




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
 import torch

if is_vision_available():
 import PIL


lowerCamelCase    :		Tuple			   =							logging.get_logger(__name__)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	np.ndarray     ,							lowercase     :	Union[int, Iterable[int]]     ,							lowercase     :	bool     ,							lowercase     :	int					):


      '''simple docstring'''



      def constraint_to_multiple_of(lowercase     :	str     ,							lowercase     :	Optional[Any]     ,							lowercase     :	int=0     ,							lowercase     :	Union[str, Any]=None					):
            lowerCamelCase_							=   round(val / multiple					) * multiple

            if max_val is not None and x > max_val:
                  lowerCamelCase_							=   math.floor(val / multiple					) * multiple

            if x < min_val:
                  lowerCamelCase_							=   math.ceil(val / multiple					) * multiple

            return x

      lowerCamelCase_							=   (output_size, output_size) if isinstance(lowerCAmelCase__     ,							lowerCAmelCase__					) else output_size

      lowerCamelCase_							=   get_image_size(lowerCAmelCase__					)
      lowerCamelCase_							=   output_size

      # determine new height and width
      lowerCamelCase_							=   output_height / input_height
      lowerCamelCase_							=   output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width					) < abs(1 - scale_height					):
                  # fit width
                  lowerCamelCase_							=   scale_width
            else:
                  # fit height
                  lowerCamelCase_							=   scale_height

      lowerCamelCase_							=   constraint_to_multiple_of(scale_height * input_height     ,							multiple=lowerCAmelCase__					)
      lowerCamelCase_							=   constraint_to_multiple_of(scale_width * input_width     ,							multiple=lowerCAmelCase__					)

      return (new_height, new_width)







class   A(						__lowercase       ):



 '''simple docstring'''


 UpperCamelCase						       =						["pixel_values"]

 def __init__(    self :  int						,							A_ :  bool = True						,							A_ :  Dict[str, int] = None						,							A_ :  PILImageResampling = PILImageResampling.BILINEAR						,							A_ :  bool = False						,							A_ :  int = 1						,							A_ :  bool = True						,							A_ :  Union[int, float] = 1 / 255						,							A_ :  bool = True						,							A_ :  Optional[Union[float, List[float]]] = None						,							A_ :  Optional[Union[float, List[float]]] = None						,							**A_ :  Tuple						,							)       ->     Optional[int]:

       """simple docstring"""






       super().__init__(**snake_case_  )
       lowerCamelCase_							=   size if size is not None else {'''height''': 384, '''width''': 384}
       lowerCamelCase_							=   get_size_dict(snake_case_  )
       lowerCamelCase_							=   do_resize
       lowerCamelCase_							=   size
       lowerCamelCase_							=   keep_aspect_ratio
       lowerCamelCase_							=   ensure_multiple_of
       lowerCamelCase_							=   resample
       lowerCamelCase_							=   do_rescale
       lowerCamelCase_							=   rescale_factor
       lowerCamelCase_							=   do_normalize
       lowerCamelCase_							=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase_							=   image_std if image_std is not None else IMAGENET_STANDARD_STD

 def     a__			(    self :  Optional[int]						,							A_ :  np.ndarray						,							A_ :  Dict[str, int]						,							A_ :  bool = False						,							A_ :  int = 1						,							A_ :  PILImageResampling = PILImageResampling.BICUBIC						,							A_ :  Optional[Union[str, ChannelDimension]] = None						,							**A_ :  int						,							)       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   get_size_dict(snake_case_  )
       if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""  )
       lowerCamelCase_							=   get_resize_output_image_size(
           snake_case_						,							output_size=(size['height'], size['width'])						,							keep_aspect_ratio=snake_case_						,							multiple=snake_case_						,							)
       return resize(snake_case_						,							size=snake_case_						,							resample=snake_case_						,							data_format=snake_case_						,							**snake_case_  )

 def     a__			(    self :  int						,							A_ :  np.ndarray						,							A_ :  Union[int, float]						,							A_ :  Optional[Union[str, ChannelDimension]] = None						,							**A_ :  List[Any]						,							)       ->     str:

       """simple docstring"""






       return rescale(snake_case_						,							scale=snake_case_						,							data_format=snake_case_						,							**snake_case_  )

 def     a__			(    self :  Tuple						,							A_ :  np.ndarray						,							A_ :  Union[float, List[float]]						,							A_ :  Union[float, List[float]]						,							A_ :  Optional[Union[str, ChannelDimension]] = None						,							**A_ :  Optional[int]						,							)       ->     Dict:

