




'''simple docstring'''



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Any        =						(IPNDMScheduler,)
       A						:				List[Any]        =						(('''num_inference_steps''', 50),)







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{'num_train_timesteps': 1_000}
             config.update(**A  )
             return config







       def        UpperCamelCase_							(      self,							A=0,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						dict(self.forward_default_kwargs  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						kwargs.pop('num_inference_steps',							A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.dummy_sample
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						0.1 * sample
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_scheduler_config(**A  )
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						scheduler_class(**A  )
                   scheduler.set_timesteps(A  )
                   # copy over dummy past residuals
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						dummy_past_residuals[:]

                   if time_step is None:
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						scheduler.timesteps[len(scheduler.timesteps  ) // 2]

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(A  )
                         SCREAMING_SNAKE_CASE   :      Any    					=						scheduler_class.from_pretrained(A  )
                         new_scheduler.set_timesteps(A  )
                         # copy over dummy past residuals
                         SCREAMING_SNAKE_CASE   :      Any    					=						dummy_past_residuals[:]

                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						new_scheduler.step(A,							A,							A,							**A  ).prev_sample

                   assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

                   SCREAMING_SNAKE_CASE   :      Dict    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      str    					=						new_scheduler.step(A,							A,							A,							**A  ).prev_sample

                   assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self,							A=0,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						dict(self.forward_default_kwargs  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						kwargs.pop('num_inference_steps',							A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.dummy_sample
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						0.1 * sample
             SCREAMING_SNAKE_CASE   :      Any    					=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_scheduler_config()
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						scheduler_class(**A  )
                   scheduler.set_timesteps(A  )

                   # copy over dummy past residuals (must be after setting timesteps)
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						dummy_past_residuals[:]

                   if time_step is None:
                         SCREAMING_SNAKE_CASE   :      Any    					=						scheduler.timesteps[len(scheduler.timesteps  ) // 2]

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(A  )
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						scheduler_class.from_pretrained(A  )
                         # copy over dummy past residuals
                         new_scheduler.set_timesteps(A  )

                         # copy over dummy past residual (must be after setting timesteps)
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						dummy_past_residuals[:]

                   SCREAMING_SNAKE_CASE   :      str    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      int    					=						new_scheduler.step(A,							A,							A,							**A  ).prev_sample

                   assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

                   SCREAMING_SNAKE_CASE   :      str    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						new_scheduler.step(A,							A,							A,							**A  ).prev_sample

                   assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.scheduler_classes[0]
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_scheduler_config(**A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						scheduler_class(**A  )

             SCREAMING_SNAKE_CASE   :      int    					=						10
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.dummy_model()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.dummy_sample_deter
             scheduler.set_timesteps(A  )

             for i, t in enumerate(scheduler.timesteps  ):
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model(A,							A  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						scheduler.step(A,							A,							A  ).prev_sample

             for i, t in enumerate(scheduler.timesteps  ):
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						model(A,							A  )
                   SCREAMING_SNAKE_CASE   :      int    					=						scheduler.step(A,							A,							A  ).prev_sample

             return sample







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						dict(self.forward_default_kwargs  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						kwargs.pop('num_inference_steps',							A  )

             for scheduler_class in self.scheduler_classes:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_scheduler_config()
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						scheduler_class(**A  )

                   SCREAMING_SNAKE_CASE   :      Any    					=						self.dummy_sample
                   SCREAMING_SNAKE_CASE   :      Any    					=						0.1 * sample

                   if num_inference_steps is not None and hasattr(A,							'set_timesteps'  ):
                         scheduler.set_timesteps(A  )
                   elif num_inference_steps is not None and not hasattr(A,							'set_timesteps'  ):
                         SCREAMING_SNAKE_CASE   :      Any    					=						num_inference_steps

                   # copy over dummy past residuals (must be done after set_timesteps)
                   SCREAMING_SNAKE_CASE   :      str    					=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						dummy_past_residuals[:]

                   SCREAMING_SNAKE_CASE   :      Any    					=						scheduler.timesteps[5]
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						scheduler.timesteps[6]

                   SCREAMING_SNAKE_CASE   :      int    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample

                   self.assertEqual(output_a.shape,							sample.shape  )
                   self.assertEqual(output_a.shape,							output_a.shape  )

