docstring'''

  _UpperCamelCase           =    Path(snake_case__ ).parent.name
  _UpperCamelCase           =    task_specific_params[f'summarization_{dataset}']["""max_position_embeddings"""]
  _UpperCamelCase           =    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           =    get_tf_weights_as_numpy(snake_case__ )
  _UpperCamelCase           =    task_specific_params[f'summarization_{dataset}']
  if dataset == "large":
    _UpperCamelCase           =    task_specific_params
  _UpperCamelCase           =    convert_pegasus(snake_case__    ,     snake_case__ )
  torch_model.save_pretrained(snake_case__ )
  _UpperCamelCase           =    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__":
   lowerCamelCase__  =					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__  =					parser.parse_args()
   if args.save_dir is None:
      lowerCamelCase__  =					Path(args.tf_ckpt_path).parent.name
      lowerCamelCase__  =					os.path.join('''pegasus''', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''ernie_m'''
     __SCREAMING_SNAKE_CASE     					=    {"dropout": "classifier_dropout", "num_classes": "num_labels"}


     def __init__(  self							,					A_ = 25_00_02							,					A_ = 7_68							,					A_ = 12							,					A_ = 12							,					A_ = 30_72							,					A_ = "gelu"							,					A_ = 0.1							,					A_ = 0.1							,					A_ = 5_14							,					A_ = 0.0_2							,					A_ = 1							,					A_ = 1e-05							,					A_=None							,					A_=False							,					A_=0.0							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					**A_    )
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							List[str]							=     hidden_size
       _UpperCAmelCase		:							Optional[int]							=     num_hidden_layers
       _UpperCAmelCase		:							Tuple							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     intermediate_size
       _UpperCAmelCase		:							List[Any]							=     hidden_act
       _UpperCAmelCase		:							Any							=     hidden_dropout_prob
       _UpperCAmelCase		:							Any							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Tuple							=     max_position_embeddings
       _UpperCAmelCase		:							Dict							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							str							=     classifier_dropout
       _UpperCAmelCase		:							Any							=     is_decoder
       _UpperCAmelCase		:							Union[str, Any]							=     act_dropout