       """simple docstring"""






       return normalize(snake_case_						,							mean=snake_case_						,							std=snake_case_						,							data_format=snake_case_						,							**snake_case_  )

 def     a__			(    self :  int						,							A_ :  ImageInput						,							A_ :  bool = None						,							A_ :  int = None						,							A_ :  bool = None						,							A_ :  int = None						,							A_ :  PILImageResampling = None						,							A_ :  bool = None						,							A_ :  float = None						,							A_ :  bool = None						,							A_ :  Optional[Union[float, List[float]]] = None						,							A_ :  Optional[Union[float, List[float]]] = None						,							A_ :  Optional[Union[str, TensorType]] = None						,							A_ :  ChannelDimension = ChannelDimension.FIRST						,							**A_ :  int						,							)       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_							=   size if size is not None else self.size
       lowerCamelCase_							=   get_size_dict(snake_case_  )
       lowerCamelCase_							=   keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
       lowerCamelCase_							=   ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
       lowerCamelCase_							=   resample if resample is not None else self.resample
       lowerCamelCase_							=   do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_							=   rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_							=   do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_							=   image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_							=   image_std if image_std is not None else self.image_std

       lowerCamelCase_							=   make_list_of_images(snake_case_  )

       if not valid_images(snake_case_  ):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'  )

       if do_resize and size is None or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'  )

       if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'  )

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'  )

       # All transformations expect numpy arrays.
       lowerCamelCase_							=   [to_numpy_array(snake_case_  ) for image in images]

       if do_resize:
             lowerCamelCase_							=   [self.resize(image=snake_case_						,							size=snake_case_						,							resample=snake_case_  ) for image in images]

       if do_rescale:
             lowerCamelCase_							=   [self.rescale(image=snake_case_						,							scale=snake_case_  ) for image in images]

       if do_normalize:
             lowerCamelCase_							=   [self.normalize(image=snake_case_						,							mean=snake_case_						,							std=snake_case_  ) for image in images]

       lowerCamelCase_							=   [to_channel_dimension_format(snake_case_						,							snake_case_  ) for image in images]

       lowerCamelCase_							=   {'''pixel_values''': images}
       return BatchFeature(data=snake_case_						,							tensor_type=snake_case_  )





 def     a__			(    self :  str						,							A_ :  Dict						,							A_ :  List[Tuple] = None  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
             if len(snake_case_  ) != len(snake_case_  ):
                   raise ValueError(
                       'Make sure that you pass in as many target sizes as the batch dimension of the logits'  )

             if is_torch_tensor(snake_case_  ):
                   lowerCamelCase_							=   target_sizes.numpy()

             lowerCamelCase_							=   []

             for idx in range(len(snake_case_  )  ):
                   lowerCamelCase_							=   torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0  )						,							size=target_sizes[idx]						,							mode='bilinear'						,							align_corners=snake_case_  )
                   lowerCamelCase_							=   resized_logits[0].argmax(dim=0  )
                   semantic_segmentation.append(snake_case_  )
       else:
             lowerCamelCase_							=   logits.argmax(dim=1  )
             lowerCamelCase_							=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

       return semantic_segmentation









from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin







@dataclass
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						42







class   A(						UpperCamelCase							,				UpperCamelCase       ):



 '''simple docstring'''


 @register_to_config
 def __init__(    self :  Tuple						,							A_ :  int = 32						,							A_ :  int = 64						,							A_ :  int = 20						,							A_ :  int = 768						,							A_ :  Optional[Any]=77						,							A_ :  Optional[int]=4						,							A_ :  float = 0.0						,							A_ :  str = "silu"						,							A_ :  Optional[str] = None						,							A_ :  Optional[str] = None						,							A_ :  Optional[str] = "linear"						,							A_ :  Optional[str] = "prd"						,							A_ :  Optional[int] = None						,							A_ :  Optional[int] = None						,							A_ :  Optional[int] = None						,							)       ->     List[Any]:

