





import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel






def 				_UpperCAmelCase			(    A				,							A=False     ):




   '''simple docstring'''

   UpperCAmelCase__						=OmegaConf.load(A     )
   if display:
      print(yaml.dump(OmegaConf.to_container(A     )     )     )
   return config






def 				_UpperCAmelCase			(    A				,							A=None				,							A=None     ):




   '''simple docstring'''

   if conf_path is None:
      UpperCAmelCase__						="./model_checkpoints/vqgan_only.yaml"
   UpperCAmelCase__						=load_config(A				,							display=A     )
   UpperCAmelCase__						=VQModel(**config.model.params     )
   if ckpt_path is None:
      UpperCAmelCase__						="./model_checkpoints/vqgan_only.pt"
   UpperCAmelCase__						=torch.load(A				,							map_location=A     )
   if ".ckpt" in ckpt_path:
      UpperCAmelCase__						=sd["state_dict"]
   model.load_state_dict(A				,							strict=A     )
   model.to(A     )
   del sd
   return model






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=model.encode(A     )
   print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""     )
   UpperCAmelCase__						=model.decode(A     )
   return xrec






def 				_UpperCAmelCase			(    A				,							A=False     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=string.rsplit("."				,							1     )
   if reload:
      UpperCAmelCase__						=importlib.import_module(A     )
      importlib.reload(A     )
   return getattr(importlib.import_module(A				,							package=A     )				,							cls     )






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if "target" not in config:
      raise KeyError("Expected key `target` to instantiate."     )
   return get_obj_from_str(config["target"]     )(**config.get("params"				,							{}     )     )






def 				_UpperCAmelCase			(    A				,							A				,							A=True				,							A=True     ):




   '''simple docstring'''

   UpperCAmelCase__						=instantiate_from_config(A     )
   if sd is not None:
      model.load_state_dict(A     )
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

   if ckpt:
      UpperCAmelCase__						=torch.load(A				,							map_location="cpu"     )
      UpperCAmelCase__						=pl_sd["global_step"]
      print(F"""loaded model from global step {global_step}."""     )
   else:
      UpperCAmelCase__						={"state_dict": None}
      UpperCAmelCase__						=None
   UpperCAmelCase__						=load_model_from_config(config.model				,							pl_sd["state_dict"]				,							gpu=A				,							eval_mode=A     )["model"]
   return model, global_step










from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'EncodecFeatureExtractor'
 __UpperCamelCase       =      ('T5Tokenizer', 'T5TokenizerFast')

 def __init__(  self,		A_,		A_		)	->       Optional[int]:
    super().__init__(A_,		A_		)
    UpperCAmelCase__						=self.feature_extractor
    UpperCAmelCase__						=False

 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_=True		)	->       Union[str, Any]:
    return self.tokenizer.get_decoder_prompt_ids(task=A_,		language=A_,		no_timestamps=A_		)

 def __call__(  self,		*A_,		**A_		)	->       Any:
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*A_,		**A_		)

    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("sampling_rate",		A_		)
    UpperCAmelCase__						=kwargs.pop("text",		A_		)
    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio is None and text is None:
       raise ValueError("You need to specify either an `audio` or `text` input to process."		)

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(A_,		**A_		)

    if audio is not None:
       UpperCAmelCase__						=self.feature_extractor(A_,		*A_,		sampling_rate=A_,		**A_		)

    if audio is None:
       return inputs

    elif text is None:
       return audio_inputs

    else:
       UpperCAmelCase__						=audio_inputs["input_values"]
       if "padding_mask" in audio_inputs:
          UpperCAmelCase__						=audio_inputs["padding_mask"]
       return inputs

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       Dict:
    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("padding_mask",		A_		)

    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio_values is not None:
       return self._decode_audio(A_,		padding_mask=A_		)
    else:
       return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_ = None		)	->       List[np.ndarray]:
    UpperCAmelCase__						=to_numpy(A_		)
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=audio_values.shape

    if padding_mask is None:
       return list(A_		)

    UpperCAmelCase__						=to_numpy(A_		)

    # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
    # token (so that the generated audio values are **not** treated as padded tokens)
    UpperCAmelCase__						=seq_len - padding_mask.shape[-1]
    UpperCAmelCase__						=1 - self.feature_extractor.padding_value
    UpperCAmelCase__						=np.pad(A_,		((0, 0), (0, difference)),		"constant",		constant_values=A_		)

    UpperCAmelCase__						=audio_values.tolist()
    for i in range(A_		):
       UpperCAmelCase__						=np.asarray(audio_values[i]		)[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       UpperCAmelCase__						=sliced_audio.reshape(A_,		-1		)

    return audio_values



