

'''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  :		Tuple  =		logging.get_logger(__name__)

@add_end_docstrings(__SCREAMING_SNAKE_CASE )
class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 def __init__(       self  , **snake_case_   ):
   super().__init__(**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  , snake_case_  , **snake_case_   ):
   return super().__call__(snake_case_  , **snake_case_   )







 def 						_A(       self  , **snake_case_   ):
   lowercase									={}
   if "candidate_labels" in kwargs:
     lowercase									=kwargs['''candidate_labels''']
   if "hypothesis_template" in kwargs:
     lowercase									=kwargs['''hypothesis_template''']

   return preprocess_params, {}, {}







 def 						_A(       self  , snake_case_  , snake_case_=None  , snake_case_="This is a sound of {}."   ):






   if isinstance(snake_case_  , 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
       lowercase									=requests.get(snake_case_   ).content
     else:
       with open(snake_case_  , '''rb'''   ) as f:
         lowercase									=f.read()

   if isinstance(snake_case_  , snake_case_   ):
     lowercase									=ffmpeg_read(snake_case_  , self.feature_extractor.sampling_rate   )

   if not isinstance(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'''   )

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







 def 						_A(       self  , snake_case_   ):
   lowercase									=model_inputs.pop('''candidate_labels'''   )
   lowercase									=model_inputs.pop('''text_inputs'''   )
   if isinstance(text_inputs[0]  , snake_case_   ):
     lowercase									=text_inputs[0]
   else:
     # Batching case.
     lowercase									=text_inputs[0][0]

   lowercase									=self.model(**snake_case_  , **snake_case_   )

   lowercase									={
       '''candidate_labels''': candidate_labels,
       '''logits''': outputs.logits_per_audio,
   }
   return model_outputs







 def 						_A(       self  , snake_case_   ):
   lowercase									=model_outputs.pop('''candidate_labels'''   )
   lowercase									=model_outputs['''logits'''][0]

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

   lowercase									=[
       {'''score''': score, '''label''': candidate_label}
       for score, candidate_label in sorted(zip(snake_case_  , snake_case_   )  , key=lambda snake_case_   : -x[0]   )
   ]
   return result



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase_   							=  HfArgumentParser(InitializationArguments)
lowercase_   							=  parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase_   							=  AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase_   							=  {
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowercase_   							=  AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase_   							=  AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)


