

"""simple docstring"""




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.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class      __UpperCamelCase					(  unittest.TestCase		):


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


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   1
							A     =   3
							A     =   (3_2, 3_2)

							A     =   floats_tensor((batch_size, num_channels) + sizes	,	rng=random.Random(0)).to(__SCREAMING_SNAKE_CASE)
							return image


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							torch.manual_seed(0)
							A     =   UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)	,	layers_per_block=2	,	sample_size=3_2	,	in_channels=4	,	out_channels=4	,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,	cross_attention_dim=3_2	,	)
							return model


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							torch.manual_seed(0)
							A     =   AutoencoderKL(
							    block_out_channels=[3_2, 6_4]	,	in_channels=3	,	out_channels=3	,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,	latent_channels=4	,	)
							return model


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							torch.manual_seed(0)
							A     =   CLIPTextConfig(
							    bos_token_id=0	,	eos_token_id=2	,	hidden_size=3_2	,	intermediate_size=3_7	,	layer_norm_eps=1E-05	,	num_attention_heads=4	,	num_hidden_layers=5	,	pad_token_id=1	,	vocab_size=1_0_0_0	,	)
							return CLIPTextModel(__SCREAMING_SNAKE_CASE)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							def extract(*__SCREAMING_SNAKE_CASE					:						List[Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[int]):





												class      __UpperCamelCase					:


														def __init__(self					:						Any):
																			A     =   torch.ones([0])


														def    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Tuple):
																			self.pixel_values.to(__SCREAMING_SNAKE_CASE)
																			return self

												return Out()

							return extract


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   "cpu"  # ensure determinism for the device-dependent torch.Generator
							A     =   self.dummy_cond_unet
							A     =   DDIMScheduler(
							    beta_start=0.0_0_0_8_5	,	beta_end=0.0_1_2	,	beta_schedule="scaled_linear"	,	clip_sample=__SCREAMING_SNAKE_CASE	,	set_alpha_to_one=__SCREAMING_SNAKE_CASE	,	)

							A     =   self.dummy_vae
							A     =   self.dummy_text_encoder
							A     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

							# make sure here that pndm scheduler skips prk
							A     =   StableDiffusionPipeline(
							    unet=__SCREAMING_SNAKE_CASE	,	scheduler=__SCREAMING_SNAKE_CASE	,	vae=__SCREAMING_SNAKE_CASE	,	text_encoder=__SCREAMING_SNAKE_CASE	,	tokenizer=__SCREAMING_SNAKE_CASE	,	safety_checker=__SCREAMING_SNAKE_CASE	,	feature_extractor=self.dummy_extractor	,	)
							A     =   sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "A painting of a squirrel eating a burger"

							A     =   torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
							A     =   sd_pipe([prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=6.0	,	num_inference_steps=2	,	output_type="np")
							A     =   output.images

							A     =   torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
							A     =   sd_pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=6.0	,	num_inference_steps=2	,	output_type="np"	,	return_dict=__SCREAMING_SNAKE_CASE	,	)[0]

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

							assert image.shape == (1, 6_4, 6_4, 3)
							A     =   np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7])

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

							# make sure here that pndm scheduler skips prk
							A     =   StableDiffusionPipeline(
							    unet=__SCREAMING_SNAKE_CASE	,	scheduler=__SCREAMING_SNAKE_CASE	,	vae=__SCREAMING_SNAKE_CASE	,	text_encoder=__SCREAMING_SNAKE_CASE	,	tokenizer=__SCREAMING_SNAKE_CASE	,	safety_checker=__SCREAMING_SNAKE_CASE	,	feature_extractor=self.dummy_extractor	,	)
							A     =   sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

							A     =   output.images

							A     =   torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
							A     =   sd_pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=6.0	,	num_inference_steps=2	,	output_type="np"	,	return_dict=__SCREAMING_SNAKE_CASE	,	)[0]

