

'''simple docstring'''



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class 				_snake_case					:
  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   _SCREAMING_SNAKE_CASE				(				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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   *lowerCAmelCase 						=							self.shape
      lowerCAmelCase 						=							int(np.prod(_SCREAMING_SNAKE_CASE	)	)

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

      lowerCAmelCase 						=							rays.view(_SCREAMING_SNAKE_CASE      , inner_batch_size * self.height * self.width      , 2      , 3	)

      return rays

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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

      lowerCAmelCase 						=							coords.view(_SCREAMING_SNAKE_CASE      , -1      , 2	)

      lowerCAmelCase 						=							self.resolution()
      lowerCAmelCase 						=							self.fov()

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

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


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''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=_SCREAMING_SNAKE_CASE      , height=_SCREAMING_SNAKE_CASE      , x_fov=self.x_fov      , y_fov=self.y_fov      , )



def       snake_case   (	snake_case      : int			) ->  DifferentiableProjectiveCamera:


    """simple docstring"""


    lowerCAmelCase 						=							[]
    lowerCAmelCase 						=							[]
    lowerCAmelCase 						=							[]
    lowerCAmelCase 						=							[]
    for theta in np.linspace(0					,		2 * np.pi					,		num=20			):
        lowerCAmelCase 						=							np.array([np.sin(snake_case			), np.cos(snake_case			), -0.5]			)
        z /= np.sqrt(np.sum(z**2			)			)
        lowerCAmelCase 						=							-z * 4
        lowerCAmelCase 						=							np.array([np.cos(snake_case			), -np.sin(snake_case			), 0.0]			)
        lowerCAmelCase 						=							np.cross(snake_case					,		snake_case			)
        origins.append(snake_case			)
        xs.append(snake_case			)
        ys.append(snake_case			)
        zs.append(snake_case			)
    return DifferentiableProjectiveCamera(
        origin=torch.from_numpy(np.stack(snake_case					,		axis=0			)			).float()					,		x=torch.from_numpy(np.stack(snake_case					,		axis=0			)			).float()					,		y=torch.from_numpy(np.stack(snake_case					,		axis=0			)			).float()					,		z=torch.from_numpy(np.stack(snake_case					,		axis=0			)			).float()					,		width=snake_case					,		height=snake_case					,		x_fov=0.7					,		y_fov=0.7					,		shape=(1, len(snake_case			))					,		)








'''simple docstring'''



import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase						:  Dict							       =				get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")



@require_sentencepiece
@require_tokenizers
class 				_snake_case					(   a_     , unittest.TestCase			):
  SCREAMING_SNAKE_CASE			:       int				=     GPTSwaTokenizer
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     False
  SCREAMING_SNAKE_CASE			:       Any				=     True
  SCREAMING_SNAKE_CASE			:       Any				=     False

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      super().setUp()

      # We have a SentencePiece fixture for testing
      lowerCAmelCase 						=							GPTSwaTokenizer(_SCREAMING_SNAKE_CASE      , eos_token='<unk>'      , bos_token='<unk>'      , pad_token='<unk>'	)

      tokenizer.save_pretrained(self.tmpdirname	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							'This is a test'
      lowerCAmelCase 						=							'This is a test'
      return input_text, output_text

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							'<s>'
      lowerCAmelCase 						=							1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE	)      , _SCREAMING_SNAKE_CASE	)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE	)      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							list(self.get_tokenizer().get_vocab().keys()	)

      self.assertEqual(vocab_keys[0]      , '<unk>'	)
      self.assertEqual(vocab_keys[1]      , '<s>'	)
      self.assertEqual(vocab_keys[-1]      , 'j'	)
      self.assertEqual(len(_SCREAMING_SNAKE_CASE	)      , 20_00	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.assertEqual(self.get_tokenizer().vocab_size      , 20_00	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							GPTSwaTokenizer(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							tokenizer.tokenize('This is a test'	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE      , ['▁This', '▁is', '▁a', '▁t', 'est']	)

      self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)      , [4_65, 2_87, 2_65, 6_31, 8_42]	)

      lowerCAmelCase 						=							tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
      # fmt: off
      self.assertListEqual(
          _SCREAMING_SNAKE_CASE      , ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']      , )
      # fmt: on

      lowerCAmelCase 						=							tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(
          _SCREAMING_SNAKE_CASE      , [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]      , )

      lowerCAmelCase 						=							tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE	)
      # fmt: off
      self.assertListEqual(
          _SCREAMING_SNAKE_CASE      , ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']	)
      # fmt: on

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							GPTSwaTokenizer(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							['This is a test', 'I was born in 92000, and this is falsé.']
      lowerCAmelCase 						=							[
          [4_65, 2_87, 2_65, 6_31, 8_42],
          [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
      ]

      # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
      for text, expected_ids in zip(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          self.assertListEqual(tokenizer.encode_fast(_SCREAMING_SNAKE_CASE	)      , _SCREAMING_SNAKE_CASE	)

      # Test that decode_fast returns the input text
      for text, token_ids in zip(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          self.assertEqual(tokenizer.decode_fast(_SCREAMING_SNAKE_CASE	)      , _SCREAMING_SNAKE_CASE	)


  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							[
          '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
          'Hey there, how are you doing this fine day?',
          'This is a text with a trailing spaces followed by a dot     .',
          'Häj sväjs lillebrör! =)',
          'Det är inget fel på Mr. Cool',
      ]

      # fmt: off
      lowerCAmelCase 						=							{'input_ids': [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
      # fmt: on
      self.tokenizer_integration_test_util(
          expected_encoding=_SCREAMING_SNAKE_CASE      , model_name='AI-Sweden/gpt-sw3-126m'      , sequences=_SCREAMING_SNAKE_CASE      , )





