




'''simple docstring'''






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class   SCREAMING_SNAKE_CASE_		(       lowercase__				):
 __snake_case	:     Tuple			   =      CONFIG_NAME



 @register_to_config
 def __init__(  self   ,		A = True   ,		A = 8   ,		A = "lanczos"   ,		A = True   ,		A = False   ,		):






        '''simple docstring'''




        super().__init__()



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        if images.ndim == 3:
               lowercase  :		Optional[int]						=      images[None, ...]
        lowercase  :		Dict						=      (images * 255).round().astype('''uint8''' )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               lowercase  :		Dict						=      [Image.fromarray(image.squeeze()   ,		mode='''L''' ) for image in images]
        else:
               lowercase  :		int						=      [Image.fromarray(_A ) for image in images]

        return pil_images



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        if not isinstance(_A   ,		_A ):
               lowercase  :		List[str]						=      [images]
        lowercase  :		str						=      [np.array(_A ).astype(np.floataa ) / 2_5_5.0 for image in images]
        lowercase  :		str						=      np.stack(_A   ,		axis=0 )

        return images



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        if images.ndim == 3:
               lowercase  :		Tuple						=      images[..., None]

        lowercase  :		List[str]						=      torch.from_numpy(images.transpose(0   ,		3   ,		1   ,		2 ) )
        return images



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        lowercase  :		str						=      images.cpu().permute(0   ,		2   ,		3   ,		1 ).float().numpy()
        return images



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        return 2.0 * images - 1.0



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        return (images / 2 + 0.5).clamp(0   ,		1 )



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      image.convert('''RGB''' )
        return image



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






        '''simple docstring'''




        if height is None:
               lowercase  :		Tuple						=      image.height
        if width is None:
               lowercase  :		Optional[int]						=      image.width

        lowercase  :		List[Any]						=      (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        lowercase  :		int						=      image.resize((width, height)   ,		resample=PIL_INTERPOLATION[self.config.resample] )
        return image



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






        '''simple docstring'''




        lowercase  :		List[Any]						=      (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(_A   ,		_A ):
               lowercase  :		Optional[int]						=      [image]
        elif not (isinstance(_A   ,		_A ) and all(isinstance(_A   ,		_A ) for i in image )):
               raise ValueError(
                   f"""Input is in incorrect format: {[type(_A ) for i in image]}. Currently, we only support {', '.join(_A )}""" )

        if isinstance(image[0]   ,		PIL.Image.Image ):
               if self.config.do_convert_rgb:
                      lowercase  :		Optional[Any]						=      [self.convert_to_rgb(_A ) for i in image]
               if self.config.do_resize:
                      lowercase  :		int						=      [self.resize(_A   ,		_A   ,		_A ) for i in image]
               lowercase  :		Dict						=      self.pil_to_numpy(_A )  # to np
               lowercase  :		int						=      self.numpy_to_pt(_A )  # to pt

        elif isinstance(image[0]   ,		np.ndarray ):
               lowercase  :		str						=      np.concatenate(_A   ,		axis=0 ) if image[0].ndim == 4 else np.stack(_A   ,		axis=0 )
               lowercase  :		Any						=      self.numpy_to_pt(_A )
               lowercase  :		Tuple						=      image.shape
               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                      raise ValueError(
                          f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                          f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor""" )

        elif isinstance(image[0]   ,		torch.Tensor ):
               lowercase  :		Optional[Any]						=      torch.cat(_A   ,		axis=0 ) if image[0].ndim == 4 else torch.stack(_A   ,		axis=0 )
               lowercase  :		List[Any]						=      image.shape

               # don't need any preprocess if the image is latents
               if channel == 4:
                      return image

               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                      raise ValueError(
                          f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                          f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor""" )

