import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=BertTokenizer
     _SCREAMING_SNAKE_CASE     :     List[Any]		=BertTokenizerFast
     _SCREAMING_SNAKE_CASE     :     int		=True
     _SCREAMING_SNAKE_CASE     :     str		=True
     _SCREAMING_SNAKE_CASE     :     List[str]		=filter_non_english



     def 			a__  (					self       ):
            super().setUp()

            _A=	[
                '[UNK]',
                '[CLS]',
                '[SEP]',
                '[PAD]',
                '[MASK]',
                'want',
                '##want',
                '##ed',
                'wa',
                'un',
                'runn',
                '##ing',
                ',',
                'low',
                'lowest',
            ]
            _A=	os.path.join(self.tmpdirname				,      VOCAB_FILES_NAMES['vocab_file']       )
            with open(self.vocab_file				,      'w'				,      encoding='utf-8'       ) as vocab_writer:
                   vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	'UNwant\u00E9d,running'
            _A=	'unwanted, running'
            return input_text, output_text



     def 			a__  (					self       ):
            _A=	self.tokenizer_class(self.vocab_file       )

            _A=	tokenizer.tokenize('UNwant\u00E9d,running'       )
            self.assertListEqual(lowerCAmelCase__				,      ['un', '##want', '##ed', ',', 'runn', '##ing']       )
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )				,      [9, 6, 7, 12, 10, 11]       )



     def 			a__  (					self       ):
            if not self.test_rust_tokenizer:
                   return

            _A=	self.get_tokenizer()
            _A=	self.get_rust_tokenizer()

            _A=	'UNwant\u00E9d,running'

            _A=	tokenizer.tokenize(lowerCAmelCase__       )
            _A=	rust_tokenizer.tokenize(lowerCAmelCase__       )
            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            _A=	tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
            _A=	rust_tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            _A=	self.get_rust_tokenizer()
            _A=	tokenizer.encode(lowerCAmelCase__       )
            _A=	rust_tokenizer.encode(lowerCAmelCase__       )
            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            # With lower casing
            _A=	self.get_tokenizer(do_lower_case=lowerCAmelCase__       )
            _A=	self.get_rust_tokenizer(do_lower_case=lowerCAmelCase__       )

            _A=	'UNwant\u00E9d,running'

            _A=	tokenizer.tokenize(lowerCAmelCase__       )
            _A=	rust_tokenizer.tokenize(lowerCAmelCase__       )
            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            _A=	tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
            _A=	rust_tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            _A=	self.get_rust_tokenizer()
            _A=	tokenizer.encode(lowerCAmelCase__       )
            _A=	rust_tokenizer.encode(lowerCAmelCase__       )
            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	BasicTokenizer()

            self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'       )				,      ['ah', '\u535A', '\u63A8', 'zz']       )



     def 			a__  (					self       ):
            _A=	BasicTokenizer(do_lower_case=lowerCAmelCase__       )

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '       )				,      ['hello', '!', 'how', 'are', 'you', '?']       )
            self.assertListEqual(tokenizer.tokenize('H\u00E9llo'       )				,      ['hello']       )



     def 			a__  (					self       ):
            _A=	BasicTokenizer(do_lower_case=lowerCAmelCase__				,      strip_accents=lowerCAmelCase__       )

            self.assertListEqual(
                tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '       )				,      ['hällo', '!', 'how', 'are', 'you', '?']       )
            self.assertListEqual(tokenizer.tokenize('H\u00E9llo'       )				,      ['h\u00E9llo']       )



     def 			a__  (					self       ):
            _A=	BasicTokenizer(do_lower_case=lowerCAmelCase__				,      strip_accents=lowerCAmelCase__       )

            self.assertListEqual(
                tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '       )				,      ['hallo', '!', 'how', 'are', 'you', '?']       )
            self.assertListEqual(tokenizer.tokenize('H\u00E9llo'       )				,      ['hello']       )



     def 			a__  (					self       ):
            _A=	BasicTokenizer(do_lower_case=lowerCAmelCase__       )

            self.assertListEqual(
                tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '       )				,      ['hallo', '!', 'how', 'are', 'you', '?']       )
            self.assertListEqual(tokenizer.tokenize('H\u00E9llo'       )				,      ['hello']       )



     def 			a__  (					self       ):
            _A=	BasicTokenizer(do_lower_case=lowerCAmelCase__       )

