





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							torch.Tensor  # [batch_size x 3]
   _SCREAMING_SNAKE_CASE				:							torch.Tensor  # [batch_size x 3]
   _SCREAMING_SNAKE_CASE				:							torch.Tensor  # [batch_size x 3]
   _SCREAMING_SNAKE_CASE				:							torch.Tensor  # [batch_size x 3]
   _SCREAMING_SNAKE_CASE				:							int
   _SCREAMING_SNAKE_CASE				:							int
   _SCREAMING_SNAKE_CASE				:							float
   _SCREAMING_SNAKE_CASE				:							float
   _SCREAMING_SNAKE_CASE				:							Tuple[int]
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    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			: List[Any]       ):
    return torch.from_numpy(np.array([self.width, self.height] ,    dtype=np.floataa       )       )
   def 				lowerCAmelCase      (self			: List[Any]       ):
    return torch.from_numpy(np.array([self.x_fov, self.y_fov] ,    dtype=np.floataa       )       )
   def 				lowerCAmelCase      (self			: Any       ):
    __a			:  str		    =  torch.arange(self.height * self.width       )
    __a			:  int		    =  torch.stack(
        [
            pixel_indices % self.width,
            torch.div(snake_case_ ,    self.width ,    rounding_mode='''trunc'''       ),
        ] ,    axis=1 ,    )
    return coords
   @property
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  Any		    =  self.shape
    __a			:  Tuple		    =  int(np.prod(snake_case_       )       )

    __a			:  int		    =  self.get_image_coords()
    __a			:  Optional[int]		    =  torch.broadcast_to(coords.unsqueeze(0       ) ,    [batch_size * inner_batch_size, *coords.shape]       )
    __a			:  Union[str, Any]		    =  self.get_camera_rays(snake_case_       )

    __a			:  Tuple		    =  rays.view(snake_case_ ,    inner_batch_size * self.height * self.width ,    2 ,    3       )

    return rays
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: torch.Tensor       ):
    __a			:  Optional[int]		    =  coords.shape
    assert n_coords == 2
    assert batch_size == self.origin.shape[0]

    __a			:  Optional[int]		    =  coords.view(snake_case_ ,    -1 ,    2       )

    __a			:  Dict		    =  self.resolution()
    __a			:  List[Any]		    =  self.fov()

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

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







   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: int ,    snake_case_			: 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=snake_case_ ,    height=snake_case_ ,    x_fov=self.x_fov ,    y_fov=self.y_fov ,    )





def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ):
 __a			:  List[str]		    =  []
 __a			:  Optional[Any]		    =  []
 __a			:  Any		    =  []
 __a			:  List[str]		    =  []
 for theta in np.linspace(0  ,       2 * np.pi  ,       num=2_0  ):
  __a			:  Optional[Any]		    =  np.array([np.sin(lowerCAmelCase__  ), np.cos(lowerCAmelCase__  ), -0.5]  )
  z /= np.sqrt(np.sum(z**2  )  )
  __a			:  str		    =  -z * 4
  __a			:  Tuple		    =  np.array([np.cos(lowerCAmelCase__  ), -np.sin(lowerCAmelCase__  ), 0.0]  )
  __a			:  Tuple		    =  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__  ))  ,       )


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase__          =50000
lowercase__          =5000

lowercase__       ,      lowercase__          =os.path.split(__file__)
lowercase__          =os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ):
 for i in range(lowerCAmelCase__  ):
  __a			:  str		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Tuple  ):
 for i in range(0  ,       len(lowerCAmelCase__  )  ,       lowerCAmelCase__  ):
  __a			:  Optional[int]		    =  dataset[i : i + batch_size]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    str  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(lowerCAmelCase__  ):
   __a			:  Dict		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    int  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(0  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ):
   __a			:  int		    =  dataset[i : i + batch_size]




def        __UpperCamelCase					(    ):
 __a			:  Any		    =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
 __a			:  List[Any]		    =  [
     (read, {'''length''': SMALL_TEST}),
     (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0_0}),
     (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0_0_0}),
 ]

 __a			:  Union[str, Any]		    =  [
     (read, {'''length''': SMALL_TEST}),
     (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0_0}),
     (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0_0_0}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print('''generating dataset'''  )
  __a			:  Optional[Any]		    =  datasets.Features(
      {'''list''': datasets.Sequence(datasets.Value('''float32'''  )  ), '''numbers''': datasets.Value('''float32'''  )}  )
  __a			:  Optional[int]		    =  generate_example_dataset(
      os.path.join(lowerCAmelCase__  ,       '''dataset.arrow'''  )  ,       lowerCAmelCase__  ,       num_examples=lowerCAmelCase__  ,       seq_shapes={'''list''': (1_0_0,)}  ,       )
  print('''first set of iterations'''  )
  for func, kwargs in functions:
   print(func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  str		    =  func(lowerCAmelCase__  ,       **lowerCAmelCase__  )

  print('''shuffling dataset'''  )
  __a			:  int		    =  dataset.shuffle()
  print('''Second set of iterations (after shuffling'''  )
  for func, kwargs in functions_shuffled:
   print('''shuffled '''  ,       func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  List[Any]		    =  func(
       lowerCAmelCase__  ,       **lowerCAmelCase__  )

 with open(lowerCAmelCase__  ,       '''wb'''  ) as f:
  f.write(json.dumps(lowerCAmelCase__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_iterating()