                   SCREAMING_SNAKE_CASE   :      str    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      int    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample

                   self.assertEqual(output_a.shape,							sample.shape  )
                   self.assertEqual(output_a.shape,							output_a.shape  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for timesteps in [100, 1_000]:
                   self.check_over_configs(num_train_timesteps=A,							time_step=A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for t, num_inference_steps in zip([1, 5, 10],							[10, 50, 100]  ):
                   self.check_over_forward(num_inference_steps=A,							time_step=A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.full_loop()
             SCREAMING_SNAKE_CASE   :      str    					=						torch.mean(torch.abs(A  )  )

             assert abs(result_mean.item() - 2_540_529  ) < 10








'''simple docstring'''



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 							_a				(			SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				str        =						LongformerTokenizer
       A						:				List[str]        =						True
       A						:				Optional[int]        =						LongformerTokenizerFast
       A						:				Tuple        =						True







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().setUp()

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

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['vocab_file']  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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(A  ) + '\n'  )
             with open(self.merges_file,							'w',							encoding='utf-8'  ) as fp:
                   fp.write('\n'.join(A  )  )







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             kwargs.update(self.special_tokens_map  )
             return self.tokenizer_class.from_pretrained(self.tmpdirname,							**A  )







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             kwargs.update(self.special_tokens_map  )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,							**A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						'lower newer'
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						'lower newer'
             return input_text, output_text







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.tokenizer_class(self.vocab_file,							self.merges_file,							**self.special_tokens_map  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'lower newer'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.tokenize(A  )  # , add_prefix_space=True)
             self.assertListEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokens + [tokenizer.unk_token]
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(A  ),							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.get_tokenizer()

             self.assertListEqual(tokenizer.encode('Hello world!',							add_special_tokens=A  ),							[0, 31_414, 232, 328, 2]  )
             self.assertListEqual(
                 tokenizer.encode('Hello world! cécé herlolip 418',							add_special_tokens=A  ),							[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2],							)







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.encode('sequence builders',							add_special_tokens=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.encode('multi-sequence build',							add_special_tokens=A  )

             SCREAMING_SNAKE_CASE   :      int    					=						tokenizer.encode(
                 'sequence builders',							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.encode(
                 'sequence builders',							'multi-sequence build',							add_special_tokens=A,							add_prefix_space=A  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.build_inputs_with_special_tokens(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer.build_inputs_with_special_tokens(A,							A  )

             assert encoded_sentence == encoded_text_from_decode
             assert encoded_pair == encoded_pair_from_decode







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_tokenizer()

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'Encode this sequence.'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.byte_encoder[' '.encode('utf-8'  )[0]]

             # Testing encoder arguments
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.encode(A,							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
             self.assertNotEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.encode(A,							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
             self.assertEqual(A,							A  )

             tokenizer.add_special_tokens({'bos_token': '<s>'}  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.encode(A,							add_special_tokens=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[1]  )[0]
             self.assertNotEqual(A,							A  )

             # Testing spaces after special tokens
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'<mask>'
             tokenizer.add_special_tokens(
                 {'mask_token': AddedToken(A,							lstrip=A,							rstrip=A  )}  )  # mask token has a left space
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.convert_tokens_to_ids(A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						'Encode <mask> sequence'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						'Encode <mask>sequence'

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.encode(A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						encoded.index(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
             self.assertEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.encode(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						encoded.index(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
             self.assertNotEqual(A,							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.rust_tokenizer_class.from_pretrained(A,							**A  )
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer_class.from_pretrained(A,							**A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'A, <mask> AllenNLP sentence.'
                         SCREAMING_SNAKE_CASE   :      Any    					=						tokenizer_r.encode_plus(A,							add_special_tokens=A,							return_token_type_ids=A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer_p.encode_plus(A,							add_special_tokens=A,							return_token_type_ids=A  )

                         # 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']  ),							)

                         SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )
                         self.assertSequenceEqual(tokens_r['input_ids'],							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for trim_offsets, add_prefix_space in itertools.product([True, False],							repeat=2  ):
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.rust_tokenizer_class.from_pretrained(
                       self.tmpdirname,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )

                   SCREAMING_SNAKE_CASE   :      Tuple    					=						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()  )

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

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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

                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ) + 1, len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ) + 1, len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      List[str]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ), len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Any    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ), len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Any    					=						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)),
                         # )

                         SCREAMING_SNAKE_CASE   :      str    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(1, 1 + len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(1 + len(A  ) + 1, 1 + len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      str    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, 1 + len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(1 + len(A  ), 1 + len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, 1 + len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(1 + len(A  ), 1 + len(A  ) + 1 + len(A  )),							)


