code: 
  
  
  '''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 _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Tuple   					=       CONFIG_NAME
  
  
  
  
  
  
  
  		@register_to_config
  		def __init__(      self	:		Union[str, Any]						,  _A	:		bool = True						,  _A	:		int = 8						,  _A	:		str = "lanczos"						,  _A	:		bool = True						,  _A	:		bool = False						,  )						->				int:
  			super().__init__()
  
  
  
  
  
  
  
  		@staticmethod
  		def      __lowerCAmelCase	(      _A	:		np.ndarray    )						->				PIL.Image.Image:
  			if images.ndim == 3:
  				__magic_name__ :					Optional[int]  			=  images[None, ...]
  			__magic_name__ :					Dict  			=  (images * 255).round().astype('uint8'    )
  			if images.shape[-1] == 1:
  				# special case for grayscale (single channel) images
  				__magic_name__ :					Dict  			=  [Image.fromarray(image.squeeze()						,  mode='L'    ) for image in images]
  			else:
  				__magic_name__ :					int  			=  [Image.fromarray(_A    ) for image in images]
  
  			return pil_images
  
  
  
  
  
  
  
  		@staticmethod
  		def      __lowerCAmelCase	(      _A	:		Union[List[PIL.Image.Image], PIL.Image.Image]    )						->				np.ndarray:
  			if not isinstance(_A						,  _A    ):
  				__magic_name__ :					List[str]  			=  [images]
  			__magic_name__ :					str  			=  [np.array(_A    ).astype(np.floataa    ) / 255.0 for image in images]
  			__magic_name__ :					str  			=  np.stack(_A						,  axis=0    )
  
  			return images
  
  
  
  
  
  
  
  		@staticmethod
  		def      __lowerCAmelCase	(      _A	:		np.ndarray    )						->				torch.FloatTensor:
  			if images.ndim == 3:
  				__magic_name__ :					Tuple  			=  images[..., None]
  
  			__magic_name__ :					List[str]  			=  torch.from_numpy(images.transpose(0						,  3						,  1						,  2    )    )
  			return images
  
  
  
  
  
  
  
  		@staticmethod
  		def      __lowerCAmelCase	(      _A	:		torch.FloatTensor    )						->				np.ndarray:
  			__magic_name__ :					str  			=  images.cpu().permute(0						,  2						,  3						,  1    ).float().numpy()
  			return images
  
  
  
  
  
  
  
  		@staticmethod
  		def      __lowerCAmelCase	(      _A	:		str    )						->				Union[str, Any]:
  			return 2.0 * images - 1.0
  
  
  
  
  
  
  
  		@staticmethod
  		def      __lowerCAmelCase	(      _A	:		Any    )						->				Union[str, Any]:
  			return (images / 2 + 0.5).clamp(0						,  1    )
  
  
  
  
  
  
  
  		@staticmethod
  		def      __lowerCAmelCase	(      _A	:		PIL.Image.Image    )						->				PIL.Image.Image:
  			__magic_name__ :					List[Any]  			=  image.convert('RGB'    )
  			return image
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		PIL.Image.Image						,  _A	:		Optional[int] = None						,  _A	:		Optional[int] = None						,  )						->				PIL.Image.Image:
  			if height is None:
  				__magic_name__ :					Tuple  			=  image.height
  			if width is None:
  				__magic_name__ :					Optional[int]  			=  image.width
  
  			__magic_name__			,       __magic_name__ :					List[Any]  			=  (
  			    x - x % self.config.vae_scale_factor for x in (width, height)
  			)  # resize to integer multiple of vae_scale_factor
  			__magic_name__ :					int  			=  image.resize((width, height)						,  resample=PIL_INTERPOLATION[self.config.resample]    )
  			return image
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]						,  _A	:		Optional[int] = None						,  _A	:		Optional[int] = None						,  )						->				torch.Tensor:
  			__magic_name__ :					List[Any]  			=  (PIL.Image.Image, np.ndarray, torch.Tensor)
  			if isinstance(_A						,  _A    ):
  				__magic_name__ :					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:
  					__magic_name__ :					Optional[Any]  			=  [self.convert_to_rgb(_A    ) for i in image]
  				if self.config.do_resize:
  					__magic_name__ :					int  			=  [self.resize(_A						,  _A						,  _A    ) for i in image]
  				__magic_name__ :					Dict  			=  self.pil_to_numpy(_A    )  # to np
  				__magic_name__ :					int  			=  self.numpy_to_pt(_A    )  # to pt
  
