"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)  # pylint: disable=invalid-name



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7_68    ):






										"""simple docstring"""

										super().__init__(lowerCAmelCase    )
										snake_case								=			proj_size

										snake_case								=			CLIPVisionModel(lowerCAmelCase    )
										snake_case								=			PaintByExampleMapper(lowerCAmelCase    )
										snake_case								=			nn.LayerNorm(config.hidden_size    )
										snake_case								=			nn.Linear(config.hidden_size		,    self.proj_size    )

										# uncondition for scaling
										snake_case								=			nn.Parameter(torch.randn((1, 1, self.proj_size)    )    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase=False    ):






										"""simple docstring"""

										snake_case								=			self.model(pixel_values=lowerCAmelCase    )
										snake_case								=			clip_output.pooler_output
										snake_case								=			self.mapper(latent_states[:, None]    )
										snake_case								=			self.final_layer_norm(lowerCAmelCase    )
										snake_case								=			self.proj_out(lowerCAmelCase    )
										if return_uncond_vector:
																return latent_states, self.uncond_vector

										return latent_states





class   lowerCAmelCase_    (			nn.Module					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										super().__init__()
										snake_case								=			(config.num_hidden_layers + 1) // 5
										snake_case								=			config.hidden_size
										snake_case								=			1
										snake_case								=			nn.ModuleList(
										    [
										        BasicTransformerBlock(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    activation_fn='gelu'		,    attention_bias=lowerCAmelCase    )
										        for _ in range(lowerCAmelCase    )
										    ]    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										for block in self.blocks:
																snake_case								=			block(lowerCAmelCase    )

										return hidden_states



"""simple docstring"""
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
					from .camera import create_pan_cameras
					from .pipeline_shap_e import ShapEPipeline
					from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
					from .renderer import (
					    BoundingBoxVolume,
					    ImportanceRaySampler,
					    MLPNeRFModelOutput,
					    MLPNeRSTFModel,
					    ShapEParamsProjModel,
					    ShapERenderer,
					    StratifiedRaySampler,
					    VoidNeRFModel,
					)


