


'''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							):

							'''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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return torch.from_numpy(np.array([self.width, self.height]					,     dtype=np.floataa							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return torch.from_numpy(np.array([self.x_fov, self.y_fov]					,     dtype=np.floataa							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				torch.arange(self.height * self.width							)
							UpperCamelCase__     :Optional[Any]							      =				torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(UpperCamelCase_					,     self.width					,     rounding_mode='''trunc'''							),
							    ]					,     axis=1					,     )
							return coords




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.shape
							UpperCamelCase__     :Optional[Any]							      =				int(np.prod(UpperCamelCase_							)							)

							UpperCamelCase__     :Optional[int]							      =				self.get_image_coords()
							UpperCamelCase__     :Optional[int]							      =				torch.broadcast_to(coords.unsqueeze(0							)					,     [batch_size * inner_batch_size, *coords.shape]							)
							UpperCamelCase__     :int							      =				self.get_camera_rays(UpperCamelCase_							)

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

							return rays




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							UpperCamelCase__     :Tuple							      =				coords.view(UpperCamelCase_					,     -1					,     2							)

							UpperCamelCase__     :Union[str, Any]							      =				self.resolution()
							UpperCamelCase__     :Tuple							      =				self.fov()

							UpperCamelCase__     :Union[str, Any]							      =				(flat.float() / (res - 1)) * 2 - 1
							UpperCamelCase__     :int							      =				fracs * torch.tan(fov / 2							)

							UpperCamelCase__     :int							      =				fracs.view(UpperCamelCase_					,     -1					,     2							)
							UpperCamelCase__     :Optional[Any]							      =				(
							    self.z.view(UpperCamelCase_					,     1					,     3							)
							    + self.x.view(UpperCamelCase_					,     1					,     3							) * fracs[:, :, :1]
							    + self.y.view(UpperCamelCase_					,     1					,     3							) * fracs[:, :, 1:]
							)
							UpperCamelCase__     :Tuple							      =				directions / directions.norm(dim=-1					,     keepdim=UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				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					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''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=UpperCamelCase_					,     height=UpperCamelCase_					,     x_fov=self.x_fov					,     y_fov=self.y_fov					,     )



def     a   (    __a     )						->			DifferentiableProjectiveCamera:



					'''simple docstring'''






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



'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'transfo-xl'
		_a									=       ['mems']
		_a									=       {
		    'n_token': 'vocab_size',
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'n_head',
		    'num_hidden_layers': 'n_layer',
		}




		def __init__(	self					,     UpperCamelCase_=267735					,     UpperCamelCase_=[20000, 40000, 200000]					,     UpperCamelCase_=1024					,     UpperCamelCase_=1024					,     UpperCamelCase_=16					,     UpperCamelCase_=64					,     UpperCamelCase_=4096					,     UpperCamelCase_=4					,     UpperCamelCase_=False					,     UpperCamelCase_=18					,     UpperCamelCase_=1600					,     UpperCamelCase_=1000					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=0					,     UpperCamelCase_=-1					,     UpperCamelCase_=True					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.0					,     UpperCamelCase_=True					,     UpperCamelCase_="normal"					,     UpperCamelCase_=0.01					,     UpperCamelCase_=0.01					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1e-5					,     UpperCamelCase_=0					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				vocab_size
							UpperCamelCase__     :Optional[int]							      =				[]
							self.cutoffs.extend(UpperCamelCase_							)
							if proj_share_all_but_first:
												UpperCamelCase__     :Any							      =				[False] + [True] * len(self.cutoffs							)
							else:
												UpperCamelCase__     :Dict							      =				[False] + [False] * len(self.cutoffs							)
							UpperCamelCase__     :Union[str, Any]							      =				d_model
							UpperCamelCase__     :List[Any]							      =				d_embed
							UpperCamelCase__     :Any							      =				d_head
							UpperCamelCase__     :Tuple							      =				d_inner
							UpperCamelCase__     :int							      =				div_val
							UpperCamelCase__     :int							      =				pre_lnorm
							UpperCamelCase__     :Any							      =				n_layer
							UpperCamelCase__     :int							      =				n_head
							UpperCamelCase__     :Tuple							      =				mem_len
							UpperCamelCase__     :List[str]							      =				same_length
							UpperCamelCase__     :Any							      =				attn_type
							UpperCamelCase__     :List[str]							      =				clamp_len
							UpperCamelCase__     :Union[str, Any]							      =				sample_softmax
							UpperCamelCase__     :List[Any]							      =				adaptive
							UpperCamelCase__     :List[Any]							      =				dropout
							UpperCamelCase__     :Optional[Any]							      =				dropatt
							UpperCamelCase__     :str							      =				untie_r
							UpperCamelCase__     :List[Any]							      =				init
							UpperCamelCase__     :Optional[Any]							      =				init_range
							UpperCamelCase__     :int							      =				proj_init_std
							UpperCamelCase__     :List[str]							      =				init_std
							UpperCamelCase__     :Optional[int]							      =				layer_norm_epsilon
							super().__init__(eos_token_id=UpperCamelCase_					,     **UpperCamelCase_							)




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''							)
							return -1




		@max_position_embeddings.setter
		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError(
							    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''							)