from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    = 42  # [batch_size x 3]
				lowerCamelCase_    = 42  # [batch_size x 3]
				lowerCamelCase_    = 42  # [batch_size x 3]
				lowerCamelCase_    = 42  # [batch_size x 3]
				lowerCamelCase_    = 42
				lowerCamelCase_    = 42
				lowerCamelCase_    = 42
				lowerCamelCase_    = 42
				lowerCamelCase_    = 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"""





						A_		:    List[Any]         =				torch.arange(self.height * self.width )
						A_		:    Union[str, Any]         =				torch.stack(
						    [
						        pixel_indices % self.width,
						        torch.div(lowercase		,     self.width		,     rounding_mode='trunc' ),
						    ]		,     axis=1		,     )
						return coords
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_       ,      *A_		:    int         =				self.shape
						A_		:    Dict         =				int(np.prod(lowercase ) )

						A_		:    Dict         =				self.get_image_coords()
						A_		:    Tuple         =				torch.broadcast_to(coords.unsqueeze(0 )		,     [batch_size * inner_batch_size, *coords.shape] )
						A_		:    int         =				self.get_camera_rays(lowercase )

						A_		:    List[Any]         =				rays.view(lowercase		,     inner_batch_size * self.height * self.width		,     2		,     3 )

						return rays
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





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

						A_		:    Tuple         =				coords.view(lowercase		,     -1		,     2 )

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

						A_		:    Tuple         =				(flat.float() / (res - 1)) * 2 - 1
						A_		:    str         =				fracs * torch.tan(fov / 2 )

						A_		:    str         =				fracs.view(lowercase		,     -1		,     2 )
						A_		:    Optional[int]         =				(
						    self.z.view(lowercase		,     1		,     3 )
						    + self.x.view(lowercase		,     1		,     3 ) * fracs[:, :, :1]
						    + self.y.view(lowercase		,     1		,     3 ) * fracs[:, :, 1:]
						)
						A_		:    Dict         =				directions / directions.norm(dim=-1		,     keepdim=lowercase )
						A_		:    int         =				torch.stack(
						    [
						        torch.broadcast_to(self.origin.view(lowercase		,     1		,     3 )		,     [batch_size, directions.shape[1], 3] ),
						        directions,
						    ]		,     dim=2		,     )
						return rays.view(lowercase		,     *lowercase		,     2		,     3 )







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase ):



						"""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=lowercase		,     height=lowercase		,     x_fov=self.x_fov		,     y_fov=self.y_fov		,     )




def     UpperCamelCase						(	__lowercase						:			int     ):
		'''simple docstring'''




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







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase            =		{
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCAmelCase            =		{
    """squeezebert/squeezebert-uncased""": 512,
    """squeezebert/squeezebert-mnli""": 512,
    """squeezebert/squeezebert-mnli-headless""": 512,
}


_UpperCAmelCase            =		{
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = PRETRAINED_INIT_CONFIGURATION
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = SqueezeBertTokenizer
				def __init__(						self		,     lowercase=None		,     lowercase=None		,     lowercase=True		,     lowercase="[UNK]"		,     lowercase="[SEP]"		,     lowercase="[PAD]"		,     lowercase="[CLS]"		,     lowercase="[MASK]"		,     lowercase=True		,     lowercase=None		,     **lowercase		,     ):



						"""simple docstring"""





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

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

						A_		:    Tuple         =				do_lower_case
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=None ):



						"""simple docstring"""





						A_		:    str         =				[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		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Dict         =				[self.sep_token_id]
						A_		:    Union[str, Any]         =				[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		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Dict         =				self._tokenizer.model.save(lowercase		,     name=lowercase )
						return tuple(lowercase )






