

"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 				lowerCamelCase  (A__     ,A__     ,unittest.TestCase     ):
			lowerCamelCase__				:						Optional[Any]   						=							StableDiffusionParadigmsPipeline
			lowerCamelCase__				:						Union[str, Any]   						=							TEXT_TO_IMAGE_PARAMS
			lowerCamelCase__				:						str   						=							TEXT_TO_IMAGE_BATCH_PARAMS
			lowerCamelCase__				:						Optional[int]   						=							TEXT_TO_IMAGE_IMAGE_PARAMS
			lowerCamelCase__				:						Any   						=							TEXT_TO_IMAGE_IMAGE_PARAMS

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      int:
						torch.manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)      ,			layers_per_block=2      ,			sample_size=3_2      ,			in_channels=4      ,			out_channels=4      ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,			cross_attention_dim=3_2      ,			attention_head_dim=(2, 4)      ,			use_linear_projection=__UpperCAmelCase      ,			)
						SCREAMING_SNAKE_CASE__									= DDIMParallelScheduler(
						    beta_start=0.00_085      ,			beta_end=0.012      ,			beta_schedule="""scaled_linear"""      ,			clip_sample=__UpperCAmelCase      ,			set_alpha_to_one=__UpperCAmelCase      ,			)
						torch.manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= AutoencoderKL(
						    block_out_channels=[3_2, 6_4]      ,			in_channels=3      ,			out_channels=3      ,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,			latent_channels=4      ,			sample_size=1_2_8      ,			)
						torch.manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= CLIPTextConfig(
						    bos_token_id=0      ,			eos_token_id=2      ,			hidden_size=3_2      ,			intermediate_size=3_7      ,			layer_norm_eps=1e-05      ,			num_attention_heads=4      ,			num_hidden_layers=5      ,			pad_token_id=1      ,			vocab_size=1_0_0_0      ,			hidden_act="""gelu"""      ,			projection_dim=5_1_2      ,			)
						SCREAMING_SNAKE_CASE__									= CLIPTextModel(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

						SCREAMING_SNAKE_CASE__									= {
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """safety_checker""": None,
						    """feature_extractor""": None,
						}
						return components

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  int=0    )				->      Tuple:
						if str(__UpperCAmelCase    ).startswith("""mps"""    ):
									SCREAMING_SNAKE_CASE__									= torch.manual_seed(__UpperCAmelCase    )
						else:
									SCREAMING_SNAKE_CASE__									= torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= {
						    """prompt""": """a photograph of an astronaut riding a horse""",
						    """generator""": generator,
						    """num_inference_steps""": 1_0,
						    """guidance_scale""": 6.0,
						    """output_type""": """numpy""",
						    """parallel""": 3,
						    """debug""": True,
						}
						return inputs

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Dict:
						SCREAMING_SNAKE_CASE__									= """cpu"""  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE__									= self.get_dummy_components()
						SCREAMING_SNAKE_CASE__									= StableDiffusionParadigmsPipeline(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= sd_pipe.to(__UpperCAmelCase    )
						sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= self.get_dummy_inputs(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= sd_pipe(**__UpperCAmelCase    ).images
						SCREAMING_SNAKE_CASE__									= image[0, -3:, -3:, -1]
						assert image.shape == (1, 6_4, 6_4, 3)

						SCREAMING_SNAKE_CASE__									= np.array([0.4_773, 0.5_417, 0.4_723, 0.4_925, 0.5_631, 0.4_752, 0.5_240, 0.4_935, 0.5_023]    )

						assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= """cpu"""  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE__									= self.get_dummy_components()
						torch.manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= DDPMParallelScheduler()
						torch.manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= StableDiffusionParadigmsPipeline(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= sd_pipe.to(__UpperCAmelCase    )
						sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= self.get_dummy_inputs(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= sd_pipe(**__UpperCAmelCase    ).images
						SCREAMING_SNAKE_CASE__									= image[0, -3:, -3:, -1]
						assert image.shape == (1, 6_4, 6_4, 3)

						SCREAMING_SNAKE_CASE__									= np.array([0.3_573, 0.4_420, 0.4_960, 0.4_799, 0.3_796, 0.3_879, 0.4_819, 0.4_365, 0.4_468]    )

						assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Optional[int]:
						super().test_inference_batch_consistent(batch_sizes=[1, 2]    )

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Optional[int]:
						super().test_inference_batch_single_identical(batch_size=2      ,			expected_max_diff=3e-3    )





			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      int:
						SCREAMING_SNAKE_CASE__									= """cpu"""  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE__									= self.get_dummy_components()
						SCREAMING_SNAKE_CASE__									= StableDiffusionParadigmsPipeline(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= sd_pipe.to(__UpperCAmelCase    )
						sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= self.get_dummy_inputs(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= """french fries"""
						SCREAMING_SNAKE_CASE__									= sd_pipe(**__UpperCAmelCase      ,			negative_prompt=__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= output.images
						SCREAMING_SNAKE_CASE__									= image[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)

						SCREAMING_SNAKE_CASE__									= np.array([0.4_771, 0.5_420, 0.4_683, 0.4_918, 0.5_636, 0.4_725, 0.5_230, 0.4_923, 0.5_015]    )

						assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2





@slow
@require_torch_gpu
class 				lowerCamelCase  (unittest.TestCase     ):

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Dict:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  Dict=0    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= {
						    """prompt""": """a photograph of an astronaut riding a horse""",
						    """generator""": generator,
						    """num_inference_steps""": 1_0,
						    """guidance_scale""": 7.5,
						    """output_type""": """numpy""",
						    """parallel""": 3,
						    """debug""": True,
						}
						return inputs





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Any:
						SCREAMING_SNAKE_CASE__									= """stabilityai/stable-diffusion-2-base"""
						SCREAMING_SNAKE_CASE__									= DDIMParallelScheduler.from_pretrained(__UpperCAmelCase      ,			subfolder="""scheduler"""    )
						SCREAMING_SNAKE_CASE__									= StableDiffusionParadigmsPipeline.from_pretrained(__UpperCAmelCase      ,			scheduler=__UpperCAmelCase      ,			safety_checker=__UpperCAmelCase    )
						pipe.to(__UpperCAmelCase    )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase    )
						pipe.enable_attention_slicing()

						SCREAMING_SNAKE_CASE__									= self.get_inputs()
						SCREAMING_SNAKE_CASE__									= pipe(**__UpperCAmelCase    ).images
						SCREAMING_SNAKE_CASE__									= image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)

						SCREAMING_SNAKE_CASE__									= np.array([0.9_622, 0.9_602, 0.9_748, 0.9_591, 0.9_630, 0.9_691, 0.9_661, 0.9_631, 0.9_741]    )

						assert np.abs(expected_slice - image_slice    ).max() < 1e-2





"""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"""    )    )


