

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   List[str]       				=  ["image_processor", "tokenizer"]
	_lowercase  :   Dict       				=  "AutoImageProcessor"
	_lowercase  :   List[Any]       				=  "AutoTokenizer"







	def __init__(   self: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





						super().__init__(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    self.image_processor







	def __call__(   self: Optional[Any]  , __UpperCamelCase: Optional[Any]=None  , __UpperCamelCase: int=None  , __UpperCamelCase: Optional[int]=None  , **__UpperCamelCase: Tuple      ):

						'''simple docstring'''





						if text is None and images is None:
											raise ValueError('You have to specify either text or images. Both cannot be none.'      )

						if text is not None:
											__magic_name__							=    self.tokenizer(__UpperCamelCase  , return_tensors=__UpperCamelCase  , **__UpperCamelCase      )

						if images is not None:
											__magic_name__							=    self.image_processor(__UpperCamelCase  , return_tensors=__UpperCamelCase  , **__UpperCamelCase      )

						if text is not None and images is not None:
											__magic_name__							=    image_features.pixel_values
											return encoding
						elif text is not None:
											return encoding
						else:
											return BatchEncoding(data=dict(**__UpperCamelCase      )  , tensor_type=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , *__UpperCamelCase: Tuple  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return self.tokenizer.batch_decode(*__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , *__UpperCamelCase: Optional[Any]  , **__UpperCamelCase: str      ):

						'''simple docstring'''





						return self.tokenizer.decode(*__UpperCamelCase  , **__UpperCamelCase      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						return ["input_ids", "attention_mask", "pixel_values"]







import torch
from torch import nn

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

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			):







	@register_to_config
	def __init__(   self: Optional[Any]  , *,
	    __UpperCamelCase: int = 4  , __UpperCamelCase: int = 7_68  , __UpperCamelCase: int  , __UpperCamelCase: Any  , ):

						'''simple docstring'''





						super().__init__()

						__magic_name__							=    nn.Parameter(torch.zeros(__UpperCamelCase      )      )

						# parameters for additional clip time embeddings
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )

						# parameters for encoder hidden states
						__magic_name__							=    clip_extra_context_tokens
						__magic_name__							=    nn.Linear(
						    __UpperCamelCase  , self.clip_extra_context_tokens * cross_attention_dim      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.LayerNorm(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , *, __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





						if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__magic_name__							=    image_embeddings.shape[0]
											__magic_name__							=    self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
											__magic_name__							=    classifier_free_guidance_embeddings.expand(
											    __UpperCamelCase  , -1      )
											__magic_name__							=    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]

						__magic_name__							=    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, ...
						__magic_name__							=    self.embedding_proj(__UpperCamelCase      )
						__magic_name__							=    self.clip_image_embeddings_project_to_time_embeddings(__UpperCamelCase      )
						__magic_name__							=    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"
						__magic_name__							=    self.clip_extra_context_tokens_proj(__UpperCamelCase      )
						__magic_name__							=    clip_extra_context_tokens.reshape(__UpperCamelCase  , -1  , self.clip_extra_context_tokens      )
						__magic_name__							=    clip_extra_context_tokens.permute(0  , 2  , 1      )

						__magic_name__							=    self.encoder_hidden_states_proj(__UpperCamelCase      )
						__magic_name__							=    self.text_encoder_hidden_states_norm(__UpperCamelCase      )
						__magic_name__							=    torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]  , dim=1      )

						return text_encoder_hidden_states, additive_clip_time_embeddings




