

"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ViltImageProcessor





class 				lowerCamelCase  (unittest.TestCase     ):

			def __init__(   self			:  Optional[int]      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  Union[str, Any]=7      ,			__UpperCAmelCase			:  Tuple=3      ,			__UpperCAmelCase			:  Any=1_8      ,			__UpperCAmelCase			:  int=3_0      ,			__UpperCAmelCase			:  str=4_0_0      ,			__UpperCAmelCase			:  List[Any]=True      ,			__UpperCAmelCase			:  List[Any]=None      ,			__UpperCAmelCase			:  List[str]=2      ,			__UpperCAmelCase			:  Union[str, Any]=True      ,			__UpperCAmelCase			:  int=[0.5, 0.5, 0.5]      ,			__UpperCAmelCase			:  Optional[Any]=[0.5, 0.5, 0.5]      ,			)				->      int:
						SCREAMING_SNAKE_CASE__									= size if size is not None else {"""shortest_edge""": 3_0}
						SCREAMING_SNAKE_CASE__									= parent
						SCREAMING_SNAKE_CASE__									= batch_size
						SCREAMING_SNAKE_CASE__									= num_channels
						SCREAMING_SNAKE_CASE__									= image_size
						SCREAMING_SNAKE_CASE__									= min_resolution
						SCREAMING_SNAKE_CASE__									= max_resolution
						SCREAMING_SNAKE_CASE__									= do_resize
						SCREAMING_SNAKE_CASE__									= size
						SCREAMING_SNAKE_CASE__									= size_divisor
						SCREAMING_SNAKE_CASE__									= do_normalize
						SCREAMING_SNAKE_CASE__									= image_mean
						SCREAMING_SNAKE_CASE__									= image_std

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      List[str]:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "size_divisor": self.size_divisor,
						}





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  List[str]=False    )				->      str:
						if not batched:
									SCREAMING_SNAKE_CASE__									= self.size["""shortest_edge"""]
									SCREAMING_SNAKE_CASE__									= image_inputs[0]
									if isinstance(__UpperCamelCase      ,			Image.Image    ):
												SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= image.size
									else:
												SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= image.shape[1], image.shape[2]
									SCREAMING_SNAKE_CASE__									= size / min(__UpperCamelCase      ,			__UpperCamelCase    )
									if h < w:
												SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= size, scale * w
									else:
												SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= scale * h, size

									SCREAMING_SNAKE_CASE__									= int((1_3_3_3 / 8_0_0) * size    )
									if max(__UpperCamelCase      ,			__UpperCamelCase    ) > max_size:
												SCREAMING_SNAKE_CASE__									= max_size / max(__UpperCamelCase      ,			__UpperCamelCase    )
												SCREAMING_SNAKE_CASE__									= newh * scale
												SCREAMING_SNAKE_CASE__									= neww * scale

									SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= int(newh + 0.5    ), int(neww + 0.5    )
									SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= (
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

						else:
									SCREAMING_SNAKE_CASE__									= []
									for image in image_inputs:
												SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.get_expected_values([image]    )
												expected_values.append((expected_height, expected_width)    )
									SCREAMING_SNAKE_CASE__									= max(__UpperCamelCase      ,			key=lambda __UpperCAmelCase    : item[0]    )[0]
									SCREAMING_SNAKE_CASE__									= max(__UpperCamelCase      ,			key=lambda __UpperCAmelCase    : item[1]    )[1]

						return expected_height, expected_width





@require_torch
@require_vision
class 				lowerCamelCase  (UpperCAmelCase_     ,unittest.TestCase     ):
			lowerCamelCase__				:						str   						=							ViltImageProcessor if is_vision_available() else None

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= ViltImageProcessingTester(self    )

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.image_processing_class(**self.image_processor_dict    )
						self.assertTrue(hasattr(__UpperCamelCase      ,			"""image_mean"""    )    )
						self.assertTrue(hasattr(__UpperCamelCase      ,			"""image_std"""    )    )
						self.assertTrue(hasattr(__UpperCamelCase      ,			"""do_normalize"""    )    )
						self.assertTrue(hasattr(__UpperCamelCase      ,			"""do_resize"""    )    )
						self.assertTrue(hasattr(__UpperCamelCase      ,			"""size"""    )    )
						self.assertTrue(hasattr(__UpperCamelCase      ,			"""size_divisor"""    )    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      int:
						SCREAMING_SNAKE_CASE__									= self.image_processing_class.from_dict(self.image_processor_dict    )
						self.assertEqual(image_processor.size      ,			{"""shortest_edge""": 3_0}    )

						SCREAMING_SNAKE_CASE__									= self.image_processing_class.from_dict(self.image_processor_dict      ,			size=4_2    )
						self.assertEqual(image_processor.size      ,			{"""shortest_edge""": 4_2}    )

