




'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     Tuple			   =      PaintByExamplePipeline
 __snake_case	:     Dict			   =      IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 __snake_case	:     int			   =      IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case	:     List[str]			   =      frozenset([]				)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        torch.manual_seed(0 )
        lowercase  :		int						=      UNetaDConditionModel(
            block_out_channels=(32, 64)   ,		layers_per_block=2   ,		sample_size=32   ,		in_channels=9   ,		out_channels=4   ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,		cross_attention_dim=32   ,		)
        lowercase  :		Tuple						=      PNDMScheduler(skip_prk_steps=A )
        torch.manual_seed(0 )
        lowercase  :		Optional[Any]						=      AutoencoderKL(
            block_out_channels=[32, 64]   ,		in_channels=3   ,		out_channels=3   ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,		latent_channels=4   ,		)
        torch.manual_seed(0 )
        lowercase  :		Any						=      CLIPVisionConfig(
            hidden_size=32   ,		projection_dim=32   ,		intermediate_size=37   ,		layer_norm_eps=1E-05   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		image_size=32   ,		patch_size=4   ,		)
        lowercase  :		Optional[Any]						=      PaintByExampleImageEncoder(A   ,		proj_size=32 )
        lowercase  :		List[str]						=      CLIPImageProcessor(crop_size=32   ,		size=32 )

        lowercase  :		Tuple						=      {
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''image_encoder''': image_encoder,
            '''safety_checker''': None,
            '''feature_extractor''': feature_extractor,
        }
        return components



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      np.array(image.convert('''RGB''' ) )
        lowercase  :		int						=      image[None].transpose(0   ,		3   ,		1   ,		2 )
        lowercase  :		Optional[int]						=      torch.from_numpy(A ).to(dtype=torch.floataa ) / 127.5 - 1.0
        return image



 def    SCREAMING_SNAKE_CASE(  self   ,		A="cpu"   ,		A=0 ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(A ) ).to(A )
        lowercase  :		int						=      image.cpu().permute(0   ,		2   ,		3   ,		1 )[0]
        lowercase  :		List[Any]						=      Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((64, 64) )
        lowercase  :		List[str]						=      Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
        lowercase  :		Optional[Any]						=      Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((32, 32) )

        if str(A ).startswith('''mps''' ):
               lowercase  :		Optional[int]						=      torch.manual_seed(A )
        else:
               lowercase  :		Optional[Any]						=      torch.Generator(device=A ).manual_seed(A )
        lowercase  :		str						=      {
            '''example_image''': example_image,
            '''image''': init_image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        lowercase  :		Optional[int]						=      PaintByExamplePipeline(**A )
        lowercase  :		Optional[int]						=      pipe.to('''cpu''' )
        pipe.set_progress_bar_config(disable=A )

        lowercase  :		List[str]						=      self.get_dummy_inputs()
        lowercase  :		Dict						=      pipe(**A )
        lowercase  :		Any						=      output.images

        lowercase  :		int						=      image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowercase  :		int						=      np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      '''cpu'''
        lowercase  :		List[Any]						=      self.get_dummy_inputs()
        inputs.pop('''mask_image''' )
        lowercase  :		Dict						=      self.convert_to_pt(inputs.pop('''image''' ) )
        lowercase  :		Optional[Any]						=      image.clamp(0   ,		1 ) / 2

        # make sure here that pndm scheduler skips prk
        lowercase  :		str						=      PaintByExamplePipeline(**self.get_dummy_components() )
        lowercase  :		int						=      pipe.to(A )
        pipe.set_progress_bar_config(disable=A )

        lowercase  :		List[str]						=      pipe(image=A   ,		mask_image=mask_image[:, 0]   ,		**A )
        lowercase  :		str						=      output.images

        lowercase  :		Dict						=      image.cpu().permute(0   ,		2   ,		3   ,		1 )[0]
        lowercase  :		Optional[int]						=      mask_image.cpu().permute(0   ,		2   ,		3   ,		1 )[0]

        lowercase  :		List[Any]						=      Image.fromarray(np.uinta(A ) ).convert('''RGB''' )
        lowercase  :		Optional[Any]						=      Image.fromarray(np.uinta(A ) ).convert('''RGB''' )

        lowercase  :		List[Any]						=      pipe(**self.get_dummy_inputs() )
        lowercase  :		Optional[int]						=      output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten() ).max() < 5E-2





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        super().test_inference_batch_single_identical(expected_max_diff=3E-3 )






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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/dog_in_bucket.png''' )
        lowercase  :		str						=      load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/mask.png''' )
        lowercase  :		Any						=      load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/panda.jpg''' )

        lowercase  :		str						=      PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example''' )
        lowercase  :		Union[str, Any]						=      pipe.to(A )
        pipe.set_progress_bar_config(disable=A )

        lowercase  :		List[str]						=      torch.manual_seed(321 )
        lowercase  :		Union[str, Any]						=      pipe(
            image=A   ,		mask_image=A   ,		example_image=A   ,		generator=A   ,		guidance_scale=5.0   ,		num_inference_steps=50   ,		output_type='''np'''   ,		)

        lowercase  :		str						=      output.images

        lowercase  :		Optional[Any]						=      image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase  :		Union[str, Any]						=      np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2










