

"""simple docstring"""




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						List[str]   						=							'config.json'

			@register_to_config
			def __init__(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  List[str]=2      ,			__UpperCAmelCase			:  Optional[int]=5      ,			__UpperCAmelCase			:  List[Any]=(2, 5)      ,			__UpperCAmelCase			:  int="for diffusion"      ,			__UpperCAmelCase			:  int=[1, 3]      ,			)				->      int:
						pass





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'config.json'

			@register_to_config
			def __init__(   self			:  Any      ,			__UpperCAmelCase			:  List[Any]=2      ,			__UpperCAmelCase			:  Optional[Any]=5      ,			__UpperCAmelCase			:  Optional[int]=(2, 5)      ,			__UpperCAmelCase			:  Optional[Any]="for diffusion"      ,			__UpperCAmelCase			:  str=[1, 3]      ,			)				->      int:
						pass





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						List[Any]   						=							'config.json'

			@register_to_config
			def __init__(   self			:  Dict      ,			__UpperCAmelCase			:  List[Any]=2      ,			__UpperCAmelCase			:  Optional[Any]=5      ,			__UpperCAmelCase			:  List[str]=(2, 5)      ,			__UpperCAmelCase			:  List[Any]="for diffusion"      ,			__UpperCAmelCase			:  Dict=[1, 3]      ,			__UpperCAmelCase			:  Optional[Any]=[1, 3]      ,			)				->      Optional[Any]:
						pass





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Dict   						=							'config.json'

			@register_to_config
			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=2      ,			__UpperCAmelCase			:  str=5      ,			__UpperCAmelCase			:  Union[str, Any]=(2, 5)      ,			__UpperCAmelCase			:  Optional[Any]="for diffusion"      ,			__UpperCAmelCase			:  Optional[Any]=[1, 5]      ,			__UpperCAmelCase			:  List[Any]=[5, 4]      ,			)				->      Tuple:
						pass





class 				lowerCamelCase  (unittest.TestCase     ):

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Dict:
						with self.assertRaises(__UpperCAmelCase    ):
									ConfigMixin.load_config("""dummy_path"""    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= SampleObject()
						SCREAMING_SNAKE_CASE__									= obj.config
						assert config["a"] == 2
						assert config["b"] == 5
						assert config["c"] == (2, 5)
						assert config["d"] == "for diffusion"
						assert config["e"] == [1, 3]

						# init ignore private arguments
						SCREAMING_SNAKE_CASE__									= SampleObject(_name_or_path="""lalala"""    )
						SCREAMING_SNAKE_CASE__									= obj.config
						assert config["a"] == 2
						assert config["b"] == 5
						assert config["c"] == (2, 5)
						assert config["d"] == "for diffusion"
						assert config["e"] == [1, 3]

						# can override default
						SCREAMING_SNAKE_CASE__									= SampleObject(c=6    )
						SCREAMING_SNAKE_CASE__									= obj.config
						assert config["a"] == 2
						assert config["b"] == 5
						assert config["c"] == 6
						assert config["d"] == "for diffusion"
						assert config["e"] == [1, 3]

						# can use positional arguments.
						SCREAMING_SNAKE_CASE__									= SampleObject(1      ,			c=6    )
						SCREAMING_SNAKE_CASE__									= obj.config
						assert config["a"] == 1
						assert config["b"] == 5
						assert config["c"] == 6
						assert config["d"] == "for diffusion"
						assert config["e"] == [1, 3]

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= SampleObject()
						SCREAMING_SNAKE_CASE__									= obj.config

						assert config["a"] == 2
						assert config["b"] == 5
						assert config["c"] == (2, 5)
						assert config["d"] == "for diffusion"
						assert config["e"] == [1, 3]

						with tempfile.TemporaryDirectory() as tmpdirname:
									obj.save_config(__UpperCAmelCase    )
									SCREAMING_SNAKE_CASE__									= SampleObject.from_config(SampleObject.load_config(__UpperCAmelCase    )    )
									SCREAMING_SNAKE_CASE__									= new_obj.config

						# unfreeze configs
						SCREAMING_SNAKE_CASE__									= dict(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= dict(__UpperCAmelCase    )

						assert config.pop("""c"""    ) == (2, 5)  # instantiated as tuple
						assert new_config.pop("""c"""    ) == [2, 5]  # saved & loaded as list because of json
						config.pop("""_use_default_values"""    )
						assert config == new_config

