"""simple docstring"""






import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
       from PIL import Image





class  _A						(       lowerCAmelCase_	):
      snake_case__			:   Tuple							  =	(
          'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
          'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
          'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
      )
      snake_case__			:   Tuple							  =	'CIDAS/clipseg-rd64-refined'
      snake_case__			:   Union[str, Any]							  =	'image_segmenter'
      snake_case__			:   int							  =	CLIPSegForImageSegmentation

      snake_case__			:   List[str]							  =	['image', 'text']
      snake_case__			:   Dict							  =	['image']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""vision"""]						)
            super().__init__(*__lowerCAmelCase		,       **__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            return self.pre_processor(text=[label]		,       images=[image]		,       padding=__lowerCAmelCase		,       return_tensors="""pt"""						)

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            with torch.no_grad():
                  lowercase														=	self.model(**__lowerCAmelCase						).logits
            return logits







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	outputs.cpu().detach().numpy()
            lowercase														=	0
            lowercase														=	1
            return Image.fromarray((array * 255).astype(np.uinta						)						)








"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
											lowerCamelCase__		:		int			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		Any			=2 ** len(self.unet.up_blocks     )
								if sample_size < 3 * down_scale_factor:
											raise ValueError(
											    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
											    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		Tuple			=(
											    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
											) * down_scale_factor
											logger.info(
											    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
											    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
											    ''' process.'''     )
								lowerCamelCase__		:		int			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		Union[str, Any]			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(lowerCamelCase,  lowerCamelCase     ) and len(lowerCamelCase     ) != batch_size:
											raise ValueError(
											    F'''You have passed a list of generators of length {len(lowerCamelCase     )}, but requested an effective batch'''
											    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,  device=audio.device     )
								lowerCamelCase__		:		Any			=self.scheduler.timesteps.to(lowerCamelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
											# 1. predict noise model_output
											lowerCamelCase__		:		str			=self.unet(lowerCamelCase,  lowerCamelCase     ).sample

											# 2. compute previous image: x_t -> t_t-1
											lowerCamelCase__		:		Dict			=self.scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								lowerCamelCase__		:		Optional[int]			=audio.clamp(-1,  1     ).float().cpu().numpy()

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )




