




"""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


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

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Dict:
       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       ,					lowerCamelCase_ = "auto")				->   str:
       if slice_size == "auto":
           UpperCamelCase              =	self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict:
       self.enable_attention_slicing(lowerCamelCase_)







   @torch.no_grad()
   def __call__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=1_6_0_0_0       ,					lowerCamelCase_ = 5_1_2       ,					lowerCamelCase_ = 5_1_2       ,					lowerCamelCase_ = 5_0       ,					lowerCamelCase_ = 7.5       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = 1       ,					lowerCamelCase_ = 0.0       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = "pil"       ,					lowerCamelCase_ = True       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = 1       ,					**lowerCamelCase_       ,					)				->   Any:
       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=4_8_0_0_0_0)

       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       ,	UpperCamelCase       ,	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       ,	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"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase       :			Tuple   	=logging.get_logger(__name__)

__lowerCAmelCase       :			Dict   	={
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Optional[int]				    =      'deta'
      snake_case__     :  Dict				    =      {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }

      def __init__(		self						,			__lowerCAmelCase=None						,			__lowerCAmelCase=900						,			__lowerCAmelCase=2048						,			__lowerCAmelCase=6						,			__lowerCAmelCase=2048						,			__lowerCAmelCase=8						,			__lowerCAmelCase=6						,			__lowerCAmelCase=1024						,			__lowerCAmelCase=8						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=True						,			__lowerCAmelCase="relu"						,			__lowerCAmelCase=256						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=1.0						,			__lowerCAmelCase=True						,			__lowerCAmelCase=False						,			__lowerCAmelCase="sine"						,			__lowerCAmelCase=5						,			__lowerCAmelCase=4						,			__lowerCAmelCase=4						,			__lowerCAmelCase=True						,			__lowerCAmelCase=300						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=1						,			__lowerCAmelCase=5						,			__lowerCAmelCase=2						,			__lowerCAmelCase=1						,			__lowerCAmelCase=1						,			__lowerCAmelCase=5						,			__lowerCAmelCase=2						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.2_5						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if backbone_config is None:
                  logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
                  lowercase								=      CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]		)
            else:
                  if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		):
                        lowercase								=      backbone_config.pop("""model_type"""		)
                        lowercase								=      CONFIG_MAPPING[backbone_model_type]
                        lowercase								=      config_class.from_dict(__lowerCAmelCase		)

            lowercase								=      backbone_config
            lowercase								=      num_queries
            lowercase								=      max_position_embeddings
            lowercase								=      d_model
            lowercase								=      encoder_ffn_dim
            lowercase								=      encoder_layers
            lowercase								=      encoder_attention_heads
            lowercase								=      decoder_ffn_dim
            lowercase								=      decoder_layers
            lowercase								=      decoder_attention_heads
            lowercase								=      dropout
            lowercase								=      attention_dropout
            lowercase								=      activation_dropout
            lowercase								=      activation_function
            lowercase								=      init_std
            lowercase								=      init_xavier_std
            lowercase								=      encoder_layerdrop
            lowercase								=      auxiliary_loss
            lowercase								=      position_embedding_type
            # deformable attributes
            lowercase								=      num_feature_levels
            lowercase								=      encoder_n_points
            lowercase								=      decoder_n_points
            lowercase								=      two_stage
            lowercase								=      two_stage_num_proposals
            lowercase								=      with_box_refine
            lowercase								=      assign_first_stage
            if two_stage is True and with_box_refine is False:
                  raise ValueError("""If two_stage is True, with_box_refine must be True."""		)
            # Hungarian matcher
            lowercase								=      class_cost
            lowercase								=      bbox_cost
            lowercase								=      giou_cost
            # Loss coefficients
            lowercase								=      mask_loss_coefficient
            lowercase								=      dice_loss_coefficient
            lowercase								=      bbox_loss_coefficient
            lowercase								=      giou_loss_coefficient
            lowercase								=      eos_coefficient
            lowercase								=      focal_alpha
            super().__init__(is_encoder_decoder=__lowerCAmelCase						,			**__lowerCAmelCase		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.encoder_attention_heads

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.d_model







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      copy.deepcopy(self.__dict__		)
            lowercase								=      self.backbone_config.to_dict()
            lowercase								=      self.__class__.model_type
            return output



