import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_lowerCamelCase   :				Any     			=						logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_lowerCamelCase   :				Any     			=						{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_lowerCamelCase   :				Optional[int]     			=						{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowerCamelCase   :				str     			=						sorted(arg_to_scheduler.keys())
_lowerCamelCase   :				Dict     			=						'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class   lowercase      (       pl.LightningModule       ):




							def __init__(  self				:   int		,							_UpperCamelCase				:   argparse.Namespace		,							_UpperCamelCase				:   int=None		,							_UpperCamelCase				:   Union[str, Any]="base"		,							_UpperCamelCase				:   int=None		,							_UpperCamelCase				:   Optional[Any]=None		,							_UpperCamelCase				:   Optional[Any]=None		,							**_UpperCamelCase				:   Dict		,							)   ->			List[Any]:



												'''simple docstring'''

												super().__init__()
												# TODO: move to self.save_hyperparameters()
												# self.save_hyperparameters()
												# can also expand arguments into trainer signature for easier reading

												self.save_hyperparameters(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    Path(self.hparams.output_dir )
												SCREAMING_SNAKE_CASE           =    self.hparams.cache_dir if self.hparams.cache_dir else None
												if config is None:
																	SCREAMING_SNAKE_CASE           =    AutoConfig.from_pretrained(
																	    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,							**({"num_labels": num_labels} if num_labels is not None else {})		,							cache_dir=_UpperCamelCase		,							**_UpperCamelCase		,							)
												else:
																	SCREAMING_SNAKE_CASE           =    config

												SCREAMING_SNAKE_CASE           =    ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
												for p in extra_model_params:
																	if getattr(self.hparams		,							_UpperCamelCase		,							_UpperCamelCase ):
																						assert hasattr(self.config		,							_UpperCamelCase ), F"model config doesn't have a `{p}` attribute"
																						setattr(self.config		,							_UpperCamelCase		,							getattr(self.hparams		,							_UpperCamelCase ) )

												if tokenizer is None:
																	SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained(
																	    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,							cache_dir=_UpperCamelCase		,							)
												else:
																	SCREAMING_SNAKE_CASE           =    tokenizer
												SCREAMING_SNAKE_CASE           =    MODEL_MODES[mode]
												if model is None:
																	SCREAMING_SNAKE_CASE           =    self.model_type.from_pretrained(
																	    self.hparams.model_name_or_path		,							from_tf=bool(".ckpt" in self.hparams.model_name_or_path )		,							config=self.config		,							cache_dir=_UpperCamelCase		,							)
												else:
																	SCREAMING_SNAKE_CASE           =    model




							def 		__snake_case(  self				:   Optional[Any]		,							*_UpperCamelCase				:   List[Any]		,							**_UpperCamelCase				:   Dict )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_type.from_pretrained(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   str )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    arg_to_scheduler[self.hparams.lr_scheduler]
												SCREAMING_SNAKE_CASE           =    get_schedule_func(
												    self.opt		,							num_warmup_steps=self.hparams.warmup_steps		,							num_training_steps=self.total_steps() )
												SCREAMING_SNAKE_CASE           =    {"scheduler": scheduler, "interval": "step", "frequency": 1}
												return scheduler




							def 		__snake_case(  self				:   int )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model
												SCREAMING_SNAKE_CASE           =    ["bias", "LayerNorm.weight"]
												SCREAMING_SNAKE_CASE           =    [
												    {
												        "params": [
												            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
												        ],  # check this named paramters
												        "weight_decay": self.hparams.weight_decay,
												    },
												    {
												        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
												        "weight_decay": 0.0,
												    },
												]
												if self.hparams.adafactor:
																	SCREAMING_SNAKE_CASE           =    Adafactor(
																	    _UpperCamelCase		,							lr=self.hparams.learning_rate		,							scale_parameter=_UpperCamelCase		,							relative_step=_UpperCamelCase )

												else:
																	SCREAMING_SNAKE_CASE           =    AdamW(
																	    _UpperCamelCase		,							lr=self.hparams.learning_rate		,							eps=self.hparams.adam_epsilon )
												SCREAMING_SNAKE_CASE           =    optimizer

												SCREAMING_SNAKE_CASE           =    self.get_lr_scheduler()

												return [optimizer], [scheduler]




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Tuple )   ->			Optional[Any]:



												'''simple docstring'''

												return self.validation_step(_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Union[str, Any] )   ->			Optional[Any]:



												'''simple docstring'''

												return self.validation_end(_UpperCamelCase )




							def 		__snake_case(  self				:   Tuple )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    max(1		,							self.hparams.gpus )  # TODO: consider num_tpu_cores
												SCREAMING_SNAKE_CASE           =    self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
												return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Tuple )   ->			Optional[Any]:



												'''simple docstring'''

												if stage == "test":
																	SCREAMING_SNAKE_CASE           =    len(self.test_dataloader().dataset )
												else:
																	SCREAMING_SNAKE_CASE           =    self.get_dataloader("train"		,							self.hparams.train_batch_size		,							shuffle=_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    len(self.train_dataloader().dataset )




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   bool = False )   ->			Optional[int]:



												'''simple docstring'''

												raise NotImplementedError("You must implement this for your task" )




							def 		__snake_case(  self				:   Any )   ->			str:



												'''simple docstring'''

												return self.train_loader




							def 		__snake_case(  self				:   str )   ->			int:



												'''simple docstring'''

												return self.get_dataloader("dev"		,							self.hparams.eval_batch_size		,							shuffle=_UpperCamelCase )




							def 		__snake_case(  self				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												return self.get_dataloader("test"		,							self.hparams.eval_batch_size		,							shuffle=_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Any )   ->			Any:



												'''simple docstring'''

												return os.path.join(
												    self.hparams.data_dir		,							"cached_{}_{}_{}".format(
												        _UpperCamelCase		,							list(filter(_UpperCamelCase		,							self.hparams.model_name_or_path.split("/" ) ) ).pop()		,							str(self.hparams.max_seq_length )		,							)		,							)




							@pl.utilities.rank_zero_only
							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Dict[str, Any] )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.output_dir.joinpath("best_tfmr" )
												SCREAMING_SNAKE_CASE           =    self.step_count
												self.model.save_pretrained(_UpperCamelCase )
												self.tokenizer.save_pretrained(_UpperCamelCase )





							@staticmethod
							def 		__snake_case(  _UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Optional[int] )   ->			Any:



												'''simple docstring'''

												parser.add_argument(
												    "--model_name_or_path"		,							default=_UpperCamelCase		,							type=_UpperCamelCase		,							required=_UpperCamelCase		,							help="Path to pretrained model or model identifier from huggingface.co/models"		,							)
												parser.add_argument(
												    "--config_name"		,							default=""		,							type=_UpperCamelCase		,							help="Pretrained config name or path if not the same as model_name" )
												parser.add_argument(
												    "--tokenizer_name"		,							default=_UpperCamelCase		,							type=_UpperCamelCase		,							help="Pretrained tokenizer name or path if not the same as model_name"		,							)
												parser.add_argument(
												    "--cache_dir"		,							default=str(Path(_UpperCamelCase ).parent / "test_run" / "cache" )		,							type=_UpperCamelCase		,							help="Where do you want to store the pre-trained models downloaded from huggingface.co"		,							)
												parser.add_argument(
												    "--encoder_layerdrop"		,							type=_UpperCamelCase		,							help="Encoder layer dropout probability (Optional). Goes into model.config"		,							)
												parser.add_argument(
												    "--decoder_layerdrop"		,							type=_UpperCamelCase		,							help="Decoder layer dropout probability (Optional). Goes into model.config"		,							)
												parser.add_argument(
												    "--dropout"		,							type=_UpperCamelCase		,							help="Dropout probability (Optional). Goes into model.config"		,							)
												parser.add_argument(
												    "--attention_dropout"		,							type=_UpperCamelCase		,							help="Attention dropout probability (Optional). Goes into model.config"		,							)
												parser.add_argument("--learning_rate"		,							default=5e-5		,							type=_UpperCamelCase		,							help="The initial learning rate for Adam." )
												parser.add_argument(
												    "--lr_scheduler"		,							default="linear"		,							choices=_UpperCamelCase		,							metavar=_UpperCamelCase		,							type=_UpperCamelCase		,							help="Learning rate scheduler"		,							)
												parser.add_argument("--weight_decay"		,							default=0.0		,							type=_UpperCamelCase		,							help="Weight decay if we apply some." )
												parser.add_argument("--adam_epsilon"		,							default=1e-8		,							type=_UpperCamelCase		,							help="Epsilon for Adam optimizer." )
												parser.add_argument("--warmup_steps"		,							default=0		,							type=_UpperCamelCase		,							help="Linear warmup over warmup_steps." )
												parser.add_argument("--num_workers"		,							default=4		,							type=_UpperCamelCase		,							help="kwarg passed to DataLoader" )
												parser.add_argument("--num_train_epochs"		,							dest="max_epochs"		,							default=3		,							type=_UpperCamelCase )
												parser.add_argument("--train_batch_size"		,							default=32		,							type=_UpperCamelCase )
												parser.add_argument("--eval_batch_size"		,							default=32		,							type=_UpperCamelCase )
												parser.add_argument("--adafactor"		,							action="store_true" )



class   lowercase      (       pl.Callback       ):