       """simple docstring"""






       super().__init__()
       lowerCamelCase_							=   num_attention_heads
       lowerCamelCase_							=   attention_head_dim
       lowerCamelCase_							=   num_attention_heads * attention_head_dim
       lowerCamelCase_							=   additional_embeddings

       lowerCamelCase_							=   time_embed_dim or inner_dim
       lowerCamelCase_							=   embedding_proj_dim or embedding_dim
       lowerCamelCase_							=   clip_embed_dim or embedding_dim

       lowerCamelCase_							=   Timesteps(A_						,							A_						,							0  )
       lowerCamelCase_							=   TimestepEmbedding(A_						,							A_						,							out_dim=A_						,							act_fn=A_  )

       lowerCamelCase_							=   nn.Linear(A_						,							A_  )

       if embedding_proj_norm_type is None:
             lowerCamelCase_							=   None
       elif embedding_proj_norm_type == "layer":
             lowerCamelCase_							=   nn.LayerNorm(A_  )
       else:
             raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""  )

       lowerCamelCase_							=   nn.Linear(A_						,							A_  )

       if encoder_hid_proj_type is None:
             lowerCamelCase_							=   None
       elif encoder_hid_proj_type == "linear":
             lowerCamelCase_							=   nn.Linear(A_						,							A_  )
       else:
             raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""  )

       lowerCamelCase_							=   nn.Parameter(torch.zeros(1						,							num_embeddings + additional_embeddings						,							A_  )  )

       if added_emb_type == "prd":
             lowerCamelCase_							=   nn.Parameter(torch.zeros(1						,							1						,							A_  )  )
       elif added_emb_type is None:
             lowerCamelCase_							=   None
       else:
             raise ValueError(
                 f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""  )

       lowerCamelCase_							=   nn.ModuleList(
           [
               BasicTransformerBlock(
                   A_						,							A_						,							A_						,							dropout=A_						,							activation_fn='gelu'						,							attention_bias=A_						,							)
               for d in range(A_  )
           ]  )

       if norm_in_type == "layer":
             lowerCamelCase_							=   nn.LayerNorm(A_  )
       elif norm_in_type is None:
             lowerCamelCase_							=   None
       else:
             raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}."""  )

       lowerCamelCase_							=   nn.LayerNorm(A_  )

       lowerCamelCase_							=   nn.Linear(A_						,							A_  )

       lowerCamelCase_							=   torch.full(
           [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]						,							-10000.0  )
       causal_attention_mask.triu_(1  )
       lowerCamelCase_							=   causal_attention_mask[None, ...]
       self.register_buffer('causal_attention_mask'						,							A_						,							persistent=A_  )

       lowerCamelCase_							=   nn.Parameter(torch.zeros(1						,							A_  )  )
       lowerCamelCase_							=   nn.Parameter(torch.zeros(1						,							A_  )  )

 @property
 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
 def     a__			(    self :  str  )       ->     Dict[str, AttentionProcessor]:

       """simple docstring"""






       lowerCamelCase_							=   {}

       def fn_recursive_add_processors(A_ :  str						,							A_ :  torch.nn.Module						,							A_ :  Dict[str, AttentionProcessor]  ):
             if hasattr(A_						,							'set_processor'  ):
                   lowerCamelCase_							=   module.processor

             for sub_name, child in module.named_children():
                   fn_recursive_add_processors(f"""{name}.{sub_name}"""						,							A_						,							A_  )

             return processors

       for name, module in self.named_children():
             fn_recursive_add_processors(A_						,							A_						,							A_  )

       return processors

 def     a__			(    self :  List[Any]						,							A_ :  Union[AttentionProcessor, Dict[str, AttentionProcessor]]  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   len(self.attn_processors.keys()  )

       if isinstance(A_						,							A_  ) and len(A_  ) != count:
             raise ValueError(
                 f"""A dict of processors was passed, but the number of processors {len(A_  )} does not match the"""
                 f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""  )

       def fn_recursive_attn_processor(A_ :  str						,							A_ :  torch.nn.Module						,							A_ :  Union[str, Any]  ):
             if hasattr(A_						,							'set_processor'  ):
                   if not isinstance(A_						,							A_  ):
                         module.set_processor(A_  )
                   else:
                         module.set_processor(processor.pop(f"""{name}.processor"""  )  )

             for sub_name, child in module.named_children():
                   fn_recursive_attn_processor(f"""{name}.{sub_name}"""						,							A_						,							A_  )

       for name, module in self.named_children():
             fn_recursive_attn_processor(A_						,							A_						,							A_  )

 def     a__			(    self :  List[Any]  )       ->     List[Any]:

