


'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Dict			      = KandinskyVaaControlnetImgaImgPipeline
	_lowerCamelCase   :					int			      = ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
	_lowerCamelCase   :					Union[str, Any]			      = ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
	_lowerCamelCase   :					Optional[int]			      = [
	    """generator""",
	    """height""",
	    """width""",
	    """strength""",
	    """guidance_scale""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	_lowerCamelCase   :					Any			      = False



	@property
	def 				lowercase     (					self    ):
		return 32



	@property
	def 				lowercase     (					self    ):
		return 32



	@property
	def 				lowercase     (					self    ):
		return self.time_input_dim



	@property
	def 				lowercase     (					self    ):
		return self.time_input_dim * 4



	@property
	def 				lowercase     (					self    ):
		return 100



	@property
	def 				lowercase     (					self    ):
		torch.manual_seed(0    )

		lowerCAmelCase					:    Optional[int]      							= {
		    'in_channels': 8,
		    # Out channels is double in channels because predicts mean and variance
		    'out_channels': 8,
		    'addition_embed_type': 'image_hint',
		    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
		    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
		    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
		    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
		    'layers_per_block': 1,
		    'encoder_hid_dim': self.text_embedder_hidden_size,
		    'encoder_hid_dim_type': 'image_proj',
		    'cross_attention_dim': self.cross_attention_dim,
		    'attention_head_dim': 4,
		    'resnet_time_scale_shift': 'scale_shift',
		    'class_embed_type': None,
		}

		lowerCAmelCase					:    int      							= UNetaDConditionModel(**snake_case__    )
		return model



	@property
	def 				lowercase     (					self    ):
		return {
		    "block_out_channels": [32, 32, 64, 64],
		    "down_block_types": [
		        "DownEncoderBlock2D",
		        "DownEncoderBlock2D",
		        "DownEncoderBlock2D",
		        "AttnDownEncoderBlock2D",
		    ],
		    "in_channels": 3,
		    "latent_channels": 4,
		    "layers_per_block": 1,
		    "norm_num_groups": 8,
		    "norm_type": "spatial",
		    "num_vq_embeddings": 12,
		    "out_channels": 3,
		    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
		    "vq_embed_dim": 4,
		}



	@property
	def 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    List[Any]      							= VQModel(**self.dummy_movq_kwargs    )
		return model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.dummy_unet
		lowerCAmelCase					:    int      							= self.dummy_movq

		lowerCAmelCase					:    str      							= {
		    'num_train_timesteps': 1000,
		    'beta_schedule': 'linear',
		    'beta_start': 0.0_0_0_8_5,
		    'beta_end': 0.0_1_2,
		    'clip_sample': False,
		    'set_alpha_to_one': False,
		    'steps_offset': 0,
		    'prediction_type': 'epsilon',
		    'thresholding': False,
		}

		lowerCAmelCase					:    Dict      							= DDIMScheduler(**snake_case__    )

		lowerCAmelCase					:    str      							= {
		    'unet': unet,
		    'scheduler': scheduler,
		    'movq': movq,
		}

		return components



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		lowerCAmelCase					:    int      							= floats_tensor((1, self.text_embedder_hidden_size) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
		lowerCAmelCase					:    Tuple      							= floats_tensor((1, self.text_embedder_hidden_size) ,					rng=random.Random(seed + 1    )    ).to(
		    snake_case__    )
		# create init_image
		lowerCAmelCase					:    Tuple      							= floats_tensor((1, 3, 64, 64) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= image.cpu().permute(0 ,					2 ,					3 ,					1    )[0]
		lowerCAmelCase					:    int      							= Image.fromarray(np.uinta(snake_case__    )    ).convert('RGB'    ).resize((256, 256)    )
		# create hint
		lowerCAmelCase					:    List[str]      							= floats_tensor((1, 3, 64, 64) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )

		if str(snake_case__    ).startswith('mps'    ):
			lowerCAmelCase					:    Dict      							= torch.manual_seed(snake_case__    )
		else:
			lowerCAmelCase					:    str      							= torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
		lowerCAmelCase					:    str      							= {
		    'image': init_image,
		    'image_embeds': image_embeds,
		    'negative_image_embeds': negative_image_embeds,
		    'hint': hint,
		    'generator': generator,
		    'height': 64,
		    'width': 64,
		    'num_inference_steps': 10,
		    'guidance_scale': 7.0,
		    'strength': 0.2,
		    'output_type': 'np',
		}
		return inputs



