

from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


__lowercase				      =						logging.get_logger(__name__)  # pylint: disable=invalid-name

class    _lowercase				(					__snake_case			):

   def __init__(				self	:    Dict			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    List[Any]			,						) ->			List[str]:
         """simple docstring"""





         super().__init__()

         if hasattr(scheduler.config			,						'''steps_offset'''       ) and scheduler.config.steps_offset != 1:
               A_							       =		(
                   F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                   F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                   "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                   " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                   " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                   " file"
               )
               deprecate('''steps_offset!=1'''			,						'''1.0.0'''			,						A_			,						standard_warn=A_       )
               A_							       =		dict(scheduler.config       )
               A_							       =		1
               A_							       =		FrozenDict(A_       )

         if hasattr(scheduler.config			,						'''skip_prk_steps'''       ) and scheduler.config.skip_prk_steps is False:
               A_							       =		(
                   F"The configuration file of this scheduler: {scheduler} has not set the configuration"
                   " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
                   " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
                   " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
                   " Hub, it would be very nice if you could open a Pull request for the"
                   " `scheduler/scheduler_config.json` file"
               )
               deprecate('''skip_prk_steps not set'''			,						'''1.0.0'''			,						A_			,						standard_warn=A_       )
               A_							       =		dict(scheduler.config       )
               A_							       =		True
               A_							       =		FrozenDict(A_       )

         if safety_checker is None:
               logger.warning(
                   F"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                   ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
                   ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
                   ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
                   ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
                   ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''       )

         self.register_modules(
             segmentation_model=A_			,						segmentation_processor=A_			,						vae=A_			,						text_encoder=A_			,						tokenizer=A_			,						unet=A_			,						scheduler=A_			,						safety_checker=A_			,						feature_extractor=A_			,						)

   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    str = "auto"       ) ->			str:
         """simple docstring"""





         if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               A_							       =		self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(A_       )

   def     UpperCamelCase					(				self	:    Dict       ) ->			Optional[int]:
         """simple docstring"""





         self.enable_attention_slicing(A_       )

   def     UpperCamelCase					(				self	:    str       ) ->			str:
         """simple docstring"""





         if is_accelerate_available():
               from accelerate import cpu_offload
         else:
               raise ImportError('''Please install accelerate via `pip install accelerate`'''       )

         A_							       =		torch.device('''cuda'''       )

         for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
               if cpu_offloaded_model is not None:
                     cpu_offload(A_			,						A_       )

   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def     UpperCamelCase					(				self	:    Any       ) ->			Optional[Any]:
         """simple docstring"""





         if self.device != torch.device('''meta'''       ) or not hasattr(self.unet			,						'''_hf_hook'''       ):
               return self.device
         for module in self.unet.modules():
               if (
                   hasattr(A_			,						'''_hf_hook'''       )
                   and hasattr(module._hf_hook			,						'''execution_device'''       )
                   and module._hf_hook.execution_device is not None
               ):
                     return torch.device(module._hf_hook.execution_device       )
         return self.device





   @torch.no_grad()
   def __call__(				self	:    Tuple			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    List[str] = 5_1_2			,						lowerCamelCase__	:    Optional[Any] = 5_1_2			,						lowerCamelCase__	:    Any = 5_0			,						lowerCamelCase__	:    Optional[int] = 7.5			,						lowerCamelCase__	:    int = None			,						lowerCamelCase__	:    Dict = 1			,						lowerCamelCase__	:    Tuple = 0.0			,						lowerCamelCase__	:    Any = None			,						lowerCamelCase__	:    Union[str, Any] = None			,						lowerCamelCase__	:    Any = "pil"			,						lowerCamelCase__	:    Dict = True			,						lowerCamelCase__	:    Any = None			,						lowerCamelCase__	:    Union[str, Any] = 1			,						**lowerCamelCase__	:    Dict			,						) ->			Any:
         """simple docstring"""





         A_							       =		self.segmentation_processor(
             text=[text]			,						images=[image]			,						padding='''max_length'''			,						return_tensors='''pt'''       ).to(self.device       )
         A_							       =		self.segmentation_model(**A_       )
         A_							       =		torch.sigmoid(outputs.logits       ).cpu().detach().unsqueeze(-1       ).numpy()
         A_							       =		self.numpy_to_pil(A_       )[0].resize(image.size       )

         # Run inpainting pipeline with the generated mask
         A_							       =		StableDiffusionInpaintPipeline(
             vae=self.vae			,						text_encoder=self.text_encoder			,						tokenizer=self.tokenizer			,						unet=self.unet			,						scheduler=self.scheduler			,						safety_checker=self.safety_checker			,						feature_extractor=self.feature_extractor			,						)
         return inpainting_pipeline(
             prompt=A_			,						image=A_			,						mask_image=A_			,						height=A_			,						width=A_			,						num_inference_steps=A_			,						guidance_scale=A_			,						negative_prompt=A_			,						num_images_per_prompt=A_			,						eta=A_			,						generator=A_			,						latents=A_			,						output_type=A_			,						return_dict=A_			,						callback=A_			,						callback_steps=A_			,						)












from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


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



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :List[str]		       =  ['input_features', 'attention_mask']





