'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Optional[int]      =       UnCLIPImageVariationPipeline
			UpperCAmelCase_			:     Optional[Any]      =       IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
			UpperCAmelCase_			:     Union[str, Any]      =       IMAGE_VARIATION_BATCH_PARAMS

			UpperCAmelCase_			:     Dict      =       [
			    '''generator''',
			    '''return_dict''',
			    '''decoder_num_inference_steps''',
			    '''super_res_num_inference_steps''',
			]
			UpperCAmelCase_			:     Optional[Any]      =       False



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return 32



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return 32



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.time_input_dim



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.time_input_dim * 4



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return 100



			@property
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
					return tokenizer



			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase   					=  CLIPTextConfig(
					    bos_token_id=0       ,    eos_token_id=2       ,    hidden_size=self.text_embedder_hidden_size       ,    projection_dim=self.text_embedder_hidden_size       ,    intermediate_size=37       ,    layer_norm_eps=1E-0_5       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    pad_token_id=1       ,    vocab_size=1000       ,    )
					return CLIPTextModelWithProjection(__lowerCAmelCase)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase   					=  CLIPVisionConfig(
					    hidden_size=self.text_embedder_hidden_size       ,    projection_dim=self.text_embedder_hidden_size       ,    num_hidden_layers=5       ,    num_attention_heads=4       ,    image_size=32       ,    intermediate_size=37       ,    patch_size=1       ,    )
					return CLIPVisionModelWithProjection(__lowerCAmelCase)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)

					lowerCAmelCase   					=  {
					    """clip_embeddings_dim""": self.text_embedder_hidden_size,
					    """time_embed_dim""": self.time_embed_dim,
					    """cross_attention_dim""": self.cross_attention_dim,
					}

					lowerCAmelCase   					=  UnCLIPTextProjModel(**__lowerCAmelCase)
					return model



			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)

					lowerCAmelCase   					=  {
					    """sample_size""": 32,
					    # RGB in channels
					    """in_channels""": 3,
					    # Out channels is double in channels because predicts mean and variance
					    """out_channels""": 6,
					    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
					    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
					    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
					    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    """layers_per_block""": 1,
					    """cross_attention_dim""": self.cross_attention_dim,
					    """attention_head_dim""": 4,
					    """resnet_time_scale_shift""": """scale_shift""",
					    """class_embed_type""": """identity""",
					}

					lowerCAmelCase   					=  UNetaDConditionModel(**__lowerCAmelCase)
					return model



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return {
					    "sample_size": 64,
					    "layers_per_block": 1,
					    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
					    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
					    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    "in_channels": 6,
					    "out_channels": 3,
					}



			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)

					lowerCAmelCase   					=  UNetaDModel(**self.dummy_super_res_kwargs)
					return model



			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(1)

					lowerCAmelCase   					=  UNetaDModel(**self.dummy_super_res_kwargs)
					return model



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.dummy_decoder
					lowerCAmelCase   					=  self.dummy_text_proj
					lowerCAmelCase   					=  self.dummy_text_encoder
					lowerCAmelCase   					=  self.dummy_tokenizer
					lowerCAmelCase   					=  self.dummy_super_res_first
					lowerCAmelCase   					=  self.dummy_super_res_last

					lowerCAmelCase   					=  UnCLIPScheduler(
					    variance_type="""learned_range"""       ,    prediction_type="""epsilon"""       ,    num_train_timesteps=1000       ,    )

					lowerCAmelCase   					=  UnCLIPScheduler(
					    variance_type="""fixed_small_log"""       ,    prediction_type="""epsilon"""       ,    num_train_timesteps=1000       ,    )

					lowerCAmelCase   					=  CLIPImageProcessor(crop_size=32       ,    size=32)

					lowerCAmelCase   					=  self.dummy_image_encoder

					return {
					    "decoder": decoder,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "text_proj": text_proj,
					    "feature_extractor": feature_extractor,
					    "image_encoder": image_encoder,
					    "super_res_first": super_res_first,
					    "super_res_last": super_res_last,
					    "decoder_scheduler": decoder_scheduler,
					    "super_res_scheduler": super_res_scheduler,
					}



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=0       ,    __lowerCAmelCase=True):




