import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Any		=LongformerTokenizer
     _SCREAMING_SNAKE_CASE     :     int		=True
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=LongformerTokenizerFast
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		=True



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

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            _A=	[
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                '\u0120',
                '\u0120l',
                '\u0120n',
                '\u0120lo',
                '\u0120low',
                'er',
                '\u0120lowest',
                '\u0120newer',
                '\u0120wider',
                '<unk>',
            ]
            _A=	dict(zip(lowerCAmelCase__				,      range(len(lowerCAmelCase__       )       )       )       )
            _A=	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
            _A=	{'unk_token': '<unk>'}

            _A=	os.path.join(self.tmpdirname				,      VOCAB_FILES_NAMES['vocab_file']       )
            _A=	os.path.join(self.tmpdirname				,      VOCAB_FILES_NAMES['merges_file']       )
            with open(self.vocab_file				,      'w'				,      encoding='utf-8'       ) as fp:
                   fp.write(json.dumps(lowerCAmelCase__       ) + '\n'       )
            with open(self.merges_file				,      'w'				,      encoding='utf-8'       ) as fp:
                   fp.write('\n'.join(lowerCAmelCase__       )       )



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            kwargs.update(self.special_tokens_map       )
            return self.tokenizer_class.from_pretrained(self.tmpdirname				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            kwargs.update(self.special_tokens_map       )
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	'lower newer'
            _A=	'lower newer'
            return input_text, output_text



     def 			a__  (					self       ):
            _A=	self.tokenizer_class(self.vocab_file				,      self.merges_file				,      **self.special_tokens_map       )
            _A=	'lower newer'
            _A=	['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
            _A=	tokenizer.tokenize(lowerCAmelCase__       )  # , add_prefix_space=True)
            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            _A=	tokens + [tokenizer.unk_token]
            _A=	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )				,      lowerCAmelCase__       )



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

            self.assertListEqual(tokenizer.encode('Hello world!'				,      add_special_tokens=lowerCAmelCase__       )				,      [0, 31414, 232, 328, 2]       )
            self.assertListEqual(
                tokenizer.encode('Hello world! cécé herlolip 418'				,      add_special_tokens=lowerCAmelCase__       )				,      [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]				,      )



     @slow
     def 			a__  (					self       ):
            _A=	self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'       )

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

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

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

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode



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

            _A=	'Encode this sequence.'
            _A=	tokenizer.byte_encoder[' '.encode('utf-8'       )[0]]

            # Testing encoder arguments
            _A=	tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__				,      add_prefix_space=lowerCAmelCase__       )
            _A=	tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
            self.assertNotEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            _A=	tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__				,      add_prefix_space=lowerCAmelCase__       )
            _A=	tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
            self.assertEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            tokenizer.add_special_tokens({'bos_token': '<s>'}       )
            _A=	tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
            _A=	tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
            self.assertNotEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            # Testing spaces after special tokens
            _A=	'<mask>'
            tokenizer.add_special_tokens(
                {'mask_token': AddedToken(lowerCAmelCase__				,      lstrip=lowerCAmelCase__				,      rstrip=lowerCAmelCase__       )}       )  # mask token has a left space
            _A=	tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )

            _A=	'Encode <mask> sequence'
            _A=	'Encode <mask>sequence'

            _A=	tokenizer.encode(lowerCAmelCase__       )
            _A=	encoded.index(lowerCAmelCase__       )
            _A=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
            self.assertEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            _A=	tokenizer.encode(lowerCAmelCase__       )
            _A=	encoded.index(lowerCAmelCase__       )
            _A=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
            self.assertNotEqual(lowerCAmelCase__				,      lowerCAmelCase__       )



     def 			a__  (					self       ):
            pass



     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=	self.tokenizer_class.from_pretrained(lowerCAmelCase__				,      **lowerCAmelCase__       )
                          _A=	'A, <mask> AllenNLP sentence.'
                          _A=	tokenizer_r.encode_plus(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__				,      return_token_type_ids=lowerCAmelCase__       )
                          _A=	tokenizer_p.encode_plus(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__				,      return_token_type_ids=lowerCAmelCase__       )

                          # token_type_ids should put 0 everywhere
                          self.assertEqual(sum(tokens_r['token_type_ids']       )				,      sum(tokens_p['token_type_ids']       )       )

                          # attention_mask should put 1 everywhere, so sum over length should be 1
                          self.assertEqual(
                              sum(tokens_r['attention_mask']       ) / len(tokens_r['attention_mask']       )				,      sum(tokens_p['attention_mask']       ) / len(tokens_p['attention_mask']       )				,      )

                          _A=	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
                          _A=	tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']       )

                          # Rust correctly handles the space before the mask while python doesnt
                          self.assertSequenceEqual(tokens_p['input_ids']				,      [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]       )
                          self.assertSequenceEqual(tokens_r['input_ids']				,      [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]       )

                          self.assertSequenceEqual(
                              lowerCAmelCase__				,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )
                          self.assertSequenceEqual(
                              lowerCAmelCase__				,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )



     def 			a__  (					self       ):

            for trim_offsets, add_prefix_space in itertools.product([True, False]				,      repeat=2       ):
                   _A=	self.rust_tokenizer_class.from_pretrained(
                       self.tmpdirname				,      use_fast=lowerCAmelCase__				,      add_prefix_space=lowerCAmelCase__				,      trim_offsets=lowerCAmelCase__       )

