




'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.swin.modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		)


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class __magic_name__:






						def __init__(   self						:						Dict					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						Optional[Any]=1_3					,   __UpperCamelCase						:						int=3_2					,   __UpperCamelCase						:						str=2					,   __UpperCamelCase						:						Optional[Any]=3					,   __UpperCamelCase						:						Union[str, Any]=1_6					,   __UpperCamelCase						:						int=[1, 2, 1]					,   __UpperCamelCase						:						Optional[int]=[2, 2, 4]					,   __UpperCamelCase						:						Any=2					,   __UpperCamelCase						:						Union[str, Any]=2.0					,   __UpperCamelCase						:						str=True					,   __UpperCamelCase						:						Dict=0.0					,   __UpperCamelCase						:						Union[str, Any]=0.0					,   __UpperCamelCase						:						int=0.1					,   __UpperCamelCase						:						Optional[Any]="gelu"					,   __UpperCamelCase						:						Tuple=False					,   __UpperCamelCase						:						Optional[Any]=True					,   __UpperCamelCase						:						Tuple=0.02					,   __UpperCamelCase						:						List[Any]=1E-5					,   __UpperCamelCase						:						str=True					,   __UpperCamelCase						:						Dict=None					,   __UpperCamelCase						:						Optional[int]=True					,   __UpperCamelCase						:						Dict=1_0					,   __UpperCamelCase						:						Dict=8					,   ):



									'''simple docstring'''






									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__          =						window_size
									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__          =						use_absolute_embeddings
									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__          =						type_sequence_label_size
									snake_case__          =						encoder_stride






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									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.type_sequence_label_size       )

									snake_case__          =						self.get_config()

									return config, pixel_values, labels






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									return SwinConfig(
									    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					,   window_size=self.window_size					,   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					,   use_absolute_embeddings=self.use_absolute_embeddings					,   path_norm=self.patch_norm					,   layer_norm_eps=self.layer_norm_eps					,   initializer_range=self.initializer_range					,   encoder_stride=self.encoder_stride					,   )






						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						TFSwinModel(config=__UpperCamelCase       )
									snake_case__          =						model(__UpperCamelCase       )

									snake_case__          =						((config.image_size // config.patch_size) ** 2) // (4 ** (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_seq_len, expected_dim)       )






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						int					,   __UpperCamelCase						:						Union[str, Any]					,   __UpperCamelCase						:						List[str]       ):



									'''simple docstring'''






									snake_case__          =						TFSwinForMaskedImageModeling(config=__UpperCamelCase       )
									snake_case__          =						model(__UpperCamelCase       )
									self.parent.assertEqual(
									    result.logits.shape					,   (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

									# test greyscale images
									snake_case__          =						1
									snake_case__          =						TFSwinForMaskedImageModeling(__UpperCamelCase       )

									snake_case__          =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
									snake_case__          =						model(__UpperCamelCase       )
									self.parent.assertEqual(result.logits.shape					,   (self.batch_size, 1, self.image_size, self.image_size)       )






						def       __lowerCAmelCase(   self						:						Dict					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						Union[str, Any]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






									snake_case__          =						self.type_sequence_label_size
									snake_case__          =						TFSwinForImageClassification(__UpperCamelCase       )
									snake_case__          =						model(__UpperCamelCase					,   labels=__UpperCamelCase       )
									self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.type_sequence_label_size)       )

									# test greyscale images
									snake_case__          =						1
									snake_case__          =						TFSwinForImageClassification(__UpperCamelCase       )
									snake_case__          =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
									snake_case__          =						model(__UpperCamelCase       )
									self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.type_sequence_label_size)       )







						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						self.prepare_config_and_inputs()
									snake_case__    ,			snake_case__    ,			snake_case__          =						config_and_inputs
									snake_case__          =						{"""pixel_values""": pixel_values}
									return config, inputs_dict







@require_tf
class __magic_name__(						__lowerCAmelCase    ,		__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					int    =  (
						    (
						        TFSwinModel,
						        TFSwinForImageClassification,
						        TFSwinForMaskedImageModeling,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase_		:					Dict    =  (
						    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
						    if is_tf_available()
						    else {}
						)

						UpperCAmelCase_		:					Tuple    =  False
						UpperCAmelCase_		:					Union[str, Any]    =  False
						UpperCAmelCase_		:					Dict    =  False
						UpperCAmelCase_		:					List[Any]    =  False






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									snake_case__          =						TFSwinModelTester(self       )
									snake_case__          =						ConfigTester(self					,   config_class=__UpperCamelCase					,   embed_dim=3_7       )






						def       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									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       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									return






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



									'''simple docstring'''






									snake_case__          =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase       )






