

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch






@dataclass
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__							=42  # [batch_size x 3]
			lowerCamelCase__							=42  # [batch_size x 3]
			lowerCamelCase__							=42  # [batch_size x 3]
			lowerCamelCase__							=42  # [batch_size x 3]
			lowerCamelCase__							=42
			lowerCamelCase__							=42
			lowerCamelCase__							=42
			lowerCamelCase__							=42
			lowerCamelCase__							=42
			def 							__UpperCamelCase       (	self							:       int		)							->				List[str]:

				"""simple docstring"""

				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 							__UpperCamelCase       (	self							:       Dict		)							->				int:

				"""simple docstring"""

				return torch.from_numpy(np.array([self.width, self.height]					,					dtype=np.floataa		)		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				str:

				"""simple docstring"""

				return torch.from_numpy(np.array([self.x_fov, self.y_fov]					,					dtype=np.floataa		)		)
			def 							__UpperCamelCase       (	self							:       Any		)							->				torch.Tensor:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				torch.arange(self.height * self.width		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				torch.stack(
				    [
				        pixel_indices % self.width,
				        torch.div(__lowercase					,					self.width					,					rounding_mode="trunc"		),
				    ]					,					axis=1					,					)
				return coords
			@property
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.shape
				SCREAMING_SNAKE_CASE      :					int    =				int(np.prod(__lowercase		)		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				self.get_image_coords()
				SCREAMING_SNAKE_CASE      :					str    =				torch.broadcast_to(coords.unsqueeze(0		)					,					[batch_size * inner_batch_size, *coords.shape]		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.get_camera_rays(__lowercase		)

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

				return rays
			def 							__UpperCamelCase       (	self							:       Optional[int]					,					a							:       str		)							->				torch.Tensor:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				coords.shape
				assert n_coords == 2
				assert batch_size == self.origin.shape[0]

				SCREAMING_SNAKE_CASE      :					int    =				coords.view(__lowercase					,					-1					,					2		)

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

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

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






			def 							__UpperCamelCase       (	self							:       Optional[Any]					,					a							:       Any					,					a							:       Union[str, Any]		)							->				"DifferentiableProjectiveCamera":

				"""simple docstring"""

				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=__lowercase					,					height=__lowercase					,					x_fov=self.x_fov					,					y_fov=self.y_fov					,					)

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



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_UpperCAmelCase	:     Dict   ={
    """susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
    """susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							"""ernie_m"""
						SCREAMING_SNAKE_CASE__					:     Dict[str, str]    =							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




						def __init__( self      ,  __lowercase = 2_5_0_0_0_2      ,  __lowercase = 7_6_8      ,  __lowercase = 1_2      ,  __lowercase = 1_2      ,  __lowercase = 3_0_7_2      ,  __lowercase = "gelu"      ,  __lowercase = 0.1      ,  __lowercase = 0.1      ,  __lowercase = 5_1_4      ,  __lowercase = 0.02      ,  __lowercase = 1      ,  __lowercase = 1e-05      ,  __lowercase=None      ,  __lowercase=False      ,  __lowercase=0.0      ,  **__lowercase      ,  )    ->			Tuple:
										super().__init__(pad_token_id=__lowercase      ,  **__lowercase							)
										lowerCAmelCase_       :					Tuple			 = vocab_size
										lowerCAmelCase_       :					Dict			 = hidden_size
										lowerCAmelCase_       :					Tuple			 = num_hidden_layers
										lowerCAmelCase_       :					int			 = num_attention_heads
										lowerCAmelCase_       :					Dict			 = intermediate_size
										lowerCAmelCase_       :					int			 = hidden_act
										lowerCAmelCase_       :					Union[str, Any]			 = hidden_dropout_prob
										lowerCAmelCase_       :					Any			 = attention_probs_dropout_prob
										lowerCAmelCase_       :					Union[str, Any]			 = max_position_embeddings
										lowerCAmelCase_       :					str			 = initializer_range
										lowerCAmelCase_       :					List[str]			 = layer_norm_eps
										lowerCAmelCase_       :					List[Any]			 = classifier_dropout
										lowerCAmelCase_       :					Any			 = is_decoder
										lowerCAmelCase_       :					List[Any]			 = act_dropout