
"""simple docstring"""





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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






class    _UpperCAmelCase(   lowerCamelCase__     ):



		def __init__( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		)	->	Tuple:






							'''simple docstring'''
							super().__init__()

							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(
							    speech_model=__lowerCamelCase    ,		speech_processor=__lowerCamelCase    ,		vae=__lowerCamelCase    ,		text_encoder=__lowerCamelCase    ,		tokenizer=__lowerCamelCase    ,		unet=__lowerCamelCase    ,		scheduler=__lowerCamelCase    ,		feature_extractor=__lowerCamelCase    ,		)



		def 						UpperCAmelCase							( self    ,		__a = "auto")	->	Optional[int]:






							'''simple docstring'''






							if slice_size == "auto":
												_UpperCamelCase										=  self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(__lowerCamelCase)



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							self.enable_attention_slicing(__lowerCamelCase)




		@torch.no_grad()
		def __call__( self    ,		__a    ,		__a=1_60_00    ,		__a = 5_12    ,		__a = 5_12    ,		__a = 50    ,		__a = 7.5    ,		__a = None    ,		__a = 1    ,		__a = 0.0    ,		__a = None    ,		__a = None    ,		__a = "pil"    ,		__a = True    ,		__a = None    ,		__a = 1    ,		**__a    ,		)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  self.speech_processor.feature_extractor(
							    __lowerCamelCase    ,		return_tensors='''pt'''    ,		sampling_rate=__lowerCamelCase).input_features.to(self.device)
							_UpperCamelCase										=  self.speech_model.generate(__lowerCamelCase    ,		max_length=48_00_00)

							_UpperCamelCase										=  self.speech_processor.tokenizer.batch_decode(__lowerCamelCase    ,		skip_special_tokens=__lowerCamelCase    ,		normalize=__lowerCamelCase)[
							    0
							]

							if isinstance(__lowerCamelCase    ,		__lowerCamelCase):
												_UpperCamelCase										=  1
							elif isinstance(__lowerCamelCase    ,		__lowerCamelCase):
												_UpperCamelCase										=  len(__lowerCamelCase)
							else:
												raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__lowerCamelCase)}''')

							if height % 8 != 0 or width % 8 != 0:
												raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''')

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(__lowerCamelCase    ,		__lowerCamelCase) or callback_steps <= 0)
							):
												raise ValueError(
												    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
												    F''' {type(__lowerCamelCase)}.''')

							# get prompt text embeddings
							_UpperCamelCase										=  self.tokenizer(
							    __lowerCamelCase    ,		padding='''max_length'''    ,		max_length=self.tokenizer.model_max_length    ,		return_tensors='''pt'''    ,		)
							_UpperCamelCase										=  text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												_UpperCamelCase										=  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
												logger.warning(
												    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
												    F''' {self.tokenizer.model_max_length} tokens: {removed_text}''')
												_UpperCamelCase										=  text_input_ids[:, : self.tokenizer.model_max_length]
							_UpperCamelCase										=  self.text_encoder(text_input_ids.to(self.device))[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							_UpperCamelCase										=  text_embeddings.shape
							_UpperCamelCase										=  text_embeddings.repeat(1    ,		__lowerCamelCase    ,		1)
							_UpperCamelCase										=  text_embeddings.view(bs_embed * num_images_per_prompt    ,		__lowerCamelCase    ,		-1)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							_UpperCamelCase										=  guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
												_UpperCamelCase										=  42
												if negative_prompt is None:
																	_UpperCamelCase										=  [''''''] * batch_size
												elif type(__lowerCamelCase) is not type(__lowerCamelCase):
																	raise TypeError(
																	    F'''`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCamelCase)} !='''
																	    F''' {type(__lowerCamelCase)}.''')
												elif isinstance(__lowerCamelCase    ,		__lowerCamelCase):
																	_UpperCamelCase										=  [negative_prompt]
												elif batch_size != len(__lowerCamelCase):
																	raise ValueError(
																	    F'''`negative_prompt`: {negative_prompt} has batch size {len(__lowerCamelCase)}, but `prompt`:'''
																	    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																	    ''' the batch size of `prompt`.''')
												else:
																	_UpperCamelCase										=  negative_prompt