                   _A=	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
                   _A=	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

                   self.assertEqual(pre_tokenizer_state['add_prefix_space']				,      lowerCAmelCase__       )

                   self.assertEqual(post_processor_state['add_prefix_space']				,      lowerCAmelCase__       )
                   self.assertEqual(post_processor_state['trim_offsets']				,      lowerCAmelCase__       )





     def 			a__  (					self       ):

            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
                          _A=	'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                          _A=	f"{text_of_1_token} {text_of_1_token}"

                          _A=	self.rust_tokenizer_class.from_pretrained(
                              lowerCAmelCase__				,      use_fast=lowerCAmelCase__				,      add_prefix_space=lowerCAmelCase__				,      trim_offsets=lowerCAmelCase__       )
                          _A=	tokenizer_r(lowerCAmelCase__				,      return_offsets_mapping=lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          self.assertEqual(encoding.offset_mapping[0]				,      (0, len(lowerCAmelCase__       ))       )
                          self.assertEqual(
                              encoding.offset_mapping[1]				,      (len(lowerCAmelCase__       ) + 1, len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))				,      )

                          _A=	self.rust_tokenizer_class.from_pretrained(
                              lowerCAmelCase__				,      use_fast=lowerCAmelCase__				,      add_prefix_space=lowerCAmelCase__				,      trim_offsets=lowerCAmelCase__       )
                          _A=	tokenizer_r(lowerCAmelCase__				,      return_offsets_mapping=lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          self.assertEqual(encoding.offset_mapping[0]				,      (0, len(lowerCAmelCase__       ))       )
                          self.assertEqual(
                              encoding.offset_mapping[1]				,      (len(lowerCAmelCase__       ) + 1, len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))				,      )

                          _A=	self.rust_tokenizer_class.from_pretrained(
                              lowerCAmelCase__				,      use_fast=lowerCAmelCase__				,      add_prefix_space=lowerCAmelCase__				,      trim_offsets=lowerCAmelCase__       )
                          _A=	tokenizer_r(lowerCAmelCase__				,      return_offsets_mapping=lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          self.assertEqual(encoding.offset_mapping[0]				,      (0, len(lowerCAmelCase__       ))       )
                          self.assertEqual(
                              encoding.offset_mapping[1]				,      (len(lowerCAmelCase__       ), len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))				,      )

                          _A=	self.rust_tokenizer_class.from_pretrained(
                              lowerCAmelCase__				,      use_fast=lowerCAmelCase__				,      add_prefix_space=lowerCAmelCase__				,      trim_offsets=lowerCAmelCase__       )
                          _A=	tokenizer_r(lowerCAmelCase__				,      return_offsets_mapping=lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          self.assertEqual(encoding.offset_mapping[0]				,      (0, len(lowerCAmelCase__       ))       )
                          self.assertEqual(
                              encoding.offset_mapping[1]				,      (len(lowerCAmelCase__       ), len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))				,      )

                          _A=	f" {text}"

                          # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                          #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                          # )
                          # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                          # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                          # self.assertEqual(
                          #     encoding.offset_mapping[1],
                          #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                          # )

                          _A=	self.rust_tokenizer_class.from_pretrained(
                              lowerCAmelCase__				,      use_fast=lowerCAmelCase__				,      add_prefix_space=lowerCAmelCase__				,      trim_offsets=lowerCAmelCase__       )
                          _A=	tokenizer_r(lowerCAmelCase__				,      return_offsets_mapping=lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          self.assertEqual(encoding.offset_mapping[0]				,      (1, 1 + len(lowerCAmelCase__       ))       )
                          self.assertEqual(
                              encoding.offset_mapping[1]				,      (1 + len(lowerCAmelCase__       ) + 1, 1 + len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))				,      )

                          _A=	self.rust_tokenizer_class.from_pretrained(
                              lowerCAmelCase__				,      use_fast=lowerCAmelCase__				,      add_prefix_space=lowerCAmelCase__				,      trim_offsets=lowerCAmelCase__       )
                          _A=	tokenizer_r(lowerCAmelCase__				,      return_offsets_mapping=lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          self.assertEqual(encoding.offset_mapping[0]				,      (0, 1 + len(lowerCAmelCase__       ))       )
                          self.assertEqual(
                              encoding.offset_mapping[1]				,      (1 + len(lowerCAmelCase__       ), 1 + len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))				,      )

                          _A=	self.rust_tokenizer_class.from_pretrained(
                              lowerCAmelCase__				,      use_fast=lowerCAmelCase__				,      add_prefix_space=lowerCAmelCase__				,      trim_offsets=lowerCAmelCase__       )
                          _A=	tokenizer_r(lowerCAmelCase__				,      return_offsets_mapping=lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          self.assertEqual(encoding.offset_mapping[0]				,      (0, 1 + len(lowerCAmelCase__       ))       )
                          self.assertEqual(
                              encoding.offset_mapping[1]				,      (1 + len(lowerCAmelCase__       ), 1 + len(lowerCAmelCase__       ) + 1 + len(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_   ))       ,					)