

import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import DinatBackbone, DinatForImageClassification, DinatModel
	from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor





class      snake_case_       :







			'''simple docstring'''

			def __init__(					self			:    Dict							,       _UpperCamelCase			:    Optional[Any]							,       _UpperCamelCase			:    Tuple=1_3							,       _UpperCamelCase			:    Optional[Any]=6_4							,       _UpperCamelCase			:    List[str]=4							,       _UpperCamelCase			:    Tuple=3							,       _UpperCamelCase			:    Optional[Any]=1_6							,       _UpperCamelCase			:    Dict=[1, 2, 1]							,       _UpperCamelCase			:    List[str]=[2, 4, 8]							,       _UpperCamelCase			:    Optional[int]=3							,       _UpperCamelCase			:    str=[[3], [1, 2], [1]]							,       _UpperCamelCase			:    Dict=2.0							,       _UpperCamelCase			:    Dict=True							,       _UpperCamelCase			:    int=0.0							,       _UpperCamelCase			:    Any=0.0							,       _UpperCamelCase			:    int=0.1							,       _UpperCamelCase			:    List[Any]="gelu"							,       _UpperCamelCase			:    Optional[int]=True							,       _UpperCamelCase			:    Optional[Any]=0.02							,       _UpperCamelCase			:    Any=1e-5							,       _UpperCamelCase			:    Any=True							,       _UpperCamelCase			:    Optional[int]=None							,       _UpperCamelCase			:    Optional[int]=True							,       _UpperCamelCase			:    Optional[int]=1_0							,       _UpperCamelCase			:    List[str]=["stage1", "stage2"]							,       _UpperCamelCase			:    Any=[1, 2]							,       )				->Optional[int]:
									snake_case_       =     parent
									snake_case_       =     batch_size
									snake_case_       =     image_size
									snake_case_       =     patch_size
									snake_case_       =     num_channels
									snake_case_       =     embed_dim
									snake_case_       =     depths
									snake_case_       =     num_heads
									snake_case_       =     kernel_size
									snake_case_       =     dilations
									snake_case_       =     mlp_ratio
									snake_case_       =     qkv_bias
									snake_case_       =     hidden_dropout_prob
									snake_case_       =     attention_probs_dropout_prob
									snake_case_       =     drop_path_rate
									snake_case_       =     hidden_act
									snake_case_       =     patch_norm
									snake_case_       =     layer_norm_eps
									snake_case_       =     initializer_range
									snake_case_       =     is_training
									snake_case_       =     scope
									snake_case_       =     use_labels
									snake_case_       =     num_labels
									snake_case_       =     out_features
									snake_case_       =     out_indices







			def     snake_case__(					self			:    List[str]							)				->Optional[Any]:
									snake_case_       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

									snake_case_       =     None
									if self.use_labels:
															snake_case_       =     ids_tensor([self.batch_size]							,       self.num_labels							)

									snake_case_       =     self.get_config()

									return config, pixel_values, labels







			def     snake_case__(					self			:    Any							)				->Optional[Any]:
									return DinatConfig(
									    num_labels=self.num_labels							,       image_size=self.image_size							,       patch_size=self.patch_size							,       num_channels=self.num_channels							,       embed_dim=self.embed_dim							,       depths=self.depths							,       num_heads=self.num_heads							,       kernel_size=self.kernel_size							,       dilations=self.dilations							,       mlp_ratio=self.mlp_ratio							,       qkv_bias=self.qkv_bias							,       hidden_dropout_prob=self.hidden_dropout_prob							,       attention_probs_dropout_prob=self.attention_probs_dropout_prob							,       drop_path_rate=self.drop_path_rate							,       hidden_act=self.hidden_act							,       patch_norm=self.patch_norm							,       layer_norm_eps=self.layer_norm_eps							,       initializer_range=self.initializer_range							,       out_features=self.out_features							,       out_indices=self.out_indices							,       )







			def     snake_case__(					self			:    int							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    List[str]							,       _UpperCamelCase			:    Optional[Any]							)				->Union[str, Any]:
									snake_case_       =     DinatModel(config=_A							)
									model.to(_A							)
									model.eval()
									snake_case_       =     model(_A							)