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '       )				,      ['HeLLo', '!', 'how', 'Are', 'yoU', '?']       )



     def 			a__  (					self       ):
            _A=	BasicTokenizer(do_lower_case=lowerCAmelCase__				,      strip_accents=lowerCAmelCase__       )

            self.assertListEqual(
                tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '       )				,      ['HäLLo', '!', 'how', 'Are', 'yoU', '?']       )



     def 			a__  (					self       ):
            _A=	BasicTokenizer(do_lower_case=lowerCAmelCase__				,      strip_accents=lowerCAmelCase__       )

            self.assertListEqual(
                tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '       )				,      ['HaLLo', '!', 'how', 'Are', 'yoU', '?']       )



     def 			a__  (					self       ):
            _A=	BasicTokenizer(do_lower_case=lowerCAmelCase__				,      never_split=['[UNK]']       )

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'       )				,      ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']       )



     def 			a__  (					self       ):
            _A=	BasicTokenizer()
            _A=	'a\n\'ll !!to?\'d of, can\'t.'
            _A=	['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
            self.assertListEqual(tokenizer.tokenize(lowerCAmelCase__       )				,      lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

            _A=	{}
            for i, token in enumerate(lowerCAmelCase__       ):
                   _A=	i
            _A=	WordpieceTokenizer(vocab=lowerCAmelCase__				,      unk_token='[UNK]'       )

            self.assertListEqual(tokenizer.tokenize(''       )				,      []       )

            self.assertListEqual(tokenizer.tokenize('unwanted running'       )				,      ['un', '##want', '##ed', 'runn', '##ing']       )

            self.assertListEqual(tokenizer.tokenize('unwantedX running'       )				,      ['[UNK]', 'runn', '##ing']       )



     def 			a__  (					self       ):
            self.assertTrue(_is_whitespace(' '       )       )
            self.assertTrue(_is_whitespace('\t'       )       )
            self.assertTrue(_is_whitespace('\r'       )       )
            self.assertTrue(_is_whitespace('\n'       )       )
            self.assertTrue(_is_whitespace('\u00A0'       )       )

            self.assertFalse(_is_whitespace('A'       )       )
            self.assertFalse(_is_whitespace('-'       )       )



     def 			a__  (					self       ):
            self.assertTrue(_is_control('\u0005'       )       )

            self.assertFalse(_is_control('A'       )       )
            self.assertFalse(_is_control(' '       )       )
            self.assertFalse(_is_control('\t'       )       )
            self.assertFalse(_is_control('\r'       )       )



     def 			a__  (					self       ):
            self.assertTrue(_is_punctuation('-'       )       )
            self.assertTrue(_is_punctuation('$'       )       )
            self.assertTrue(_is_punctuation('`'       )       )
            self.assertTrue(_is_punctuation('.'       )       )

            self.assertFalse(_is_punctuation('A'       )       )
            self.assertFalse(_is_punctuation(' '       )       )



     def 			a__  (					self       ):
            _A=	self.get_tokenizer()
            _A=	self.get_rust_tokenizer()

            # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
            self.assertListEqual([tokenizer.tokenize(lowerCAmelCase__       ) for t in ['Test', '\xad', 'test']]				,      [['[UNK]'], [], ['[UNK]']]       )

            self.assertListEqual(
                [rust_tokenizer.tokenize(lowerCAmelCase__       ) for t in ['Test', '\xad', 'test']]				,      [['[UNK]'], [], ['[UNK]']]       )



     @slow
     def 			a__  (					self       ):
            _A=	self.tokenizer_class.from_pretrained('bert-base-uncased'       )

            _A=	tokenizer.encode('sequence builders'				,      add_special_tokens=lowerCAmelCase__       )
            _A=	tokenizer.encode('multi-sequence build'				,      add_special_tokens=lowerCAmelCase__       )

            _A=	tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__       )
            _A=	tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__				,      lowerCAmelCase__       )

            assert encoded_sentence == [101] + text + [102]
            assert encoded_pair == [101] + text + [102] + text_a + [102]



     def 			a__  (					self       ):
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
                          _A=	self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__				,      **lowerCAmelCase__       )

                          _A=	f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
                          _A=	tokenizer_r.encode_plus(
                              lowerCAmelCase__				,      return_attention_mask=lowerCAmelCase__				,      return_token_type_ids=lowerCAmelCase__				,      return_offsets_mapping=lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__				,      )