       """simple docstring"""






       self.set_attn_processor(AttnProcessor()  )

 def     a__			(    self :  Dict						,							A_ :  List[Any]						,							A_ :  Union[torch.Tensor, float, int]						,							A_ :  torch.FloatTensor						,							A_ :  Optional[torch.FloatTensor] = None						,							A_ :  Optional[torch.BoolTensor] = None						,							A_ :  bool = True						,							)       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   hidden_states.shape[0]

       lowerCamelCase_							=   timestep
       if not torch.is_tensor(A_  ):
             lowerCamelCase_							=   torch.tensor([timesteps]						,							dtype=torch.long						,							device=hidden_states.device  )
       elif torch.is_tensor(A_  ) and len(timesteps.shape  ) == 0:
             lowerCamelCase_							=   timesteps[None].to(hidden_states.device  )

       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       lowerCamelCase_							=   timesteps * torch.ones(A_						,							dtype=timesteps.dtype						,							device=timesteps.device  )

       lowerCamelCase_							=   self.time_proj(A_  )

       # timesteps does not contain any weights and will always return f32 tensors
       # but time_embedding might be fp16, so we need to cast here.
       lowerCamelCase_							=   timesteps_projected.to(dtype=self.dtype  )
       lowerCamelCase_							=   self.time_embedding(A_  )

       if self.embedding_proj_norm is not None:
             lowerCamelCase_							=   self.embedding_proj_norm(A_  )

       lowerCamelCase_							=   self.embedding_proj(A_  )
       if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
             lowerCamelCase_							=   self.encoder_hidden_states_proj(A_  )
       elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
             raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'  )

       lowerCamelCase_							=   self.proj_in(A_  )

       lowerCamelCase_							=   self.positional_embedding.to(hidden_states.dtype  )

       lowerCamelCase_							=   []
       lowerCamelCase_							=   0

       if encoder_hidden_states is not None:
             additional_embeds.append(A_  )
             additional_embeddings_len += encoder_hidden_states.shape[1]

       if len(proj_embeddings.shape  ) == 2:
             lowerCamelCase_							=   proj_embeddings[:, None, :]

       if len(hidden_states.shape  ) == 2:
             lowerCamelCase_							=   hidden_states[:, None, :]

       lowerCamelCase_							=   additional_embeds + [
           proj_embeddings,
           time_embeddings[:, None, :],
           hidden_states,
       ]

       if self.prd_embedding is not None:
             lowerCamelCase_							=   self.prd_embedding.to(hidden_states.dtype  ).expand(A_						,							-1						,							-1  )
             additional_embeds.append(A_  )

       lowerCamelCase_							=   torch.cat(
           A_						,							dim=1						,							)

       # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
       lowerCamelCase_							=   additional_embeddings_len + proj_embeddings.shape[1] + 1
       if positional_embeddings.shape[1] < hidden_states.shape[1]:
             lowerCamelCase_							=   F.pad(
                 A_						,							(
                     0,
                     0,
                     additional_embeddings_len,
                     self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                 )						,							value=0.0						,							)

       lowerCamelCase_							=   hidden_states + positional_embeddings

       if attention_mask is not None:
             lowerCamelCase_							=   (1 - attention_mask.to(hidden_states.dtype  )) * -10000.0
             lowerCamelCase_							=   F.pad(A_						,							(0, self.additional_embeddings)						,							value=0.0  )
             lowerCamelCase_							=   (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype  )
             lowerCamelCase_							=   attention_mask.repeat_interleave(self.config.num_attention_heads						,							dim=0  )

       if self.norm_in is not None:
             lowerCamelCase_							=   self.norm_in(A_  )

       for block in self.transformer_blocks:
             lowerCamelCase_							=   block(A_						,							attention_mask=A_  )

       lowerCamelCase_							=   self.norm_out(A_  )

       if self.prd_embedding is not None:
             lowerCamelCase_							=   hidden_states[:, -1]
       else:
             lowerCamelCase_							=   hidden_states[:, additional_embeddings_len:]

       lowerCamelCase_							=   self.proj_to_clip_embeddings(A_  )

       if not return_dict:
             return (predicted_image_embedding,)

       return PriorTransformerOutput(predicted_image_embedding=A_  )





 def     a__			(    self :  Tuple						,							A_ :  List[Any]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   (prior_latents * self.clip_std) + self.clip_mean
       return prior_latents