									snake_case_       =     (config.image_size // config.patch_size) // (2 ** (len(config.depths							) - 1))
									snake_case_       =     int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

									self.parent.assertEqual(
									    result.last_hidden_state.shape							,       (self.batch_size, expected_height, expected_width, expected_dim)							)







			def     snake_case__(					self			:    Optional[Any]							,       _UpperCamelCase			:    Optional[int]							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    Optional[Any]							)				->Union[str, Any]:
									snake_case_       =     DinatForImageClassification(_A							)
									model.to(_A							)
									model.eval()
									snake_case_       =     model(_A							,       labels=_A							)
									self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)							)

									# test greyscale images
									snake_case_       =     1
									snake_case_       =     DinatForImageClassification(_A							)
									model.to(_A							)
									model.eval()

									snake_case_       =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
									snake_case_       =     model(_A							)
									self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)							)







			def     snake_case__(					self			:    Dict							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							)				->Union[str, Any]:
									snake_case_       =     DinatBackbone(config=_A							)
									model.to(_A							)
									model.eval()
									snake_case_       =     model(_A							)

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps							)							,       len(config.out_features							)							)
									self.parent.assertListEqual(list(result.feature_maps[0].shape							)							,       [self.batch_size, model.channels[0], 1_6, 1_6]							)

									# verify channels
									self.parent.assertEqual(len(model.channels							)							,       len(config.out_features							)							)

									# verify backbone works with out_features=None
									snake_case_       =     None
									snake_case_       =     DinatBackbone(config=_A							)
									model.to(_A							)
									model.eval()
									snake_case_       =     model(_A							)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps							)							,       1							)
									self.parent.assertListEqual(list(result.feature_maps[0].shape							)							,       [self.batch_size, model.channels[-1], 4, 4]							)

									# verify channels
									self.parent.assertEqual(len(model.channels							)							,       1							)







			def     snake_case__(					self			:    Optional[int]							)				->List[Any]:
									snake_case_       =     self.prepare_config_and_inputs()
									snake_case_       =     config_and_inputs
									snake_case_       =     {"pixel_values": pixel_values}
									return config, inputs_dict






@require_natten
@require_torch
class      snake_case_       (		A__     ,	A__     ,	unittest.TestCase ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       Dict						      =    (
			    (
			        DinatModel,
			        DinatForImageClassification,
			        DinatBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			SCREAMING_SNAKE_CASE					:       List[str]						      =    (
			    {"feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
			    if is_torch_available()
			    else {}
			)
			SCREAMING_SNAKE_CASE					:       str						      =    False

			SCREAMING_SNAKE_CASE					:       int						      =    False
			SCREAMING_SNAKE_CASE					:       Any						      =    False
			SCREAMING_SNAKE_CASE					:       int						      =    False
			SCREAMING_SNAKE_CASE					:       Any						      =    False







			def     snake_case__(					self			:    str							)				->Union[str, Any]:
									snake_case_       =     DinatModelTester(self							)
									snake_case_       =     ConfigTester(self							,       config_class=_A							,       embed_dim=3_7							)







			def     snake_case__(					self			:    List[Any]							)				->int:
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()







			def     snake_case__(					self			:    Optional[Any]							)				->Optional[int]:
									return







			def     snake_case__(					self			:    List[Any]							)				->Optional[int]:
									snake_case_       =     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_A							)







			def     snake_case__(					self			:    List[str]							)				->List[str]:
									snake_case_       =     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_A							)







			def     snake_case__(					self			:    List[Any]							)				->Dict:
									snake_case_       =     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*_A							)







			@unittest.skip(reason='''Dinat does not use inputs_embeds'''							)
			def     snake_case__(					self			:    Union[str, Any]							)				->Optional[Any]:
									pass







