
import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
			from transformers import (
			    FlaxBertModel,
			    FlaxCLIPVisionModel,
			    FlaxVisionTextDualEncoderModel,
			    FlaxViTModel,
			    VisionTextDualEncoderConfig,
			    VisionTextDualEncoderProcessor,
			)
			from transformers.modeling_flax_pytorch_utils import (
			    convert_pytorch_state_dict_to_flax,
			    load_flax_weights_in_pytorch_model,
			)


if is_torch_available():
			import torch

			from transformers import VisionTextDualEncoderModel

if is_vision_available():
			from PIL import Image






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[Any]				):




			"""simple docstring"""


			if isinstance(__snake_case    ,							collections.abc.Iterable				):
						return x
			return (x, x)





@require_flax
class 	_lowerCAmelCase					:




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							pass




				def      __a						(					self     )				->      int:
							pass




				def      __a						(					self     )				->      Tuple:
							pass




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[Any]:
							lowerCAmelCase_						=	np.abs((a - b)     ).max()
							self.assertLessEqual(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE				,				f"""Difference between torch and flax is {diff} (>= {tol})."""     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=None				,				**_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	model(input_ids=_SCREAMING_SNAKE_CASE				,				pixel_values=_SCREAMING_SNAKE_CASE				,				attention_mask=_SCREAMING_SNAKE_CASE     )

							self.assertEqual(output["text_embeds"].shape				,				(input_ids.shape[0], config.projection_dim)     )
							self.assertEqual(output["image_embeds"].shape				,				(pixel_values.shape[0], config.projection_dim)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=None				,				**_UpperCamelCase     )				->      Dict:
							lowerCAmelCase_						=	self.get_vision_text_model(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase_						=	{"vision_model": vision_model, "text_model": text_model}
							lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	model(input_ids=_SCREAMING_SNAKE_CASE				,				pixel_values=_SCREAMING_SNAKE_CASE				,				attention_mask=_SCREAMING_SNAKE_CASE     )

							self.assertEqual(output["text_embeds"].shape				,				(input_ids.shape[0], model.config.projection_dim)     )
							self.assertEqual(output["image_embeds"].shape				,				(pixel_values.shape[0], model.config.projection_dim)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=None				,				**_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	self.get_vision_text_model(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase_						=	{"vision_model": vision_model, "text_model": text_model}
							lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	model(input_ids=_SCREAMING_SNAKE_CASE				,				pixel_values=_SCREAMING_SNAKE_CASE				,				attention_mask=_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase_						=	output[0]

							with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(_SCREAMING_SNAKE_CASE     )
										lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE     )

										lowerCAmelCase_						=	model(input_ids=_SCREAMING_SNAKE_CASE				,				pixel_values=_SCREAMING_SNAKE_CASE				,				attention_mask=_SCREAMING_SNAKE_CASE     )
										lowerCAmelCase_						=	after_output[0]
										lowerCAmelCase_						=	np.amax(np.abs(out_a - out_a     )     )
										self.assertLessEqual(_SCREAMING_SNAKE_CASE				,				1e-3     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=None				,				**_UpperCamelCase     )				->      Optional[int]:
							lowerCAmelCase_						=	self.get_vision_text_model(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase_						=	{"vision_model": vision_model, "text_model": text_model}
							lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	model(
							    input_ids=_SCREAMING_SNAKE_CASE				,				pixel_values=_SCREAMING_SNAKE_CASE				,				attention_mask=_SCREAMING_SNAKE_CASE				,				output_attentions=_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	output.vision_model_output.attentions
							self.assertEqual(len(_SCREAMING_SNAKE_CASE     )				,				vision_config.num_hidden_layers     )

							# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
							lowerCAmelCase_						=	to_atuple(vision_model.config.image_size     )
							lowerCAmelCase_						=	to_atuple(vision_model.config.patch_size     )
							lowerCAmelCase_						=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							lowerCAmelCase_						=	num_patches + 1
							self.assertEqual(vision_attentions[0].shape[-3:]				,				(vision_config.num_attention_heads, seq_len, seq_len)     )

							lowerCAmelCase_						=	output.text_model_output.attentions
							self.assertEqual(len(_SCREAMING_SNAKE_CASE     )				,				text_config.num_hidden_layers     )

							self.assertEqual(
							    text_attentions[0].shape[-3:]				,				(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])				,				)




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							pt_model.to(_SCREAMING_SNAKE_CASE     )
							pt_model.eval()

							# prepare inputs
							lowerCAmelCase_						=	inputs_dict
							lowerCAmelCase_						=	{k: torch.tensor(v.tolist()     ) for k, v in flax_inputs.items()}

							with torch.no_grad():
										lowerCAmelCase_						=	pt_model(**_SCREAMING_SNAKE_CASE     ).to_tuple()

							lowerCAmelCase_						=	fx_model(**_SCREAMING_SNAKE_CASE     ).to_tuple()
							self.assertEqual(len(_SCREAMING_SNAKE_CASE     )				,				len(_SCREAMING_SNAKE_CASE     )				,				"Output lengths differ between Flax and PyTorch"     )
							for fx_output, pt_output in zip(fx_outputs[:4]				,				pt_outputs[:4]     ):
										self.assert_almost_equals(_SCREAMING_SNAKE_CASE				,				pt_output.numpy()				,				4e-2     )