					"""simple docstring"""


					lowerCAmelCase   					=  floats_tensor((1, 3, 32, 32)       ,    rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					if str(__lowerCAmelCase).startswith("""mps"""):
							lowerCAmelCase   					=  torch.manual_seed(__lowerCAmelCase)
					else:
							lowerCAmelCase   					=  torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)

					if pil_image:
							lowerCAmelCase   					=  input_image * 0.5 + 0.5
							lowerCAmelCase   					=  input_image.clamp(0       ,    1)
							lowerCAmelCase   					=  input_image.cpu().permute(0       ,    2       ,    3       ,    1).float().numpy()
							lowerCAmelCase   					=  DiffusionPipeline.numpy_to_pil(__lowerCAmelCase)[0]

					return {
					    "image": input_image,
					    "generator": generator,
					    "decoder_num_inference_steps": 2,
					    "super_res_num_inference_steps": 2,
					    "output_type": "np",
					}



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """cpu"""

					lowerCAmelCase   					=  self.get_dummy_components()

					lowerCAmelCase   					=  self.pipeline_class(**__lowerCAmelCase)
					lowerCAmelCase   					=  pipe.to(__lowerCAmelCase)

					pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  self.get_dummy_inputs(__lowerCAmelCase       ,    pil_image=__lowerCAmelCase)

					lowerCAmelCase   					=  pipe(**__lowerCAmelCase)
					lowerCAmelCase   					=  output.images

					lowerCAmelCase   					=  self.get_dummy_inputs(__lowerCAmelCase       ,    pil_image=__lowerCAmelCase)

					lowerCAmelCase   					=  pipe(
					    **__lowerCAmelCase       ,    return_dict=__lowerCAmelCase       ,    )[0]

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]
					lowerCAmelCase   					=  image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)

					lowerCAmelCase   					=  np.array(
					    [
					        0.9997,
					        0.0002,
					        0.9997,
					        0.9997,
					        0.9969,
					        0.0023,
					        0.9997,
					        0.9969,
					        0.9970,
					    ])

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """cpu"""

					lowerCAmelCase   					=  self.get_dummy_components()

					lowerCAmelCase   					=  self.pipeline_class(**__lowerCAmelCase)
					lowerCAmelCase   					=  pipe.to(__lowerCAmelCase)

					pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  self.get_dummy_inputs(__lowerCAmelCase       ,    pil_image=__lowerCAmelCase)

					lowerCAmelCase   					=  pipe(**__lowerCAmelCase)
					lowerCAmelCase   					=  output.images

					lowerCAmelCase   					=  self.get_dummy_inputs(__lowerCAmelCase       ,    pil_image=__lowerCAmelCase)

					lowerCAmelCase   					=  pipe(
					    **__lowerCAmelCase       ,    return_dict=__lowerCAmelCase       ,    )[0]

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]
					lowerCAmelCase   					=  image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)

					lowerCAmelCase   					=  np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971])

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """cpu"""

					lowerCAmelCase   					=  self.get_dummy_components()

					lowerCAmelCase   					=  self.pipeline_class(**__lowerCAmelCase)
					lowerCAmelCase   					=  pipe.to(__lowerCAmelCase)

					pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  self.get_dummy_inputs(__lowerCAmelCase       ,    pil_image=__lowerCAmelCase)
					lowerCAmelCase   					=  [
					    pipeline_inputs["""image"""],
					    pipeline_inputs["""image"""],
					]

					lowerCAmelCase   					=  pipe(**__lowerCAmelCase)
					lowerCAmelCase   					=  output.images

					lowerCAmelCase   					=  self.get_dummy_inputs(__lowerCAmelCase       ,    pil_image=__lowerCAmelCase)
					lowerCAmelCase   					=  [
					    tuple_pipeline_inputs["""image"""],
					    tuple_pipeline_inputs["""image"""],
					]

					lowerCAmelCase   					=  pipe(
					    **__lowerCAmelCase       ,    return_dict=__lowerCAmelCase       ,    )[0]

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]
					lowerCAmelCase   					=  image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (2, 64, 64, 3)

					lowerCAmelCase   					=  np.array(
					    [
					        0.9997,
					        0.9989,
					        0.0008,
					        0.0021,
					        0.9960,
					        0.0018,
					        0.0014,
					        0.0002,
					        0.9933,
					    ])

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  torch.device("""cpu""")






					class 				a__:


								'''simple docstring'''






