
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


lowercase__		:Union[str, Any]			  =   logging.get_logger(__name__)  # pylint: disable=invalid-name







class     lowercase		( SCREAMING_SNAKE_CASE__		):




       def __init__(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,):
            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=A__       ,speech_processor=A__       ,vae=A__       ,text_encoder=A__       ,tokenizer=A__       ,unet=A__       ,scheduler=A__       ,feature_extractor=A__       ,)




       def 			A__				(    self       ,A__ = "auto"):
            if slice_size == "auto":
                 lowercase						     =						self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(A__)




       def 			A__				(    self):
            self.enable_attention_slicing(A__)





       @torch.no_grad()
       def __call__(    self       ,A__       ,A__=1_6_0_0_0       ,A__ = 5_1_2       ,A__ = 5_1_2       ,A__ = 5_0       ,A__ = 7.5       ,A__ = None       ,A__ = 1       ,A__ = 0.0       ,A__ = None       ,A__ = None       ,A__ = "pil"       ,A__ = True       ,A__ = None       ,A__ = 1       ,**A__       ,):
            lowercase						     =						self.speech_processor.feature_extractor(
                A__       ,return_tensors='''pt'''       ,sampling_rate=A__).input_features.to(self.device)
            lowercase						     =						self.speech_model.generate(A__       ,max_length=4_8_0_0_0_0)

            lowercase						     =						self.speech_processor.tokenizer.batch_decode(A__       ,skip_special_tokens=A__       ,normalize=A__)[
                0
            ]

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

            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(A__       ,A__) or callback_steps <= 0)
            ):
                 raise ValueError(
                     f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                     f' {type(A__)}.')

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

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 lowercase						     =						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}')
                 lowercase						     =						text_input_ids[:, : self.tokenizer.model_max_length]
            lowercase						     =						self.text_encoder(text_input_ids.to(self.device))[0]

            # duplicate text embeddings for each generation per prompt, using mps friendly method
            lowercase     ,						lowercase     ,						lowercase						     =						text_embeddings.shape
            lowercase						     =						text_embeddings.repeat(1       ,A__       ,1)
            lowercase						     =						text_embeddings.view(bs_embed * num_images_per_prompt       ,A__       ,-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.
            lowercase						     =						guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                 lowercase						     =						4_2
                 if negative_prompt is None:
                      lowercase						     =						[''''''] * batch_size
                 elif type(A__) is not type(A__):
                      raise TypeError(
                          f'`negative_prompt` should be the same type to `prompt`, but got {type(A__)} !='
                          f' {type(A__)}.')
                 elif isinstance(A__       ,A__):
                      lowercase						     =						[negative_prompt]
                 elif batch_size != len(A__):
                      raise ValueError(
                          f'`negative_prompt`: {negative_prompt} has batch size {len(A__)}, but `prompt`:'
                          f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                          ''' the batch size of `prompt`.''')
                 else:
                      lowercase						     =						negative_prompt

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

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 lowercase						     =						uncond_embeddings.shape[1]
                 lowercase						     =						uncond_embeddings.repeat(1       ,A__       ,1)
                 lowercase						     =						uncond_embeddings.view(batch_size * num_images_per_prompt       ,A__       ,-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
                 lowercase						     =						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`.
            lowercase						     =						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            lowercase						     =						text_embeddings.dtype
            if latents is None:
                 if self.device.type == "mps":
                      # randn does not exist on mps
                      lowercase						     =						torch.randn(A__       ,generator=A__       ,device='''cpu'''       ,dtype=A__).to(
                          self.device)
                 else:
                      lowercase						     =						torch.randn(A__       ,generator=A__       ,device=self.device       ,dtype=A__)
            else:
                 if latents.shape != latents_shape:
                      raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
                 lowercase						     =						latents.to(self.device)

            # set timesteps
            self.scheduler.set_timesteps(A__)

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

            # scale the initial noise by the standard deviation required by the scheduler
            lowercase						     =						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]
            lowercase						     =						'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
            lowercase						     =						{}
            if accepts_eta:
                 lowercase						     =						eta

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

                 # predict the noise residual
                 lowercase						     =						self.unet(A__       ,A__       ,encoder_hidden_states=A__).sample

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

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

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

            lowercase						     =						1 / 0.18215 * latents
            lowercase						     =						self.vae.decode(A__).sample

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

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

            if output_type == "pil":
                 lowercase						     =						self.numpy_to_pil(A__)

            if not return_dict:
                 return image

            return StableDiffusionPipelineOutput(images=A__       ,nsfw_content_detected=A__)






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowercase__		:Union[str, Any]			  =   {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:List[Any]			  =   ["DPTFeatureExtractor"]
							lowercase__		:List[Any]			  =   ["DPTImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DPTForDepthEstimation",
							    "DPTForSemanticSegmentation",
							    "DPTModel",
							    "DPTPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_dpt import DPTFeatureExtractor
														from .image_processing_dpt import DPTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_dpt import (
														    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DPTForDepthEstimation,
														    DPTForSemanticSegmentation,
														    DPTModel,
														    DPTPreTrainedModel,
														)


else:
							import sys

							lowercase__		:List[Any]			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




