




import math

import tensorflow as tf
from packaging import version

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Union[str, Any]:
     a     =				tf.convert_to_tensor(__UpperCamelCase)
     a     =				0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0)		, x.dtype)))

     return x * cdf

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					List[Any]:
     a     =				tf.convert_to_tensor(__UpperCamelCase)
     a     =				tf.cast(math.pi		, x.dtype)
     a     =				tf.cast(0.044_715		, x.dtype)
     a     =				0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi) * (x + coeff * tf.pow(__UpperCamelCase		, 3))))

     return x * cdf

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Union[str, Any]:
     a     =				tf.convert_to_tensor(__UpperCamelCase)

     return x * tf.tanh(tf.math.softplus(__UpperCamelCase))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				tf.convert_to_tensor(__UpperCamelCase)
     a     =				tf.cast(0.044_715		, x.dtype)
     a     =				tf.cast(0.7_978_845_608		, x.dtype)

     return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     a     =				tf.convert_to_tensor(__UpperCamelCase)
     a     =				tf.cast(1.702		, x.dtype)
     return x * tf.math.sigmoid(coeff * x)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     return tf.clip_by_value(_gelu(__UpperCamelCase)		, -10		, 10)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=-1)      ->					Any:
     a  ,				a     =				tf.split(__UpperCamelCase		, 2		, axis=__UpperCamelCase)
     return a * tf.math.sigmoid(__UpperCamelCase)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):

   def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Optional[int]:
        return tf.keras.activations.gelu(__UpperCamelCase		, approximate=__UpperCamelCase)

   lowercase__				:      int	   = tf.keras.activations.gelu





   lowercase__				:      Optional[int]	   = approximate_gelu_wrap
else:
   lowercase__				:      Any	   = _gelu
   lowercase__				:      str	   = _gelu_new


lowercase__				:      Union[str, Any]	   = {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}





def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys())}''')







import argparse

import torch

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


logging.set_verbosity_info()
lowercase__				:      Union[str, Any]	   = logging.get_logger(__name__)

lowercase__				:      Dict	   = [
    ["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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          a     =				k.replace(__UpperCamelCase		, __UpperCamelCase)

     if k.startswith("encoder"):
          a     =				k.replace(".attn"		, ".self_attn")
          a     =				k.replace("norm1"		, "self_attn_layer_norm")
          a     =				k.replace("norm2"		, "final_layer_norm")
     elif k.startswith("decoder"):
          a     =				k.replace("norm1"		, "self_attn_layer_norm")
          a     =				k.replace("norm2"		, "encoder_attn_layer_norm")
          a     =				k.replace("norm3"		, "final_layer_norm")
     return k

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				[
         "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     =				sd.pop(__UpperCamelCase)
          a     =				k.replace("layernorm_embedding"		, "layer_norm")
          assert new_k not in sd
          a     =				v


lowercase__				:      Optional[Any]	   = ["START"]

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:
     a     =				torch.load(__UpperCamelCase		, map_location="cpu")
     a     =				model["model"]
     a     =				BlenderbotConfig.from_json_file(__UpperCamelCase)
     a     =				BlenderbotForConditionalGeneration(__UpperCamelCase)
     a     =				m.model.state_dict().keys()
     a     =				[]
     a     =				{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

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


if __name__ == "__main__":
   lowercase__				:      int	   = 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__				:      str	   = parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