			@unittest.skip(reason='''Dinat does not use feedforward chunking'''							)
			def     snake_case__(					self			:    Dict							)				->Optional[Any]:
									pass







			def     snake_case__(					self			:    List[str]							)				->Dict:
									snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case_       =     model_class(_A							)
															self.assertIsInstance(model.get_input_embeddings()							,       (nn.Module)							)
															snake_case_       =     model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(_A							,       nn.Linear							)							)







			def     snake_case__(					self			:    Optional[int]							)				->List[Any]:
									snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case_       =     model_class(_A							)
															snake_case_       =     inspect.signature(model.forward							)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															snake_case_       =     [*signature.parameters.keys()]

															snake_case_       =     ["pixel_values"]
															self.assertListEqual(arg_names[:1]							,       _A							)







			def     snake_case__(					self			:    int							)				->List[str]:
									self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.'''							)







			def     snake_case__(					self			:    Any							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    Any							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    str							)				->Dict:
									snake_case_       =     model_class(_A							)
									model.to(_A							)
									model.eval()

									with torch.no_grad():
															snake_case_       =     model(**self._prepare_for_class(_A							,       _A							)							)

									snake_case_       =     outputs.hidden_states

									snake_case_       =     getattr(
									    self.model_tester							,       '''expected_num_hidden_layers'''							,       len(self.model_tester.depths							) + 1							)
									self.assertEqual(len(_A							)							,       _A							)

									# Dinat has a different seq_length
									snake_case_       =     (
									    config.patch_size
									    if isinstance(config.patch_size							,       collections.abc.Iterable							)
									    else (config.patch_size, config.patch_size)
									)

									snake_case_       =     image_size[0] // patch_size[0]
									snake_case_       =     image_size[1] // patch_size[1]

									self.assertListEqual(
									    list(hidden_states[0].shape[-3:]							)							,       [height, width, self.model_tester.embed_dim]							,       )

									if model_class.__name__ != "DinatBackbone":
															snake_case_       =     outputs.reshaped_hidden_states
															self.assertEqual(len(_A							)							,       _A							)

															snake_case_       =     reshaped_hidden_states[0].shape
															snake_case_       =     (
															    reshaped_hidden_states[0].view(_A							,       _A							,       _A							,       _A							).permute(0							,       2							,       3							,       1							)
															)
															self.assertListEqual(
															    list(reshaped_hidden_states.shape[-3:]							)							,       [height, width, self.model_tester.embed_dim]							,       )







			def     snake_case__(					self			:    str							)				->str:
									snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()

									snake_case_       =     (
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size							,       collections.abc.Iterable							)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)

									for model_class in self.all_model_classes:
															snake_case_       =     True
															self.check_hidden_states_output(_A							,       _A							,       _A							,       _A							)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case_       =     True

															self.check_hidden_states_output(_A							,       _A							,       _A							,       _A							)







			@slow
			def     snake_case__(					self			:    List[Any]							)				->int:






									for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_       =     DinatModel.from_pretrained(_A							)
															self.assertIsNotNone(_A							)







			def     snake_case__(					self			:    str							)				->str:
									snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()

									snake_case_       =     _config_zero_init(_A							)
									for model_class in self.all_model_classes:
															snake_case_       =     model_class(config=_A							)
															for name, param in model.named_parameters():
																					if "embeddings" not in name and param.requires_grad:
																											self.assertIn(
																											    ((param.data.mean() * 1e9).round() / 1e9).item()							,       [0.0, 1.0]							,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,       )






@require_natten
@require_vision
@require_torch
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			@cached_property
			def     snake_case__(					self			:    Any							)				->Tuple:
									return AutoImageProcessor.from_pretrained('''shi-labs/dinat-mini-in1k-224'''							) if is_vision_available() else None







			@slow
			def     snake_case__(					self			:    int							)				->Optional[Any]:
									snake_case_       =     DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''							).to(_A							)
									snake_case_       =     self.default_image_processor

									snake_case_       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
									snake_case_       =     image_processor(images=_A							,       return_tensors='''pt'''							).to(_A							)

									# forward pass
									with torch.no_grad():
															snake_case_       =     model(**_A							)

									# verify the logits
									snake_case_       =     torch.Size((1, 1_0_0_0)							)
									self.assertEqual(outputs.logits.shape							,       _A							)
									snake_case_       =     torch.tensor([-0.1545, -0.7667, 0.4642]							).to(_A							)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]							,       _A							,       atol=1e-4							)							)