								UpperCAmelCase_			:     Dict      =       1







					lowerCAmelCase   					=  self.get_dummy_components()

					lowerCAmelCase   					=  self.pipeline_class(**__lowerCAmelCase)
					lowerCAmelCase   					=  pipe.to(__lowerCAmelCase)

					pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.Generator(device=__lowerCAmelCase).manual_seed(0)
					lowerCAmelCase   					=  pipe.decoder.dtype
					lowerCAmelCase   					=  1

					lowerCAmelCase   					=  (
					    batch_size,
					    pipe.decoder.config.in_channels,
					    pipe.decoder.config.sample_size,
					    pipe.decoder.config.sample_size,
					)
					lowerCAmelCase   					=  pipe.prepare_latents(
					    __lowerCAmelCase       ,    dtype=__lowerCAmelCase       ,    device=__lowerCAmelCase       ,    generator=__lowerCAmelCase       ,    latents=__lowerCAmelCase       ,    scheduler=DummyScheduler())

					lowerCAmelCase   					=  (
					    batch_size,
					    pipe.super_res_first.config.in_channels // 2,
					    pipe.super_res_first.config.sample_size,
					    pipe.super_res_first.config.sample_size,
					)
					lowerCAmelCase   					=  pipe.prepare_latents(
					    __lowerCAmelCase       ,    dtype=__lowerCAmelCase       ,    device=__lowerCAmelCase       ,    generator=__lowerCAmelCase       ,    latents=__lowerCAmelCase       ,    scheduler=DummyScheduler())

					lowerCAmelCase   					=  self.get_dummy_inputs(__lowerCAmelCase       ,    pil_image=__lowerCAmelCase)

					lowerCAmelCase   					=  pipe(
					    **__lowerCAmelCase       ,    decoder_latents=__lowerCAmelCase       ,    super_res_latents=__lowerCAmelCase).images

					lowerCAmelCase   					=  self.get_dummy_inputs(__lowerCAmelCase       ,    pil_image=__lowerCAmelCase)
					# Don't pass image, instead pass embedding
					lowerCAmelCase   					=  pipeline_inputs.pop("""image""")
					lowerCAmelCase   					=  pipe.image_encoder(__lowerCAmelCase).image_embeds

					lowerCAmelCase   					=  pipe(
					    **__lowerCAmelCase       ,    decoder_latents=__lowerCAmelCase       ,    super_res_latents=__lowerCAmelCase       ,    image_embeddings=__lowerCAmelCase       ,    ).images

					# make sure passing text embeddings manually is identical
					assert np.abs(img_out_a - img_out_a).max() < 1E-4



			@skip_mps
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  torch_device == """cpu"""

					# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
					lowerCAmelCase   					=  1E-2

					self._test_attention_slicing_forward_pass(
					    test_max_difference=__lowerCAmelCase       ,    expected_max_diff=__lowerCAmelCase)



			@skip_mps
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  torch_device == """cpu"""
					lowerCAmelCase   					=  True
					lowerCAmelCase   					=  [
					    """decoder_num_inference_steps""",
					    """super_res_num_inference_steps""",
					]

					self._test_inference_batch_single_identical(
					    test_max_difference=__lowerCAmelCase       ,    relax_max_difference=__lowerCAmelCase       ,    additional_params_copy_to_batched_inputs=__lowerCAmelCase       ,    )



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  [
					    """decoder_num_inference_steps""",
					    """super_res_num_inference_steps""",
					]

					if torch_device == "mps":
							# TODO: MPS errors with larger batch sizes
							lowerCAmelCase   					=  [2, 3]
							self._test_inference_batch_consistent(
							    batch_sizes=__lowerCAmelCase       ,    additional_params_copy_to_batched_inputs=__lowerCAmelCase       ,    )
					else:
							self._test_inference_batch_consistent(
							    additional_params_copy_to_batched_inputs=__lowerCAmelCase)



			@skip_mps
			def 							a_       (   self):




					"""simple docstring"""


					return super().test_dict_tuple_outputs_equivalent()



			@skip_mps
			def 							a_       (   self):




					"""simple docstring"""


					return super().test_save_load_local()







			@skip_mps
			def 							a_       (   self):




					"""simple docstring"""


					return super().test_save_load_optional_components()