'''simple docstring'''






import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
      from tokenizers import Tokenizer
      from tokenizers.models import WordPiece

class   SCREAMING_SNAKE_CASE_		(       unittest.TestCase				):



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      list(tokenizer_class.max_model_input_sizes.keys() )
        for model_name in sa_models[:1]:
               lowercase  :		Dict						=      tokenizer_class.from_pretrained(A )
               self.assertIsNotNone(A )
               self.assertIsInstance(A   ,		A )
               self.assertIsInstance(A   ,		A )

               for special_tok in tokenizer.all_special_tokens:
                      self.assertIsInstance(A   ,		A )
                      lowercase  :		Tuple						=      tokenizer.convert_tokens_to_ids(A )
                      self.assertIsInstance(A   ,		A )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = None ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      pickle.dumps(A )
        self.assertIsNotNone(A )

        lowercase  :		str						=      pickle.loads(A )

        # Ensure is_fast is correctly restored
        self.assertEqual(be_restored.is_fast   ,		be_original.is_fast )

        # Ensure encodings are potentially correctly restored
        if be_original.is_fast:
               self.assertIsNotNone(be_restored.encodings )
        else:
               self.assertIsNone(be_restored.encodings )

        # Ensure the keys are the same
        for original_v, restored_v in zip(be_original.values()   ,		be_restored.values() ):
               if equal_op:
                      self.assertTrue(equal_op(A   ,		A ) )
               else:
                      self.assertEqual(A   ,		A )



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.check_tokenizer_from_pretrained(A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.assertEqual(TensorType('''tf''' )   ,		TensorType.TENSORFLOW )
        self.assertEqual(TensorType('''pt''' )   ,		TensorType.PYTORCH )
        self.assertEqual(TensorType('''np''' )   ,		TensorType.NUMPY )



 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import numpy as np

        lowercase  :		Union[str, Any]						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		str						=      BertTokenizerFast.from_pretrained('''bert-base-cased''' )

        # Python no tensor
        with self.subTest('''BatchEncoding (Python, return_tensors=None)''' ):
               self.assert_dump_and_restore(tokenizer_p('''Small example to encode''' ) )

        with self.subTest('''BatchEncoding (Python, return_tensors=NUMPY)''' ):
               self.assert_dump_and_restore(
                   tokenizer_p('''Small example to encode'''   ,		return_tensors=TensorType.NUMPY )   ,		np.array_equal )

        with self.subTest('''BatchEncoding (Rust, return_tensors=None)''' ):
               self.assert_dump_and_restore(tokenizer_r('''Small example to encode''' ) )

        with self.subTest('''BatchEncoding (Rust, return_tensors=NUMPY)''' ):
               self.assert_dump_and_restore(
                   tokenizer_r('''Small example to encode'''   ,		return_tensors=TensorType.NUMPY )   ,		np.array_equal )



 @require_tf
 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import tensorflow as tf

        def tf_array_equals(A   ,		A ):
               return tf.reduce_all(tf.equal(A   ,		A ) )

        lowercase  :		Optional[Any]						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		Optional[Any]						=      BertTokenizerFast.from_pretrained('''bert-base-cased''' )

        with self.subTest('''BatchEncoding (Python, return_tensors=TENSORFLOW)''' ):
               self.assert_dump_and_restore(
                   tokenizer_p('''Small example to encode'''   ,		return_tensors=TensorType.TENSORFLOW )   ,		A )

        with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)''' ):
               self.assert_dump_and_restore(
                   tokenizer_r('''Small example to encode'''   ,		return_tensors=TensorType.TENSORFLOW )   ,		A )



 @require_torch
 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import torch

        lowercase  :		Tuple						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		Any						=      BertTokenizerFast.from_pretrained('''bert-base-cased''' )

        with self.subTest('''BatchEncoding (Python, return_tensors=PYTORCH)''' ):
               self.assert_dump_and_restore(
                   tokenizer_p('''Small example to encode'''   ,		return_tensors=TensorType.PYTORCH )   ,		torch.equal )

        with self.subTest('''BatchEncoding (Rust, return_tensors=PYTORCH)''' ):
               self.assert_dump_and_restore(
                   tokenizer_r('''Small example to encode'''   ,		return_tensors=TensorType.PYTORCH )   ,		torch.equal )



 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		List[Any]						=      BertTokenizerFast.from_pretrained('''bert-base-cased''' )

        with self.subTest('''Python Tokenizer''' ):
               self.assertFalse(tokenizer_p('''Small example to_encode''' ).is_fast )

        with self.subTest('''Rust Tokenizer''' ):
               self.assertTrue(tokenizer_r('''Small example to_encode''' ).is_fast )