        # expected range [0,1], normalize to [-1,1]
        lowercase  :		str						=      self.config.do_normalize
        if image.min() < 0:
               warnings.warn(
                   '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
                   f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""   ,		_A   ,		)
               lowercase  :		str						=      False

        if do_normalize:
               lowercase  :		int						=      self.normalize(_A )

        return image





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






        '''simple docstring'''




        if not isinstance(_A   ,		torch.Tensor ):
               raise ValueError(
                   f"""Input for postprocessing is in incorrect format: {type(_A )}. We only support pytorch tensor""" )
        if output_type not in ["latent", "pt", "np", "pil"]:
               lowercase  :		Optional[Any]						=      (
                   f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                   '`pil`, `np`, `pt`, `latent`'
               )
               deprecate('''Unsupported output_type'''   ,		'''1.0.0'''   ,		_A   ,		standard_warn=_A )
               lowercase  :		Union[str, Any]						=      'np'

        if output_type == "latent":
               return image

        if do_denormalize is None:
               lowercase  :		Any						=      [self.config.do_normalize] * image.shape[0]

        lowercase  :		Optional[int]						=      torch.stack(
            [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

        if output_type == "pt":
               return image

        lowercase  :		Any						=      self.pt_to_numpy(_A )

        if output_type == "np":
               return image

        if output_type == "pil":
               return self.numpy_to_pil(_A )






class   SCREAMING_SNAKE_CASE_		(       lowercase__				):
 __snake_case	:     Optional[int]			   =      CONFIG_NAME



 @register_to_config
 def __init__(  self   ,		A = True   ,		A = 8   ,		A = "lanczos"   ,		A = True   ,		):






        '''simple docstring'''




        super().__init__()



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        if images.ndim == 3:
               lowercase  :		Union[str, Any]						=      images[None, ...]
        lowercase  :		Optional[Any]						=      (images * 255).round().astype('''uint8''' )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               lowercase  :		Optional[int]						=      [Image.fromarray(image.squeeze()   ,		mode='''L''' ) for image in images]
        else:
               lowercase  :		Optional[Any]						=      [Image.fromarray(image[:, :, :3] ) for image in images]

        return pil_images



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        return image[:, :, 1] * 2**8 + image[:, :, 2]



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        if images.ndim == 3:
               lowercase  :		Tuple						=      images[None, ...]
        lowercase  :		Any						=      images[:, :, :, 3:]
        if images.shape[-1] == 6:
               lowercase  :		List[str]						=      (images_depth * 255).round().astype('''uint8''' )
               lowercase  :		Any						=      [
                   Image.fromarray(self.rgblike_to_depthmap(_A )   ,		mode='''I;16''' ) for image_depth in images_depth
               ]
        elif images.shape[-1] == 4:
               lowercase  :		Optional[Any]						=      (images_depth * 65535.0).astype(np.uintaa )
               lowercase  :		Any						=      [Image.fromarray(_A   ,		mode='''I;16''' ) for image_depth in images_depth]
        else:
               raise Exception('''Not supported''' )

        return pil_images





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






        '''simple docstring'''




        if not isinstance(_A   ,		torch.Tensor ):
               raise ValueError(
                   f"""Input for postprocessing is in incorrect format: {type(_A )}. We only support pytorch tensor""" )
        if output_type not in ["latent", "pt", "np", "pil"]:
               lowercase  :		List[Any]						=      (
                   f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                   '`pil`, `np`, `pt`, `latent`'
               )
               deprecate('''Unsupported output_type'''   ,		'''1.0.0'''   ,		_A   ,		standard_warn=_A )
               lowercase  :		List[str]						=      'np'

        if do_denormalize is None:
               lowercase  :		Tuple						=      [self.config.do_normalize] * image.shape[0]

        lowercase  :		Tuple						=      torch.stack(
            [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

        lowercase  :		Optional[int]						=      self.pt_to_numpy(_A )

        if output_type == "np":
               if image.shape[-1] == 6:
                      lowercase  :		str						=      np.stack([self.rgblike_to_depthmap(im[:, :, 3:] ) for im in image]   ,		axis=0 )
               else:
                      lowercase  :		Tuple						=      image[:, :, :, 3:]
               return image[:, :, :, :3], image_depth

        if output_type == "pil":
               return self.numpy_to_pil(_A ), self.numpy_to_depth(_A )
        else:
               raise Exception(f"""This type {output_type} is not supported""" )










'''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''' ) )




