

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__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				Union[str, Any]											=							CONFIG_NAME

						@register_to_config
						def __init__(				self , __UpperCamelCase = True , __UpperCamelCase = 8 , __UpperCamelCase = "lanczos" , __UpperCamelCase = True , __UpperCamelCase = False , )							->	Dict:




									'''simple docstring'''


									super().__init__()

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	PIL.Image.Image:




									'''simple docstring'''


									if images.ndim == 3:
												__UpperCamelCase    :    str								=      images[None, ...]
									__UpperCamelCase    :    int								=      (images * 2_55).round().astype("uint8"    )
									if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												__UpperCamelCase    :    int								=      [Image.fromarray(image.squeeze() , mode="L"    ) for image in images]
									else:
												__UpperCamelCase    :    Any								=      [Image.fromarray(__UpperCamelCase    ) for image in images]

									return pil_images

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	np.ndarray:




									'''simple docstring'''


									if not isinstance(__UpperCamelCase , __UpperCamelCase    ):
												__UpperCamelCase    :    Tuple								=      [images]
									__UpperCamelCase    :    Any								=      [np.array(__UpperCamelCase    ).astype(np.floataa    ) / 255.0 for image in images]
									__UpperCamelCase    :    str								=      np.stack(__UpperCamelCase , axis=0    )

									return images

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	torch.FloatTensor:




									'''simple docstring'''


									if images.ndim == 3:
												__UpperCamelCase    :    Dict								=      images[..., None]

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

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      images.cpu().permute(0 , 2 , 3 , 1    ).float().numpy()
									return images

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	Union[str, Any]:




									'''simple docstring'''


									return 2.0 * images - 1.0

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	List[Any]:




									'''simple docstring'''


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

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	PIL.Image.Image:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      image.convert("RGB"    )
									return image

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , )							->	PIL.Image.Image:




									'''simple docstring'''


									if height is None:
												__UpperCamelCase    :    Dict								=      image.height
									if width is None:
												__UpperCamelCase    :    Tuple								=      image.width

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

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , )							->	torch.Tensor:




									'''simple docstring'''


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

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

									elif isinstance(image[0] , np.ndarray    ):
												__UpperCamelCase    :    List[str]								=      np.concatenate(__UpperCamelCase , axis=0    ) if image[0].ndim == 4 else np.stack(__UpperCamelCase , axis=0    )
												__UpperCamelCase    :    List[Any]								=      self.numpy_to_pt(__UpperCamelCase    )
												__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      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    ):
												__UpperCamelCase    :    List[str]								=      torch.cat(__UpperCamelCase , axis=0    ) if image[0].ndim == 4 else torch.stack(__UpperCamelCase , axis=0    )
												__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase    :    Dict								=      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]
									__UpperCamelCase    :    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()}]''' , __UpperCamelCase , )
												__UpperCamelCase    :    List[Any]								=      False

									if do_normalize:
												__UpperCamelCase    :    List[Any]								=      self.normalize(__UpperCamelCase    )

									return image




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = "pil" , __UpperCamelCase = None , )							->	List[Any]:




									'''simple docstring'''


									if not isinstance(__UpperCamelCase , torch.Tensor    ):
												raise ValueError(
												    f'''Input for postprocessing is in incorrect format: {type(__UpperCamelCase    )}. We only support pytorch tensor'''    )
									if output_type not in ["latent", "pt", "np", "pil"]:
												__UpperCamelCase    :    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" , __UpperCamelCase , standard_warn=__UpperCamelCase    )
												__UpperCamelCase    :    Optional[int]								=      "np"

									if output_type == "latent":
												return image

									if do_denormalize is None:
												__UpperCamelCase    :    Union[str, Any]								=      [self.config.do_normalize] * image.shape[0]

									__UpperCamelCase    :    str								=      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

									__UpperCamelCase    :    Any								=      self.pt_to_numpy(__UpperCamelCase    )

									if output_type == "np":
												return image

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







class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				Optional[Any]											=							CONFIG_NAME

