from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class       lowercase__		:
						UpperCamelCase_  =	42  # [batch_size x 3]
						UpperCamelCase_  =	42  # [batch_size x 3]
						UpperCamelCase_  =	42  # [batch_size x 3]
						UpperCamelCase_  =	42  # [batch_size x 3]
						UpperCamelCase_  =	42
						UpperCamelCase_  =	42
						UpperCamelCase_  =	42
						UpperCamelCase_  =	42
						UpperCamelCase_  =	42


						def  __A ( self			:       List[Any]		):





												'''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  __A ( self			:       str		):





												'''simple docstring'''





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


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       str		):





												'''simple docstring'''





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


						@property
						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





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

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

												SCREAMING_SNAKE_CASE							:					str           =							rays.view(UpperCamelCase__					,	inner_batch_size * self.height * self.width					,	2					,	3		)

												return rays


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       torch.Tensor		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							coords.view(UpperCamelCase__					,	-1					,	2		)

												SCREAMING_SNAKE_CASE							:					Any           =							self.resolution()
												SCREAMING_SNAKE_CASE							:					str           =							self.fov()

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

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





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






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCamelCase	: Dict    		=  [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
						while ask_again:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input(_lowercase		)
												try:
																		if default is not None and len(_lowercase		) == 0:
																								return default
																		return convert_value(_lowercase		) if convert_value is not None else result
												except Exception:
																		if error_message is not None:
																								print(_lowercase		)
def    A			(					_lowercase	,						_lowercase=[]	,						_lowercase=None	,						_lowercase=0		):
						SCREAMING_SNAKE_CASE							:					Dict           =							BulletMenu(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							menu.run(default_choice=_lowercase		)
						return convert_value(_lowercase		) if convert_value is not None else result
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							int(_lowercase		)
						return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return DynamoBackend(DYNAMO_BACKENDS[value]		).value
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]		)
def    A			(					_lowercase		):
						return {"yes": True, "no": False}[value.lower()]





class       lowercase__		(     argparse.RawDescriptionHelpFormatter):


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							super()._format_usage(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							usage.replace('''<command> [<args>] '''					,	''''''		)
												return usage