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


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png""")
					lowerCAmelCase   					=  load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/unclip/karlo_v1_alpha_cat_variation_fp16.npy""")

					lowerCAmelCase   					=  UnCLIPImageVariationPipeline.from_pretrained(
					    """kakaobrain/karlo-v1-alpha-image-variations"""       ,    torch_dtype=torch.floataa)
					lowerCAmelCase   					=  pipeline.to(__lowerCAmelCase)
					pipeline.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.Generator(device="""cpu""").manual_seed(0)
					lowerCAmelCase   					=  pipeline(
					    __lowerCAmelCase       ,    generator=__lowerCAmelCase       ,    output_type="""np"""       ,    )

					lowerCAmelCase   					=  output.images[0]

					assert image.shape == (256, 256, 3)

					assert_mean_pixel_difference(__lowerCAmelCase       ,    __lowerCAmelCase       ,    15)





'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       MvpTokenizer
			UpperCAmelCase_			:     Optional[Any]      =       MvpTokenizerFast
			UpperCAmelCase_			:     str      =       True
			UpperCAmelCase_			:     List[Any]      =       filter_roberta_detectors



			def 							a_       (   self):




					"""simple docstring"""


					super().setUp()
					lowerCAmelCase   					=  [
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """<unk>""",
					]
					lowerCAmelCase   					=  dict(zip(__lowerCAmelCase       ,    range(len(__lowerCAmelCase))))
					lowerCAmelCase   					=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					lowerCAmelCase   					=  {"""unk_token""": """<unk>"""}

					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""merges_file"""])
					with open(self.vocab_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write(json.dumps(__lowerCAmelCase) + """\n""")
					with open(self.merges_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write("""\n""".join(__lowerCAmelCase))



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return "lower newer", "lower newer"



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""")



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""")



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					lowerCAmelCase   					=  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    max_length=len(__lowerCAmelCase)       ,    padding=__lowerCAmelCase       ,    return_tensors="""pt""")
							self.assertIsInstance(__lowerCAmelCase       ,    __lowerCAmelCase)

							self.assertEqual((2, 9)       ,    batch.input_ids.shape)
							self.assertEqual((2, 9)       ,    batch.attention_mask.shape)
							lowerCAmelCase   					=  batch.input_ids.tolist()[0]
							self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)
							# Test that special tokens are reset



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    return_tensors="""pt""")
							# check if input_ids are returned and no labels
							self.assertIn("""input_ids"""       ,    __lowerCAmelCase)
							self.assertIn("""attention_mask"""       ,    __lowerCAmelCase)
							self.assertNotIn("""labels"""       ,    __lowerCAmelCase)
							self.assertNotIn("""decoder_attention_mask"""       ,    __lowerCAmelCase)



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					    """Another summary.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(text_target=__lowerCAmelCase       ,    max_length=32       ,    padding="""max_length"""       ,    return_tensors="""pt""")
							self.assertEqual(32       ,    targets["""input_ids"""].shape[1])



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(
							    ["""I am a small frog""" * 1024, """I am a small frog"""]       ,    padding=__lowerCAmelCase       ,    truncation=__lowerCAmelCase       ,    return_tensors="""pt""")
							self.assertIsInstance(__lowerCAmelCase       ,    __lowerCAmelCase)
							self.assertEqual(batch.input_ids.shape       ,    (2, 1024))



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization."""]
					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    text_target=__lowerCAmelCase       ,    return_tensors="""pt""")
							lowerCAmelCase   					=  inputs["""input_ids"""]
							lowerCAmelCase   					=  inputs["""labels"""]
							self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
							self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
							self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
							self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



			def 							a_       (   self):




					"""simple docstring"""


					pass







			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
									lowerCAmelCase   					=  self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  """A, <mask> AllenNLP sentence."""
									lowerCAmelCase   					=  tokenizer_r.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r["""token_type_ids"""])       ,    sum(tokens_p["""token_type_ids"""]))

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r["""attention_mask"""]) / len(tokens_r["""attention_mask"""])       ,    sum(tokens_p["""attention_mask"""]) / len(tokens_p["""attention_mask"""])       ,    )

									lowerCAmelCase   					=  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
									lowerCAmelCase   					=  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
									self.assertSequenceEqual(tokens_r["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

									self.assertSequenceEqual(
									    __lowerCAmelCase       ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
									self.assertSequenceEqual(
									    __lowerCAmelCase       ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])




