

'''simple docstring'''





import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    StableDiffusionInstructPixaPixPipeline
				lowerCamelCase_		       =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
				lowerCamelCase_		       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowerCamelCase_		       =    IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowerCamelCase_		       =    IMAGE_TO_IMAGE_IMAGE_PARAMS







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							Any    		=UNetaDConditionModel(
										    block_out_channels=(32, 64)		,					layers_per_block=2		,					sample_size=32		,					in_channels=8		,					out_channels=4		,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,					cross_attention_dim=32		,					)
										lowercase :							Optional[int]    		=PNDMScheduler(skip_prk_steps=UpperCAmelCase__   )
										torch.manual_seed(0   )
										lowercase :							Union[str, Any]    		=AutoencoderKL(
										    block_out_channels=[32, 64]		,					in_channels=3		,					out_channels=3		,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,					latent_channels=4		,					)
										torch.manual_seed(0   )
										lowercase :							Optional[int]    		=CLIPTextConfig(
										    bos_token_id=0		,					eos_token_id=2		,					hidden_size=32		,					intermediate_size=37		,					layer_norm_eps=1E-05		,					num_attention_heads=4		,					num_hidden_layers=5		,					pad_token_id=1		,					vocab_size=1000		,					)
										lowercase :							int    		=CLIPTextModel(UpperCAmelCase__   )
										lowercase :							List[Any]    		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

										lowercase :							List[str]    		={
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Optional[int]=0   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=floats_tensor((1, 3, 32, 32)		,					rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
										lowercase :							str    		=image.cpu().permute(0		,					2		,					3		,					1   )[0]
										lowercase :							Any    		=Image.fromarray(np.uinta(UpperCAmelCase__   )   ).convert('''RGB'''   )
										if str(UpperCAmelCase__   ).startswith('''mps'''   ):
																lowercase :							Dict    		=torch.manual_seed(UpperCAmelCase__   )
										else:
																lowercase :							List[Any]    		=torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )
										lowercase :							Dict    		={
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''image''': image,
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 6.0,
										    '''image_guidance_scale''': 1,
										    '''output_type''': '''numpy''',
										}
										return inputs







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							str    		=self.get_dummy_components()
										lowercase :							str    		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Tuple    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Any    		=sd_pipe(**UpperCAmelCase__   ).images
										lowercase :							int    		=image[0, -3:, -3:, -1]
										assert image.shape == (1, 32, 32, 3)
										lowercase :							Union[str, Any]    		=np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]   )

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							str    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							Tuple    		=self.get_dummy_components()
										lowercase :							Optional[int]    		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Optional[Any]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Tuple    		='''french fries'''
										lowercase :							Dict    		=sd_pipe(**UpperCAmelCase__		,					negative_prompt=UpperCAmelCase__   )
										lowercase :							List[str]    		=output.images
										lowercase :							Tuple    		=image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowercase :							Any    		=np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]   )

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							List[str]    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							str    		=self.get_dummy_components()
										lowercase :							Any    		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Optional[Any]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Any    		=[inputs['''prompt''']] * 2

										lowercase :							int    		=np.array(inputs['''image''']   ).astype(np.floataa   ) / 2_55.0
										lowercase :							Tuple    		=torch.from_numpy(UpperCAmelCase__   ).unsqueeze(0   ).to(UpperCAmelCase__   )
										lowercase :							List[Any]    		=image / 2 + 0.5
										lowercase :							List[str]    		=image.permute(0		,					3		,					1		,					2   )
										lowercase :							List[str]    		=image.repeat(2		,					1		,					1		,					1   )

										lowercase :							List[Any]    		=sd_pipe(**UpperCAmelCase__   ).images
										lowercase :							Optional[int]    		=image[-1, -3:, -3:, -1]

										assert image.shape == (2, 32, 32, 3)
										lowercase :							Union[str, Any]    		=np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]   )

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Any    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							Union[str, Any]    		=self.get_dummy_components()
										lowercase :							Any    		=EulerAncestralDiscreteScheduler(
										    beta_start=0.0_00_85		,					beta_end=0.0_12		,					beta_schedule='''scaled_linear'''   )
										lowercase :							Any    		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Dict    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Any    		=sd_pipe(**UpperCAmelCase__   ).images
										lowercase :							Any    		=image[0, -3:, -3:, -1]

										lowercase :							List[str]    		=[round(UpperCAmelCase__		,					4   ) for x in image_slice.flatten().tolist()]
										print(''','''.join([str(UpperCAmelCase__   ) for x in slice]   )   )

										assert image.shape == (1, 32, 32, 3)
										lowercase :							Dict    		=np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]   )

