'''simple docstring'''
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a=  logging.get_logger(__name__)






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						['''pixel_values''']







     def __init__(		self     ,			_lowerCamelCase = True     ,			_lowerCamelCase = 1 / 2_5_5     ,			_lowerCamelCase = True     ,			_lowerCamelCase = 8     ,			**_lowerCamelCase     ,			):
         super().__init__(**_lowerCamelCase    )

         __UpperCamelCase	:	List[Any]		    =  do_rescale
         __UpperCamelCase	:	List[Any]		    =  rescale_factor
         __UpperCamelCase	:	List[str]		    =  do_pad
         __UpperCamelCase	:	str		    =  pad_size







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase    ):
         return rescale(_lowerCamelCase     ,			scale=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  get_image_size(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  (old_height // size + 1) * size - old_height
         __UpperCamelCase	:	int		    =  (old_width // size + 1) * size - old_width

         return pad(_lowerCamelCase     ,			((0, pad_height), (0, pad_width))     ,			mode='symmetric'     ,			data_format=_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = ChannelDimension.FIRST     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	str		    =  do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase	:	Any		    =  rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase	:	Tuple		    =  do_pad if do_pad is not None else self.do_pad
         __UpperCamelCase	:	List[Any]		    =  pad_size if pad_size is not None else self.pad_size

         __UpperCamelCase	:	str		    =  make_list_of_images(_lowerCamelCase    )

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

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

         # All transformations expect numpy arrays.
         __UpperCamelCase	:	str		    =  [to_numpy_array(_lowerCamelCase    ) for image in images]

         if do_rescale:
             __UpperCamelCase	:	Optional[int]		    =  [self.rescale(image=_lowerCamelCase     ,			scale=_lowerCamelCase    ) for image in images]

         if do_pad:
             __UpperCamelCase	:	Optional[Any]		    =  [self.pad(_lowerCamelCase     ,			size=_lowerCamelCase    ) for image in images]

         __UpperCamelCase	:	Optional[Any]		    =  [to_channel_dimension_format(_lowerCamelCase     ,			_lowerCamelCase    ) for image in images]

         __UpperCamelCase	:	List[Any]		    =  {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase     ,			tensor_type=_lowerCamelCase    )




'''simple docstring'''
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     @register_to_config
     def __init__(		self     ,			*,
         _lowerCamelCase = 4     ,			_lowerCamelCase = 7_6_8     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         super().__init__()

         __UpperCamelCase	:	Optional[int]		    =  nn.Parameter(torch.zeros(_lowerCamelCase    )    )

         # parameters for additional clip time embeddings
         __UpperCamelCase	:	Dict		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )

         # parameters for encoder hidden states
         __UpperCamelCase	:	Optional[Any]		    =  clip_extra_context_tokens
         __UpperCamelCase	:	Dict		    =  nn.Linear(
             _lowerCamelCase     ,			self.clip_extra_context_tokens * cross_attention_dim    )
         __UpperCamelCase	:	str		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  nn.LayerNorm(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*, _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __UpperCamelCase	:	int		    =  image_embeddings.shape[0]
             __UpperCamelCase	:	Optional[Any]		    =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
             __UpperCamelCase	:	List[Any]		    =  classifier_free_guidance_embeddings.expand(
                 _lowerCamelCase     ,			-1    )
             __UpperCamelCase	:	Optional[Any]		    =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,			dim=0    )

         # The image embeddings batch size and the text embeddings batch size are equal
         assert image_embeddings.shape[0] == prompt_embeds.shape[0]

         __UpperCamelCase	:	Optional[Any]		    =  prompt_embeds.shape[0]

         # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
         # adding CLIP embeddings to the existing timestep embedding, ...
         __UpperCamelCase	:	Optional[int]		    =  self.embedding_proj(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  self.clip_image_embeddings_project_to_time_embeddings(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  time_projected_image_embeddings + time_projected_prompt_embeds

         # ... and by projecting CLIP embeddings into four
         # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
         __UpperCamelCase	:	Optional[int]		    =  self.clip_extra_context_tokens_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  clip_extra_context_tokens.reshape(_lowerCamelCase     ,			-1     ,			self.clip_extra_context_tokens    )
         __UpperCamelCase	:	Dict		    =  clip_extra_context_tokens.permute(0     ,			2     ,			1    )

         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder_hidden_states_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.text_encoder_hidden_states_norm(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,			dim=1    )

         return text_encoder_hidden_states, additive_clip_time_embeddings