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



									'''simple docstring'''






									snake_case__          =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase       )






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



									'''simple docstring'''






									snake_case__          =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase       )






						@unittest.skip(reason="""Swin does not use inputs_embeds"""       )
						def       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									pass






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												snake_case__          =						model_class(__UpperCamelCase       )
												self.assertIsInstance(model.get_input_embeddings()					,   tf.keras.layers.Layer       )
												snake_case__          =						model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__UpperCamelCase					,   tf.keras.layers.Dense       )       )






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



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												snake_case__          =						model_class(__UpperCamelCase       )
												snake_case__          =						inspect.signature(model.call       )
												# 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]					,   __UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.model_tester.prepare_config_and_inputs_for_common()
									snake_case__          =						True

									for model_class in self.all_model_classes:
												snake_case__          =						True
												snake_case__          =						False
												snake_case__          =						True
												snake_case__          =						model_class(__UpperCamelCase       )
												snake_case__          =						model(**self._prepare_for_class(__UpperCamelCase					,   __UpperCamelCase       )       )
												snake_case__          =						outputs.attentions
												snake_case__          =						len(self.model_tester.depths       )
												self.assertEqual(len(__UpperCamelCase       )					,   __UpperCamelCase       )

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												snake_case__          =						True
												snake_case__          =						config.window_size**2
												snake_case__          =						model_class(__UpperCamelCase       )
												snake_case__          =						model(**self._prepare_for_class(__UpperCamelCase					,   __UpperCamelCase       )       )
												snake_case__          =						outputs.attentions
												self.assertEqual(len(__UpperCamelCase       )					,   __UpperCamelCase       )

												self.assertListEqual(
												    list(attentions[0].shape[-3:]       )					,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,   )
												snake_case__          =						len(__UpperCamelCase       )

												# Check attention is always last and order is fine
												snake_case__          =						True
												snake_case__          =						True
												snake_case__          =						model_class(__UpperCamelCase       )
												snake_case__          =						model(**self._prepare_for_class(__UpperCamelCase					,   __UpperCamelCase       )       )

												if hasattr(self.model_tester					,   """num_hidden_states_types"""       ):
															snake_case__          =						self.model_tester.num_hidden_states_types
												else:
															# also another +1 for reshaped_hidden_states
															snake_case__          =						2
												self.assertEqual(out_len + added_hidden_states					,   len(__UpperCamelCase       )       )

												snake_case__          =						outputs.attentions

												self.assertEqual(len(__UpperCamelCase       )					,   __UpperCamelCase       )

												self.assertListEqual(
												    list(self_attentions[0].shape[-3:]       )					,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,   )






						def       __lowerCAmelCase(   self						:						List[str]					,   __UpperCamelCase						:						Tuple					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						Optional[Any]					,   __UpperCamelCase						:						List[str]       ):



									'''simple docstring'''






									snake_case__          =						model_class(__UpperCamelCase       )
									snake_case__          =						model(**self._prepare_for_class(__UpperCamelCase					,   __UpperCamelCase       )       )
									snake_case__          =						outputs.hidden_states

									snake_case__          =						getattr(
									    self.model_tester					,   """expected_num_hidden_layers"""					,   len(self.model_tester.depths       ) + 1       )
									self.assertEqual(len(__UpperCamelCase       )					,   __UpperCamelCase       )

									# Swin has a different seq_length
									snake_case__          =						to_atuple(config.patch_size       )

									snake_case__          =						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]       )					,   [num_patches, self.model_tester.embed_dim]					,   )

									snake_case__          =						outputs.reshaped_hidden_states
									self.assertEqual(len(__UpperCamelCase       )					,   __UpperCamelCase       )

									snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__          =						reshaped_hidden_states[0].shape

									snake_case__          =						tf.reshape(reshaped_hidden_states[0]					,   (batch_size, num_channels, height * width)       )
									snake_case__          =						tf.transpose(__UpperCamelCase					,   (0, 2, 1)       )

									self.assertListEqual(
									    list(reshaped_hidden_states.shape[-2:]       )					,   [num_patches, self.model_tester.embed_dim]					,   )






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.model_tester.prepare_config_and_inputs_for_common()

									snake_case__          =						to_atuple(self.model_tester.image_size       )

									for model_class in self.all_model_classes:
												snake_case__          =						True
												self.check_hidden_states_output(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase       )

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

												self.check_hidden_states_output(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.model_tester.prepare_config_and_inputs_for_common()
									snake_case__          =						3

									snake_case__          =						to_atuple(self.model_tester.image_size       )
									snake_case__          =						to_atuple(config.patch_size       )

									snake_case__          =						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									snake_case__          =						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
												snake_case__          =						True
												self.check_hidden_states_output(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   (padded_height, padded_width)       )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												snake_case__          =						True
												self.check_hidden_states_output(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   (padded_height, padded_width)       )







