"""simple docstring"""



import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 						__UpperCAmelCase    (__A							,   __A     ):







			'''simple docstring'''





			@register_to_config
			def __init__(			self   ,      *,
			    snake_case_ = 4   ,      snake_case_ = 768   ,      snake_case_   ,      snake_case_   ,      ):







										'''simple docstring'''


										super().__init__()

										A__       :     Dict								=			nn.Parameter(torch.zeros(snake_case_							)							)

										# parameters for additional clip time embeddings
										A__       :     List[str]								=			nn.Linear(snake_case_   ,      snake_case_							)
										A__       :     Dict								=			nn.Linear(snake_case_   ,      snake_case_							)

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





			def 		lowerCamelCase					(			self   ,      *, snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


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

										A__       :     Optional[int]								=			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, ...
										A__       :     Tuple								=			self.embedding_proj(snake_case_							)
										A__       :     Optional[Any]								=			self.clip_image_embeddings_project_to_time_embeddings(snake_case_							)
										A__       :     str								=			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"
										A__       :     Union[str, Any]								=			self.clip_extra_context_tokens_proj(snake_case_							)
										A__       :     Optional[int]								=			clip_extra_context_tokens.reshape(snake_case_   ,      -1   ,      self.clip_extra_context_tokens							)
										A__       :     str								=			clip_extra_context_tokens.permute(0   ,      2   ,      1							)

										A__       :     Optional[int]								=			self.encoder_hidden_states_proj(snake_case_							)
										A__       :     List[str]								=			self.text_encoder_hidden_states_norm(snake_case_							)
										A__       :     str								=			torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]   ,      dim=1							)

										return text_encoder_hidden_states, additive_clip_time_embeddings






"""simple docstring"""



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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'glpn'





			def __init__(			self   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=[2, 2, 2, 2]   ,      snake_case_=[8, 4, 2, 1]   ,      snake_case_=[32, 64, 160, 256]   ,      snake_case_=[7, 3, 3, 3]   ,      snake_case_=[4, 2, 2, 2]   ,      snake_case_=[1, 2, 5, 8]   ,      snake_case_=[4, 4, 4, 4]   ,      snake_case_="gelu"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.02   ,      snake_case_=0.1   ,      snake_case_=1E-6   ,      snake_case_=64   ,      snake_case_=10   ,      snake_case_=-1   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     str								=			num_channels
										A__       :     List[Any]								=			num_encoder_blocks
										A__       :     List[str]								=			depths
										A__       :     int								=			sr_ratios
										A__       :     Optional[Any]								=			hidden_sizes
										A__       :     int								=			patch_sizes
										A__       :     str								=			strides
										A__       :     int								=			mlp_ratios
										A__       :     Optional[int]								=			num_attention_heads
										A__       :     Optional[Any]								=			hidden_act
										A__       :     Optional[Any]								=			hidden_dropout_prob
										A__       :     Optional[Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			initializer_range
										A__       :     Dict								=			drop_path_rate
										A__       :     Any								=			layer_norm_eps
										A__       :     Any								=			decoder_hidden_size
										A__       :     Optional[Any]								=			max_depth
										A__       :     List[Any]								=			head_in_index





