
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	'biogpt'







	def __init__(     self      :Tuple   ,     lowercase      :Union[str, Any]=4_2_3_8_4   ,     lowercase      :str=1_0_2_4   ,     lowercase      :Any=2_4   ,     lowercase      :List[str]=1_6   ,     lowercase      :List[str]=4_0_9_6   ,     lowercase      :int="gelu"   ,     lowercase      :List[Any]=0.1   ,     lowercase      :int=0.1   ,     lowercase      :int=1_0_2_4   ,     lowercase      :Optional[Any]=0.02   ,     lowercase      :Union[str, Any]=1e-12   ,     lowercase      :Tuple=True   ,     lowercase      :int=True   ,     lowercase      :Optional[int]=0.0   ,     lowercase      :Any=0.0   ,     lowercase      :str=1   ,     lowercase      :Any=0   ,     lowercase      :int=2   ,     **lowercase      :Tuple   ,     )		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= vocab_size
				SCREAMING_SNAKE_CASE     						= max_position_embeddings
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= intermediate_size
				SCREAMING_SNAKE_CASE     						= hidden_act
				SCREAMING_SNAKE_CASE     						= hidden_dropout_prob
				SCREAMING_SNAKE_CASE     						= attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE     						= initializer_range
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= scale_embedding
				SCREAMING_SNAKE_CASE     						= use_cache
				SCREAMING_SNAKE_CASE     						= layerdrop
				SCREAMING_SNAKE_CASE     						= activation_dropout
				super().__init__(pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     **lowercase			)

import torch
from torch import nn

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





class     lowerCamelCase      (       __lowerCamelCase ,		__lowerCamelCase					):







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







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE     						= nn.Parameter(torch.zeros(lowercase			)			)

				# parameters for additional clip time embeddings
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)

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







	def  snake_case__     (     self      :Union[str, Any]   ,     *, lowercase      :Optional[int]   ,     lowercase      :Union[str, Any]   ,     lowercase      :Union[str, Any]   ,     lowercase      :Optional[Any]			)		->      Union[str, Any]:







				"""simple docstring"""






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

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

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

				return text_encoder_hidden_states, additive_clip_time_embeddings