			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Any:
						SCREAMING_SNAKE_CASE__									= logging.get_logger("""diffusers.configuration_utils"""    )
						# 30 for warning
						logger.setLevel(3_0    )

						with CaptureLogger(__UpperCAmelCase    ) as cap_logger:
									SCREAMING_SNAKE_CASE__									= DDIMScheduler.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-torch"""      ,			subfolder="""scheduler"""    )

						assert ddim.__class__ == DDIMScheduler
						# no warning should be thrown
						assert cap_logger.out == ""

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Any:
						SCREAMING_SNAKE_CASE__									= logging.get_logger("""diffusers.configuration_utils"""    )
						# 30 for warning
						logger.setLevel(3_0    )

						with CaptureLogger(__UpperCAmelCase    ) as cap_logger:
									SCREAMING_SNAKE_CASE__									= EulerDiscreteScheduler.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-torch"""      ,			subfolder="""scheduler"""    )

						assert euler.__class__ == EulerDiscreteScheduler
						# no warning should be thrown
						assert cap_logger.out == ""

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= logging.get_logger("""diffusers.configuration_utils"""    )
						# 30 for warning
						logger.setLevel(3_0    )

						with CaptureLogger(__UpperCAmelCase    ) as cap_logger:
									SCREAMING_SNAKE_CASE__									= EulerAncestralDiscreteScheduler.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-torch"""      ,			subfolder="""scheduler"""    )

						assert euler.__class__ == EulerAncestralDiscreteScheduler
						# no warning should be thrown
						assert cap_logger.out == ""

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= logging.get_logger("""diffusers.configuration_utils"""    )
						# 30 for warning
						logger.setLevel(3_0    )

						with CaptureLogger(__UpperCAmelCase    ) as cap_logger:
									SCREAMING_SNAKE_CASE__									= PNDMScheduler.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-torch"""      ,			subfolder="""scheduler"""    )

						assert pndm.__class__ == PNDMScheduler
						# no warning should be thrown
						assert cap_logger.out == ""

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      str:
						SCREAMING_SNAKE_CASE__									= logging.get_logger("""diffusers.configuration_utils"""    )
						# 30 for warning
						logger.setLevel(3_0    )

						with CaptureLogger(__UpperCAmelCase    ) as cap_logger:
									SCREAMING_SNAKE_CASE__									= DDPMScheduler.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-torch"""      ,			subfolder="""scheduler"""      ,			prediction_type="""sample"""      ,			beta_end=8      ,			)

						with CaptureLogger(__UpperCAmelCase    ) as cap_logger_a:
									SCREAMING_SNAKE_CASE__									= DDPMScheduler.from_pretrained("""google/ddpm-celebahq-256"""      ,			beta_start=8_8    )

						assert ddpm.__class__ == DDPMScheduler
						assert ddpm.config.prediction_type == "sample"
						assert ddpm.config.beta_end == 8
						assert ddpm_a.config.beta_start == 8_8

						# no warning should be thrown
						assert cap_logger.out == ""
						assert cap_logger_a.out == ""

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= logging.get_logger("""diffusers.configuration_utils"""    )
						# 30 for warning
						logger.setLevel(3_0    )

						with CaptureLogger(__UpperCAmelCase    ) as cap_logger:
									SCREAMING_SNAKE_CASE__									= DPMSolverMultistepScheduler.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-torch"""      ,			subfolder="""scheduler"""    )

						assert dpm.__class__ == DPMSolverMultistepScheduler
						# no warning should be thrown
						assert cap_logger.out == ""





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      List[Any]:
						# let's first save a config that should be in the form
						#    a=2,
						#    b=5,
						#    c=(2, 5),
						#    d="for diffusion",
						#    e=[1, 3],

						SCREAMING_SNAKE_CASE__									= SampleObject()

						SCREAMING_SNAKE_CASE__									= {k: v for k, v in config.config.items() if not k.startswith("""_"""    )}

						# make sure that default config has all keys in `_use_default_values`
						assert set(config_dict.keys()    ) == set(config.config._use_default_values    )

						with tempfile.TemporaryDirectory() as tmpdirname:
									config.save_config(__UpperCAmelCase    )

									# now loading it with SampleObject2 should put f into `_use_default_values`
									SCREAMING_SNAKE_CASE__									= SampleObjecta.from_config(__UpperCAmelCase    )

									assert "f" in config._use_default_values
									assert config.f == [1, 3]

						# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
						# **BECAUSE** it is part of `config._use_default_values`
						SCREAMING_SNAKE_CASE__									= SampleObjecta.from_config(config.config    )
						assert new_config.f == [5, 4]

						config.config._use_default_values.pop()
						SCREAMING_SNAKE_CASE__									= SampleObjecta.from_config(config.config    )
						assert new_config_a.f == [1, 3]

						# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
						assert new_config_a.e == [1, 3]





"""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"""    )    )