  			elif isinstance(image[0]						,  np.ndarray    ):
  				__magic_name__ :					str  			=  np.concatenate(_A						,  axis=0    ) if image[0].ndim == 4 else np.stack(_A						,  axis=0    )
  				__magic_name__ :					Any  			=  self.numpy_to_pt(_A    )
  				__magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__ :					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    ):
  				__magic_name__ :					Optional[Any]  			=  torch.cat(_A						,  axis=0    ) if image[0].ndim == 4 else torch.stack(_A						,  axis=0    )
  				__magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__ :					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]
  			__magic_name__ :					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						,  )
  				__magic_name__ :					str  			=  False
  
  			if do_normalize:
  				__magic_name__ :					int  			=  self.normalize(_A    )
  
  			return image
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		torch.FloatTensor						,  _A	:		str = "pil"						,  _A	:		Optional[List[bool]] = None						,  )						->				Optional[Any]:
  			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"]:
  				__magic_name__ :					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    )
  				__magic_name__ :					Union[str, Any]  			=  'np'
  
  			if output_type == "latent":
  				return image
  
  			if do_denormalize is None:
  				__magic_name__ :					Any  			=  [self.config.do_normalize] * image.shape[0]
  
  			__magic_name__ :					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
  
  			__magic_name__ :					Any  			=  self.pt_to_numpy(_A    )
  
  			if output_type == "np":
  				return image
  
  			if output_type == "pil":
  				return self.numpy_to_pil(_A    )
  
  
  
  
  
  
  
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[int]   					=       CONFIG_NAME
  
  
  
  
  
  
  
  		@register_to_config
  		def __init__(      self	:		List[str]						,  _A	:		bool = True						,  _A	:		int = 8						,  _A	:		str = "lanczos"						,  _A	:		bool = True						,  )						->				Tuple:
  			super().__init__()
  
  
  
  
  
  
  
  		@staticmethod
  		def      __lowerCAmelCase	(      _A	:		Union[str, Any]    )						->				Optional[int]:
  			if images.ndim == 3:
  				__magic_name__ :					Union[str, Any]  			=  images[None, ...]
  			__magic_name__ :					Optional[Any]  			=  (images * 255).round().astype('uint8'    )
  			if images.shape[-1] == 1:
  				# special case for grayscale (single channel) images
  				__magic_name__ :					Optional[int]  			=  [Image.fromarray(image.squeeze()						,  mode='L'    ) for image in images]
  			else:
  				__magic_name__ :					Optional[Any]  			=  [Image.fromarray(image[:, :, :3]    ) for image in images]
  
  			return pil_images
  
  
  
  
  
  
  
  		@staticmethod
  		def      __lowerCAmelCase	(      _A	:		Optional[int]    )						->				Optional[Any]:
  			return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		Dict    )						->				Dict:
  			if images.ndim == 3:
  				__magic_name__ :					Tuple  			=  images[None, ...]
  			__magic_name__ :					Any  			=  images[:, :, :, 3:]
  			if images.shape[-1] == 6:
  				__magic_name__ :					List[str]  			=  (images_depth * 255).round().astype('uint8'    )
  				__magic_name__ :					Any  			=  [
  				    Image.fromarray(self.rgblike_to_depthmap(_A    )						,  mode='I;16'    ) for image_depth in images_depth
  				]
  			elif images.shape[-1] == 4:
  				__magic_name__ :					Optional[Any]  			=  (images_depth * 6_5535.0).astype(np.uintaa    )
  				__magic_name__ :					Any  			=  [Image.fromarray(_A						,  mode='I;16'    ) for image_depth in images_depth]
  			else:
  				raise Exception('Not supported'    )
  
  			return pil_images
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]						,  _A	:		torch.FloatTensor						,  _A	:		str = "pil"						,  _A	:		Optional[List[bool]] = None						,  )						->				int:
  			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"]:
  				__magic_name__ :					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    )
  				__magic_name__ :					List[str]  			=  'np'
  
  			if do_denormalize is None:
  				__magic_name__ :					Tuple  			=  [self.config.do_normalize] * image.shape[0]
  
  			__magic_name__ :					Tuple  			=  torch.stack(
  			    [self.denormalize(image[i]    ) if do_denormalize[i] else image[i] for i in range(image.shape[0]    )]    )
  
  			__magic_name__ :					Optional[int]  			=  self.pt_to_numpy(_A    )
  
  			if output_type == "np":
  				if image.shape[-1] == 6:
  					__magic_name__ :					str  			=  np.stack([self.rgblike_to_depthmap(im[:, :, 3:]    ) for im in image]						,  axis=0    )
  				else:
  					__magic_name__ :					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'    )
style_context: 
  
