



"""simple docstring"""







from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)





class    UpperCamelCase     (			snake_case    ):




	"""simple docstring"""






	def __init__(			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,):
			super().__init__()

			self.register_modules(vae=UpperCAmelCase_							,text_encoder=UpperCAmelCase_							,tokenizer=UpperCAmelCase_							,unet=UpperCAmelCase_							,scheduler=UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			with torch.no_grad():
					_lowercase							:      str			=					self.vae.encode(tfms.ToTensor()(UpperCAmelCase_					).unsqueeze(0					).to(self.device					) * 2 - 1					)
					_lowercase							:      Union[str, Any]			=					0.18215 * latent.latent_dist.sample()
			return latent






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			_lowercase							:      Dict			=					(1 / 0.18215) * latent
			with torch.no_grad():
					_lowercase							:      Tuple			=					self.vae.decode(UpperCAmelCase_					).sample
			_lowercase							:      Optional[Any]			=					(img / 2 + 0.5).clamp(0							,1					)
			_lowercase							:      List[Any]			=					img.detach().cpu().permute(0							,2							,3							,1					).numpy()
			_lowercase							:      List[Any]			=					(img * 2_55).round().astype("""uint8"""					)
			return Image.fromarray(img[0]					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			_lowercase							:      Optional[int]			=					self.tokenizer(
			    UpperCAmelCase_							,padding="""max_length"""							,max_length=self.tokenizer.model_max_length							,truncation=UpperCAmelCase_							,return_tensors="""pt"""							,)

			_lowercase							:      Dict			=					self.text_encoder(text_input.input_ids.to(self.device					)					)[0]

			_lowercase							:      Tuple			=					self.tokenizer(
			    """"""							,padding="""max_length"""							,max_length=self.tokenizer.model_max_length							,truncation=UpperCAmelCase_							,return_tensors="""pt"""							,)

			_lowercase							:      Any			=					self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

			return torch.cat([uncond_embedding, text_embedding]					)






	def __call__(			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_ = 0.3							,UpperCAmelCase_ = 0.6							,UpperCAmelCase_ = 0.5							,UpperCAmelCase_ = 42							,UpperCAmelCase_ = 50							,UpperCAmelCase_ = 7.5							,):
			_lowercase							:      List[str]			=					steps - int(kmin * steps					)
			_lowercase							:      str			=					steps - int(kmax * steps					)

			_lowercase							:      List[Any]			=					self.prep_text(UpperCAmelCase_					)

			self.scheduler.set_timesteps(UpperCAmelCase_					)

			_lowercase			,   _lowercase							:      Optional[Any]			=					img.size
			_lowercase							:      Any			=					self.encode(UpperCAmelCase_					)

			torch.manual_seed(UpperCAmelCase_					)
			_lowercase							:      Dict			=					torch.randn(
			    (1, self.unet.config.in_channels, height // 8, width // 8)							,).to(self.device					)

			_lowercase							:      Any			=					self.scheduler.add_noise(
			    UpperCAmelCase_							,UpperCAmelCase_							,timesteps=self.scheduler.timesteps[tmax]							,)

			_lowercase							:      int			=					torch.cat([latents] * 2					)

			_lowercase							:      int			=					self.scheduler.scale_model_input(UpperCAmelCase_							,self.scheduler.timesteps[tmax]					)

			with torch.no_grad():
					_lowercase							:      Optional[int]			=					self.unet(
					    UpperCAmelCase_							,self.scheduler.timesteps[tmax]							,encoder_hidden_states=UpperCAmelCase_							,).sample

			_lowercase			,   _lowercase							:      int			=					pred.chunk(2					)
			_lowercase							:      str			=					pred_uncond + guidance_scale * (pred_text - pred_uncond)

			_lowercase							:      Dict			=					self.scheduler.step(UpperCAmelCase_							,self.scheduler.timesteps[tmax]							,UpperCAmelCase_					).prev_sample

			for i, t in enumerate(tqdm(self.scheduler.timesteps					)					):
					if i > tmax:
							if i < tmin:  # layout generation phase
									_lowercase							:      Tuple			=					self.scheduler.add_noise(
									    UpperCAmelCase_							,UpperCAmelCase_							,timesteps=UpperCAmelCase_							,)

									_lowercase							:      int			=					(mix_factor * latents) + (
									    1 - mix_factor
									) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
									_lowercase							:      Optional[Any]			=					torch.cat([input] * 2					)

							else:  # content generation phase
									_lowercase							:      List[Any]			=					torch.cat([latents] * 2					)

