
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class  lowercase__(				UpperCAmelCase ,						UpperCAmelCase							):



	"""simple docstring"""




	@register_to_config
	def __init__(       self  :			Tuple       ,							*,
	    SCREAMING_SNAKE_CASE_  :			int = 4       ,							SCREAMING_SNAKE_CASE_  :			int = 7_6_8       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							)       ->     Optional[int]:
					super().__init__()

					lowercase_       =					nn.Parameter(torch.zeros(SCREAMING_SNAKE_CASE_       )       )

					# parameters for additional clip time embeddings
					lowercase_       =					nn.Linear(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					nn.Linear(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

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




	def 						_lowercase  (       self  :			Tuple       ,							*, SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     List[str]:
					if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									lowercase_       =					image_embeddings.shape[0]
									lowercase_       =					self.learned_classifier_free_guidance_embeddings.unsqueeze(0       )
									lowercase_       =					classifier_free_guidance_embeddings.expand(
									    SCREAMING_SNAKE_CASE_       ,							-1       )
									lowercase_       =					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]

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

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

					return text_encoder_hidden_states, additive_clip_time_embeddings






import logging

from transformers.configuration_utils import PretrainedConfig


__a  			=					logging.getLogger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[int]         =	'masked_bert'
	def __init__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=3_0_5_2_2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=7_6_8       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=1_2       ,							SCREAMING_SNAKE_CASE_  :			Any=1_2       ,							SCREAMING_SNAKE_CASE_  :			str=3_0_7_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			str=2       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.02       ,							SCREAMING_SNAKE_CASE_  :			Any=1e-12       ,							SCREAMING_SNAKE_CASE_  :			Any=0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]="topK"       ,							SCREAMING_SNAKE_CASE_  :			Dict="constant"       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.0       ,							**SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							)       ->     Optional[Any]:
					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					hidden_act
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					initializer_range
					lowercase_       =					layer_norm_eps
					lowercase_       =					pruning_method
					lowercase_       =					mask_init
					lowercase_       =					mask_scale




