"""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       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Any			=    KandinskyVaaControlnetImgaImgPipeline
					UpperCamelCase_ :	Dict			=    ["image_embeds", "negative_image_embeds", "image", "hint"]
					UpperCamelCase_ :	int			=    ["image_embeds", "negative_image_embeds", "image", "hint"]
					UpperCamelCase_ :	Optional[Any]			=    [
					    "generator",
					    "height",
					    "width",
					    "strength",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					UpperCamelCase_ :	str			=    False




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							return 32




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							return 32




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							return self.time_input_dim




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							return self.time_input_dim * 4




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							return 100




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							torch.manual_seed(0     )

							lowerCAmelCase__        =   {
							    """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__        =   UNetaDConditionModel(**snake_case__     )
							return model




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							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 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							torch.manual_seed(0     )
							lowerCAmelCase__        =   VQModel(**self.dummy_movq_kwargs     )
							return model




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.dummy_unet
							lowerCAmelCase__        =   self.dummy_movq

							lowerCAmelCase__        =   {
							    """num_train_timesteps""": 1000,
							    """beta_schedule""": """linear""",
							    """beta_start""": 0.0_0085,
							    """beta_end""": 0.012,
							    """clip_sample""": False,
							    """set_alpha_to_one""": False,
							    """steps_offset""": 0,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							}

							lowerCAmelCase__        =   DDIMScheduler(**snake_case__     )

							lowerCAmelCase__        =   {
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     Any  ,						snake_case__						:     Dict=0     ):
							lowerCAmelCase__        =   floats_tensor((1, self.text_embedder_hidden_size)  ,						rng=random.Random(snake_case__     )     ).to(snake_case__     )
							lowerCAmelCase__        =   floats_tensor((1, self.text_embedder_hidden_size)  ,						rng=random.Random(seed + 1     )     ).to(
							    snake_case__     )
							# create init_image
							lowerCAmelCase__        =   floats_tensor((1, 3, 64, 64)  ,						rng=random.Random(snake_case__     )     ).to(snake_case__     )
							lowerCAmelCase__        =   image.cpu().permute(0  ,						2  ,						3  ,						1     )[0]
							lowerCAmelCase__        =   Image.fromarray(np.uinta(snake_case__     )     ).convert("""RGB"""     ).resize((256, 256)     )
							# create hint
							lowerCAmelCase__        =   floats_tensor((1, 3, 64, 64)  ,						rng=random.Random(snake_case__     )     ).to(snake_case__     )

							if str(snake_case__     ).startswith("""mps"""     ):
									lowerCAmelCase__        =   torch.manual_seed(snake_case__     )
							else:
									lowerCAmelCase__        =   torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
							lowerCAmelCase__        =   {
							    """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 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   """cpu"""

							lowerCAmelCase__        =   self.get_dummy_components()

							lowerCAmelCase__        =   self.pipeline_class(**snake_case__     )
							lowerCAmelCase__        =   pipe.to(snake_case__     )

							pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   pipe(**self.get_dummy_inputs(snake_case__     )     )
							lowerCAmelCase__        =   output.images

							lowerCAmelCase__        =   pipe(
							    **self.get_dummy_inputs(snake_case__     )  ,						return_dict=snake_case__  ,						)[0]

							lowerCAmelCase__        =   image[0, -3:, -3:, -1]
							lowerCAmelCase__        =   image_from_tuple[0, -3:, -3:, -1]

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

							lowerCAmelCase__        =   np.array(
							    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]     )
							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       a_      (     unittest.TestCase			):




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""     )

							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""     )
							lowerCAmelCase__        =   init_image.resize((512, 512)     )

							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinskyv22/hint_image_cat.png"""     )
							lowerCAmelCase__        =   torch.from_numpy(np.array(snake_case__     )     ).float() / 255.0
							lowerCAmelCase__        =   hint.permute(2  ,						0  ,						1     ).unsqueeze(0     )

							lowerCAmelCase__        =   """A robot, 4k photo"""

							lowerCAmelCase__        =   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-2-prior"""  ,						torch_dtype=torch.floataa     )
							pipe_prior.to(snake_case__     )

							lowerCAmelCase__        =   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-2-controlnet-depth"""  ,						torch_dtype=torch.floataa     )
							lowerCAmelCase__        =   pipeline.to(snake_case__     )

							pipeline.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   torch.Generator(device="""cpu"""     ).manual_seed(0     )

							lowerCAmelCase__   ,					lowerCAmelCase__        =   pipe_prior(
							    snake_case__  ,						image=snake_case__  ,						strength=0.85  ,						generator=snake_case__  ,						negative_prompt=""""""  ,						).to_tuple()

							lowerCAmelCase__        =   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__        =   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    :							Optional[Any]													=						get_tests_dir("fixtures/test_sentencepiece_no_bos.model")

@require_sentencepiece
@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Tuple			=    PegasusTokenizer
					UpperCamelCase_ :	Any			=    PegasusTokenizerFast
					UpperCamelCase_ :	int			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase__        =   PegasusTokenizer(snake_case__     )
							tokenizer.save_pretrained(self.tmpdirname     )




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							return PegasusTokenizer.from_pretrained("""google/pegasus-large"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						**snake_case__						:     Optional[Any]     ):
							return PegasusTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Optional[Any]     ):
							return ("This is a test", "This is a test")




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   """</s>"""
							lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							self.assertEqual(self.get_tokenizer().vocab_size  ,						1103     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   self.tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   (
							    """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__        =   rust_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							lowerCAmelCase__        =   py_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self._large_tokenizer
							# <mask_1> masks whole sentence while <mask_2> masks single word
							lowerCAmelCase__        =   """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
							lowerCAmelCase__        =   [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
							lowerCAmelCase__        =   tokenizer([raw_input_str]  ,						return_tensors=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self._large_tokenizer
							# The tracebacks for the following asserts are **better** without messages or self.assertEqual
							assert tokenizer.vocab_size == 96103
							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__        =   """To ensure a smooth flow of bank resolutions."""
							lowerCAmelCase__        =   [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
							lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   ["""This is going to be way too long.""" * 150, """short example"""]
							lowerCAmelCase__        =   ["""not super long but more than 5 tokens""", """tiny"""]
							lowerCAmelCase__        =   self._large_tokenizer(snake_case__  ,						padding=snake_case__  ,						truncation=snake_case__  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							# fmt: off
							lowerCAmelCase__        =   {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 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, 18289, 17780, 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       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    PegasusTokenizer
					UpperCamelCase_ :	Optional[int]			=    PegasusTokenizerFast
					UpperCamelCase_ :	Union[str, Any]			=    True
					UpperCamelCase_ :	Optional[int]			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							super().setUp()

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




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						**snake_case__						:     List[Any]     ):
							return PegasusTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Dict     ):
							return ("This is a test", "This is a test")




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   self.tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   (
							    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
							    """ <pad> <pad> <pad>"""
							)
							lowerCAmelCase__        =   rust_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   ["""This is going to be way too long.""" * 1000, """short example"""]
							lowerCAmelCase__        =   ["""not super long but more than 5 tokens""", """tiny"""]
							lowerCAmelCase__        =   self._large_tokenizer(snake_case__  ,						padding=snake_case__  ,						truncation=snake_case__  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   (
							    """This is an example string that is used to test the original TF implementation against the HF"""
							    """ implementation"""
							)

							lowerCAmelCase__        =   self._large_tokenizer(snake_case__     ).input_ids

							self.assertListEqual(
							    snake_case__  ,						[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]  ,						)