							_lowercase							:      Union[str, Any]			=					self.scheduler.scale_model_input(UpperCAmelCase_							,UpperCAmelCase_					)

							with torch.no_grad():
									_lowercase							:      Optional[Any]			=					self.unet(
									    UpperCAmelCase_							,UpperCAmelCase_							,encoder_hidden_states=UpperCAmelCase_							,).sample

							_lowercase			,   _lowercase							:      Dict			=					pred.chunk(2					)
							_lowercase							:      Any			=					pred_uncond + guidance_scale * (pred_text - pred_uncond)

							_lowercase							:      Tuple			=					self.scheduler.step(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					).prev_sample

			return self.decode(UpperCAmelCase_					)





from typing import List, Optional, Union

import numpy as np

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


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		["input_values", "attention_mask"]





     def __init__(self      :    Tuple				,    _A      :    Optional[Any]=1				,    _A      :    Dict=1_6_0_0_0				,    _A      :    int=0.0				,    _A      :    List[str]=False				,    _A      :    Optional[int]=True				,    **_A      :    List[Any]				,    )						->						str:
         super().__init__(feature_size=_A				,    sampling_rate=_A				,    padding_value=_A				,    **_A   )
         snake_case      =					return_attention_mask
         snake_case      =					do_normalize





     @staticmethod
     def 		UpperCAmelCase(_A      :    List[np.ndarray]				,    _A      :    List[np.ndarray]				,    _A      :    float = 0.0   )						->						List[np.ndarray]:
         if attention_mask is not None:
             snake_case      =					np.array(_A				,    np.intaa   )
             snake_case      =					[]

             for vector, length in zip(_A				,    attention_mask.sum(-1   )   ):
                 snake_case      =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7   )
                 if length < normed_slice.shape[0]:
                     snake_case      =					padding_value

                 normed_input_values.append(_A   )
         else:
             snake_case      =					[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

         return normed_input_values





     def __call__(self      :    Optional[Any]				,    _A      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,    _A      :    Union[bool, str, PaddingStrategy] = False				,    _A      :    Optional[int] = None				,    _A      :    bool = False				,    _A      :    Optional[int] = None				,    _A      :    Optional[bool] = None				,    _A      :    Optional[Union[str, TensorType]] = None				,    _A      :    Optional[int] = None				,    **_A      :    List[Any]				,    )						->						BatchFeature:

         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."   )

         snake_case      =					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}'   )
         snake_case      =					is_batched_numpy or (
             isinstance(_A				,    (list, tuple)   ) and (isinstance(raw_speech[0]				,    (np.ndarray, tuple, list)   ))
         )

         # always return batch
         if not is_batched:
             snake_case      =					[raw_speech]

         # convert into correct format for padding
         snake_case      =					BatchFeature({"input_values": raw_speech}   )

         snake_case      =					self.pad(
             _A				,    padding=_A				,    max_length=_A				,    truncation=_A				,    pad_to_multiple_of=_A				,    return_attention_mask=_A				,    )

         # convert input values to correct format
         snake_case      =					padded_inputs["input_values"]
         if not isinstance(input_values[0]				,    np.ndarray   ):
             snake_case      =					[np.asarray(_A				,    dtype=np.floataa   ) for array in input_values]
         elif (
             not isinstance(_A				,    np.ndarray   )
             and isinstance(input_values[0]				,    np.ndarray   )
             and input_values[0].dtype is np.dtype(np.floataa   )
         ):
             snake_case      =					[array.astype(np.floataa   ) for array in input_values]
         elif isinstance(_A				,    np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
             snake_case      =					input_values.astype(np.floataa   )

         # convert attention_mask to correct format
         snake_case      =					padded_inputs.get("attention_mask"   )
         if attention_mask is not None:
             snake_case      =					[np.asarray(_A				,    dtype=np.intaa   ) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if self.do_normalize:
             snake_case      =					(
                 attention_mask
                 if self._get_padding_strategies(_A				,    max_length=_A   ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             snake_case      =					self.zero_mean_unit_var_norm(
                 padded_inputs["input_values"]				,    attention_mask=_A				,    padding_value=self.padding_value   )

         if return_tensors is not None:
             snake_case      =					padded_inputs.convert_to_tensors(_A   )

         return padded_inputs

