


'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch
@dataclass
class   lowercase      :

		"""simple docstring"""

		_a       					=							42  # [batch_size x 3]
		_a       					=							42  # [batch_size x 3]
		_a       					=							42  # [batch_size x 3]
		_a       					=							42  # [batch_size x 3]
		_a       					=							42
		_a       					=							42
		_a       					=							42
		_a       					=							42
		_a       					=							42




		def        lowerCAmelCase__							(						self				):

							'''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				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__   ,							*UpperCamelCase__  :Optional[int]				   =					self.shape
							UpperCamelCase__  :Union[str, Any]				   =					int(np.prod(UpperCamelCase_				)				)

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

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

							return rays




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__   ,							*UpperCamelCase__   ,							UpperCamelCase__  :Dict				   =					coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							UpperCamelCase__  :Tuple				   =					coords.view(UpperCamelCase_       ,  -1       ,  2				)

							UpperCamelCase__  :str				   =					self.resolution()
							UpperCamelCase__  :Optional[int]				   =					self.fov()

							UpperCamelCase__  :Dict				   =					(flat.float() / (res - 1)) * 2 - 1
							UpperCamelCase__  :int				   =					fracs * torch.tan(fov / 2				)

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

							'''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 (     __a  )    ->      DifferentiableProjectiveCamera:



					'''simple docstring'''






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



'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


__snake_case				 =	{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case				 =	{
    '''vocab_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-generator''': (
            '''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case				 =	{
    '''google/electra-small-generator''': 512,
    '''google/electra-base-generator''': 512,
    '''google/electra-large-generator''': 512,
    '''google/electra-small-discriminator''': 512,
    '''google/electra-base-discriminator''': 512,
    '''google/electra-large-discriminator''': 512,
}

__snake_case				 =	{
    '''google/electra-small-generator''': {'''do_lower_case''': True},
    '''google/electra-base-generator''': {'''do_lower_case''': True},
    '''google/electra-large-generator''': {'''do_lower_case''': True},
    '''google/electra-small-discriminator''': {'''do_lower_case''': True},
    '''google/electra-base-discriminator''': {'''do_lower_case''': True},
    '''google/electra-large-discriminator''': {'''do_lower_case''': True},
}
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							VOCAB_FILES_NAMES
		_a       					=							PRETRAINED_VOCAB_FILES_MAP
		_a       					=							PRETRAINED_INIT_CONFIGURATION
		_a       					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a       					=							ElectraTokenizer




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=True       ,  UpperCamelCase_="[UNK]"       ,  UpperCamelCase_="[SEP]"       ,  UpperCamelCase_="[PAD]"       ,  UpperCamelCase_="[CLS]"       ,  UpperCamelCase_="[MASK]"       ,  UpperCamelCase_=True       ,  UpperCamelCase_=None       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__(
							    UpperCamelCase_       ,  tokenizer_file=UpperCamelCase_       ,  do_lower_case=UpperCamelCase_       ,  unk_token=UpperCamelCase_       ,  sep_token=UpperCamelCase_       ,  pad_token=UpperCamelCase_       ,  cls_token=UpperCamelCase_       ,  mask_token=UpperCamelCase_       ,  tokenize_chinese_chars=UpperCamelCase_       ,  strip_accents=UpperCamelCase_       ,  **UpperCamelCase_       ,  )

							UpperCamelCase__  :str				   =					json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
							if (
							    normalizer_state.get('''lowercase'''       ,  UpperCamelCase_				) != do_lower_case
							    or normalizer_state.get('''strip_accents'''       ,  UpperCamelCase_				) != strip_accents
							    or normalizer_state.get('''handle_chinese_chars'''       ,  UpperCamelCase_				) != tokenize_chinese_chars
							):
												UpperCamelCase__  :Any				   =					getattr(UpperCamelCase_       ,  normalizer_state.pop('''type'''				)				)
												UpperCamelCase__  :Any				   =					do_lower_case
												UpperCamelCase__  :Dict				   =					strip_accents
												UpperCamelCase__  :Optional[int]				   =					tokenize_chinese_chars
												UpperCamelCase__  :Dict				   =					normalizer_class(**UpperCamelCase_				)

							UpperCamelCase__  :str				   =					do_lower_case




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=None				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

							return output




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					[self.sep_token_id]
							UpperCamelCase__  :str				   =					[self.cls_token_id]
							if token_ids_a is None:
												return len(cls + token_ids_a + sep				) * [0]
							return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					self._tokenizer.model.save(UpperCamelCase_       ,  name=UpperCamelCase_				)
							return tuple(UpperCamelCase_				)