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

							assert image.shape == (1, 6_4, 6_4, 3)
							A     =   np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4])

							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    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   StableDiffusionPipeline.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"	,	safety_checker=__SCREAMING_SNAKE_CASE)
							assert isinstance(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							assert isinstance(pipe.scheduler	,	__SCREAMING_SNAKE_CASE)
							assert pipe.safety_checker is None

							A     =   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(__SCREAMING_SNAKE_CASE)
												A     =   StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE)

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							A     =   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    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   self.dummy_cond_unet
							A     =   PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
							A     =   self.dummy_vae
							A     =   self.dummy_text_encoder
							A     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

							# put models in fp16
							A     =   unet.half()
							A     =   vae.half()
							A     =   bert.half()

							# make sure here that pndm scheduler skips prk
							A     =   StableDiffusionPipeline(
							    unet=__SCREAMING_SNAKE_CASE	,	scheduler=__SCREAMING_SNAKE_CASE	,	vae=__SCREAMING_SNAKE_CASE	,	text_encoder=__SCREAMING_SNAKE_CASE	,	tokenizer=__SCREAMING_SNAKE_CASE	,	safety_checker=__SCREAMING_SNAKE_CASE	,	feature_extractor=self.dummy_extractor	,	)
							A     =   sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "A painting of a squirrel eating a burger"
							A     =   sd_pipe([prompt]	,	num_inference_steps=2	,	output_type="np").images

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





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


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


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "a photo of a cat"
							A     =   {
							    "editing_prompt": ["sunglasses"],
							    "reverse_editing_direction": [False],
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 6,
							    "edit_threshold": 0.9_5,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   3
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.3_4_6_7_3_1_1_3,
							    0.3_8_4_9_2_7_3_3,
							    0.3_7_5_9_7_3_5_2,
							    0.3_4_0_8_6_3_3_5,
							    0.3_5_6_5_0_7_4_8,
							    0.3_5_5_7_9_2_0_5,
							    0.3_3_8_4_7_6_3,
							    0.3_4_3_4_0_2_3_6,
							    0.3_5_7_3_2_7_1,
							]

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

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.4_1_8_8_7_8_2_6,
							    0.3_7_7_2_8_7_6_6,
							    0.3_0_1_3_8_2_7_2,
							    0.4_1_4_1_6_3_3_5,
							    0.4_1_6_6_4_9_8_5,
							    0.3_6_2_8_3_3_9_2,
							    0.3_6_1_9_1_2_4_6,
							    0.4_3_3_6_4_4_6_5,
							    0.4_3_0_0_1_7_3_2,
							]

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "an image of a crowded boulevard, realistic, 4k"
							A     =   {
							    "editing_prompt": "crowd, crowded, people",
							    "reverse_editing_direction": True,
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 8.3,
							    "edit_threshold": 0.9,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   9
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.4_3_4_9_7_9_9_8,
							    0.9_1_8_1_4_0_6_5,
							    0.7_5_4_0_7_3_9,
							    0.5_5_5_8_0_2_0_5,
							    0.8_4_6_7_2_6_5,
							    0.5_3_8_9_6_9_1,
							    0.6_2_5_7_4_5_0_6,
							    0.5_8_8_9_7_7_6_3,
							    0.5_0_9_2_6_7_5_7,
							]

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

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.3_0_8_9_7_1_9,
							    0.3_0_5_0_0_1_4_4,
							    0.2_9_0_1_6_0_4_2,
							    0.3_0_6_3_0_9_6_4,
							    0.3_2_5_6_8_7,
							    0.2_9_4_1_9_2_2_5,
							    0.2_9_0_8_0_9_1,
							    0.2_8_7_2_3_5_9_8,
							    0.2_7_6_9_6_2_9_4,
							]