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= 'cpu'

		lowerCAmelCase					:    int      							= self.get_dummy_components()

		lowerCAmelCase					:    Optional[Any]      							= self.pipeline_class(**snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= pipe.to(snake_case__    )

		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    int      							= pipe(**self.get_dummy_inputs(snake_case__    )    )
		lowerCAmelCase					:    Any      							= output.images

		lowerCAmelCase					:    str      							= pipe(
		    **self.get_dummy_inputs(snake_case__    ) ,					return_dict=snake_case__ ,					)[0]

		lowerCAmelCase					:    int      							= image[0, -3:, -3:, -1]
		lowerCAmelCase					:    List[str]      							= image_from_tuple[0, -3:, -3:, -1]

		assert image.shape == (1, 64, 64, 3)

		lowerCAmelCase					:    List[Any]      							= np.array(
		    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]    )
		assert (
		    np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
		), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
		assert (
		    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
		), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"



@slow
@require_torch_gpu
class  lowerCAmelCase				(				unittest.TestCase							):



	def 				lowercase     (					self    ):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'    )

		lowerCAmelCase					:    List[str]      							= load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'    )
		lowerCAmelCase					:    List[Any]      							= init_image.resize((512, 512)    )

		lowerCAmelCase					:    str      							= load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/kandinskyv22/hint_image_cat.png'    )
		lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(np.array(snake_case__    )    ).float() / 2_5_5.0
		lowerCAmelCase					:    int      							= hint.permute(2 ,					0 ,					1    ).unsqueeze(0    )

		lowerCAmelCase					:    List[str]      							= 'A robot, 4k photo'

		lowerCAmelCase					:    int      							= KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
		    'kandinsky-community/kandinsky-2-2-prior' ,					torch_dtype=torch.floataa    )
		pipe_prior.to(snake_case__    )

		lowerCAmelCase					:    Union[str, Any]      							= KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
		    'kandinsky-community/kandinsky-2-2-controlnet-depth' ,					torch_dtype=torch.floataa    )
		lowerCAmelCase					:    str      							= pipeline.to(snake_case__    )

		pipeline.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    Dict      							= torch.Generator(device='cpu'    ).manual_seed(0    )

		lowerCAmelCase							,   lowerCAmelCase					:    int      							= pipe_prior(
		    snake_case__ ,					image=snake_case__ ,					strength=0.8_5 ,					generator=snake_case__ ,					negative_prompt='' ,					).to_tuple()

		lowerCAmelCase					:    int      							= pipeline(
		    image=snake_case__ ,					image_embeds=snake_case__ ,					negative_image_embeds=snake_case__ ,					hint=snake_case__ ,					generator=snake_case__ ,					num_inference_steps=100 ,					height=512 ,					width=512 ,					strength=0.5 ,					output_type='np' ,					)

		lowerCAmelCase					:    List[str]      							= output.images[0]

		assert image.shape == (512, 512, 3)

		assert_mean_pixel_difference(snake_case__ ,					snake_case__    )




'''simple docstring'''




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase  :		Union[str, Any]		     =	get_tests_dir('fixtures/test_sentencepiece_no_bos.model')



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					str			      = PegasusTokenizer
	_lowerCamelCase   :					Union[str, Any]			      = PegasusTokenizerFast
	_lowerCamelCase   :					Optional[Any]			      = True
	_lowerCamelCase   :					Optional[Any]			      = True



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

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    List[Any]      							= PegasusTokenizer(snake_case__    )
		tokenizer.save_pretrained(self.tmpdirname    )



	@cached_property
	def 				lowercase     (					self    ):
		return PegasusTokenizer.from_pretrained('google/pegasus-large'    )



	def 				lowercase     (					self ,					**snake_case__    ):
		return PegasusTokenizer.from_pretrained(self.tmpdirname ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		return ("This is a test", "This is a test")