							# PT -> Flax
							with tempfile.TemporaryDirectory() as tmpdirname:
										pt_model.save_pretrained(_SCREAMING_SNAKE_CASE     )
										lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE				,				from_pt=_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	fx_model_loaded(**_SCREAMING_SNAKE_CASE     ).to_tuple()
							self.assertEqual(len(_SCREAMING_SNAKE_CASE     )				,				len(_SCREAMING_SNAKE_CASE     )				,				"Output lengths differ between Flax and PyTorch"     )
							for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]				,				pt_outputs[:4]     ):
										self.assert_almost_equals(_SCREAMING_SNAKE_CASE				,				pt_output.numpy()				,				4e-2     )

							# Flax -> PT
							with tempfile.TemporaryDirectory() as tmpdirname:
										fx_model.save_pretrained(_SCREAMING_SNAKE_CASE     )
										lowerCAmelCase_						=	VisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE				,				from_flax=_SCREAMING_SNAKE_CASE     )

							pt_model_loaded.to(_SCREAMING_SNAKE_CASE     )
							pt_model_loaded.eval()

							with torch.no_grad():
										lowerCAmelCase_						=	pt_model_loaded(**_SCREAMING_SNAKE_CASE     ).to_tuple()

							self.assertEqual(len(_SCREAMING_SNAKE_CASE     )				,				len(_SCREAMING_SNAKE_CASE     )				,				"Output lengths differ between Flax and PyTorch"     )
							for fx_output, pt_output_loaded in zip(fx_outputs[:4]				,				pt_outputs_loaded[:4]     ):
										self.assert_almost_equals(_SCREAMING_SNAKE_CASE				,				pt_output_loaded.numpy()				,				4e-2     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Optional[Any]:
							lowerCAmelCase_						=	VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	VisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,				_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase_						=	fx_state

							self.check_pt_flax_equivalence(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	VisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase_						=	load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE				,				fx_model.params     )

							self.check_pt_flax_equivalence(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE     )




				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	self.prepare_config_and_inputs()
							self.check_model_from_pretrained_configs(**_SCREAMING_SNAKE_CASE     )




				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_from_pretrained(**_SCREAMING_SNAKE_CASE     )




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	self.prepare_config_and_inputs()
							self.check_save_load(**_SCREAMING_SNAKE_CASE     )




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	self.prepare_config_and_inputs()
							self.check_vision_text_output_attention(**_SCREAMING_SNAKE_CASE     )




				@is_pt_flax_cross_test
				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.prepare_config_and_inputs()
							lowerCAmelCase_						=	config_inputs_dict.pop("vision_config"     )
							lowerCAmelCase_						=	config_inputs_dict.pop("text_config"     )

							lowerCAmelCase_						=	config_inputs_dict

							self.check_equivalence_pt_to_flax(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE     )
							self.check_equivalence_flax_to_pt(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE     )




				@slow
				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	self.get_pretrained_model_and_inputs()

							lowerCAmelCase_						=	model_a(**_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase_						=	outputs[0]

							with tempfile.TemporaryDirectory() as tmp_dirname:
										model_a.save_pretrained(_SCREAMING_SNAKE_CASE     )
										lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE     )

										lowerCAmelCase_						=	model_a(**_SCREAMING_SNAKE_CASE     )
										lowerCAmelCase_						=	after_outputs[0]
										lowerCAmelCase_						=	np.amax(np.abs(out_a - out_a     )     )
										self.assertLessEqual(_SCREAMING_SNAKE_CASE				,				1e-5     )





@require_flax
class 	_lowerCAmelCase					(	__SCREAMING_SNAKE_CASE					,      unittest.TestCase       ):




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    "hf-internal-testing/tiny-random-vit"				,				"hf-internal-testing/tiny-bert"				,				vision_from_pt=_SCREAMING_SNAKE_CASE				,				text_from_pt=_SCREAMING_SNAKE_CASE				,				)
							lowerCAmelCase_						=	13
							lowerCAmelCase_						=	floats_tensor(
							    [
							        batch_size,
							        model.config.vision_config.num_channels,
							        model.config.vision_config.image_size,
							        model.config.vision_config.image_size,
							    ]     )
							lowerCAmelCase_						=	ids_tensor([batch_size, 4]				,				model.config.text_config.vocab_size     )
							lowerCAmelCase_						=	random_attention_mask([batch_size, 4]     )
							lowerCAmelCase_						=	{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

							return model, inputs




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Optional[int]:
							lowerCAmelCase_						=	FlaxViTModel(_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase_						=	FlaxBertModel(_SCREAMING_SNAKE_CASE     )
							return vision_model, text_model




