


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



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






		def _snake_case		(						self				:     Tuple):
				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				:     Any):
				return torch.from_numpy(np.array([self.width, self.height]   ,  dtype=np.floataa))






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






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






		@property
		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_     ,				*SCREAMING_SNAKE_CASE_				:Tuple				     =						self.shape
				SCREAMING_SNAKE_CASE_				:Tuple				     =						int(np.prod(UpperCAmelCase))

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

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						rays.view(UpperCAmelCase   ,  inner_batch_size * self.height * self.width   ,  2   ,  3)

				return rays






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     torch.Tensor):
				SCREAMING_SNAKE_CASE_     ,				*SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[str]				     =						coords.shape
				assert n_coords == 2
				assert batch_size == self.origin.shape[0]

				SCREAMING_SNAKE_CASE_				:str				     =						coords.view(UpperCAmelCase   ,  -1   ,  2)

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

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

				SCREAMING_SNAKE_CASE_				:str				     =						fracs.view(UpperCAmelCase   ,  -1   ,  2)
				SCREAMING_SNAKE_CASE_				:str				     =						(
				    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_				:Optional[int]				     =						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):
				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     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[]
		SCREAMING_SNAKE_CASE_				:Any				     =						[]
		SCREAMING_SNAKE_CASE_				:int				     =						[]
		SCREAMING_SNAKE_CASE_				:Any				     =						[]
		for theta in np.linspace(0     , 2 * np.pi     , num=20 ):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						np.array([np.sin(a ), np.cos(a ), -0.5] )
				z /= np.sqrt(np.sum(z**2 ) )
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						-z * 4
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						np.array([np.cos(a ), -np.sin(a ), 0.0] )
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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 ))     , )










from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     lowercase	(    a ):



		'''simple docstring'''




		if not path:
				return "pipe"

		for ext in PipelineDataFormat.SUPPORTED_FORMATS:
				if path.endswith(a ):
						return ext

		raise Exception(
		    F"Unable to determine file format from file extension {path}. "
		    F"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}" )


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						pipeline(
		    task=args.task     , model=args.model if args.model else None     , config=args.config     , tokenizer=args.tokenizer     , device=args.device     , )
		SCREAMING_SNAKE_CASE_				:Any				     =						try_infer_format_from_ext(args.input ) if args.format == "infer" else args.format
		SCREAMING_SNAKE_CASE_				:str				     =						PipelineDataFormat.from_str(
		    format=a     , output_path=args.output     , input_path=args.input     , column=args.column if args.column else nlp.default_input_names     , overwrite=args.overwrite     , )
		return RunCommand(a     , a )



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     List[Any]   ,  UpperCAmelCase				:     Pipeline   ,  UpperCAmelCase				:     PipelineDataFormat):
				SCREAMING_SNAKE_CASE_				:int				     =						nlp
				SCREAMING_SNAKE_CASE_				:Any				     =						reader






		@staticmethod
		def _snake_case		(						UpperCAmelCase				:     ArgumentParser):
				SCREAMING_SNAKE_CASE_				:Any				     =						parser.add_parser("run"   ,  help="Run a pipeline through the CLI")
				run_parser.add_argument("--task"   ,  choices=get_supported_tasks()   ,  help="Task to run")
				run_parser.add_argument("--input"   ,  type=UpperCAmelCase   ,  help="Path to the file to use for inference")
				run_parser.add_argument("--output"   ,  type=UpperCAmelCase   ,  help="Path to the file that will be used post to write results.")
				run_parser.add_argument("--model"   ,  type=UpperCAmelCase   ,  help="Name or path to the model to instantiate.")
				run_parser.add_argument("--config"   ,  type=UpperCAmelCase   ,  help="Name or path to the model's config to instantiate.")
				run_parser.add_argument(
				    "--tokenizer"   ,  type=UpperCAmelCase   ,  help="Name of the tokenizer to use. (default: same as the model name)")
				run_parser.add_argument(
				    "--column"   ,  type=UpperCAmelCase   ,  help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"   ,  )
				run_parser.add_argument(
				    "--format"   ,  type=UpperCAmelCase   ,  default="infer"   ,  choices=PipelineDataFormat.SUPPORTED_FORMATS   ,  help="Input format to read from"   ,  )
				run_parser.add_argument(
				    "--device"   ,  type=UpperCAmelCase   ,  default=-1   ,  help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"   ,  )
				run_parser.add_argument("--overwrite"   ,  action="store_true"   ,  help="Allow overwriting the output file.")
				run_parser.set_defaults(func=UpperCAmelCase)







		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self._nlp, []

				for entry in self._reader:
						SCREAMING_SNAKE_CASE_				:int				     =						nlp(**UpperCAmelCase) if self._reader.is_multi_columns else nlp(UpperCAmelCase)
						if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
								outputs.append(UpperCAmelCase)
						else:
								outputs += output

        # Saving data
				if self._nlp.binary_output:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self._reader.save_binary(UpperCAmelCase)
						logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}")
				else:
						self._reader.save(UpperCAmelCase)






