






import argparse

import torch

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


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

lowerCamelCase     :							Optional[int]    =[
    ['''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							( __lowerCAmelCase      )			->     int:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          UpperCamelCase__      :       Tuple	    =  k.replace(__lowerCAmelCase ,   __lowerCAmelCase      )

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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Dict:
     UpperCamelCase__      :       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:
          UpperCamelCase__      :       Any	    =  sd.pop(__lowerCAmelCase      )
          UpperCamelCase__      :       Optional[Any]	    =  k.replace("layernorm_embedding" ,   "layer_norm"      )
          assert new_k not in sd
          UpperCamelCase__      :       Union[str, Any]	    =  v


lowerCamelCase     :							Union[str, Any]    =['''START''']







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

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


if __name__ == "__main__":
 lowerCamelCase     :							Union[str, Any]    =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'''
 )
 lowerCamelCase     :							Tuple    =parser.parse_args()
 convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

lowerCamelCase     :							str    =logging.get_logger(__name__)





@add_end_docstrings(A__	)
class       __a  (   A__	):







 def __init__(		self :       List[str]							,       **SCREAMING_SNAKE_CASE :       Tuple						):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)

      requires_backends(self							,       "vision"						)
      self.check_model_type(
          TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == "tf"
          else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING						)







 def __call__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       Union[str, List[str], "Image", List["Image"]]							,       **SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






      return super().__call__(SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[str]							,       **SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  {}
      if "candidate_labels" in kwargs:
           UpperCamelCase__      :       Optional[Any]	    =  kwargs["candidate_labels"]
      if "hypothesis_template" in kwargs:
           UpperCamelCase__      :       int	    =  kwargs["hypothesis_template"]

      return preprocess_params, {}, {}







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       List[Any]							,       SCREAMING_SNAKE_CASE :       Tuple=None							,       SCREAMING_SNAKE_CASE :       Optional[int]="This is a photo of {}."						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  load_image(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[Any]	    =  self.image_processor(images=[image]							,       return_tensors=self.framework						)
      UpperCamelCase__      :       Any	    =  candidate_labels
      UpperCamelCase__      :       Dict	    =  [hypothesis_template.format(SCREAMING_SNAKE_CASE						) for x in candidate_labels]
      UpperCamelCase__      :       Optional[Any]	    =  self.tokenizer(SCREAMING_SNAKE_CASE							,       return_tensors=self.framework							,       padding=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[Any]	    =  [text_inputs]
      return inputs







 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  model_inputs.pop("candidate_labels"						)
      UpperCamelCase__      :       List[str]	    =  model_inputs.pop("text_inputs"						)
      if isinstance(text_inputs[0]							,       SCREAMING_SNAKE_CASE						):
           UpperCamelCase__      :       Dict	    =  text_inputs[0]
      else:
           # Batching case.
           UpperCamelCase__      :       Union[str, Any]	    =  text_inputs[0][0]

      UpperCamelCase__      :       Any	    =  self.model(**SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Dict	    =  {
          "candidate_labels": candidate_labels,
          "logits": outputs.logits_per_image,
      }
      return model_outputs







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  model_outputs.pop("candidate_labels"						)
      UpperCamelCase__      :       int	    =  model_outputs["logits"][0]
      if self.framework == "pt":
           UpperCamelCase__      :       Dict	    =  logits.softmax(dim=-1						).squeeze(-1						)
           UpperCamelCase__      :       Optional[Any]	    =  probs.tolist()
           if not isinstance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						):
                UpperCamelCase__      :       List[Any]	    =  [scores]
      elif self.framework == "tf":
           UpperCamelCase__      :       Optional[Any]	    =  stable_softmax(SCREAMING_SNAKE_CASE							,       axis=-1						)
           UpperCamelCase__      :       Optional[int]	    =  probs.numpy().tolist()
      else:
           raise ValueError(F'Unsupported framework: {self.framework}'						)

      UpperCamelCase__      :       Optional[int]	    =  [
          {"score": score, "label": candidate_label}
          for score, candidate_label in sorted(zip(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)							,       key=lambda SCREAMING_SNAKE_CASE						: -x[0]						)
      ]
      return result