				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	FlaxViTModelTester(self     )
							lowerCAmelCase_						=	FlaxBertModelTester(self     )
							lowerCAmelCase_						=	vit_model_tester.prepare_config_and_inputs()
							lowerCAmelCase_						=	bert_model_tester.prepare_config_and_inputs()

							lowerCAmelCase_						=	vision_config_and_inputs

							lowerCAmelCase_						=	text_config_and_inputs

							# make sure that cross attention layers are added
							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": attention_mask,
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							}





@require_torch
class 	_lowerCAmelCase					(	__SCREAMING_SNAKE_CASE					,      unittest.TestCase       ):




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    "hf-internal-testing/tiny-random-clip"				,				"hf-internal-testing/tiny-bert"				,				vision_from_pt=_SCREAMING_SNAKE_CASE				,				text_from_pt=_SCREAMING_SNAKE_CASE				,				)
							lowerCAmelCase_						=	13
							lowerCAmelCase_						=	floats_tensor(
							    [
							        batch_size,
							        model.config.vision_config.num_channels,
							        model.config.vision_config.image_size,
							        model.config.vision_config.image_size,
							    ]     )
							lowerCAmelCase_						=	ids_tensor([batch_size, 4]				,				model.config.text_config.vocab_size     )
							lowerCAmelCase_						=	random_attention_mask([batch_size, 4]     )
							lowerCAmelCase_						=	{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

							return model, inputs




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      int:
							lowerCAmelCase_						=	FlaxCLIPVisionModel(_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase_						=	FlaxBertModel(_SCREAMING_SNAKE_CASE     )
							return vision_model, text_model




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	FlaxCLIPVisionModelTester(self     )
							lowerCAmelCase_						=	FlaxBertModelTester(self     )
							lowerCAmelCase_						=	clip_model_tester.prepare_config_and_inputs()
							lowerCAmelCase_						=	bert_model_tester.prepare_config_and_inputs()

							lowerCAmelCase_						=	vision_config_and_inputs

							lowerCAmelCase_						=	text_config_and_inputs

							# make sure that cross attention layers are added
							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": attention_mask,
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							}





@require_flax
@require_vision
class 	_lowerCAmelCase					(	unittest.TestCase       ):




				@slow
				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"				,				logit_scale_init_value=1.0     )
							lowerCAmelCase_						=	VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"     )

							lowerCAmelCase_						=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							lowerCAmelCase_						=	processor(
							    text=["una foto di un gatto", "una foto di un cane"]				,				images=_SCREAMING_SNAKE_CASE				,				padding=_SCREAMING_SNAKE_CASE				,				return_tensors="np"     )

							lowerCAmelCase_						=	model(**_SCREAMING_SNAKE_CASE     )

							# verify the logits
							self.assertEqual(outputs.logits_per_image.shape				,				(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])     )
							self.assertEqual(
							    outputs.logits_per_text.shape				,				(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])				,				)

							lowerCAmelCase_						=	np.array([[1.2284727, 0.3104122]]     )

							self.assertTrue(np.allclose(outputs.logits_per_image				,				_SCREAMING_SNAKE_CASE				,				atol=1e-3     )     )








"""simple docstring"""






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 			A__   (  __SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	FunnelTokenizer
	SCREAMING_SNAKE_CASE          =	FunnelTokenizerFast
	SCREAMING_SNAKE_CASE          =	True
	SCREAMING_SNAKE_CASE          =	True

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Optional[int]:

				"""simple docstring"""

				super().setUp()

				__lowerCAmelCase      :  str												=       [
				    "<unk>",
				    "<cls>",
				    "<sep>",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				__lowerCAmelCase      :  int												=       os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["vocab_file"])
				with open(self.vocab_file       ,			"w"       ,			encoding="utf-8") as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       int:

				"""simple docstring"""

				return FunnelTokenizer.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			**_SCREAMING_SNAKE_CASE:		Any)				->       str:

				"""simple docstring"""

				return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		str)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       "UNwant\u00E9d,running"
				__lowerCAmelCase      :  str												=       "unwanted, running"
				return input_text, output_text

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.tokenizer_class(self.vocab_file)

				__lowerCAmelCase      :  Any												=       tokenizer.tokenize("UNwant\u00E9d,running")
				self.assertListEqual(_SCREAMING_SNAKE_CASE       ,			["un", "##want", "##ed", ",", "runn", "##ing"])
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)       ,			[7, 4, 5, 10, 8, 9])







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE)
				for tokenizer in tokenizers:
							__lowerCAmelCase      :  List[str]												=       tokenizer("UNwant\u00E9d,running")
							__lowerCAmelCase      :  Optional[int]												=       len(inputs["input_ids"]) - 1
							self.assertListEqual(inputs["token_type_ids"]       ,			[2] + [0] * sentence_len)

							__lowerCAmelCase      :  List[str]												=       tokenizer("UNwant\u00E9d,running"       ,			"UNwant\u00E9d,running")
							self.assertListEqual(inputs["token_type_ids"]       ,			[2] + [0] * sentence_len + [1] * sentence_len)