@require_torch
@require_natten
class      snake_case_       (		unittest.TestCase     ,	A__ ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       Dict						      =    (DinatBackbone,) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE					:       int						      =    DinatConfig







			def     snake_case__(					self			:    Dict							)				->str:
									snake_case_       =     DinatModelTester(self							)


import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class      snake_case_       (		__A     ,	unittest.TestCase ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    PreTrainedTokenizerFast
			SCREAMING_SNAKE_CASE					:       List[str]						      =    False
			SCREAMING_SNAKE_CASE					:       Tuple						      =    True
			SCREAMING_SNAKE_CASE					:       str						      =    "tokenizer_file"







			def     snake_case__(					self			:    Dict							)				->int:
									snake_case_       =     False  # because we don't have pretrained_vocab_files_map
									super().setUp()
									snake_case_       =     True

									snake_case_       =     ['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
									snake_case_       =     '''SaulLu/dummy-tokenizer-bytelevel-bpe'''

									# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
									snake_case_       =     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

									snake_case_       =     PreTrainedTokenizerFast.from_pretrained(model_paths[0]							)
									tokenizer.save_pretrained(self.tmpdirname							)







			def     snake_case__(					self			:    Union[str, Any]							)				->Dict:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->Any:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    List[Any]							)				->str:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->List[Any]:
									# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
									pass







			def     snake_case__(					self			:    Any							)				->Union[str, Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    List[str]							)				->List[Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer_with_special_tokens_change()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    Optional[int]							)				->Optional[Any]:
									snake_case_       =     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name							)

									snake_case_       =     ('''a''' for _ in range(1_0_0_0							))
									snake_case_       =     tokenizer.train_new_from_iterator(text_iterator=_UpperCamelCase							,       length=1_0_0_0							,       vocab_size=5_0							)

									snake_case_       =     new_tokenizer.encode('''a🤗'''							)
									self.assertEqual(_UpperCamelCase							,       [6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    Optional[int]							)				->int:
									snake_case_       =     AutoTokenizer.from_pretrained('''bert-base-cased'''							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									snake_case_       =     len(_UpperCamelCase							)

									with tempfile.TemporaryDirectory() as tmp_dir:
															# Hack to save this in the tokenizer_config.json
															snake_case_       =     ['''tokenizer.4.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															json.dump(_UpperCamelCase							,       open(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       '''w'''							)							)

															# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							) + 1							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

															# Will need to be adjusted if we reach v42 and this test is still here.
															# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
															shutil.move(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       os.path.join(_UpperCamelCase							,       '''tokenizer.42.0.0.json'''							)							)
															snake_case_       =     ['''tokenizer.42.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							)							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)







			def     snake_case__(					self			:    List[str]							)				->Optional[Any]:
									# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
									snake_case_       =     '''hf-internal-testing/test-two-tokenizers'''

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_7							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

									# Testing an older version by monkey-patching the version in the module it's used.
									import transformers as old_transformers

									snake_case_       =     '''3.0.0'''
									snake_case_       =     old_transformers.models.auto.AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_6							)
									snake_case_       =     json.loads(old_tokenizer._tokenizer.to_str()							)
									self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    str							)				->Optional[int]:
									# See https://github.com/huggingface/transformers/pull/12550
									# and https://github.com/huggingface/tokenizers/issues/537
									snake_case_       =     PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''							)
									snake_case_       =     '''The Matrix is a 1999 science fiction action film.'''

									with concurrent.futures.ThreadPoolExecutor() as executor:
															snake_case_       =     [executor.submit(self.fetch							,       _UpperCamelCase							,       _UpperCamelCase							) for i in range(1_0							)]
															snake_case_       =     [future.result() for future in futures]
															self.assertEqual(_UpperCamelCase							,       [[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0							)]							)







			def     snake_case__(					self			:    Optional[int]							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    str							)				->Tuple:
									return tokenizer.encode(_UpperCamelCase							,       truncation='''longest_first'''							,       padding='''longest'''							)