






'''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


a_					= [
    # 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(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      for pegasus_name, hf_name in PATTERNS:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=k.replace(UpperCamelCase__,	UpperCamelCase__							)
      return k





def  _a(  UpperCamelCase__				:						dict,	UpperCamelCase__				:						dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[str]									=DEFAULTS.copy()
      cfg_kwargs.update(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Any									=PegasusConfig(**UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				int									=PegasusForConditionalGeneration(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				str									=torch_model.model.state_dict()
      SCREAMING_SNAKE_CASE__     :				List[Any]									={}
      for k, v in tf_weights.items():
            SCREAMING_SNAKE_CASE__     :				Dict									=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__     :				Union[str, Any]									=v.T
            SCREAMING_SNAKE_CASE__     :				Any									=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__     :				Dict									=torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]							)
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=mapping['''shared.weight''']
      SCREAMING_SNAKE_CASE__     :				Dict									=mapping['''shared.weight''']
      SCREAMING_SNAKE_CASE__     :				Tuple									={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__     :				Optional[int]									=torch_model.model.load_state_dict(UpperCamelCase__,	strict=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=[
          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(  UpperCamelCase__				:						Dict="./ckpt/aeslc/model.ckpt-32000"							):




      '''simple docstring'''






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





def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Any									=Path(UpperCamelCase__							).parent.name
      SCREAMING_SNAKE_CASE__     :				Dict									=task_specific_params[f"summarization_{dataset}"]['''max_position_embeddings''']
      SCREAMING_SNAKE_CASE__     :				Tuple									=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__     :				int									=get_tf_weights_as_numpy(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=task_specific_params[f"summarization_{dataset}"]
      if dataset == "large":
            SCREAMING_SNAKE_CASE__     :				Dict									=task_specific_params
      SCREAMING_SNAKE_CASE__     :				List[str]									=convert_pegasus(UpperCamelCase__,	UpperCamelCase__							)
      torch_model.save_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=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__":
  a_					= 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.')
  a_					= parser.parse_args()
  if args.save_dir is None:
    a_					= Path(args.tf_ckpt_path).parent.name
    a_					= os.path.join('pegasus', dataset)
  convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					= logging.get_logger(__name__)

a_					= {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							"""gpt_neox"""




 def __init__(      self	:							List[Any]					,      __lowercase	:							Union[str, Any]=5_04_32					,      __lowercase	:							int=61_44					,      __lowercase	:							Tuple=44					,      __lowercase	:							List[str]=64					,      __lowercase	:							str=2_45_76					,      __lowercase	:							Dict="gelu"					,      __lowercase	:							Tuple=0.25					,      __lowercase	:							Tuple=1_00_00					,      __lowercase	:							Tuple=0.0					,      __lowercase	:							str=0.0					,      __lowercase	:							List[Any]=0.1					,      __lowercase	:							Dict=20_48					,      __lowercase	:							Any=0.02					,      __lowercase	:							Dict=1e-5					,      __lowercase	:							List[Any]=True					,      __lowercase	:							str=0					,      __lowercase	:							Optional[Any]=2					,      __lowercase	:							Tuple=False					,      __lowercase	:							List[Any]=True					,      __lowercase	:							Optional[Any]=None					,      **__lowercase	:							Any					,      )	->						Dict:
       super().__init__(bos_token_id=__lowercase					,      eos_token_id=__lowercase					,      **__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=vocab_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=max_position_embeddings
       SCREAMING_SNAKE_CASE__     :				Any									=hidden_size
       SCREAMING_SNAKE_CASE__     :				str									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				Any									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				Dict									=hidden_act
       SCREAMING_SNAKE_CASE__     :				str									=rotary_pct
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=rotary_emb_base
       SCREAMING_SNAKE_CASE__     :				List[Any]									=attention_dropout
       SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_dropout
       SCREAMING_SNAKE_CASE__     :				str									=classifier_dropout
       SCREAMING_SNAKE_CASE__     :				Any									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Dict									=layer_norm_eps
       SCREAMING_SNAKE_CASE__     :				Any									=use_cache
       SCREAMING_SNAKE_CASE__     :				Tuple									=tie_word_embeddings
       SCREAMING_SNAKE_CASE__     :				Tuple									=use_parallel_residual
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=rope_scaling
       self._rope_scaling_validation()

       if self.hidden_size % self.num_attention_heads != 0:
             raise ValueError(
                 '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[Any]:
       if self.rope_scaling is None:
             return

       if not isinstance(self.rope_scaling					,      __lowercase      ) or len(self.rope_scaling      ) != 2:
             raise ValueError(
                 '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                 F"got {self.rope_scaling}"      )
       SCREAMING_SNAKE_CASE__     :				int									=self.rope_scaling.get('''type'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.rope_scaling.get('''factor'''					,      __lowercase      )
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"      )
       if rope_scaling_factor is None or not isinstance(__lowercase					,      __lowercase      ) or rope_scaling_factor <= 1.0:
             raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"      )