'''simple docstring'''






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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def    __UpperCAmelCase			(  a_:       str							):
   _UpperCAmelCase							,       _UpperCAmelCase							:	List[str]							=   image.size
   _UpperCAmelCase							,       _UpperCAmelCase							:	int							=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
   _UpperCAmelCase							:	Optional[Any]							=   image.resize((w, h),					resample=PIL_INTERPOLATION["lanczos"]							)
   _UpperCAmelCase							:	Dict							=   np.array(snake_case_							).astype(np.floataa							) / 2_55.0
   _UpperCAmelCase							:	int							=   image[None].transpose(0,					3,					1,					2							)
   _UpperCAmelCase							:	Tuple							=   torch.from_numpy(snake_case_							)
   return 2.0 * image - 1.0






class     A__							(       UpperCamelCase__   ):





   """simple docstring"""





   def __init__( self     :		Any						,     lowerCAmelCase__     :		VQModel						,     lowerCAmelCase__     :		UNetaDModel						,     lowerCAmelCase__     :		Union[
           DDIMScheduler,
           PNDMScheduler,
           LMSDiscreteScheduler,
           EulerDiscreteScheduler,
           EulerAncestralDiscreteScheduler,
           DPMSolverMultistepScheduler,
       ]						,     )       ->    Optional[Any]:







      """simple docstring"""




      super().__init__()
      self.register_modules(vqvae=__a						,     unet=__a						,     scheduler=__a			)




   @torch.no_grad()
   def __call__( self     :		List[Any]						,     lowerCAmelCase__     :		Union[torch.Tensor, PIL.Image.Image] = None						,     lowerCAmelCase__     :		Optional[int] = 1						,     lowerCAmelCase__     :		Optional[int] = 1_0_0						,     lowerCAmelCase__     :		Optional[float] = 0.0						,     lowerCAmelCase__     :		Optional[Union[torch.Generator, List[torch.Generator]]] = None						,     lowerCAmelCase__     :		Optional[str] = "pil"						,     lowerCAmelCase__     :		bool = True						,     )       ->    Union[str, Any]:







      """simple docstring"""




      if isinstance(__a						,     PIL.Image.Image			):
         _UpperCAmelCase							:	Optional[Any]							=   1
      elif isinstance(__a						,     torch.Tensor			):
         _UpperCAmelCase							:	List[Any]							=   image.shape[0]
      else:
         raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__a			)}"""			)

      if isinstance(__a						,     PIL.Image.Image			):
         _UpperCAmelCase							:	Any							=   preprocess(__a			)

      _UpperCAmelCase							,       _UpperCAmelCase							:	Tuple							=   image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      _UpperCAmelCase							:	Optional[int]							=   (batch_size, self.unet.config.in_channels // 2, height, width)
      _UpperCAmelCase							:	List[Any]							=   next(self.unet.parameters()			).dtype

      _UpperCAmelCase							:	Any							=   randn_tensor(__a						,     generator=__a						,     device=self.device						,     dtype=__a			)

      _UpperCAmelCase							:	int							=   image.to(device=self.device						,     dtype=__a			)

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(__a						,     device=self.device			)
      _UpperCAmelCase							:	str							=   self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      _UpperCAmelCase							:	List[Any]							=   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							:	List[Any]							=   "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
      _UpperCAmelCase							:	List[str]							=   {}
      if accepts_eta:
         _UpperCAmelCase							:	int							=   eta

      for t in self.progress_bar(__a			):
         # concat latents and low resolution image in the channel dimension.
         _UpperCAmelCase							:	int							=   torch.cat([latents, image]						,     dim=1			)
         _UpperCAmelCase							:	Union[str, Any]							=   self.scheduler.scale_model_input(__a						,     __a			)
         # predict the noise residual
         _UpperCAmelCase							:	List[Any]							=   self.unet(__a						,     __a			).sample
         # compute the previous noisy sample x_t -> x_t-1
         _UpperCAmelCase							:	Any							=   self.scheduler.step(__a						,     __a						,     __a						,     **__a			).prev_sample

      # decode the image latents with the VQVAE
      _UpperCAmelCase							:	int							=   self.vqvae.decode(__a			).sample
      _UpperCAmelCase							:	Optional[int]							=   torch.clamp(__a						,     -1.0						,     1.0			)
      _UpperCAmelCase							:	Optional[Any]							=   image / 2 + 0.5
      _UpperCAmelCase							:	Any							=   image.cpu().permute(0						,     2						,     3						,     1			).numpy()

      if output_type == "pil":
         _UpperCAmelCase							:	Tuple							=   self.numpy_to_pil(__a			)

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=__a			)
'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:     Dict  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Tuple  ={}




class        __A      ( UpperCamelCase__	):
    a__       :						int          =  """llama"""
    a__       :						Any          =  ["""past_key_values"""]



    def __init__(self    :    List[str]   ,			__a    :    List[str]=32000   ,			__a    :    Tuple=4096   ,			__a    :    List[Any]=11008   ,			__a    :    Dict=32   ,			__a    :    Tuple=32   ,			__a    :    Any=None   ,			__a    :    Any="silu"   ,			__a    :    List[Any]=2048   ,			__a    :    List[Any]=0.02   ,			__a    :    str=1E-6   ,			__a    :    Optional[Any]=True   ,			__a    :    Union[str, Any]=0   ,			__a    :    Any=1   ,			__a    :    Dict=2   ,			__a    :    Dict=1   ,			__a    :    str=False   ,			__a    :    str=None   ,			**__a    :    Optional[Any]   ,			):
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
              UpperCAmelCase_           =   num_attention_heads

         UpperCAmelCase_           =   num_key_value_heads
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   rms_norm_eps
         UpperCAmelCase_           =   pretraining_tp
         UpperCAmelCase_           =   use_cache
         UpperCAmelCase_           =   rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=__a   ,			bos_token_id=__a   ,			eos_token_id=__a   ,			tie_word_embeddings=__a   ,			**__a   ,			)






    def 					_lowercase						(self    :    List[str] ):
         if self.rope_scaling is None:
              return

         if not isinstance(self.rope_scaling   ,			__a ) or len(self.rope_scaling ) != 2:
              raise ValueError(
                  "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                  f"""got {self.rope_scaling}""" )
         UpperCAmelCase_           =   self.rope_scaling.get("type"   ,			__a )
         UpperCAmelCase_           =   self.rope_scaling.get("factor"   ,			__a )
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
              raise ValueError(
                  f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""" )
         if rope_scaling_factor is None or not isinstance(__a   ,			__a ) or rope_scaling_factor <= 1.0:
              raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )