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Any )   ->			Tuple:



												'''simple docstring'''

												if (
												    trainer.is_global_zero and trainer.global_rank == 0
												):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
																	pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.



class   lowercase      (       pl.Callback       ):




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   int )   ->			Optional[int]:



												'''simple docstring'''

												for name, param in pl_module.model.rag.named_parameters():
																	if param.grad is None:
																						print(_UpperCamelCase )



class   lowercase      (       pl.Callback       ):




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   int )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    trainer.lr_schedulers[0]["scheduler"]
												SCREAMING_SNAKE_CASE           =    {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
												pl_module.logger.log_metrics(_UpperCamelCase )




							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   pl.Trainer		,							_UpperCamelCase				:   pl.LightningModule )   ->			List[str]:



												'''simple docstring'''

												rank_zero_info("***** Validation results *****" )
												SCREAMING_SNAKE_CASE           =    trainer.callback_metrics
												# Log results
												for key in sorted(_UpperCamelCase ):
																	if key not in ["log", "progress_bar"]:
																						rank_zero_info("{} = {}\n".format(_UpperCamelCase		,							str(metrics[key] ) ) )





							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   pl.Trainer		,							_UpperCamelCase				:   pl.LightningModule )   ->			Dict:



												'''simple docstring'''

												rank_zero_info("***** Test results *****" )
												SCREAMING_SNAKE_CASE           =    trainer.callback_metrics
												# Log and save results to file
												SCREAMING_SNAKE_CASE           =    os.path.join(pl_module.hparams.output_dir		,							"test_results.txt" )
												with open(_UpperCamelCase		,							"w" ) as writer:
																	for key in sorted(_UpperCamelCase ):
																						if key not in ["log", "progress_bar"]:
																											rank_zero_info("{} = {}\n".format(_UpperCamelCase		,							str(metrics[key] ) ) )
																											writer.write("{} = {}\n".format(_UpperCamelCase		,							str(metrics[key] ) ) )





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Tuple					):
					#  To allow all pl args uncomment the following line
					#  parser = pl.Trainer.add_argparse_args(parser)
					parser.add_argument(
					    "--output_dir" ,	default=str(Path(UpperCAmelCase__					).parent / "test_run" / "model_checkpoints"					) ,	type=UpperCAmelCase__ ,	help="The output directory where the model predictions and checkpoints will be written." ,	)
					parser.add_argument(
					    "--fp16" ,	action="store_true" ,	help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" ,	)

					parser.add_argument(
					    "--fp16_opt_level" ,	type=UpperCAmelCase__ ,	default="O2" ,	help=(
					        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
					        "See details at https://nvidia.github.io/apex/amp.html"
					    ) ,	)
					parser.add_argument("--n_tpu_cores" ,	dest="tpu_cores" ,	type=UpperCAmelCase__					)
					parser.add_argument("--max_grad_norm" ,	dest="gradient_clip_val" ,	default=1.0 ,	type=UpperCAmelCase__ ,	help="Max gradient norm"					)
					parser.add_argument("--do_train" ,	action="store_true" ,	help="Whether to run training."					)
					parser.add_argument("--do_predict" ,	action="store_true" ,	help="Whether to run predictions on the test set."					)
					parser.add_argument(
					    "--gradient_accumulation_steps" ,	dest="accumulate_grad_batches" ,	type=UpperCAmelCase__ ,	default=1 ,	help="Number of updates steps to accumulate before performing a backward/update pass." ,	)
					parser.add_argument("--seed" ,	type=UpperCAmelCase__ ,	default=4_2 ,	help="random seed for initialization"					)
					parser.add_argument(
					    "--data_dir" ,	default=str(Path(UpperCAmelCase__					).parent / "test_run" / "dummy-train-data"					) ,	type=UpperCAmelCase__ ,	help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." ,	)





def 		__lowerCamelCase							(UpperCAmelCase__       :							BaseTransformer ,	UpperCAmelCase__       :							argparse.Namespace ,	UpperCAmelCase__       :							Optional[int]=None ,	UpperCAmelCase__       :							Dict=True ,	UpperCAmelCase__       :							List[str]=[] ,	UpperCAmelCase__       :							str=None ,	UpperCAmelCase__       :							Optional[Any]=None ,	**UpperCAmelCase__       :							Union[str, Any] ,	):
					pl.seed_everything(args.seed					)