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= '</s>'
		lowerCAmelCase					:    int      							= 1

		self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__    ) ,					snake_case__    )
		self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__    ) ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= list(self.get_tokenizer().get_vocab().keys()    )

		self.assertEqual(vocab_keys[0] ,					'<pad>'    )
		self.assertEqual(vocab_keys[1] ,					'</s>'    )
		self.assertEqual(vocab_keys[-1] ,					'v'    )
		self.assertEqual(len(snake_case__    ) ,					1103    )



	def 				lowercase     (					self    ):
		self.assertEqual(self.get_tokenizer().vocab_size ,					1103    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    List[Any]      							= self.tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    Optional[Any]      							= (
		    'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
		    ' </s> <pad> <pad> <pad>'
		)
		lowerCAmelCase					:    Optional[Any]      							= rust_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		lowerCAmelCase					:    Optional[int]      							= py_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self._large_tokenizer
		# <mask_1> masks whole sentence while <mask_2> masks single word
		lowerCAmelCase					:    List[str]      							= '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
		lowerCAmelCase					:    Optional[Any]      							= [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
		lowerCAmelCase					:    Optional[Any]      							= tokenizer([raw_input_str] ,					return_tensors=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self._large_tokenizer
		# The tracebacks for the following asserts are **better** without messages or self.assertEqual
		assert tokenizer.vocab_size == 9_6103
		assert tokenizer.pad_token_id == 0
		assert tokenizer.eos_token_id == 1
		assert tokenizer.offset == 103
		assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
		assert tokenizer.unk_token == "<unk>"
		assert tokenizer.model_max_length == 1024
		lowerCAmelCase					:    List[Any]      							= 'To ensure a smooth flow of bank resolutions.'
		lowerCAmelCase					:    Optional[int]      							= [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
		lowerCAmelCase					:    Any      							= tokenizer([raw_input_str] ,					return_tensors=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )
		assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]    ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



	@require_torch
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= ['This is going to be way too long.' * 150, 'short example']
		lowerCAmelCase					:    int      							= ['not super long but more than 5 tokens', 'tiny']
		lowerCAmelCase					:    Dict      							= self._large_tokenizer(snake_case__ ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )
		lowerCAmelCase					:    Dict      							= self._large_tokenizer(
		    text_target=snake_case__ ,					max_length=5 ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )

		assert batch.input_ids.shape == (2, 1024)
		assert batch.attention_mask.shape == (2, 1024)
		assert targets["input_ids"].shape == (2, 5)
		assert len(snake_case__    ) == 2  # input_ids, attention_mask.



	@slow
	def 				lowercase     (					self    ):
		# fmt: off
		lowerCAmelCase					:    Tuple      							= {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
		# fmt: on

		self.tokenizer_integration_test_util(
		    expected_encoding=snake_case__ ,					model_name='google/bigbird-pegasus-large-arxiv' ,					revision='ba85d0851d708441f91440d509690f1ab6353415' ,					)



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Optional[Any]			      = PegasusTokenizer
	_lowerCamelCase   :					str			      = PegasusTokenizerFast
	_lowerCamelCase   :					Tuple			      = True
	_lowerCamelCase   :					int			      = True



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

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    int      							= PegasusTokenizer(snake_case__ ,					offset=0 ,					mask_token_sent=snake_case__ ,					mask_token='[MASK]'    )
		tokenizer.save_pretrained(self.tmpdirname    )



	@cached_property
	def 				lowercase     (					self    ):
		return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'    )



	def 				lowercase     (					self ,					**snake_case__    ):
		return PegasusTokenizer.from_pretrained(self.tmpdirname ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		return ("This is a test", "This is a test")



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    Union[str, Any]      							= self.tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    List[str]      							= (
		    'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
		    ' <pad> <pad> <pad>'
		)
		lowerCAmelCase					:    Dict      							= rust_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		lowerCAmelCase					:    Union[str, Any]      							= py_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	@require_torch
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= ['This is going to be way too long.' * 1000, 'short example']
		lowerCAmelCase					:    Union[str, Any]      							= ['not super long but more than 5 tokens', 'tiny']
		lowerCAmelCase					:    List[str]      							= self._large_tokenizer(snake_case__ ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )
		lowerCAmelCase					:    List[str]      							= self._large_tokenizer(
		    text_target=snake_case__ ,					max_length=5 ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )

		assert batch.input_ids.shape == (2, 4096)
		assert batch.attention_mask.shape == (2, 4096)
		assert targets["input_ids"].shape == (2, 5)
		assert len(snake_case__    ) == 2  # input_ids, attention_mask.



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= (
		    'This is an example string that is used to test the original TF implementation against the HF'
		    ' implementation'
		)

		lowerCAmelCase					:    Tuple      							= self._large_tokenizer(snake_case__    ).input_ids

		self.assertListEqual(
		    snake_case__ ,					[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] ,					)