						@register_to_config
						def __init__(				self , __UpperCamelCase = True , __UpperCamelCase = 8 , __UpperCamelCase = "lanczos" , __UpperCamelCase = True , )							->	Optional[Any]:




									'''simple docstring'''


									super().__init__()

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	int:




									'''simple docstring'''


									if images.ndim == 3:
												__UpperCamelCase    :    Dict								=      images[None, ...]
									__UpperCamelCase    :    Dict								=      (images * 2_55).round().astype("uint8"    )
									if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												__UpperCamelCase    :    Tuple								=      [Image.fromarray(image.squeeze() , mode="L"    ) for image in images]
									else:
												__UpperCamelCase    :    Dict								=      [Image.fromarray(image[:, :, :3]    ) for image in images]

									return pil_images

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	Dict:




									'''simple docstring'''


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

						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	Optional[int]:




									'''simple docstring'''


									if images.ndim == 3:
												__UpperCamelCase    :    str								=      images[None, ...]
									__UpperCamelCase    :    Tuple								=      images[:, :, :, 3:]
									if images.shape[-1] == 6:
												__UpperCamelCase    :    Optional[Any]								=      (images_depth * 2_55).round().astype("uint8"    )
												__UpperCamelCase    :    str								=      [
												    Image.fromarray(self.rgblike_to_depthmap(__UpperCamelCase    ) , mode="I;16"    ) for image_depth in images_depth
												]
									elif images.shape[-1] == 4:
												__UpperCamelCase    :    str								=      (images_depth * 65535.0).astype(np.uintaa    )
												__UpperCamelCase    :    Optional[int]								=      [Image.fromarray(__UpperCamelCase , mode="I;16"    ) for image_depth in images_depth]
									else:
												raise Exception("Not supported"    )

									return pil_images




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = "pil" , __UpperCamelCase = None , )							->	List[Any]:




									'''simple docstring'''


									if not isinstance(__UpperCamelCase , torch.Tensor    ):
												raise ValueError(
												    f'''Input for postprocessing is in incorrect format: {type(__UpperCamelCase    )}. We only support pytorch tensor'''    )
									if output_type not in ["latent", "pt", "np", "pil"]:
												__UpperCamelCase    :    int								=      (
												    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" , __UpperCamelCase , standard_warn=__UpperCamelCase    )
												__UpperCamelCase    :    List[str]								=      "np"

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

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

									__UpperCamelCase    :    Tuple								=      self.pt_to_numpy(__UpperCamelCase    )

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

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


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   ):







						"""simple docstring"""




						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      list(tokenizer_class.max_model_input_sizes.keys()    )
									for model_name in sa_models[:1]:
												__UpperCamelCase    :    int								=      tokenizer_class.from_pretrained(__UpperCamelCase    )
												self.assertIsNotNone(__UpperCamelCase    )
												self.assertIsInstance(__UpperCamelCase , __UpperCamelCase    )
												self.assertIsInstance(__UpperCamelCase , __UpperCamelCase    )

												for special_tok in tokenizer.all_special_tokens:
															self.assertIsInstance(__UpperCamelCase , __UpperCamelCase    )
															__UpperCamelCase    :    str								=      tokenizer.convert_tokens_to_ids(__UpperCamelCase    )
															self.assertIsInstance(__UpperCamelCase , __UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None    )							->	Tuple:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      pickle.dumps(__UpperCamelCase    )
									self.assertIsNotNone(__UpperCamelCase    )

									__UpperCamelCase    :    List[str]								=      pickle.loads(__UpperCamelCase    )

									# 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(__UpperCamelCase , __UpperCamelCase    )    )
												else:
															self.assertEqual(__UpperCamelCase , __UpperCamelCase    )

						@slow
						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									self.check_tokenizer_from_pretrained(__UpperCamelCase    )

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									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]:




									'''simple docstring'''


									import numpy as np

									__UpperCamelCase    :    str								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Optional[Any]								=      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    )							->	Union[str, Any]:




									'''simple docstring'''


									import tensorflow as tf

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

									__UpperCamelCase    :    int								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Dict								=      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    ) , __UpperCamelCase    )

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

						@require_torch
						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									import torch

									__UpperCamelCase    :    List[Any]								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Optional[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 __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[int]								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Tuple								=      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]:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      BertTokenizerFast.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Dict								=      tokenizer_r(["Test", "\xad", "test"] , is_split_into_words=__UpperCamelCase    )

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

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    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:
												__UpperCamelCase    :    Optional[Any]								=      batch.convert_to_tensors(tensor_type="np"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    Union[str, Any]								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    int								=      batch.convert_to_tensors(tensor_type="np" , prepend_batch_axis=__UpperCamelCase    )
									self.assertEqual(tensor_batch["inputs"].shape , (1, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (1,)    )

						@require_torch
						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    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:
												__UpperCamelCase    :    Tuple								=      batch.convert_to_tensors(tensor_type="pt"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

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

						@require_tf
						def __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    Tuple								=      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:
												__UpperCamelCase    :    Optional[int]								=      batch.convert_to_tensors(tensor_type="tf"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    str								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    Union[str, Any]								=      batch.convert_to_tensors(tensor_type="tf" , prepend_batch_axis=__UpperCamelCase    )
									self.assertEqual(tensor_batch["inputs"].shape , (1, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (1,)    )

						@require_flax
						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    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:
												__UpperCamelCase    :    Dict								=      batch.convert_to_tensors(tensor_type="jax"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    Tuple								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    Union[str, Any]								=      batch.convert_to_tensors(tensor_type="jax" , prepend_batch_axis=__UpperCamelCase    )
									self.assertEqual(tensor_batch["inputs"].shape , (1, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (1,)    )

						def __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      [{"input_ids": np.array([0, 1, 2]    )}, {"input_ids": np.array([0, 1, 2, 3]    )}]
									__UpperCamelCase    :    Any								=      BertTokenizer.from_pretrained("bert-base-cased"    )

									__UpperCamelCase    :    Optional[Any]								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase    )
									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]]    )
									__UpperCamelCase    :    Optional[Any]								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase , 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    )							->	Optional[int]:




									'''simple docstring'''


									import torch

									__UpperCamelCase    :    List[str]								=      [{"input_ids": torch.tensor([0, 1, 2]    )}, {"input_ids": torch.tensor([0, 1, 2, 3]    )}]
									__UpperCamelCase    :    str								=      BertTokenizer.from_pretrained("bert-base-cased"    )

									__UpperCamelCase    :    str								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase    )
									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]]    )
									__UpperCamelCase    :    int								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase , 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    )							->	List[Any]:




									'''simple docstring'''


									import tensorflow as tf

									__UpperCamelCase    :    List[Any]								=      [{"input_ids": tf.constant([0, 1, 2]    )}, {"input_ids": tf.constant([0, 1, 2, 3]    )}]
									__UpperCamelCase    :    int								=      BertTokenizer.from_pretrained("bert-base-cased"    )

									__UpperCamelCase    :    Optional[Any]								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase    )
									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]]    )
									__UpperCamelCase    :    List[str]								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase , 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    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      Tokenizer(WordPiece(unk_token="[UNK]"    )    )
									PreTrainedTokenizerFast(tokenizer_object=__UpperCamelCase    )




						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      Tokenizer(WordPiece(unk_token="[UNK]"    )    )
									with tempfile.TemporaryDirectory() as tmpdirname:
												bert_tokenizer.save(os.path.join(__UpperCamelCase , "tokenizer.json"    )    )
												PreTrainedTokenizerFast(tokenizer_file=os.path.join(__UpperCamelCase , "tokenizer.json"    )    )