import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


__lowerCamelCase    :				List[str]							     =	logging.get_logger(__name__)







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Union[str, Any] ,			_lowercase    :       Union[List[ControlNetModel], Tuple[ControlNetModel]]   ):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__    							=   nn.ModuleList(_lowercase   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       torch.FloatTensor ,			_lowercase    :       Union[torch.Tensor, float, int] ,			_lowercase    :       torch.Tensor ,			_lowercase    :       List[torch.tensor] ,			_lowercase    :       List[float] ,			_lowercase    :       Optional[torch.Tensor] = None ,			_lowercase    :       Optional[torch.Tensor] = None ,			_lowercase    :       Optional[torch.Tensor] = None ,			_lowercase    :       Optional[Dict[str, Any]] = None ,			_lowercase    :       bool = False ,			_lowercase    :       bool = True ,			):



     """simple docstring"""






     for i, (image, scale, controlnet) in enumerate(zip(_lowercase ,			_lowercase ,			self.nets   )   ):
       SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   controlnet(
           _lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			)

       # merge samples
       if i == 0:
         SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   down_samples, mid_sample
       else:
         SCREAMING_SNAKE_CASE__    							=   [
             samples_prev + samples_curr
             for samples_prev, samples_curr in zip(_lowercase ,			_lowercase   )
         ]
         mid_block_res_sample += mid_sample

     return down_block_res_samples, mid_block_res_sample




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       Union[str, os.PathLike] ,			_lowercase    :       bool = True ,			_lowercase    :       Callable = None ,			_lowercase    :       bool = False ,			_lowercase    :       Optional[str] = None ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   0
     SCREAMING_SNAKE_CASE__    							=   save_directory
     for controlnet in self.nets:
       controlnet.save_pretrained(
           _lowercase ,			is_main_process=_lowercase ,			save_function=_lowercase ,			safe_serialization=_lowercase ,			variant=_lowercase ,			)

       idx += 1
       SCREAMING_SNAKE_CASE__    							=   model_path_to_save + f"""_{idx}"""




   @classmethod
   def 			__a		(					cls    :       Optional[int] ,			_lowercase    :       Optional[Union[str, os.PathLike]] ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   0
     SCREAMING_SNAKE_CASE__    							=   []

     # load controlnet and append to list until no controlnet directory exists anymore
     # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
     # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
     SCREAMING_SNAKE_CASE__    							=   pretrained_model_path
     while os.path.isdir(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   ControlNetModel.from_pretrained(_lowercase ,			**_lowercase   )
       controlnets.append(_lowercase   )

       idx += 1
       SCREAMING_SNAKE_CASE__    							=   pretrained_model_path + f"""_{idx}"""

     logger.info(f"""{len(_lowercase   )} controlnets loaded from {pretrained_model_path}."""   )

     if len(_lowercase   ) == 0:
       raise ValueError(
           f"""No ControlNets found under {os.path.dirname(_lowercase   )}. Expected at least {pretrained_model_path + '_0'}."""   )

     return cls(_lowercase   )

from typing import List, Optional

import numpy as np

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







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"EncodecFeatureExtractor"
   lowerCAmelCase_														=							("T5Tokenizer", "T5TokenizerFast")




   def __init__(					self    :       List[Any] ,			_lowercase    :       int ,			_lowercase    :       Any   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.feature_extractor
     SCREAMING_SNAKE_CASE__    							=   False




   def 			__a		(					self    :       Tuple ,			_lowercase    :       str=None ,			_lowercase    :       int=None ,			_lowercase    :       Any=True   ):



     """simple docstring"""






     return self.tokenizer.get_decoder_prompt_ids(task=_lowercase ,			language=_lowercase ,			no_timestamps=_lowercase   )




   def __call__(					self    :       Tuple ,			*_lowercase    :       Tuple ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     if self._in_target_context_manager:
       return self.current_processor(*_lowercase ,			**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""sampling_rate""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""text""" ,			_lowercase   )
     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   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:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase ,			**_lowercase   )

     if audio is not None:
       SCREAMING_SNAKE_CASE__    							=   self.feature_extractor(_lowercase ,			*_lowercase ,			sampling_rate=_lowercase ,			**_lowercase   )

     if audio is None:
       return inputs

     elif text is None:
       return audio_inputs

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




   def 			__a		(					self    :       Dict ,			*_lowercase    :       Any ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""padding_mask""" ,			_lowercase   )

     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

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




   def 			__a		(					self    :       List[str] ,			*_lowercase    :       Tuple ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     return self.tokenizer.decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   audio_values.shape

     if padding_mask is None:
       return list(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )

     # 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)
     SCREAMING_SNAKE_CASE__    							=   seq_len - padding_mask.shape[-1]
     SCREAMING_SNAKE_CASE__    							=   1 - self.feature_extractor.padding_value
     SCREAMING_SNAKE_CASE__    							=   np.pad(_lowercase ,			((0, 0), (0, difference)) ,			"""constant""" ,			constant_values=_lowercase   )

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

     return audio_values