  
  '''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 _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		Optional[Any]    )						->				Optional[int]:
  			__magic_name__ :					Optional[int]  			=  list(tokenizer_class.max_model_input_sizes.keys()    )
  			for model_name in sa_models[:1]:
  				__magic_name__ :					List[str]  			=  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    )
  					__magic_name__ :					Optional[Any]  			=  tokenizer.convert_tokens_to_ids(_A    )
  					self.assertIsInstance(_A						,  _A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		BatchEncoding						,  _A	:		Optional[Callable] = None    )						->				Tuple:
  			__magic_name__ :					Union[str, Any]  			=  pickle.dumps(_A    )
  			self.assertIsNotNone(_A    )
  
  			__magic_name__ :					Optional[Any]  			=  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      __lowerCAmelCase	(      self	:		Dict    )						->				Optional[Any]:
  			self.check_tokenizer_from_pretrained(_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				List[Any]:
  			self.assertEqual(TensorType('tf'    )						,  TensorType.TENSORFLOW    )
  			self.assertEqual(TensorType('pt'    )						,  TensorType.PYTORCH    )
  			self.assertEqual(TensorType('np'    )						,  TensorType.NUMPY    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Optional[int]:
  			import numpy as np
  
  			__magic_name__ :					Dict  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					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      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[str]:
  			import tensorflow as tf
  
  			def tf_array_equals(_A	:		str						,  _A	:		Union[str, Any]    ):
  				return tf.reduce_all(tf.equal(_A						,  _A    )    )
  
  			__magic_name__ :					str  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					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      __lowerCAmelCase	(      self	:		Any    )						->				List[str]:
  			import torch
  
  			__magic_name__ :					Union[str, Any]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Optional[int]  			=  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      __lowerCAmelCase	(      self	:		List[str]    )						->				Dict:
  			__magic_name__ :					Optional[int]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Dict  			=  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      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Optional[int]:
  			__magic_name__ :					Optional[int]  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Tuple  			=  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      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Optional[Any]:
  			__magic_name__ :					Any  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					Dict  			=  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:
  				__magic_name__ :					Any  			=  batch.convert_to_tensors(tensor_type='np'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Optional[Any]  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					List[Any]  			=  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      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Any:
  			__magic_name__ :					Optional[int]  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					List[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:
  				__magic_name__ :					Union[str, Any]  			=  batch.convert_to_tensors(tensor_type='pt'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Any  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					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      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
  			__magic_name__ :					str  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					List[str]  			=  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:
  				__magic_name__ :					Optional[int]  			=  batch.convert_to_tensors(tensor_type='tf'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Union[str, Any]  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					Union[str, Any]  			=  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      __lowerCAmelCase	(      self	:		str    )						->				Optional[Any]:
  			__magic_name__ :					List[Any]  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					int  			=  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:
  				__magic_name__ :					Optional[Any]  			=  batch.convert_to_tensors(tensor_type='jax'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					str  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					Dict  			=  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      __lowerCAmelCase	(      self	:		str    )						->				int:
  			__magic_name__ :					int  			=  [{'input_ids': np.array([0, 1, 2]    )}, {'input_ids': np.array([0, 1, 2, 3]    )}]
  			__magic_name__ :					Dict  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					Optional[int]  			=  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]]    )
  			__magic_name__ :					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      __lowerCAmelCase	(      self	:		List[Any]    )						->				str:
  			import torch
  
  			__magic_name__ :					List[str]  			=  [{'input_ids': torch.tensor([0, 1, 2]    )}, {'input_ids': torch.tensor([0, 1, 2, 3]    )}]
  			__magic_name__ :					List[str]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					List[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]]    )
  			__magic_name__ :					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      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
  			import tensorflow as tf
  
  			__magic_name__ :					Tuple  			=  [{'input_ids': tf.constant([0, 1, 2]    )}, {'input_ids': tf.constant([0, 1, 2, 3]    )}]
  			__magic_name__ :					Any  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					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]]    )
  			__magic_name__ :					Union[str, 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      __lowerCAmelCase	(      self	:		Any    )						->				str:
  			__magic_name__ :					str  			=  Tokenizer(WordPiece(unk_token='[UNK]'    )    )
  			PreTrainedTokenizerFast(tokenizer_object=_A    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Any:
  			__magic_name__ :					List[Any]  			=  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'    )    )