


import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu



class A__     ( unittest.TestCase  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  Any       ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  @property
  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Any     =   1
        a__	:  Optional[int]     =   3
        a__	:  Optional[int]     =   (32, 32)

        a__	:  Union[str, Any]     =   floats_tensor((batch_size, num_channels) + sizes				,				rng=random.Random(0       )       ).to(lowerCamelCase__       )
        return image

  @property
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        torch.manual_seed(0       )
        a__	:  Dict     =   UNetaDConditionModel(
            block_out_channels=(32, 64)				,				layers_per_block=2				,				sample_size=32				,				in_channels=4				,				out_channels=4				,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,				up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				,				cross_attention_dim=32				,				)
        return model

  @property
  def    _UpperCamelCase( self			:  Optional[Any]       ):
        torch.manual_seed(0       )
        a__	:  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				,				)
        return model

  @property
  def    _UpperCamelCase( self			:  List[str]       ):
        torch.manual_seed(0       )
        a__	:  Dict     =   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=1_000				,				)
        return CLIPTextModel(lowerCamelCase__       )

  @property
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        def extract(*lowerCamelCase__			:  List[Any]				,				**lowerCamelCase__			:  int       ):



              class A__     :



                """simple docstring"""



                def __init__( self			:  Tuple       ):
                      a__	:  Dict     =   torch.ones([0]       )

                def    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  Tuple       ):
                      self.pixel_values.to(lowerCamelCase__       )
                      return self




              return Out()

        return extract

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Any     =   "cpu"  # ensure determinism for the device-dependent torch.Generator
        a__	:  Dict     =   self.dummy_cond_unet
        a__	:  Optional[Any]     =   DDIMScheduler(
            beta_start=0.0_0085				,				beta_end=0.012				,				beta_schedule="scaled_linear"				,				clip_sample=lowerCamelCase__				,				set_alpha_to_one=lowerCamelCase__				,				)

        a__	:  Tuple     =   self.dummy_vae
        a__	:  Optional[int]     =   self.dummy_text_encoder
        a__	:  Any     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

        # make sure here that pndm scheduler skips prk
        a__	:  Tuple     =   StableDiffusionPipeline(
            unet=lowerCamelCase__				,				scheduler=lowerCamelCase__				,				vae=lowerCamelCase__				,				text_encoder=lowerCamelCase__				,				tokenizer=lowerCamelCase__				,				safety_checker=lowerCamelCase__				,				feature_extractor=self.dummy_extractor				,				)
        a__	:  Any     =   sd_pipe.to(lowerCamelCase__       )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Tuple     =   "A painting of a squirrel eating a burger"

        a__	:  Union[str, Any]     =   torch.Generator(device=lowerCamelCase__       ).manual_seed(0       )
        a__	:  List[Any]     =   sd_pipe([prompt]				,				generator=lowerCamelCase__				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type="np"       )
        a__	:  List[Any]     =   output.images

        a__	:  Optional[Any]     =   torch.Generator(device=lowerCamelCase__       ).manual_seed(0       )
        a__	:  Dict     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type="np"				,				return_dict=lowerCamelCase__				,				)[0]

        a__	:  Tuple     =   image[0, -3:, -3:, -1]
        a__	:  Dict     =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        a__	:  Optional[Any]     =   np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]       )

        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    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  str     =   "cpu"  # ensure determinism for the device-dependent torch.Generator
        a__	:  Optional[Any]     =   self.dummy_cond_unet
        a__	:  List[Any]     =   PNDMScheduler(skip_prk_steps=lowerCamelCase__       )
        a__	:  Any     =   self.dummy_vae
        a__	:  List[Any]     =   self.dummy_text_encoder
        a__	:  List[Any]     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

        # make sure here that pndm scheduler skips prk
        a__	:  Union[str, Any]     =   StableDiffusionPipeline(
            unet=lowerCamelCase__				,				scheduler=lowerCamelCase__				,				vae=lowerCamelCase__				,				text_encoder=lowerCamelCase__				,				tokenizer=lowerCamelCase__				,				safety_checker=lowerCamelCase__				,				feature_extractor=self.dummy_extractor				,				)
        a__	:  int     =   sd_pipe.to(lowerCamelCase__       )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Dict     =   "A painting of a squirrel eating a burger"
        a__	:  int     =   torch.Generator(device=lowerCamelCase__       ).manual_seed(0       )
        a__	:  Optional[int]     =   sd_pipe([prompt]				,				generator=lowerCamelCase__				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type="np"       )

        a__	:  int     =   output.images

        a__	:  int     =   torch.Generator(device=lowerCamelCase__       ).manual_seed(0       )
        a__	:  Tuple     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type="np"				,				return_dict=lowerCamelCase__				,				)[0]

        a__	:  str     =   image[0, -3:, -3:, -1]
        a__	:  Optional[Any]     =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        a__	:  Tuple     =   np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]       )