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Optional[int]:
						pass

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Tuple:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__									= self.image_processing_class(**self.image_processor_dict    )
						# create random PIL images
						SCREAMING_SNAKE_CASE__									= prepare_image_inputs(self.image_processor_tester      ,			equal_resolution=__UpperCamelCase    )
						for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase      ,			Image.Image    )

						# Test not batched input
						SCREAMING_SNAKE_CASE__									= image_processing(image_inputs[0]      ,			return_tensors="""pt"""    ).pixel_values

						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.image_processor_tester.get_expected_values(__UpperCamelCase    )
						self.assertEqual(
						    encoded_images.shape      ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,			)

						# Test batched
						SCREAMING_SNAKE_CASE__									= image_processing(__UpperCamelCase      ,			return_tensors="""pt"""    ).pixel_values

						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.image_processor_tester.get_expected_values(__UpperCamelCase      ,			batched=__UpperCamelCase    )
						self.assertEqual(
						    encoded_images.shape      ,			(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )      ,			)

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      str:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__									= self.image_processing_class(**self.image_processor_dict    )
						# create random numpy tensors
						SCREAMING_SNAKE_CASE__									= prepare_image_inputs(self.image_processor_tester      ,			equal_resolution=__UpperCamelCase      ,			numpify=__UpperCamelCase    )
						for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase      ,			np.ndarray    )

						# Test not batched input
						SCREAMING_SNAKE_CASE__									= image_processing(image_inputs[0]      ,			return_tensors="""pt"""    ).pixel_values

						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.image_processor_tester.get_expected_values(__UpperCamelCase    )
						self.assertEqual(
						    encoded_images.shape      ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,			)

						# Test batched
						SCREAMING_SNAKE_CASE__									= image_processing(__UpperCamelCase      ,			return_tensors="""pt"""    ).pixel_values

						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.image_processor_tester.get_expected_values(__UpperCamelCase      ,			batched=__UpperCamelCase    )
						self.assertEqual(
						    encoded_images.shape      ,			(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )      ,			)





			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Optional[Any]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__									= self.image_processing_class(**self.image_processor_dict    )
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE__									= prepare_image_inputs(self.image_processor_tester      ,			equal_resolution=__UpperCamelCase      ,			torchify=__UpperCamelCase    )
						for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase      ,			torch.Tensor    )

						# Test not batched input
						SCREAMING_SNAKE_CASE__									= image_processing(image_inputs[0]      ,			return_tensors="""pt"""    ).pixel_values

						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.image_processor_tester.get_expected_values(__UpperCamelCase    )
						self.assertEqual(
						    encoded_images.shape      ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,			)

						# Test batched
						SCREAMING_SNAKE_CASE__									= image_processing(__UpperCamelCase      ,			return_tensors="""pt"""    ).pixel_values

						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.image_processor_tester.get_expected_values(__UpperCamelCase      ,			batched=__UpperCamelCase    )
						self.assertEqual(
						    encoded_images.shape      ,			(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )      ,			)





"""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     ):

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  str    )				->      int:
						SCREAMING_SNAKE_CASE__									= list(tokenizer_class.max_model_input_sizes.keys()    )
						for model_name in sa_models[:1]:
									SCREAMING_SNAKE_CASE__									= 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    )
												SCREAMING_SNAKE_CASE__									= tokenizer.convert_tokens_to_ids(__UpperCAmelCase    )
												self.assertIsInstance(__UpperCAmelCase      ,			__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  BatchEncoding      ,			__UpperCAmelCase			:  Optional[Callable] = None    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= pickle.dumps(__UpperCAmelCase    )
						self.assertIsNotNone(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Optional[Any]:
						self.check_tokenizer_from_pretrained(__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Optional[int]:
						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			:  Dict    )				->      List[str]:
						import numpy as np

						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= 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			:  Dict    )				->      int:
						import tensorflow as tf

						def tf_array_equals(__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  str    ):
									return tf.reduce_all(tf.equal(__UpperCAmelCase      ,			__UpperCAmelCase    )    )

						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Optional[int]:
						import torch

						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= 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			:  Any    )				->      int:
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= 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			:  Optional[int]    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= 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:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""np"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= 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:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""pt"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      int:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= 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:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""tf"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= 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:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""jax"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= [{"""input_ids""": np.array([0, 1, 2]    )}, {"""input_ids""": np.array([0, 1, 2, 3]    )}]
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )

						SCREAMING_SNAKE_CASE__									= 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]]    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Optional[int]:
						import torch

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

						SCREAMING_SNAKE_CASE__									= 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]]    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Dict:
						import tensorflow as tf

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

						SCREAMING_SNAKE_CASE__									= 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]]    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= Tokenizer(WordPiece(unk_token="""[UNK]"""    )    )
						PreTrainedTokenizerFast(tokenizer_object=__UpperCAmelCase    )





			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      int:
						SCREAMING_SNAKE_CASE__									= 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"""    )    )