					# init model
					SCREAMING_SNAKE_CASE           =    Path(model.hparams.output_dir					)
					odir.mkdir(exist_ok=UpperCAmelCase__					)

					# add custom checkpoints
					if checkpoint_callback is None:
										SCREAMING_SNAKE_CASE           =    pl.callbacks.ModelCheckpoint(
										    filepath=args.output_dir ,	prefix="checkpoint" ,	monitor="val_loss" ,	mode="min" ,	save_top_k=1					)
					if early_stopping_callback:
										extra_callbacks.append(UpperCAmelCase__					)
					if logging_callback is None:
										SCREAMING_SNAKE_CASE           =    LoggingCallback()

					SCREAMING_SNAKE_CASE           =    {}

					if args.fpaa:
										SCREAMING_SNAKE_CASE           =    1_6

					if args.gpus > 1:
										SCREAMING_SNAKE_CASE           =    "auto"
										SCREAMING_SNAKE_CASE           =    "ddp"

					SCREAMING_SNAKE_CASE           =    args.accumulate_grad_batches
					SCREAMING_SNAKE_CASE           =    None
					SCREAMING_SNAKE_CASE           =    "auto"

					SCREAMING_SNAKE_CASE           =    pl.Trainer.from_argparse_args(
					    UpperCAmelCase__ ,	weights_summary=UpperCAmelCase__ ,	callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] ,	logger=UpperCAmelCase__ ,	val_check_interval=1 ,	num_sanity_val_steps=2 ,	**UpperCAmelCase__ ,	)

					if args.do_train:
										trainer.fit(UpperCAmelCase__					)

					else:
										print("RAG modeling tests with new set functions successfuly executed!"					)
					return trainer





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class   lowercase      :
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    int
							lowercase__  :    int
							lowercase__  :    float
							lowercase__  :    float
							lowercase__  :    Tuple[int]




							def 		__snake_case(  self				:   str )   ->			Dict:



												'''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 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Tuple )   ->			List[str]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Any )   ->			torch.Tensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    torch.arange(self.height * self.width )
												SCREAMING_SNAKE_CASE           =    torch.stack(
												    [
												        pixel_indices % self.width,
												        torch.div(_UpperCamelCase		,							self.width		,							rounding_mode="trunc" ),
												    ]		,							axis=1		,							)
												return coords




							@property
							def 		__snake_case(  self				:   Any )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				*SCREAMING_SNAKE_CASE           =    self.shape
												SCREAMING_SNAKE_CASE           =    int(np.prod(_UpperCamelCase ) )

												SCREAMING_SNAKE_CASE           =    self.get_image_coords()
												SCREAMING_SNAKE_CASE           =    torch.broadcast_to(coords.unsqueeze(0 )		,							[batch_size * inner_batch_size, *coords.shape] )
												SCREAMING_SNAKE_CASE           =    self.get_camera_rays(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    rays.view(_UpperCamelCase		,							inner_batch_size * self.height * self.width		,							2		,							3 )

												return rays




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   torch.Tensor )   ->			torch.Tensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				*SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    coords.shape
												assert n_coords == 2
												assert batch_size == self.origin.shape[0]

												SCREAMING_SNAKE_CASE           =    coords.view(_UpperCamelCase		,							-1		,							2 )

												SCREAMING_SNAKE_CASE           =    self.resolution()
												SCREAMING_SNAKE_CASE           =    self.fov()

												SCREAMING_SNAKE_CASE           =    (flat.float() / (res - 1)) * 2 - 1
												SCREAMING_SNAKE_CASE           =    fracs * torch.tan(fov / 2 )

												SCREAMING_SNAKE_CASE           =    fracs.view(_UpperCamelCase		,							-1		,							2 )
												SCREAMING_SNAKE_CASE           =    (
												    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           =    directions / directions.norm(dim=-1		,							keepdim=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    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 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int )   ->			"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=_UpperCamelCase		,							height=_UpperCamelCase		,							x_fov=self.x_fov		,							y_fov=self.y_fov		,							)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					for theta in np.linspace(0 ,	2 * np.pi ,	num=2_0					):
										SCREAMING_SNAKE_CASE           =    np.array([np.sin(UpperCAmelCase__					), np.cos(UpperCAmelCase__					), -0.5]					)
										z /= np.sqrt(np.sum(z**2					)					)
										SCREAMING_SNAKE_CASE           =    -z * 4
										SCREAMING_SNAKE_CASE           =    np.array([np.cos(UpperCAmelCase__					), -np.sin(UpperCAmelCase__					), 0.0]					)
										SCREAMING_SNAKE_CASE           =    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__					)) ,	)




