


import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class  lowerCAmelCase__:






    '''simple docstring'''





    def __init__(	self				:   str       ,   __snake_case				:   str=2       ,   __snake_case				:   Any=3       ,   __snake_case				:   Optional[Any]=64       ,   __snake_case				:   List[Any]=None	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   np.random.default_rng(__snake_case	)
       UpperCAmelCase_							:      str						=   length
       UpperCAmelCase_							:      Union[str, Any]						=   rng.normal(size=(length,)	).astype(np.floataa	)
       UpperCAmelCase_							:      int						=   a * self.x + b + rng.normal(scale=0.1       ,   size=(length,)	).astype(np.floataa	)





    def __len__(	self				:   Tuple	):





       '''simple docstring'''
       return self.length





    def __getitem__(	self				:   List[Any]       ,   __snake_case				:   List[str]	):





       '''simple docstring'''
       return {"x": self.x[i], "y": self.y[i]}





class  lowerCAmelCase__(    torch.nn.Module						):






    '''simple docstring'''





    def __init__(	self				:   List[Any]       ,   __snake_case				:   List[Any]=0       ,   __snake_case				:   Union[str, Any]=0       ,   __snake_case				:   str=False	):





       '''simple docstring'''
       super().__init__()
       UpperCAmelCase_							:      Optional[Any]						=   torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
       UpperCAmelCase_							:      Any						=   torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
       UpperCAmelCase_							:      List[Any]						=   True





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   Any=None	):





       '''simple docstring'''
       if self.first_batch:
          print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''	)
          UpperCAmelCase_							:      Any						=   False
       return x * self.a[0] + self.b[0]





class  lowerCAmelCase__(    torch.nn.Module						):






    '''simple docstring'''





    def __init__(	self				:   Dict       ,   __snake_case				:   List[str]=0       ,   __snake_case				:   Optional[int]=0       ,   __snake_case				:   Union[str, Any]=False	):





       '''simple docstring'''
       super().__init__()
       UpperCAmelCase_							:      Optional[int]						=   torch.nn.Parameter(torch.tensor(__snake_case	).float()	)
       UpperCAmelCase_							:      Dict						=   torch.nn.Parameter(torch.tensor(__snake_case	).float()	)
       UpperCAmelCase_							:      List[str]						=   True





    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   str=None	):





       '''simple docstring'''
       if self.first_batch:
          print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''	)
          UpperCAmelCase_							:      int						=   False
       return x * self.a + self.b





def      snake_case_						( __lowercase			,       __lowercase = 1_6       ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

   UpperCAmelCase_							:      str						=   AutoTokenizer.from_pretrained('''bert-base-cased'''       )
   UpperCAmelCase_							:      Dict						=   {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
   UpperCAmelCase_							:      Optional[Any]						=   load_dataset('''csv'''			,       data_files=__lowercase       )
   UpperCAmelCase_							:      Optional[int]						=   datasets['''train'''].unique('''label'''       )

   UpperCAmelCase_							:      str						=   {v: i for i, v in enumerate(__lowercase       )}

   def tokenize_function(__lowercase       ):
      # max_length=None => use the model max length (it's actually the default)
      UpperCAmelCase_							:      Optional[Any]						=   tokenizer(
          examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=__lowercase			,       max_length=__lowercase			,       padding='''max_length'''       )
      if "label" in examples:
         UpperCAmelCase_							:      int						=   [label_to_id[l] for l in examples['''label''']]
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   UpperCAmelCase_							:      int						=   datasets.map(
       __lowercase			,       batched=__lowercase			,       remove_columns=['''sentence1''', '''sentence2''', '''label''']			,       )

   def collate_fn(__lowercase       ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(__lowercase			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''       )
      return tokenizer.pad(__lowercase			,       padding='''longest'''			,       return_tensors='''pt'''       )

   # Instantiate dataloaders.
   UpperCAmelCase_							:      Tuple						=   DataLoader(tokenized_datasets['''train''']			,       shuffle=__lowercase			,       collate_fn=__lowercase			,       batch_size=2       )
   UpperCAmelCase_							:      Dict						=   DataLoader(tokenized_datasets['''validation''']			,       shuffle=__lowercase			,       collate_fn=__lowercase			,       batch_size=1       )

   return train_dataloader, eval_dataloader



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class  lowerCAmelCase__:






    '''simple docstring'''





    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     int
    A_  :     int
    A_  :     float
    A_  :     float
    A_  :     Tuple[int]





    def 					_lowerCamelCase (	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 					_lowerCamelCase (	self				:   Tuple	):





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





    def 					_lowerCamelCase (	self				:   Tuple	):





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





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   torch.arange(self.height * self.width	)
       UpperCAmelCase_							:      Any						=   torch.stack(
           [
               pixel_indices % self.width,
               torch.div(__snake_case       ,   self.width       ,   rounding_mode='''trunc'''	),
           ]       ,   axis=1       ,   )
       return coords





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_							:      List[Any]						=   self.shape
       UpperCAmelCase_							:      Optional[Any]						=   int(np.prod(__snake_case	)	)

       UpperCAmelCase_							:      str						=   self.get_image_coords()
       UpperCAmelCase_							:      List[str]						=   torch.broadcast_to(coords.unsqueeze(0	)       ,   [batch_size * inner_batch_size, *coords.shape]	)
       UpperCAmelCase_							:      Tuple						=   self.get_camera_rays(__snake_case	)

       UpperCAmelCase_							:      Union[str, Any]						=   rays.view(__snake_case       ,   inner_batch_size * self.height * self.width       ,   2       ,   3	)

       return rays





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   torch.Tensor	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_		,      UpperCAmelCase_							:      int						=   coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       UpperCAmelCase_							:      str						=   coords.view(__snake_case       ,   -1       ,   2	)

       UpperCAmelCase_							:      List[Any]						=   self.resolution()
       UpperCAmelCase_							:      Optional[Any]						=   self.fov()

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

       UpperCAmelCase_							:      Optional[Any]						=   fracs.view(__snake_case       ,   -1       ,   2	)
       UpperCAmelCase_							:      List[Any]						=   (
           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:]
       )
       UpperCAmelCase_							:      Union[str, Any]						=   directions / directions.norm(dim=-1       ,   keepdim=__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   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				:   Any       ,   __snake_case				:   int       ,   __snake_case				:   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=__snake_case       ,   height=__snake_case       ,   x_fov=self.x_fov       ,   y_fov=self.y_fov       ,   )





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