





'''simple docstring'''




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			None
     lowerCAmelCase						     =			None
     lowerCAmelCase						     =			None
     lowerCAmelCase						     =			None




class 					SCREAMING_SNAKE_CASE       (a__				):


     def __init__(		self		,      _UpperCAmelCase=1		,      _UpperCAmelCase=0		,      _UpperCAmelCase=2		,      _UpperCAmelCase=512		,      _UpperCAmelCase="cls"		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase		,      bos_token_id=_UpperCAmelCase		,      eos_token_id=_UpperCAmelCase		,      **_UpperCAmelCase)
          __A		:					Any						=			project_dim
          __A		:					List[str]						=			pooler_fn
          __A		:					Tuple						=			learn_encoder
          __A		:					Optional[int]						=			use_attention_mask




class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			[r'''pooler''', r'''logit_scale''']
     lowerCAmelCase						     =			[r'''position_ids''', r'''predictions.decoder.bias''']
     lowerCAmelCase						     =			'''roberta'''
     lowerCAmelCase						     =			RobertaSeriesConfig


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          super().__init__(_UpperCAmelCase)
          __A		:					List[str]						=			XLMRobertaModel(_UpperCAmelCase)
          __A		:					List[str]						=			nn.Linear(config.hidden_size		,      config.project_dim)
          __A		:					List[Any]						=			getattr(_UpperCAmelCase		,      'has_pre_transformation'		,      _UpperCAmelCase)
          if self.has_pre_transformation:
               __A		:					Tuple						=			nn.Linear(config.hidden_size		,      config.project_dim)
               __A		:					Optional[int]						=			nn.LayerNorm(config.hidden_size		,      eps=config.layer_norm_eps)
          self.post_init()


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      ):

          '''simple docstring'''






          __A		:					Optional[Any]						=			return_dict if return_dict is not None else self.config.use_return_dict

          __A		:					Any						=			self.base_model(
              input_ids=_UpperCAmelCase		,      attention_mask=_UpperCAmelCase		,      token_type_ids=_UpperCAmelCase		,      position_ids=_UpperCAmelCase		,      head_mask=_UpperCAmelCase		,      inputs_embeds=_UpperCAmelCase		,      encoder_hidden_states=_UpperCAmelCase		,      encoder_attention_mask=_UpperCAmelCase		,      output_attentions=_UpperCAmelCase		,      output_hidden_states=True if self.has_pre_transformation else output_hidden_states		,      return_dict=_UpperCAmelCase		,      )

          if self.has_pre_transformation:
               __A		:					Optional[int]						=			outputs['hidden_states'][-2]
               __A		:					Dict						=			self.pre_LN(_UpperCAmelCase)
               __A		:					Optional[Any]						=			self.transformation_pre(_UpperCAmelCase)

               return TransformationModelOutput(
                   projection_state=_UpperCAmelCase		,      last_hidden_state=outputs.last_hidden_state		,      hidden_states=outputs.hidden_states		,      attentions=outputs.attentions		,      )
          else:
               __A		:					Optional[Any]						=			self.transformation(outputs.last_hidden_state)
               return TransformationModelOutput(
                   projection_state=_UpperCAmelCase		,      last_hidden_state=outputs.last_hidden_state		,      hidden_states=outputs.hidden_states		,      attentions=outputs.attentions		,      )






'''simple docstring'''




import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)

lowercase__       : str         =       [
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __A		:					Optional[Any]						=			k.replace(__snake_case					,				__snake_case     )

     if k.startswith('encoder'     ):
          __A		:					Any						=			k.replace('.attn'					,				'.self_attn'     )
          __A		:					Any						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'final_layer_norm'     )
     elif k.startswith('decoder'     ):
          __A		:					Tuple						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'encoder_attn_layer_norm'     )
          __A		:					int						=			k.replace('norm3'					,				'final_layer_norm'     )
     return k






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Dict:
     __A		:					Optional[int]						=			[
         'model.encoder.layernorm_embedding.weight',
         'model.encoder.layernorm_embedding.bias',
         'model.decoder.layernorm_embedding.weight',
         'model.decoder.layernorm_embedding.bias',
     ]
     for k in keys:
          __A		:					Tuple						=			sd.pop(__snake_case     )
          __A		:					Union[str, Any]						=			k.replace('layernorm_embedding'					,				'layer_norm'     )
          assert new_k not in sd
          __A		:					str						=			v


lowercase__       : Tuple         =       ['''START''']






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any					,				__snake_case    :					List[Any]     )	->	int:
     __A		:					List[str]						=			torch.load(__snake_case					,				map_location='cpu'     )
     __A		:					Tuple						=			model['model']
     __A		:					str						=			BlenderbotConfig.from_json_file(__snake_case     )
     __A		:					int						=			BlenderbotForConditionalGeneration(__snake_case     )
     __A		:					List[Any]						=			m.model.state_dict().keys()
     __A		:					Optional[int]						=			[]
     __A		:					Optional[int]						=			{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          __A		:					Union[str, Any]						=			rename_state_dict_key(__snake_case     )
          if new_k not in valid_keys:
               failures.append([k, new_k]     )
          else:
               __A		:					Optional[Any]						=			v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(__snake_case     )
     m.model.load_state_dict(__snake_case					,				strict=__snake_case     )
     m.half()
     m.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
  parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
  parser.add_argument(
      '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)