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.get_dummy_components()
										lowercase :							Union[str, Any]    		=StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=VaeImageProcessor(do_resize=UpperCAmelCase__		,					do_normalize=UpperCAmelCase__   )
										lowercase :							str    		=pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							List[str]    		=pipe(**self.get_dummy_inputs_by_type(UpperCAmelCase__		,					input_image_type='''pt'''   )   )[0]

										lowercase :							List[str]    		=components['''vae''']
										lowercase :							str    		=self.get_dummy_inputs_by_type(UpperCAmelCase__		,					input_image_type='''pt'''   )

										for image_param in self.image_latents_params:
																if image_param in inputs.keys():
																						lowercase :							List[str]    		=vae.encode(inputs[image_param]   ).latent_dist.mode()

										lowercase :							List[Any]    		=pipe(**UpperCAmelCase__   )[0]

										lowercase :							Optional[int]    		=np.abs(out - out_latents_inputs   ).max()
										self.assertLess(UpperCAmelCase__		,					1E-4		,					'''passing latents as image input generate different result from passing image'''   )





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







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		str=0   ):
										'''simple docstring'''
										lowercase :							Any    		=torch.manual_seed(UpperCAmelCase__   )
										lowercase :							List[Any]    		=load_image(
										    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''   )
										lowercase :							Optional[int]    		={
										    '''prompt''': '''turn him into a cyborg''',
										    '''image''': image,
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''guidance_scale''': 7.5,
										    '''image_guidance_scale''': 1.0,
										    '''output_type''': '''numpy''',
										}
										return inputs







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							str    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    '''timbrooks/instruct-pix2pix'''		,					safety_checker=UpperCAmelCase__   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							Optional[Any]    		=self.get_inputs()
										lowercase :							Any    		=pipe(**UpperCAmelCase__   ).images
										lowercase :							Union[str, Any]    		=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowercase :							Any    		=np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]   )

										assert np.abs(expected_slice - image_slice   ).max() < 1E-3







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    '''timbrooks/instruct-pix2pix'''		,					safety_checker=UpperCAmelCase__   )
										lowercase :							int    		=LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							Tuple    		=self.get_inputs()
										lowercase :							Optional[Any]    		=pipe(**UpperCAmelCase__   ).images
										lowercase :							Any    		=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowercase :							Any    		=np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]   )

										assert np.abs(expected_slice - image_slice   ).max() < 1E-3







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    '''timbrooks/instruct-pix2pix'''		,					safety_checker=UpperCAmelCase__   )
										lowercase :							Dict    		=DDIMScheduler.from_config(pipe.scheduler.config   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							List[Any]    		=self.get_inputs()
										lowercase :							Dict    		=pipe(**UpperCAmelCase__   ).images
										lowercase :							List[str]    		=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowercase :							Optional[int]    		=np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]   )

										assert np.abs(expected_slice - image_slice   ).max() < 1E-3







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							str    		=0

										def callback_fn(UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		torch.FloatTensor   ) -> None:
																lowercase :							str    		=True
																nonlocal number_of_steps
																number_of_steps += 1
																if step == 1:
																						lowercase :							Optional[int]    		=latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 64)
																						lowercase :							Any    		=latents[0, -3:, -3:, -1]
																						lowercase :							int    		=np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]   )

																						assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
																elif step == 2:
																						lowercase :							Union[str, Any]    		=latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 64)
																						lowercase :							Dict    		=latents[0, -3:, -3:, -1]
																						lowercase :							Any    		=np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]   )

																						assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2

										lowercase :							Union[str, Any]    		=False

										lowercase :							Union[str, Any]    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    '''timbrooks/instruct-pix2pix'''		,					safety_checker=UpperCAmelCase__		,					torch_dtype=torch.floataa   )
										lowercase :							List[str]    		=pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							Optional[int]    		=self.get_inputs()
										pipe(**UpperCAmelCase__		,					callback=UpperCAmelCase__		,					callback_steps=1   )
										assert callback_fn.has_been_called
										assert number_of_steps == 3







