


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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[int]:
  snake_case__				,				snake_case__						= image.size
  snake_case__				,				snake_case__						= (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  snake_case__						= image.resize((w, h)							,   resample=PIL_INTERPOLATION['''lanczos''']				)
  snake_case__						= np.array(__lowerCAmelCase				).astype(np.floataa				) / 255.0
  snake_case__						= image[None].transpose(0							,   3							,   1							,   2				)
  snake_case__						= torch.from_numpy(__lowerCAmelCase				)
  return 2.0 * image - 1.0


class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     def __init__(					self:List[str]     ,		_a:VQModel     ,		_a:UNetaDModel     ,		_a:Union[
             DDIMScheduler,
             PNDMScheduler,
             LMSDiscreteScheduler,
             EulerDiscreteScheduler,
             EulerAncestralDiscreteScheduler,
             DPMSolverMultistepScheduler,
         ]     ,		):
       super().__init__()
       self.register_modules(vqvae=_a     ,		unet=_a     ,		scheduler=_a   )







     @torch.no_grad()
     def __call__(					self:int     ,		_a:Union[torch.Tensor, PIL.Image.Image] = None     ,		_a:Optional[int] = 1     ,		_a:Optional[int] = 1_00     ,		_a:Optional[float] = 0.0     ,		_a:Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		_a:Optional[str] = "pil"     ,		_a:bool = True     ,		):
       if isinstance(_a     ,		PIL.Image.Image   ):
         snake_case__						= 1
       elif isinstance(_a     ,		torch.Tensor   ):
         snake_case__						= 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   ):
         snake_case__						= preprocess(_a   )

       snake_case__				,				snake_case__						= image.shape[-2:]

       # in_channels should be 6: 3 for latents, 3 for low resolution image
       snake_case__						= (batch_size, self.unet.config.in_channels // 2, height, width)
       snake_case__						= next(self.unet.parameters()   ).dtype

       snake_case__						= randn_tensor(_a     ,		generator=_a     ,		device=self.device     ,		dtype=_a   )

       snake_case__						= image.to(device=self.device     ,		dtype=_a   )

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(_a     ,		device=self.device   )
       snake_case__						= self.scheduler.timesteps

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

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

       # decode the image latents with the VQVAE
       snake_case__						= self.vqvae.decode(_a   ).sample
       snake_case__						= torch.clamp(_a     ,		-1.0     ,		1.0   )
       snake_case__						= image / 2 + 0.5
       snake_case__						= image.cpu().permute(0     ,		2     ,		3     ,		1   ).numpy()

       if output_type == "pil":
         snake_case__						= self.numpy_to_pil(_a   )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=_a   )








import tensorflow as tf

from ...tf_utils import shape_list
class     UpperCamelCase__					(     tf.keras.layers.Layer				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__=1,   snake_case__=False,   **snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									super().__init__(**snake_case__  )

									lowercase_	:   Tuple  				=     vocab_size
									lowercase_	:   Union[str, Any]  				=     d_embed
									lowercase_	:   Optional[Any]  				=     d_proj

									lowercase_	:   Optional[Any]  				=     cutoffs + [vocab_size]
									lowercase_	:   Optional[int]  				=     [0] + self.cutoffs
									lowercase_	:   Union[str, Any]  				=     div_val

									lowercase_	:   Union[str, Any]  				=     self.cutoffs[0]
									lowercase_	:   List[str]  				=     len(self.cutoffs  ) - 1
									lowercase_	:   List[str]  				=     self.shortlist_size + self.n_clusters
									lowercase_	:   Tuple  				=     keep_order

									lowercase_	:   Dict  				=     []
									lowercase_	:   int  				=     []






		def 						snake_case__				(							self,   snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									if self.n_clusters > 0:
																lowercase_	:   List[str]  				=     self.add_weight(
																    shape=(self.n_clusters, self.d_embed),   initializer="""zeros""",   trainable=snake_case__,   name="""cluster_weight"""  )
																lowercase_	:   List[Any]  				=     self.add_weight(
																    shape=(self.n_clusters,),   initializer="""zeros""",   trainable=snake_case__,   name="""cluster_bias"""  )

									if self.div_val == 1:
																for i in range(len(self.cutoffs  )  ):
																							if self.d_proj != self.d_embed:
																														lowercase_	:   Any  				=     self.add_weight(
																														    shape=(self.d_embed, self.d_proj),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_projs_._{i}""",   )
																														self.out_projs.append(snake_case__  )
																							else:
																														self.out_projs.append(snake_case__  )
																							lowercase_	:   List[str]  				=     self.add_weight(
																							    shape=(self.vocab_size, self.d_embed),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_layers_._{i}_._weight""",   )
																							lowercase_	:   List[str]  				=     self.add_weight(
																							    shape=(self.vocab_size,),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_layers_._{i}_._bias""",   )
																							self.out_layers.append((weight, bias)  )
									else:
																for i in range(len(self.cutoffs  )  ):
																							lowercase_				,		lowercase_	:   Optional[Any]  				=     self.cutoff_ends[i], self.cutoff_ends[i + 1]
																							lowercase_	:   Union[str, Any]  				=     self.d_embed // (self.div_val**i)

																							lowercase_	:   int  				=     self.add_weight(
																							    shape=(d_emb_i, self.d_proj),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_projs_._{i}"""  )
																							self.out_projs.append(snake_case__  )
																							lowercase_	:   Any  				=     self.add_weight(
																							    shape=(r_idx - l_idx, d_emb_i),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_layers_._{i}_._weight""",   )
																							lowercase_	:   Tuple  				=     self.add_weight(
																							    shape=(r_idx - l_idx,),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_layers_._{i}_._bias""",   )
																							self.out_layers.append((weight, bias)  )
									super().build(snake_case__  )