      def __init__(					self			,				A_=80			,				A_=1_6000			,				A_=80			,				A_=0.0			,				A_=True			,				A_=True			,				A_=True			,				**A_			,				):
       '''simple docstring'''




       super().__init__(feature_size=A_			,				sampling_rate=A_			,				padding_value=A_			,				**A_  )
       UpperCamelCase      :   List[Any]			      =  num_mel_bins
       UpperCamelCase      :   Any			      =  do_ceptral_normalize
       UpperCamelCase      :   int			      =  normalize_means
       UpperCamelCase      :   Tuple			      =  normalize_vars
       UpperCamelCase      :   List[Any]			      =  True





      def 			__UpperCamelCase(					self			,				A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
       UpperCamelCase      :   Optional[Any]			      =  torch.from_numpy(A_  ).unsqueeze(0  )
       UpperCamelCase      :   Dict			      =  ta_kaldi.fbank(A_			,				num_mel_bins=self.num_mel_bins			,				sample_frequency=self.sampling_rate  )
       return features.numpy()





      @staticmethod
      def 			__UpperCamelCase(					A_			,				A_			,				A_ = True			,				A_ = True			,				A_ = 0.0			,				):
       '''simple docstring'''




       if normalize_means:
        UpperCamelCase      :   str			      =  x[:input_length].mean(axis=0  )
        UpperCamelCase      :   Any			      =  np.subtract(A_			,				A_  )
       if normalize_vars:
        UpperCamelCase      :   List[str]			      =  x[:input_length].std(axis=0  )
        UpperCamelCase      :   Optional[int]			      =  np.divide(A_			,				A_  )

       if input_length < x.shape[0]:
        UpperCamelCase      :   Dict			      =  padding_value

       # make sure array is in float32
       UpperCamelCase      :   Union[str, Any]			      =  x.astype(np.floataa  )

       return x





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [
           self.utterance_cmvn(A_			,				A_			,				self.normalize_means			,				self.normalize_vars			,				self.padding_value  )
           for x, n in zip(A_			,				A_  )
       ]





      def __call__(					self			,				A_			,				A_ = False			,				A_ = None			,				A_ = False			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				**A_			,				):
       '''simple docstring'''




       if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
         raise ValueError(
             F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
             F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
             F""" {self.sampling_rate} and not {sampling_rate}."""  )
       else:
        logger.warning(
            "It is strongly recommended to pass the `sampling_rate` argument to this function. "
            "Failing to do so can result in silent errors that might be hard to debug."  )

       UpperCamelCase      :   str			      =  isinstance(A_			,				np.ndarray  ) and len(raw_speech.shape  ) > 1
       if is_batched_numpy and len(raw_speech.shape  ) > 2:
        raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""  )
       UpperCamelCase      :   List[Any]			      =  is_batched_numpy or (
           isinstance(A_			,				(list, tuple)  ) and (isinstance(raw_speech[0]			,				(np.ndarray, tuple, list)  ))
       )

       if is_batched:
        UpperCamelCase      :   List[str]			      =  [np.asarray(A_			,				dtype=np.floataa  ) for speech in raw_speech]
       elif not is_batched and not isinstance(A_			,				np.ndarray  ):
        UpperCamelCase      :   int			      =  np.asarray(A_			,				dtype=np.floataa  )
       elif isinstance(A_			,				np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
        UpperCamelCase      :   Union[str, Any]			      =  raw_speech.astype(np.floataa  )

       # always return batch
       if not is_batched:
        UpperCamelCase      :   Optional[int]			      =  [raw_speech]

       # extract fbank features
       UpperCamelCase      :   Optional[int]			      =  [self._extract_fbank_features(A_  ) for waveform in raw_speech]

       # convert into correct format for padding
       UpperCamelCase      :   List[Any]			      =  BatchFeature({"input_features": features}  )

       UpperCamelCase      :   Any			      =  self.pad(
           A_			,				padding=A_			,				max_length=A_			,				truncation=A_			,				pad_to_multiple_of=A_			,				return_attention_mask=A_			,				**A_			,				)

       # make sure list is in array format
       UpperCamelCase      :   Optional[int]			      =  padded_inputs.get("input_features"  )
       if isinstance(input_features[0]			,				A_  ):
        UpperCamelCase      :   str			      =  [np.asarray(A_			,				dtype=np.floataa  ) for feature in input_features]

       UpperCamelCase      :   Optional[int]			      =  padded_inputs.get("attention_mask"  )
       if attention_mask is not None:
        UpperCamelCase      :   Optional[int]			      =  [np.asarray(A_			,				dtype=np.intaa  ) for array in attention_mask]

       # Utterance-level cepstral mean and variance normalization
       if self.do_ceptral_normalize:
        UpperCamelCase      :   List[Any]			      =  (
            np.array(A_			,				dtype=np.intaa  )
            if self._get_padding_strategies(A_			,				max_length=A_  ) is not PaddingStrategy.DO_NOT_PAD
            else None
        )
        UpperCamelCase      :   int			      =  self.normalize(
            padded_inputs["input_features"]			,				attention_mask=A_  )

       if return_tensors is not None:
        UpperCamelCase      :   List[str]			      =  padded_inputs.convert_to_tensors(A_  )

       return padded_inputs