												_UpperCamelCase										=  text_input_ids.shape[-1]
												_UpperCamelCase										=  self.tokenizer(
												    __lowerCamelCase    ,		padding='''max_length'''    ,		max_length=__lowerCamelCase    ,		truncation=__lowerCamelCase    ,		return_tensors='''pt'''    ,		)
												_UpperCamelCase										=  self.text_encoder(uncond_input.input_ids.to(self.device))[0]

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												_UpperCamelCase										=  uncond_embeddings.shape[1]
												_UpperCamelCase										=  uncond_embeddings.repeat(1    ,		__lowerCamelCase    ,		1)
												_UpperCamelCase										=  uncond_embeddings.view(batch_size * num_images_per_prompt    ,		__lowerCamelCase    ,		-1)

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												_UpperCamelCase										=  torch.cat([uncond_embeddings, text_embeddings])

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							_UpperCamelCase										=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							_UpperCamelCase										=  text_embeddings.dtype
							if latents is None:
												if self.device.type == "mps":
																	# randn does not exist on mps
																	_UpperCamelCase										=  torch.randn(__lowerCamelCase    ,		generator=__lowerCamelCase    ,		device='''cpu'''    ,		dtype=__lowerCamelCase).to(
																	    self.device)
												else:
																	_UpperCamelCase										=  torch.randn(__lowerCamelCase    ,		generator=__lowerCamelCase    ,		device=self.device    ,		dtype=__lowerCamelCase)
							else:
												if latents.shape != latents_shape:
																	raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
												_UpperCamelCase										=  latents.to(self.device)

							# set timesteps
							self.scheduler.set_timesteps(__lowerCamelCase)

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							_UpperCamelCase										=  self.scheduler.timesteps.to(self.device)

							# scale the initial noise by the standard deviation required by the scheduler
							_UpperCamelCase										=  latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							_UpperCamelCase										=  '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
							_UpperCamelCase										=  {}
							if accepts_eta:
												_UpperCamelCase										=  eta

							for i, t in enumerate(self.progress_bar(__lowerCamelCase)):
												# expand the latents if we are doing classifier free guidance
												_UpperCamelCase										=  torch.cat([latents] * 2) if do_classifier_free_guidance else latents
												_UpperCamelCase										=  self.scheduler.scale_model_input(__lowerCamelCase    ,		__lowerCamelCase)

												# predict the noise residual
												_UpperCamelCase										=  self.unet(__lowerCamelCase    ,		__lowerCamelCase    ,		encoder_hidden_states=__lowerCamelCase).sample

												# perform guidance
												if do_classifier_free_guidance:
																	_UpperCamelCase										=  noise_pred.chunk(2)
																	_UpperCamelCase										=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

												# compute the previous noisy sample x_t -> x_t-1
												_UpperCamelCase										=  self.scheduler.step(__lowerCamelCase    ,		__lowerCamelCase    ,		__lowerCamelCase    ,		**__lowerCamelCase).prev_sample

												# call the callback, if provided
												if callback is not None and i % callback_steps == 0:
																	callback(__lowerCamelCase    ,		__lowerCamelCase    ,		__lowerCamelCase)

							_UpperCamelCase										=  1 / 0.1_8215 * latents
							_UpperCamelCase										=  self.vae.decode(__lowerCamelCase).sample

							_UpperCamelCase										=  (image / 2 + 0.5).clamp(0    ,		1)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							_UpperCamelCase										=  image.cpu().permute(0    ,		2    ,		3    ,		1).float().numpy()

							if output_type == "pil":
												_UpperCamelCase										=  self.numpy_to_pil(__lowerCamelCase)

							if not return_dict:
												return image

							return StableDiffusionPipelineOutput(images=__lowerCamelCase    ,		nsfw_content_detected=__lowerCamelCase)








"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				(
		    '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.'
		)
		lowercase__      =				'CIDAS/clipseg-rd64-refined'
		lowercase__      =				'image_segmenter'
		lowercase__      =				CLIPSegForImageSegmentation

		lowercase__      =				['image', 'text']
		lowercase__      =				['image']



		def __init__( self    ,		*__a    ,		**__a)	->	Any:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(text=[label]    ,		images=[image]    ,		padding=__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												_UpperCamelCase										=  self.model(**__a).logits
							return logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.cpu().detach().numpy()
							_UpperCamelCase										=  0
							_UpperCamelCase										=  1
							return Image.fromarray((array * 2_55).astype(np.uinta))