		@staticmethod
		def 						snake_case__				(							snake_case__,   snake_case__,   snake_case__,   snake_case__=None  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   Dict  				=     x
									if proj is not None:
																lowercase_	:   List[Any]  				=     tf.einsum("""ibd,ed->ibe""",   snake_case__,   snake_case__  )
									return tf.einsum("""ibd,nd->ibn""",   snake_case__,   snake_case__  ) + b






		@staticmethod
		def 						snake_case__				(							snake_case__,   snake_case__  )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     shape_list(snake_case__  )
									lowercase_	:   Optional[Any]  				=     tf.range(lp_size[0],   dtype=target.dtype  )
									lowercase_	:   str  				=     tf.stack([r, target],   1  )
									return tf.gather_nd(snake_case__,   snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__=True,   snake_case__=False  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   Any  				=     0
									if self.n_clusters == 0:
																lowercase_	:   Union[str, Any]  				=     self._logit(snake_case__,   self.out_layers[0][0],   self.out_layers[0][1],   self.out_projs[0]  )
																if target is not None:
																							lowercase_	:   str  				=     tf.nn.sparse_softmax_cross_entropy_with_logits(labels=snake_case__,   logits=snake_case__  )
																lowercase_	:   Optional[int]  				=     tf.nn.log_softmax(snake_case__,   axis=-1  )
									else:
																lowercase_	:   int  				=     shape_list(snake_case__  )
																lowercase_	:   List[Any]  				=     []
																lowercase_	:   Tuple  				=     tf.zeros(hidden_sizes[:2]  )
																for i in range(len(self.cutoffs  )  ):
																							lowercase_				,		lowercase_	:   Optional[int]  				=     self.cutoff_ends[i], self.cutoff_ends[i + 1]
																							if target is not None:
																														lowercase_	:   Tuple  				=     (target >= l_idx) & (target < r_idx)
																														lowercase_	:   Union[str, Any]  				=     tf.where(snake_case__  )
																														lowercase_	:   Optional[Any]  				=     tf.boolean_mask(snake_case__,   snake_case__  ) - l_idx

																							if self.div_val == 1:
																														lowercase_	:   Optional[Any]  				=     self.out_layers[0][0][l_idx:r_idx]
																														lowercase_	:   Union[str, Any]  				=     self.out_layers[0][1][l_idx:r_idx]
																							else:
																														lowercase_	:   Dict  				=     self.out_layers[i][0]
																														lowercase_	:   int  				=     self.out_layers[i][1]

																							if i == 0:
																														lowercase_	:   Optional[int]  				=     tf.concat([cur_W, self.cluster_weight],   0  )
																														lowercase_	:   Optional[Any]  				=     tf.concat([cur_b, self.cluster_bias],   0  )

																														lowercase_	:   List[str]  				=     self._logit(snake_case__,   snake_case__,   snake_case__,   self.out_projs[0]  )
																														lowercase_	:   List[str]  				=     tf.nn.log_softmax(snake_case__  )
																														out.append(head_logprob[..., : self.cutoffs[0]]  )
																														if target is not None:
																																					lowercase_	:   int  				=     tf.boolean_mask(snake_case__,   snake_case__  )
																																					lowercase_	:   Optional[int]  				=     self._gather_logprob(snake_case__,   snake_case__  )
																							else:
																														lowercase_	:   List[str]  				=     self._logit(snake_case__,   snake_case__,   snake_case__,   self.out_projs[i]  )
																														lowercase_	:   Dict  				=     tf.nn.log_softmax(snake_case__  )
																														lowercase_	:   Optional[int]  				=     self.cutoffs[0] + i - 1  # No probability for the head cluster
																														lowercase_	:   int  				=     head_logprob[..., cluster_prob_idx, None] + tail_logprob
																														out.append(snake_case__  )
																														if target is not None:
																																					lowercase_	:   Optional[Any]  				=     tf.boolean_mask(snake_case__,   snake_case__  )
																																					lowercase_	:   Optional[int]  				=     tf.boolean_mask(snake_case__,   snake_case__  )
																																					lowercase_	:   Union[str, Any]  				=     self._gather_logprob(snake_case__,   snake_case__  )
																																					cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
																							if target is not None:
																														loss += tf.scatter_nd(snake_case__,   -cur_logprob,   shape_list(snake_case__  )  )
																lowercase_	:   List[str]  				=     tf.concat(snake_case__,   axis=-1  )

									if target is not None:
																if return_mean:
																							lowercase_	:   Tuple  				=     tf.reduce_mean(snake_case__  )
																# Add the training-time loss value to the layer using `self.add_loss()`.
																self.add_loss(snake_case__  )

																# Log the loss as a metric (we could log arbitrary metrics,
																# including different metrics for training and inference.
																self.add_metric(snake_case__,   name=self.name,   aggregation="""mean""" if return_mean else """"""  )

									return out