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										lowercase :							Tuple    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    '''timbrooks/instruct-pix2pix'''		,					safety_checker=UpperCAmelCase__		,					torch_dtype=torch.floataa   )
										lowercase :							List[str]    		=pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing(1   )
										pipe.enable_sequential_cpu_offload()

										lowercase :							Dict    		=self.get_inputs()
										lowercase :							List[Any]    		=pipe(**UpperCAmelCase__   )

										lowercase :							List[str]    		=torch.cuda.max_memory_allocated()
										# make sure that less than 2.2 GB is allocated
										assert mem_bytes < 2.2 * 10**9







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.get_inputs()
										# resize to resolution that is divisible by 8 but not 16 or 32
										lowercase :							Dict    		=inputs['''image'''].resize((504, 504)   )

										lowercase :							Any    		='''timbrooks/instruct-pix2pix'''
										lowercase :							str    		=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    UpperCAmelCase__		,					safety_checker=UpperCAmelCase__		,					)
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							Optional[Any]    		=pipe(**UpperCAmelCase__   )
										lowercase :							Dict    		=output.images[0]

										lowercase :							Tuple    		=image[255:258, 383:386, -1]

										assert image.shape == (504, 504, 3)
										lowercase :							Optional[Any]    		=np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]   )

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5E-3













"""simple docstring"""







import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : List[Any]												=		LongformerTokenizer
						_SCREAMING_SNAKE_CASE  : int												=		True
						_SCREAMING_SNAKE_CASE  : List[str]												=		LongformerTokenizerFast
						_SCREAMING_SNAKE_CASE  : Dict												=		True






						def      __snake_case      (		self  :Dict				)      ->Tuple:
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowercase    :      Dict       = [
										    """l""",
										    """o""",
										    """w""",
										    """e""",
										    """r""",
										    """s""",
										    """t""",
										    """i""",
										    """d""",
										    """n""",
										    """\u0120""",
										    """\u0120l""",
										    """\u0120n""",
										    """\u0120lo""",
										    """\u0120low""",
										    """er""",
										    """\u0120lowest""",
										    """\u0120newer""",
										    """\u0120wider""",
										    """<unk>""",
										]
										lowercase    :      Tuple       = dict(zip(__magic_name__			, range(len(__magic_name__				)				)				)				)
										lowercase    :      Tuple       = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
										lowercase    :      Dict       = {"""unk_token""": """<unk>"""}

										lowercase    :      Dict       = os.path.join(self.tmpdirname			, VOCAB_FILES_NAMES["""vocab_file"""]				)
										lowercase    :      Optional[int]       = 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(__magic_name__				) + """\n"""				)
										with open(self.merges_file			, """w"""			, encoding="""utf-8"""				) as fp:
														fp.write("""\n""".join(__magic_name__				)				)






						def      __snake_case      (		self  :List[Any]			, **__magic_name__  :Any				)      ->Tuple:
										kwargs.update(self.special_tokens_map				)
										return self.tokenizer_class.from_pretrained(self.tmpdirname			, **__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, **__magic_name__  :Optional[Any]				)      ->Dict:
										kwargs.update(self.special_tokens_map				)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			, **__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Dict				)      ->str:
										lowercase    :      List[str]       = """lower newer"""
										lowercase    :      Any       = """lower newer"""
										return input_text, output_text






						def      __snake_case      (		self  :Tuple				)      ->Union[str, Any]:
										lowercase    :      Union[str, Any]       = self.tokenizer_class(self.vocab_file			, self.merges_file			, **self.special_tokens_map				)
										lowercase    :      List[str]       = """lower newer"""
										lowercase    :      Tuple       = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
										lowercase    :      Optional[int]       = tokenizer.tokenize(__magic_name__				)  # , add_prefix_space=True)
										self.assertListEqual(__magic_name__			, __magic_name__				)

										lowercase    :      str       = tokens + [tokenizer.unk_token]
										lowercase    :      Any       = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__				)			, __magic_name__				)






						def      __snake_case      (		self  :Any				)      ->str:
										lowercase    :      int       = self.get_tokenizer()

										self.assertListEqual(tokenizer.encode("""Hello world!"""			, add_special_tokens=__magic_name__				)			, [0, 31_414, 232, 328, 2]				)
										self.assertListEqual(
										    tokenizer.encode("""Hello world! cécé herlolip 418"""			, add_special_tokens=__magic_name__				)			, [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]			, )






