






'''simple docstring'''







from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class _lowercase			:

							'''simple docstring'''





							a          = 42  # [batch_size x 3]
							a          = 42  # [batch_size x 3]
							a          = 42  # [batch_size x 3]
							a          = 42  # [batch_size x 3]
							a          = 42
							a          = 42
							a          = 42
							a          = 42
							a          = 42




							def 	lowerCamelCase_       (							self:       str  ):
														assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
														assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
														assert len(self.x.shape  ) == len(self.y.shape  ) == len(self.z.shape  ) == len(self.origin.shape  ) == 2




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														return torch.from_numpy(np.array([self.width, self.height]    ,							dtype=np.floataa  )  )




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														return torch.from_numpy(np.array([self.x_fov, self.y_fov]    ,							dtype=np.floataa  )  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			Tuple 						= torch.arange(self.height * self.width  )
														lowerCamelCase__      :			List[str] 						= torch.stack(
														    [
														        pixel_indices % self.width,
														        torch.div(UpperCamelCase__    ,							self.width    ,							rounding_mode="""trunc"""  ),
														    ]    ,							axis=1    ,							)
														return coords




							@property
							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Any 						= self.shape
														lowerCamelCase__      :			Optional[Any] 						= int(np.prod(UpperCamelCase__  )  )

														lowerCamelCase__      :			Union[str, Any] 						= self.get_image_coords()
														lowerCamelCase__      :			List[str] 						= torch.broadcast_to(coords.unsqueeze(0  )    ,							[batch_size * inner_batch_size, *coords.shape]  )
														lowerCamelCase__      :			str 						= self.get_camera_rays(UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= rays.view(UpperCamelCase__    ,							inner_batch_size * self.height * self.width    ,							2    ,							3  )

														return rays




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       torch.Tensor  ):
														lowerCamelCase__      :			Optional[int] 						= coords.shape
														assert n_coords == 2
														assert batch_size == self.origin.shape[0]

														lowerCamelCase__      :			List[str] 						= coords.view(UpperCamelCase__    ,							-1    ,							2  )

														lowerCamelCase__      :			Optional[int] 						= self.resolution()
														lowerCamelCase__      :			Optional[Any] 						= self.fov()

														lowerCamelCase__      :			Optional[Any] 						= (flat.float() / (res - 1)) * 2 - 1
														lowerCamelCase__      :			Dict 						= fracs * torch.tan(fov / 2  )

														lowerCamelCase__      :			Union[str, Any] 						= fracs.view(UpperCamelCase__    ,							-1    ,							2  )
														lowerCamelCase__      :			Tuple 						= (
														    self.z.view(UpperCamelCase__    ,							1    ,							3  )
														    + self.x.view(UpperCamelCase__    ,							1    ,							3  ) * fracs[:, :, :1]
														    + self.y.view(UpperCamelCase__    ,							1    ,							3  ) * fracs[:, :, 1:]
														)
														lowerCamelCase__      :			str 						= directions / directions.norm(dim=-1    ,							keepdim=UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= torch.stack(
														    [
														        torch.broadcast_to(self.origin.view(UpperCamelCase__    ,							1    ,							3  )    ,							[batch_size, directions.shape[1], 3]  ),
														        directions,
														    ]    ,							dim=2    ,							)
														return rays.view(UpperCamelCase__    ,							*UpperCamelCase__    ,							2    ,							3  )




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       int  ):
														assert width * self.height == height * self.width, "The aspect ratio should not change."
														return DifferentiableProjectiveCamera(
														    origin=self.origin    ,							x=self.x    ,							y=self.y    ,							z=self.z    ,							width=UpperCamelCase__    ,							height=UpperCamelCase__    ,							x_fov=self.x_fov    ,							y_fov=self.y_fov    ,							)





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> DifferentiableProjectiveCamera:
							lowerCamelCase__      :			Optional[int] 						= []
							lowerCamelCase__      :			Union[str, Any] 						= []
							lowerCamelCase__      :			Dict 						= []
							lowerCamelCase__      :			List[Any] 						= []
							for theta in np.linspace(0       ,    2 * np.pi       ,    num=20   ):
														lowerCamelCase__      :			List[str] 						= np.array([np.sin(UpperCamelCase   ), np.cos(UpperCamelCase   ), -0.5]   )
														z /= np.sqrt(np.sum(z**2   )   )
														lowerCamelCase__      :			Any 						= -z * 4
														lowerCamelCase__      :			int 						= np.array([np.cos(UpperCamelCase   ), -np.sin(UpperCamelCase   ), 0.0]   )
														lowerCamelCase__      :			List[Any] 						= np.cross(UpperCamelCase       ,    UpperCamelCase   )
														origins.append(UpperCamelCase   )
														xs.append(UpperCamelCase   )
														ys.append(UpperCamelCase   )
														zs.append(UpperCamelCase   )
							return DifferentiableProjectiveCamera(
							    origin=torch.from_numpy(np.stack(UpperCamelCase       ,    axis=0   )   ).float()       ,    x=torch.from_numpy(np.stack(UpperCamelCase       ,    axis=0   )   ).float()       ,    y=torch.from_numpy(np.stack(UpperCamelCase       ,    axis=0   )   ).float()       ,    z=torch.from_numpy(np.stack(UpperCamelCase       ,    axis=0   )   ).float()       ,    width=UpperCamelCase       ,    height=UpperCamelCase       ,    x_fov=0.7       ,    y_fov=0.7       ,    shape=(1, len(UpperCamelCase   ))       ,    )









'''simple docstring'''







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


_A			:    Optional[Any]					  =logging.get_logger(__name__)

_A			:    Optional[int]					  ={
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
							a          = """rwkv"""
							a          = {"""max_position_embeddings""": """context_length"""}




							def __init__(							self:       Tuple    ,							UpperCamelCase__:       Optional[Any]=50_277    ,							UpperCamelCase__:       Union[str, Any]=1_024    ,							UpperCamelCase__:       Tuple=4_096    ,							UpperCamelCase__:       List[Any]=32    ,							UpperCamelCase__:       Dict=None    ,							UpperCamelCase__:       Dict=None    ,							UpperCamelCase__:       int=1e-5    ,							UpperCamelCase__:       Any=0    ,							UpperCamelCase__:       str=0    ,							UpperCamelCase__:       Union[str, Any]=6    ,							UpperCamelCase__:       Optional[int]=False    ,							UpperCamelCase__:       Dict=True    ,							**UpperCamelCase__:       Dict    ,							):
														lowerCamelCase__      :			Dict 						= vocab_size
														lowerCamelCase__      :			Optional[Any] 						= context_length
														lowerCamelCase__      :			Optional[Any] 						= hidden_size
														lowerCamelCase__      :			Any 						= num_hidden_layers
														lowerCamelCase__      :			int 						= attention_hidden_size if attention_hidden_size is not None else hidden_size
														lowerCamelCase__      :			Union[str, Any] 						= intermediate_size if intermediate_size is not None else 4 * hidden_size
														lowerCamelCase__      :			List[str] 						= layer_norm_epsilon
														lowerCamelCase__      :			int 						= rescale_every
														lowerCamelCase__      :			Optional[int] 						= use_cache

														lowerCamelCase__      :			Dict 						= bos_token_id
														lowerCamelCase__      :			Any 						= eos_token_id

														super().__init__(
														    tie_word_embeddings=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							**UpperCamelCase__  )

