




import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



class    lowercase_		(SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =ProphetNetTokenizer
						lowerCAmelCase__			      =False



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								super().setUp()

								SCREAMING_SNAKE_CASE_					      =							[
								    '[UNK]',
								    '[CLS]',
								    '[SEP]',
								    '[PAD]',
								    '[MASK]',
								    'want',
								    '##want',
								    '##ed',
								    'wa',
								    'un',
								    'runn',
								    '##ing',
								    ',',
								    'low',
								    'lowest',
								]
								SCREAMING_SNAKE_CASE_					      =							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	:   Dict					,							snake_case__	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'UNwant\u00E9d,running'
								SCREAMING_SNAKE_CASE_					      =							'unwanted, running'
								return input_text, output_text



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.tokenizer_class(self.vocab_file  )

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



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							BasicTokenizer()

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



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							BasicTokenizer(do_lower_case=snake_case__  )

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



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							BasicTokenizer(do_lower_case=snake_case__					,							strip_accents=snake_case__  )

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



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							BasicTokenizer(do_lower_case=snake_case__					,							strip_accents=snake_case__  )

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



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							BasicTokenizer(do_lower_case=snake_case__  )

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



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							BasicTokenizer(do_lower_case=snake_case__  )

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



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							BasicTokenizer(do_lower_case=snake_case__					,							strip_accents=snake_case__  )

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



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							BasicTokenizer(do_lower_case=snake_case__					,							strip_accents=snake_case__  )

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



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							BasicTokenizer(do_lower_case=snake_case__					,							never_split=['[UNK]']  )

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



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

								SCREAMING_SNAKE_CASE_					      =							{}
								for i, token in enumerate(snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							i
								SCREAMING_SNAKE_CASE_					      =							WordpieceTokenizer(vocab=snake_case__					,							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']  )



						@require_torch
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased'  )

								SCREAMING_SNAKE_CASE_					      =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
								SCREAMING_SNAKE_CASE_					      =							[10_37, 21_46, 2_04_23, 20_05, 76_80, 78_49, 39_89, 10_12, 1_02]
								SCREAMING_SNAKE_CASE_					      =							tokenizer(snake_case__					,							padding=snake_case__					,							return_tensors='pt'  )
								self.assertIsInstance(snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							list(batch.input_ids.numpy()[0]  )
								self.assertListEqual(snake_case__					,							snake_case__  )

								self.assertEqual((2, 9)					,							batch.input_ids.shape  )
								self.assertEqual((2, 9)					,							batch.attention_mask.shape  )



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								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	:   List[Any]  ):

								"""simple docstring"""

								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	:   Dict  ):

								"""simple docstring"""

								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(' '  )  )






						@slow
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased'  )

								SCREAMING_SNAKE_CASE_					      =							tokenizer.encode('sequence builders'					,							add_special_tokens=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tokenizer.encode('multi-sequence build'					,							add_special_tokens=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							tokenizer.build_inputs_with_special_tokens(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tokenizer.build_inputs_with_special_tokens(snake_case__					,							snake_case__  )

								assert encoded_sentence == text + [1_02]
								assert encoded_pair == text + [1_02] + text_a + [1_02]





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class    lowercase_		:
						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 	__a					(			self	:   Optional[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 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							torch.arange(self.height * self.width  )
								SCREAMING_SNAKE_CASE_					      =							torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(snake_case__					,							self.width					,							rounding_mode='trunc'  ),
								    ]					,							axis=1					,							)
								return coords



						@property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_					      =							self.shape
								SCREAMING_SNAKE_CASE_					      =							int(np.prod(snake_case__  )  )

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

								SCREAMING_SNAKE_CASE_					      =							rays.view(snake_case__					,							inner_batch_size * self.height * self.width					,							2					,							3  )

								return rays



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   torch.Tensor  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								SCREAMING_SNAKE_CASE_					      =							coords.view(snake_case__					,							-1					,							2  )

								SCREAMING_SNAKE_CASE_					      =							self.resolution()
								SCREAMING_SNAKE_CASE_					      =							self.fov()

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

								SCREAMING_SNAKE_CASE_					      =							fracs.view(snake_case__					,							-1					,							2  )
								SCREAMING_SNAKE_CASE_					      =							(
								    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:]
								)
								SCREAMING_SNAKE_CASE_					      =							directions / directions.norm(dim=-1					,							keepdim=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Optional[int]					,							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 					_a							(					lowerCAmelCase )->	DifferentiableProjectiveCamera:
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		for theta in np.linspace(0    ,						2 * np.pi    ,						num=20 ):
				SCREAMING_SNAKE_CASE_					      =							np.array([np.sin(lowerCAmelCase ), np.cos(lowerCAmelCase ), -0.5] )
				z /= np.sqrt(np.sum(z**2 ) )
				SCREAMING_SNAKE_CASE_					      =							-z * 4
				SCREAMING_SNAKE_CASE_					      =							np.array([np.cos(lowerCAmelCase ), -np.sin(lowerCAmelCase ), 0.0] )
				SCREAMING_SNAKE_CASE_					      =							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 ))    ,						)