




'''simple docstring'''





from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


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

@add_end_docstrings(__lowerCAmelCase				)
class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,**_SCREAMING_SNAKE_CASE       )				->     int:
          super().__init__(**_SCREAMING_SNAKE_CASE       )

          if self.framework != "pt":
               raise ValueError(f"""The {self.__class__} is only available in PyTorch."""       )
          # No specific FOR_XXX available yet






     def __call__(					self   ,_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     Optional[Any]:
          return super().__call__(_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,**_SCREAMING_SNAKE_CASE       )				->     List[Any]:
          _snake_case				= {}
          if "candidate_labels" in kwargs:
               _snake_case				= kwargs["candidate_labels"]
          if "hypothesis_template" in kwargs:
               _snake_case				= kwargs["hypothesis_template"]

          return preprocess_params, {}, {}






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE="This is a sound of {}."       )				->     Tuple:
          if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               if audio.startswith("http://"       ) or audio.startswith("https://"       ):
                    # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                    # like http_huggingface_co.png
                    _snake_case				= requests.get(_SCREAMING_SNAKE_CASE       ).content
               else:
                    with open(_SCREAMING_SNAKE_CASE   ,"rb"       ) as f:
                         _snake_case				= f.read()

          if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= ffmpeg_read(_SCREAMING_SNAKE_CASE   ,self.feature_extractor.sampling_rate       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,np.ndarray       ):
               raise ValueError("We expect a numpy ndarray as input"       )
          if len(audio.shape       ) != 1:
               raise ValueError("We expect a single channel audio input for ZeroShotAudioClassificationPipeline"       )

          _snake_case				= self.feature_extractor(
              [audio]   ,sampling_rate=self.feature_extractor.sampling_rate   ,return_tensors="pt"       )
          _snake_case				= candidate_labels
          _snake_case				= [hypothesis_template.format(_SCREAMING_SNAKE_CASE       ) for x in candidate_labels]
          _snake_case				= self.tokenizer(_SCREAMING_SNAKE_CASE   ,return_tensors=self.framework   ,padding=_SCREAMING_SNAKE_CASE       )
          _snake_case				= [text_inputs]
          return inputs






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Union[str, Any]:
          _snake_case				= model_inputs.pop("candidate_labels"       )
          _snake_case				= model_inputs.pop("text_inputs"       )
          if isinstance(text_inputs[0]   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= text_inputs[0]
          else:
               # Batching case.
               _snake_case				= text_inputs[0][0]

          _snake_case				= self.model(**_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )

          _snake_case				= {
              "candidate_labels": candidate_labels,
              "logits": outputs.logits_per_audio,
          }
          return model_outputs






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= model_outputs.pop("candidate_labels"       )
          _snake_case				= model_outputs["logits"][0]

          if self.framework == "pt":
               _snake_case				= logits.softmax(dim=0       )
               _snake_case				= probs.tolist()
          else:
               raise ValueError("`tf` framework not supported."       )

          _snake_case				= [
              {"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








'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase_		:     int          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		[
    ['''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       __a      (  _UpperCamelCase:       List[Any]       )		->					Union[str, Any]:

     """simple docstring"""




     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          _snake_case				= k.replace(_UpperCamelCase   ,		_UpperCamelCase       )

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





def       __a      (  _UpperCamelCase:       Dict       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= [
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          _snake_case				= sd.pop(_UpperCamelCase       )
          _snake_case				= k.replace("layernorm_embedding"   ,		"layer_norm"       )
          assert new_k not in sd
          _snake_case				= v


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





@torch.no_grad()
def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Union[str, Any]       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= torch.load(_UpperCamelCase   ,		map_location="cpu"       )
     _snake_case				= model["model"]
     _snake_case				= BlenderbotConfig.from_json_file(_UpperCamelCase       )
     _snake_case				= BlenderbotForConditionalGeneration(_UpperCamelCase       )
     _snake_case				= m.model.state_dict().keys()
     _snake_case				= []
     _snake_case				= {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

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