        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    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Union[str, Any]     =   StableDiffusionPipeline.from_pretrained(
            "hf-internal-testing/tiny-stable-diffusion-lms-pipe"				,				safety_checker=lowerCamelCase__       )
        assert isinstance(lowerCamelCase__				,				lowerCamelCase__       )
        assert isinstance(pipe.scheduler				,				lowerCamelCase__       )
        assert pipe.safety_checker is None

        a__	:  Optional[int]     =   pipe("example prompt"				,				num_inference_steps=2       ).images[0]
        assert image is not None

        # check that there's no error when saving a pipeline with one of the models being None
        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowerCamelCase__       )
              a__	:  Union[str, Any]     =   StableDiffusionPipeline.from_pretrained(lowerCamelCase__       )

        # sanity check that the pipeline still works
        assert pipe.safety_checker is None
        a__	:  Optional[Any]     =   pipe("example prompt"				,				num_inference_steps=2       ).images[0]
        assert image is not None

  @unittest.skipIf(torch_device != "cuda"				,				"This test requires a GPU"       )
  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  List[Any]     =   self.dummy_cond_unet
        a__	:  Optional[Any]     =   PNDMScheduler(skip_prk_steps=lowerCamelCase__       )
        a__	:  Tuple     =   self.dummy_vae
        a__	:  str     =   self.dummy_text_encoder
        a__	:  Union[str, Any]     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

        # put models in fp16
        a__	:  Union[str, Any]     =   unet.half()
        a__	:  List[Any]     =   vae.half()
        a__	:  Optional[int]     =   bert.half()

        # make sure here that pndm scheduler skips prk
        a__	:  Tuple     =   StableDiffusionPipeline(
            unet=lowerCamelCase__				,				scheduler=lowerCamelCase__				,				vae=lowerCamelCase__				,				text_encoder=lowerCamelCase__				,				tokenizer=lowerCamelCase__				,				safety_checker=lowerCamelCase__				,				feature_extractor=self.dummy_extractor				,				)
        a__	:  List[Any]     =   sd_pipe.to(lowerCamelCase__       )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  List[str]     =   "A painting of a squirrel eating a burger"
        a__	:  Optional[Any]     =   sd_pipe([prompt]				,				num_inference_steps=2				,				output_type="np"       ).images

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




@nightly
@require_torch_gpu
class A__     ( unittest.TestCase  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  Optional[Any]       ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  Tuple     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"				,				safety_checker=lowerCamelCase__       )
        a__	:  Optional[Any]     =   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
        a__	:  Optional[Any]     =   sd_pipe.to(lowerCamelCase__       )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Union[str, Any]     =   (
            "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
            " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
            " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
            " children from bahnhof zoo, detailed "
        )
        a__	:  Optional[Any]     =   4_003_660_346
        a__	:  List[str]     =   7

        # without safety guidance (sld_guidance_scale = 0)
        a__	:  str     =   torch.manual_seed(lowerCamelCase__       )
        a__	:  Any     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=lowerCamelCase__				,				num_inference_steps=50				,				output_type="np"				,				width=512				,				height=512				,				sld_guidance_scale=0				,				)

        a__	:  str     =   output.images
        a__	:  List[Any]     =   image[0, -3:, -3:, -1]
        a__	:  Tuple     =   [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

        assert image.shape == (1, 512, 512, 3)

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

        # without safety guidance (strong configuration)
        a__	:  List[Any]     =   torch.manual_seed(lowerCamelCase__       )
        a__	:  Optional[Any]     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=lowerCamelCase__				,				num_inference_steps=50				,				output_type="np"				,				width=512				,				height=512				,				sld_guidance_scale=2_000				,				sld_warmup_steps=7				,				sld_threshold=0.025				,				sld_momentum_scale=0.5				,				sld_mom_beta=0.7				,				)

        a__	:  Dict     =   output.images
        a__	:  Union[str, Any]     =   image[0, -3:, -3:, -1]
        a__	:  Dict     =   [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

        assert image.shape == (1, 512, 512, 3)

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

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Tuple     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"				,				safety_checker=lowerCamelCase__       )
        a__	:  Optional[int]     =   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
        a__	:  Optional[int]     =   sd_pipe.to(lowerCamelCase__       )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Optional[int]     =   "padme amidala taking a bath artwork, safe for work, no nudity"
        a__	:  Tuple     =   2_734_971_755
        a__	:  Union[str, Any]     =   7

        a__	:  List[Any]     =   torch.manual_seed(lowerCamelCase__       )
        a__	:  List[str]     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=lowerCamelCase__				,				num_inference_steps=50				,				output_type="np"				,				width=512				,				height=512				,				sld_guidance_scale=0				,				)

        a__	:  Tuple     =   output.images
        a__	:  List[str]     =   image[0, -3:, -3:, -1]
        a__	:  Optional[Any]     =   [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

        assert image.shape == (1, 512, 512, 3)

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

        a__	:  Optional[Any]     =   torch.manual_seed(lowerCamelCase__       )
        a__	:  List[str]     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=lowerCamelCase__				,				num_inference_steps=50				,				output_type="np"				,				width=512				,				height=512				,				sld_guidance_scale=2_000				,				sld_warmup_steps=7				,				sld_threshold=0.025				,				sld_momentum_scale=0.5				,				sld_mom_beta=0.7				,				)

        a__	:  List[Any]     =   output.images
        a__	:  Optional[Any]     =   image[0, -3:, -3:, -1]
        a__	:  str     =   [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

        assert image.shape == (1, 512, 512, 3)