						@slow
						def      __snake_case      (		self  :Tuple				)      ->Union[str, Any]:
										lowercase    :      Any       = self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""				)

										lowercase    :      Tuple       = tokenizer.encode("""sequence builders"""			, add_special_tokens=__magic_name__				)
										lowercase    :      List[Any]       = tokenizer.encode("""multi-sequence build"""			, add_special_tokens=__magic_name__				)

										lowercase    :      Union[str, Any]       = tokenizer.encode(
										    """sequence builders"""			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)
										lowercase    :      List[Any]       = tokenizer.encode(
										    """sequence builders"""			, """multi-sequence build"""			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)

										lowercase    :      int       = tokenizer.build_inputs_with_special_tokens(__magic_name__				)
										lowercase    :      List[Any]       = tokenizer.build_inputs_with_special_tokens(__magic_name__			, __magic_name__				)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode






						def      __snake_case      (		self  :Optional[Any]				)      ->int:
										lowercase    :      Optional[int]       = self.get_tokenizer()

										lowercase    :      Tuple       = """Encode this sequence."""
										lowercase    :      Dict       = tokenizer.byte_encoder[""" """.encode("""utf-8"""				)[0]]

										# Testing encoder arguments
										lowercase    :      List[Any]       = tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)
										lowercase    :      int       = tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
										self.assertNotEqual(__magic_name__			, __magic_name__				)

										lowercase    :      Optional[Any]       = tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)
										lowercase    :      Tuple       = tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
										self.assertEqual(__magic_name__			, __magic_name__				)

										tokenizer.add_special_tokens({"""bos_token""": """<s>"""}				)
										lowercase    :      Union[str, Any]       = tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__				)
										lowercase    :      List[str]       = tokenizer.convert_ids_to_tokens(encoded[1]				)[0]
										self.assertNotEqual(__magic_name__			, __magic_name__				)

										# Testing spaces after special tokens
										lowercase    :      Any       = """<mask>"""
										tokenizer.add_special_tokens(
										    {"""mask_token""": AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__				)}				)  # mask token has a left space
										lowercase    :      Any       = tokenizer.convert_tokens_to_ids(__magic_name__				)

										lowercase    :      Any       = """Encode <mask> sequence"""
										lowercase    :      str       = """Encode <mask>sequence"""

										lowercase    :      Optional[int]       = tokenizer.encode(__magic_name__				)
										lowercase    :      List[str]       = encoded.index(__magic_name__				)
										lowercase    :      Tuple       = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
										self.assertEqual(__magic_name__			, __magic_name__				)

										lowercase    :      Tuple       = tokenizer.encode(__magic_name__				)
										lowercase    :      List[str]       = encoded.index(__magic_name__				)
										lowercase    :      Optional[Any]       = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
										self.assertNotEqual(__magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :Any				)      ->int:
										pass






						def      __snake_case      (		self  :List[Any]				)      ->str:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																		lowercase    :      int       = self.rust_tokenizer_class.from_pretrained(__magic_name__			, **__magic_name__				)
																		lowercase    :      List[str]       = self.tokenizer_class.from_pretrained(__magic_name__			, **__magic_name__				)
																		lowercase    :      Optional[int]       = """A, <mask> AllenNLP sentence."""
																		lowercase    :      Any       = tokenizer_r.encode_plus(__magic_name__			, add_special_tokens=__magic_name__			, return_token_type_ids=__magic_name__				)
																		lowercase    :      str       = tokenizer_p.encode_plus(__magic_name__			, add_special_tokens=__magic_name__			, return_token_type_ids=__magic_name__				)

																		# 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"""]				)			, )

																		lowercase    :      Union[str, Any]       = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
																		lowercase    :      Dict       = 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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)
																		self.assertSequenceEqual(tokens_r["""input_ids"""]			, [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)

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






						def      __snake_case      (		self  :List[str]				)      ->Tuple:
										for trim_offsets, add_prefix_space in itertools.product([True, False]			, repeat=2				):
														lowercase    :      str       = self.rust_tokenizer_class.from_pretrained(
														    self.tmpdirname			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)

														lowercase    :      str       = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
														lowercase    :      Optional[Any]       = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()				)

														self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]			, __magic_name__				)

														self.assertEqual(post_processor_state["""add_prefix_space"""]			, __magic_name__				)
														self.assertEqual(post_processor_state["""trim_offsets"""]			, __magic_name__				)






						def      __snake_case      (		self  :Dict				)      ->List[str]:
										# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
										# `trim_offsets`
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																		lowercase    :      Optional[Any]       = """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																		lowercase    :      Optional[Any]       = f"""{text_of_1_token} {text_of_1_token}"""