 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      BertTokenizerFast.from_pretrained('''bert-base-cased''' )
        lowercase  :		str						=      tokenizer_r(['''Test''', '''\xad''', '''test''']   ,		is_split_into_words=A )

        self.assertEqual(encoded.word_to_tokens(0 )   ,		TokenSpan(start=1   ,		end=2 ) )
        self.assertEqual(encoded.word_to_tokens(1 )   ,		A )
        self.assertEqual(encoded.word_to_tokens(2 )   ,		TokenSpan(start=2   ,		end=3 ) )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
        lowercase  :		List[str]						=      batch.convert_to_tensors(tensor_type='''np''' )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(2, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(2,) )
        # test converting the converted
        with CaptureStderr() as cs:
               lowercase  :		Any						=      batch.convert_to_tensors(tensor_type='''np''' )
        self.assertFalse(len(cs.err )   ,		msg=f"""should have no warning, but got {cs.err}""" )

        lowercase  :		Dict						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		List[str]						=      batch.convert_to_tensors(tensor_type='''np'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 @require_torch
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
        lowercase  :		Union[str, Any]						=      batch.convert_to_tensors(tensor_type='''pt''' )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(2, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(2,) )
        # test converting the converted
        with CaptureStderr() as cs:
               lowercase  :		Tuple						=      batch.convert_to_tensors(tensor_type='''pt''' )
        self.assertFalse(len(cs.err )   ,		msg=f"""should have no warning, but got {cs.err}""" )

        lowercase  :		Optional[Any]						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		List[str]						=      batch.convert_to_tensors(tensor_type='''pt'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 @require_tf
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
        lowercase  :		Dict						=      batch.convert_to_tensors(tensor_type='''tf''' )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(2, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(2,) )
        # test converting the converted
        with CaptureStderr() as cs:
               lowercase  :		Optional[int]						=      batch.convert_to_tensors(tensor_type='''tf''' )
        self.assertFalse(len(cs.err )   ,		msg=f"""should have no warning, but got {cs.err}""" )

        lowercase  :		str						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		List[str]						=      batch.convert_to_tensors(tensor_type='''tf'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 @require_flax
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
        lowercase  :		List[Any]						=      batch.convert_to_tensors(tensor_type='''jax''' )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(2, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(2,) )
        # test converting the converted
        with CaptureStderr() as cs:
               lowercase  :		Dict						=      batch.convert_to_tensors(tensor_type='''jax''' )
        self.assertFalse(len(cs.err )   ,		msg=f"""should have no warning, but got {cs.err}""" )

        lowercase  :		str						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		str						=      batch.convert_to_tensors(tensor_type='''jax'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      [{'''input_ids''': np.array([0, 1, 2] )}, {'''input_ids''': np.array([0, 1, 2, 3] )}]
        lowercase  :		Tuple						=      BertTokenizer.from_pretrained('''bert-base-cased''' )

        lowercase  :		Dict						=      tokenizer.pad(A   ,		padding=A )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		np.ndarray ) )
        self.assertEqual(batch['''input_ids'''].tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
        lowercase  :		Dict						=      tokenizer.pad(A   ,		padding=A   ,		return_tensors='''np''' )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		np.ndarray ) )
        self.assertEqual(batch['''input_ids'''].tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )



 @require_torch
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import torch

        lowercase  :		Optional[Any]						=      [{'''input_ids''': torch.tensor([0, 1, 2] )}, {'''input_ids''': torch.tensor([0, 1, 2, 3] )}]
        lowercase  :		Tuple						=      BertTokenizer.from_pretrained('''bert-base-cased''' )

        lowercase  :		Any						=      tokenizer.pad(A   ,		padding=A )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		torch.Tensor ) )
        self.assertEqual(batch['''input_ids'''].tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
        lowercase  :		Union[str, Any]						=      tokenizer.pad(A   ,		padding=A   ,		return_tensors='''pt''' )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		torch.Tensor ) )
        self.assertEqual(batch['''input_ids'''].tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )



 @require_tf
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import tensorflow as tf

        lowercase  :		Optional[int]						=      [{'''input_ids''': tf.constant([0, 1, 2] )}, {'''input_ids''': tf.constant([0, 1, 2, 3] )}]
        lowercase  :		Dict						=      BertTokenizer.from_pretrained('''bert-base-cased''' )

        lowercase  :		List[Any]						=      tokenizer.pad(A   ,		padding=A )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		tf.Tensor ) )
        self.assertEqual(batch['''input_ids'''].numpy().tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
        lowercase  :		Any						=      tokenizer.pad(A   ,		padding=A   ,		return_tensors='''tf''' )
        self.assertTrue(isinstance(batch['''input_ids''']   ,		tf.Tensor ) )
        self.assertEqual(batch['''input_ids'''].numpy().tolist()   ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )



 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
        PreTrainedTokenizerFast(tokenizer_object=A )





 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
        with tempfile.TemporaryDirectory() as tmpdirname:
               bert_tokenizer.save(os.path.join(A   ,		'''tokenizer.json''' ) )
               PreTrainedTokenizerFast(tokenizer_file=os.path.join(A   ,		'''tokenizer.json''' ) )