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

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  Any     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       )
        a__	:  Dict     =   sd_pipe.to(lowerCamelCase__       )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Any     =   (
            "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
            " leyendecker"
        )
        a__	:  Tuple     =   1_044_355_234
        a__	:  List[Any]     =   12

        a__	:  str     =   torch.manual_seed(lowerCamelCase__       )
        a__	:  Any     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=lowerCamelCase__				,				num_inference_steps=50				,				output_type="np"				,				width=512				,				height=512				,				sld_guidance_scale=0				,				)

        a__	:  int     =   output.images
        a__	:  Optional[int]     =   image[0, -3:, -3:, -1]
        a__	:  Tuple     =   np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]       )

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-7

        a__	:  Dict     =   torch.manual_seed(lowerCamelCase__       )
        a__	:  Tuple     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=lowerCamelCase__				,				num_inference_steps=50				,				output_type="np"				,				width=512				,				height=512				,				sld_guidance_scale=2_000				,				sld_warmup_steps=7				,				sld_threshold=0.025				,				sld_momentum_scale=0.5				,				sld_mom_beta=0.7				,				)

        a__	:  Dict     =   output.images
        a__	:  Union[str, Any]     =   image[0, -3:, -3:, -1]
        a__	:  Tuple     =   np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]       )
        assert image.shape == (1, 512, 512, 3)

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







'''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 							_a				(			SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				str        =						LongformerTokenizer
       A						:				List[str]        =						True
       A						:				Optional[int]        =						LongformerTokenizerFast
       A						:				Tuple        =						True







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             SCREAMING_SNAKE_CASE   :      Any    					=						[
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						dict(zip(A,							range(len(A  )  )  )  )
             SCREAMING_SNAKE_CASE   :      str    					=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             SCREAMING_SNAKE_CASE   :      Tuple    					=						{'unk_token': '<unk>'}

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['vocab_file']  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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(A  ) + '\n'  )
             with open(self.merges_file,							'w',							encoding='utf-8'  ) as fp:
                   fp.write('\n'.join(A  )  )







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						'lower newer'
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						'lower newer'
             return input_text, output_text







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.tokenizer_class(self.vocab_file,							self.merges_file,							**self.special_tokens_map  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'lower newer'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.tokenize(A  )  # , add_prefix_space=True)
             self.assertListEqual(A,							A  )

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.get_tokenizer()

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







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.encode('sequence builders',							add_special_tokens=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.encode('multi-sequence build',							add_special_tokens=A  )

             SCREAMING_SNAKE_CASE   :      int    					=						tokenizer.encode(
                 'sequence builders',							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.encode(
                 'sequence builders',							'multi-sequence build',							add_special_tokens=A,							add_prefix_space=A  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.build_inputs_with_special_tokens(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer.build_inputs_with_special_tokens(A,							A  )

             assert encoded_sentence == encoded_text_from_decode
             assert encoded_pair == encoded_pair_from_decode







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_tokenizer()

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'Encode this sequence.'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.byte_encoder[' '.encode('utf-8'  )[0]]

             # Testing encoder arguments
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.encode(A,							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
             self.assertNotEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.encode(A,							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
             self.assertEqual(A,							A  )

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

             # Testing spaces after special tokens
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'<mask>'
             tokenizer.add_special_tokens(
                 {'mask_token': AddedToken(A,							lstrip=A,							rstrip=A  )}  )  # mask token has a left space
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.convert_tokens_to_ids(A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						'Encode <mask> sequence'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						'Encode <mask>sequence'

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.encode(A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						encoded.index(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
             self.assertEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.encode(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						encoded.index(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
             self.assertNotEqual(A,							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.rust_tokenizer_class.from_pretrained(A,							**A  )
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer_class.from_pretrained(A,							**A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'A, <mask> AllenNLP sentence.'
                         SCREAMING_SNAKE_CASE   :      Any    					=						tokenizer_r.encode_plus(A,							add_special_tokens=A,							return_token_type_ids=A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer_p.encode_plus(A,							add_special_tokens=A,							return_token_type_ids=A  )

                         # 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']  ),							)

                         SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						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(
                             A,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
                         self.assertSequenceEqual(
                             A,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for trim_offsets, add_prefix_space in itertools.product([True, False],							repeat=2  ):
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.rust_tokenizer_class.from_pretrained(
                       self.tmpdirname,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )

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

                   self.assertEqual(pre_tokenizer_state['add_prefix_space'],							A  )

                   self.assertEqual(post_processor_state['add_prefix_space'],							A  )
                   self.assertEqual(post_processor_state['trim_offsets'],							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                         SCREAMING_SNAKE_CASE   :      str    					=						'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						F"{text_of_1_token} {text_of_1_token}"

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

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

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

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

                         SCREAMING_SNAKE_CASE   :      Any    					=						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)),
                         # )

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

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

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


