




from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
  from .features import FeatureType







@dataclass
class 						lowercase							:
     _a							 =						42
     _a							 =						None
     # Automatically constructed
     _a							 =						"dict"
     _a							 =						None
     _a							 =						field(default="Translation",init=UpperCamelCase__,repr=UpperCamelCase__		)

     def __call__(  self				)						->							int:
           return pa.struct({lang: pa.string() for lang in sorted(self.languages				)}				)



     def 					a__	(  self				)						->							Union["FeatureType", Dict[str, "FeatureType"]]:
           from .features import Value

           return {k: Value("""string"""				) for k in sorted(self.languages				)}







@dataclass
class 						lowercase							:
     _a							 =						None
     _a							 =						None
     _a							 =						None
     # Automatically constructed
     _a							 =						"dict"
     _a							 =						None
     _a							 =						field(default="TranslationVariableLanguages",init=UpperCamelCase__,repr=UpperCamelCase__		)

     def 					a__	(  self				)						->							str:
           _A							:     str			=						sorted(set(self.languages				)				) if self.languages else None
           _A							:     Any			=						len(self.languages				) if self.languages else None

     def __call__(  self				)						->							Union[str, Any]:
           return pa.struct({"""language""": pa.list_(pa.string()				), """translation""": pa.list_(pa.string()				)}				)

     def 					a__	(  self		,     _a				)						->							Optional[int]:
           _A							:     Optional[Any]			=						set(self.languages				)
           if self.languages and set(_a				) - lang_set:
                 raise ValueError(
                     F'''Some languages in example ({", ".join(sorted(set(_a				) - lang_set				)				)}) are not in valid set ({", ".join(_a				)}).'''				)

           # Convert dictionary into tuples, splitting out cases where there are
           # multiple translations for a single language.
           _A							:     Any			=						[]
           for lang, text in translation_dict.items():
                 if isinstance(_a		,     _a				):
                       translation_tuples.append((lang, text)				)
                 else:
                       translation_tuples.extend([(lang, el) for el in text]				)

        # Ensure translations are in ascending order by language code.
           _A			,   _A							:     str			=						zip(*sorted(_a				)				)

           return {"language": languages, "translation": translations}



     def 					a__	(  self				)						->							Union["FeatureType", Dict[str, "FeatureType"]]:
           from .features import Sequence, Value

           return {
               "language": Sequence(Value("""string"""				)				),
               "translation": Sequence(Value("""string"""				)				),
           }







import argparse

import torch

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


logging.set_verbosity_info()
_snake_case										=       logging.get_logger(__name__)

_snake_case										=       [
    ["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_     ):
      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            _A							:     str			=						k.replace(snake_case_,snake_case_     )

      if k.startswith("""encoder"""     ):
            _A							:     Optional[Any]			=						k.replace(""".attn""",""".self_attn"""     )
            _A							:     Dict			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Optional[Any]			=						k.replace("""norm2""","""final_layer_norm"""     )
      elif k.startswith("""decoder"""     ):
            _A							:     str			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Any			=						k.replace("""norm2""","""encoder_attn_layer_norm"""     )
            _A							:     Optional[int]			=						k.replace("""norm3""","""final_layer_norm"""     )
      return k







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[Any]			=						[
          """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							:     str			=						sd.pop(snake_case_     )
            _A							:     Optional[int]			=						k.replace("""layernorm_embedding""","""layer_norm"""     )
            assert new_k not in sd
            _A							:     Optional[int]			=						v


_snake_case										=       ["START"]







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )
      _A							:     List[Any]			=						model["""model"""]
      _A							:     Optional[Any]			=						BlenderbotConfig.from_json_file(snake_case_     )
      _A							:     List[str]			=						BlenderbotForConditionalGeneration(snake_case_     )
      _A							:     Tuple			=						m.model.state_dict().keys()
      _A							:     Any			=						[]
      _A							:     Dict			=						{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

            _A							:     Optional[int]			=						rename_state_dict_key(snake_case_     )
            if new_k not in valid_keys:
                  failures.append([k, new_k]     )
            else:
                  _A							:     Dict			=						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__":
  _snake_case										=       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"
  )
  _snake_case										=       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