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



									'''simple docstring'''






									for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												snake_case__          =						TFSwinModel.from_pretrained(__UpperCamelCase       )
												self.assertIsNotNone(__UpperCamelCase       )







@require_vision
@require_tf
class __magic_name__(						unittest.TestCase      ):






						@cached_property
						def       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									return (
									    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""       )
									    if is_vision_available()
									    else None
									)







						@slow
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""       )
									snake_case__          =						self.default_image_processor

									snake_case__          =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
									snake_case__          =						image_processor(images=__UpperCamelCase					,   return_tensors="""tf"""       )

									# forward pass
									snake_case__          =						model(__UpperCamelCase       )

									# verify the logits
									snake_case__          =						tf.TensorShape((1, 1_0_0_0)       )
									self.assertEqual(outputs.logits.shape					,   __UpperCamelCase       )
									snake_case__          =						tf.constant([-0.09_48, -0.64_54, -0.09_21]       )
									self.assertTrue(np.allclose(outputs.logits[0, :3]					,   __UpperCamelCase					,   atol=1E-4       )       )




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     Any     =    BartTokenizer
					__A  :     str     =    BartTokenizerFast
					__A  :     Optional[int]     =    True
					__A  :     Optional[int]     =    filter_roberta_detectors







					def 			_lowercase      (							self   :  Dict):
											super().setUp()
											A__						:				Union[str, Any]         =			[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											A__						:				List[str]         =			dict(zip(_A      ,		range(len(_A))))
											A__						:				List[str]         =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											A__						:				str         =			{"unk_token": "<unk>"}

											A__						:				Dict         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["vocab_file"])
											A__						:				Optional[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 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  str):
											kwargs.update(self.special_tokens_map)
											return self.tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  List[Any]):
											kwargs.update(self.special_tokens_map)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  Union[str, Any]):
											return "lower newer", "lower newer"







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizer.from_pretrained("facebook/bart-large")







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizerFast.from_pretrained("facebook/bart-large")







					@require_torch
					def 			_lowercase      (							self   :  Dict):
											A__						:				Optional[int]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											A__						:				List[Any]         =			[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		max_length=len(_A)      ,		padding=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)

																	self.assertEqual((2, 9)      ,		batch.input_ids.shape)
																	self.assertEqual((2, 9)      ,		batch.attention_mask.shape)
																	A__						:				Dict         =			batch.input_ids.tolist()[0]
																	self.assertListEqual(_A      ,		_A)
																	# Test that special tokens are reset







					@require_torch
					def 			_lowercase      (							self   :  List[Any]):
											A__						:				Optional[Any]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		padding=_A      ,		return_tensors="pt")
																	# check if input_ids are returned and no labels
																	self.assertIn("input_ids"      ,		_A)
																	self.assertIn("attention_mask"      ,		_A)
																	self.assertNotIn("labels"      ,		_A)
																	self.assertNotIn("decoder_attention_mask"      ,		_A)







					@require_torch
					def 			_lowercase      (							self   :  int):
											A__						:				Optional[Any]         =			[
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				int         =			tokenizer(text_target=_A      ,		max_length=32      ,		padding="max_length"      ,		return_tensors="pt")
																	self.assertEqual(32      ,		targets["input_ids"].shape[1])







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				List[str]         =			tokenizer(
																	    ["I am a small frog" * 1024, "I am a small frog"]      ,		padding=_A      ,		truncation=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)
																	self.assertEqual(batch.input_ids.shape      ,		(2, 1024))







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											A__						:				int         =			["A long paragraph for summarization."]
											A__						:				List[str]         =			[
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Optional[Any]         =			tokenizer(_A      ,		return_tensors="pt")
																	A__						:				Union[str, Any]         =			tokenizer(text_target=_A      ,		return_tensors="pt")
																	A__						:				Tuple         =			inputs["input_ids"]
																	A__						:				str         =			targets["input_ids"]
																	self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
																	self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())







					def 			_lowercase      (							self   :  Optional[int]):
											pass







					def 			_lowercase      (							self   :  Any):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
																							A__						:				Union[str, Any]         =			self.rust_tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				int         =			self.tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				Union[str, Any]         =			"A, <mask> AllenNLP sentence."
																							A__						:				Dict         =			tokenizer_r.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)
																							A__						:				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"])      ,		)

																							A__						:				Optional[int]         =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
																							A__						:				Dict         =			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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
																							self.assertSequenceEqual(tokens_r["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

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