                          _A=	tokenizer_r.do_lower_case if hasattr(lowerCAmelCase__				,      'do_lower_case'       ) else False
                          _A=	(
                              [
                                  ((0, 0), tokenizer_r.cls_token),
                                  ((0, 1), 'A'),
                                  ((1, 2), ','),
                                  ((3, 5), 'na'),
                                  ((5, 6), '##ï'),
                                  ((6, 8), '##ve'),
                                  ((9, 15), tokenizer_r.mask_token),
                                  ((16, 21), 'Allen'),
                                  ((21, 23), '##NL'),
                                  ((23, 24), '##P'),
                                  ((25, 33), 'sentence'),
                                  ((33, 34), '.'),
                                  ((0, 0), tokenizer_r.sep_token),
                              ]
                              if not do_lower_case
                              else [
                                  ((0, 0), tokenizer_r.cls_token),
                                  ((0, 1), 'a'),
                                  ((1, 2), ','),
                                  ((3, 8), 'naive'),
                                  ((9, 15), tokenizer_r.mask_token),
                                  ((16, 21), 'allen'),
                                  ((21, 23), '##nl'),
                                  ((23, 24), '##p'),
                                  ((25, 33), 'sentence'),
                                  ((33, 34), '.'),
                                  ((0, 0), tokenizer_r.sep_token),
                              ]
                          )

                          self.assertEqual(
                              [e[1] for e in expected_results]				,      tokenizer_r.convert_ids_to_tokens(tokens['input_ids']       )       )
                          self.assertEqual([e[0] for e in expected_results]				,      tokens['offset_mapping']       )





     def 			a__  (					self       ):
            _A=	['的', '人', '有']
            _A=	''.join(lowerCAmelCase__       )
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
                          _A=	True
                          _A=	self.tokenizer_class.from_pretrained(lowerCAmelCase__				,      **lowerCAmelCase__       )
                          _A=	self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__				,      **lowerCAmelCase__       )

                          _A=	tokenizer_p.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          _A=	tokenizer_r.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )

                          _A=	tokenizer_r.convert_ids_to_tokens(lowerCAmelCase__       )
                          _A=	tokenizer_p.convert_ids_to_tokens(lowerCAmelCase__       )

                          # it is expected that each Chinese character is not preceded by "##"
                          self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )
                          self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	False
                          _A=	self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__				,      **lowerCAmelCase__       )
                          _A=	self.tokenizer_class.from_pretrained(lowerCAmelCase__				,      **lowerCAmelCase__       )

                          _A=	tokenizer_r.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          _A=	tokenizer_p.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )

                          _A=	tokenizer_r.convert_ids_to_tokens(lowerCAmelCase__       )
                          _A=	tokenizer_p.convert_ids_to_tokens(lowerCAmelCase__       )

                          # it is expected that only the first Chinese character is not preceded by "##".
                          _A=	[
                              f"##{token}" if idx != 0 else token for idx, token in enumerate(lowerCAmelCase__       )
                          ]
                          self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )
                          self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class      lowerCAmelCase						:
     _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 			a__  (					self       ):
            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 			a__  (					self       ):
            return torch.from_numpy(np.array([self.width, self.height]				,      dtype=np.floataa       )       )



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



     def 			a__  (					self       ):
            _A=	torch.arange(self.height * self.width       )
            _A=	torch.stack(
                [
                    pixel_indices % self.width,
                    torch.div(lowerCAmelCase__				,      self.width				,      rounding_mode='trunc'       ),
                ]				,      axis=1				,      )
            return coords



     @property
     def 			a__  (					self       ):
            _A,			*_A=	self.shape
            _A=	int(np.prod(lowerCAmelCase__       )       )

            _A=	self.get_image_coords()
            _A=	torch.broadcast_to(coords.unsqueeze(0       )				,      [batch_size * inner_batch_size, *coords.shape]       )
            _A=	self.get_camera_rays(lowerCAmelCase__       )

            _A=	rays.view(lowerCAmelCase__				,      inner_batch_size * self.height * self.width				,      2				,      3       )

            return rays



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A,			*_A,			_A=	coords.shape
            assert n_coords == 2
            assert batch_size == self.origin.shape[0]

            _A=	coords.view(lowerCAmelCase__				,      -1				,      2       )

            _A=	self.resolution()
            _A=	self.fov()

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

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





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            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=lowerCAmelCase__				,      height=lowerCAmelCase__				,      x_fov=self.x_fov				,      y_fov=self.y_fov				,      )


def 							UpperCamelCase				(    lowerCAmelCase_   )		->						DifferentiableProjectiveCamera:




       '''simple docstring'''






       _A=	[]
       _A=	[]
       _A=	[]
       _A=	[]
       for theta in np.linspace(0       ,					2 * np.pi       ,					num=20   ):
              _A=	np.array([np.sin(lowerCAmelCase_   ), np.cos(lowerCAmelCase_   ), -0.5]   )
              z /= np.sqrt(np.sum(z**2   )   )
              _A=	-z * 4
              _A=	np.array([np.cos(lowerCAmelCase_   ), -np.sin(lowerCAmelCase_   ), 0.0]   )
              _A=	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_   ))       ,					)