																		lowercase    :      List[str]       = self.rust_tokenizer_class.from_pretrained(
																		    __magic_name__			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)
																		lowercase    :      List[Any]       = tokenizer_r(__magic_name__			, return_offsets_mapping=__magic_name__			, add_special_tokens=__magic_name__				)
																		self.assertEqual(encoding.offset_mapping[0]			, (0, len(__magic_name__				))				)
																		self.assertEqual(
																		    encoding.offset_mapping[1]			, (len(__magic_name__				) + 1, len(__magic_name__				) + 1 + len(__magic_name__				))			, )

																		lowercase    :      List[Any]       = self.rust_tokenizer_class.from_pretrained(
																		    __magic_name__			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)
																		lowercase    :      List[Any]       = tokenizer_r(__magic_name__			, return_offsets_mapping=__magic_name__			, add_special_tokens=__magic_name__				)
																		self.assertEqual(encoding.offset_mapping[0]			, (0, len(__magic_name__				))				)
																		self.assertEqual(
																		    encoding.offset_mapping[1]			, (len(__magic_name__				) + 1, len(__magic_name__				) + 1 + len(__magic_name__				))			, )

																		lowercase    :      Tuple       = self.rust_tokenizer_class.from_pretrained(
																		    __magic_name__			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)
																		lowercase    :      List[str]       = tokenizer_r(__magic_name__			, return_offsets_mapping=__magic_name__			, add_special_tokens=__magic_name__				)
																		self.assertEqual(encoding.offset_mapping[0]			, (0, len(__magic_name__				))				)
																		self.assertEqual(
																		    encoding.offset_mapping[1]			, (len(__magic_name__				), len(__magic_name__				) + 1 + len(__magic_name__				))			, )

																		lowercase    :      Optional[Any]       = self.rust_tokenizer_class.from_pretrained(
																		    __magic_name__			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)
																		lowercase    :      Optional[int]       = tokenizer_r(__magic_name__			, return_offsets_mapping=__magic_name__			, add_special_tokens=__magic_name__				)
																		self.assertEqual(encoding.offset_mapping[0]			, (0, len(__magic_name__				))				)
																		self.assertEqual(
																		    encoding.offset_mapping[1]			, (len(__magic_name__				), len(__magic_name__				) + 1 + len(__magic_name__				))			, )

																		lowercase    :      Optional[int]       = f""" {text}"""

																		# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																		#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																		# )
																		# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																		# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																		# self.assertEqual(
																		#     encoding.offset_mapping[1],
																		#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																		# )

																		lowercase    :      Union[str, Any]       = self.rust_tokenizer_class.from_pretrained(
																		    __magic_name__			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)
																		lowercase    :      Union[str, Any]       = tokenizer_r(__magic_name__			, return_offsets_mapping=__magic_name__			, add_special_tokens=__magic_name__				)
																		self.assertEqual(encoding.offset_mapping[0]			, (1, 1 + len(__magic_name__				))				)
																		self.assertEqual(
																		    encoding.offset_mapping[1]			, (1 + len(__magic_name__				) + 1, 1 + len(__magic_name__				) + 1 + len(__magic_name__				))			, )

																		lowercase    :      int       = self.rust_tokenizer_class.from_pretrained(
																		    __magic_name__			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)
																		lowercase    :      Optional[int]       = tokenizer_r(__magic_name__			, return_offsets_mapping=__magic_name__			, add_special_tokens=__magic_name__				)
																		self.assertEqual(encoding.offset_mapping[0]			, (0, 1 + len(__magic_name__				))				)
																		self.assertEqual(
																		    encoding.offset_mapping[1]			, (1 + len(__magic_name__				), 1 + len(__magic_name__				) + 1 + len(__magic_name__				))			, )

																		lowercase    :      str       = self.rust_tokenizer_class.from_pretrained(
																		    __magic_name__			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)
																		lowercase    :      Any       = tokenizer_r(__magic_name__			, return_offsets_mapping=__magic_name__			, add_special_tokens=__magic_name__				)
																		self.assertEqual(encoding.offset_mapping[0]			, (0, 1 + len(__magic_name__				))				)
																		self.assertEqual(
																		    encoding.offset_mapping[1]			, (1 + len(__magic_name__				), 1 + len(__magic_name__				) + 1 + len(__magic_name__				))			, )