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "a castle next to a river"
							A     =   {
							    "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
							    "reverse_editing_direction": False,
							    "edit_warmup_steps": [1_5, 1_8],
							    "edit_guidance_scale": 6,
							    "edit_threshold": [0.9, 0.8],
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   4_8
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.7_5_1_6_3_5_5_5,
							    0.7_6_0_3_7_1_4_5,
							    0.6_1_7_8_5,
							    0.9_1_8_9_6_7_3,
							    0.8_6_2_7_7_0_1,
							    0.8_5_1_8_9_6_9_4,
							    0.8_5_1_2_8_1_3,
							    0.8_7_0_1_2_0_7_6,
							    0.8_3_1_2_8_5_7,
							]

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

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.7_3_5_5_3_3_6_5,
							    0.7_5_3_7_2_7_1,
							    0.7_4_3_4_1_9_0_5,
							    0.6_6_4_8_0_3_5_6,
							    0.6_4_7_2_9_2_5,
							    0.6_3_0_3_9_4_1_6,
							    0.6_4_8_1_2_9_0_5,
							    0.6_7_4_9_7_1_7,
							    0.6_5_1_7_1_0_2,
							]

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	,	torch_dtype=torch.floataa)
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "a photo of a cat"
							A     =   {
							    "editing_prompt": ["sunglasses"],
							    "reverse_editing_direction": [False],
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 6,
							    "edit_threshold": 0.9_5,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   3
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.3_4_8_8_7_6_9_5,
							    0.3_8_7_6_9_5_3,
							    0.3_7_5,
							    0.3_4_4_2_3_8_2_8,
							    0.3_5_8_1_5_4_3,
							    0.3_5_7_1_7_7_7_3,
							    0.3_3_8_3_7_8_9,
							    0.3_4_5_7_0_3_1_2,
							    0.3_5_9_3_7_5,
							]

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

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.4_2_2_8_5_1_5_6,
							    0.3_6_9_1_4_0_6_2,
							    0.2_9_0_7_7_1_4_8,
							    0.4_2_0_4_1_0_1_6,
							    0.4_1_9_1_8_9_4_5,
							    0.3_5_4_9_8_0_4_7,
							    0.3_6_1_8_1_6_4,
							    0.4_4_2_3_8_2_8,
							    0.4_3_1_1_5_2_3_4,
							]

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

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








"""simple docstring"""




import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin





class      __UpperCamelCase					(  _A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       SpeechaTextaTokenizer
		SCREAMING_SNAKE_CASE														=       False


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().setUp()

							A     =   "<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" ")
							A     =   ["he re</w> 123", "here a 1456"]
							A     =   dict(zip(__SCREAMING_SNAKE_CASE	,	range(len(__SCREAMING_SNAKE_CASE))))

							A     =   {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

							A     =   tempfile.mkdtemp()
							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["vocab_file"])
							A     =   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(__SCREAMING_SNAKE_CASE) + "\n")

							with open(self.merges_file	,	"w") as fp:
												fp.write("\n".join(__SCREAMING_SNAKE_CASE))


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   list(self.get_tokenizer().get_vocab().keys())

							self.assertEqual(vocab_keys[0]	,	"<s>")
							self.assertEqual(vocab_keys[1]	,	"<pad>")
							self.assertEqual(vocab_keys[-1]	,	"vocab")
							self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,	1_4)


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							self.assertEqual(self.get_tokenizer().vocab_size	,	1_4)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   SpeechaTextaTokenizer.from_pretrained(self.tmpdirname)

							# make sure @@ is correctly concatenated
							A     =   [4, 6, 8, 7, 1_0]  # ["here@@", "couple", "words", "of@@", "the"]
							A     =   tokenizer.decode(__SCREAMING_SNAKE_CASE)

							self.assertTrue(output_string == "herecouple words ofthe")


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   SpeechaTextaTokenizer.from_pretrained(self.tmpdirname)

							with tempfile.TemporaryDirectory() as tmp_dirname:
												tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE)
												os.remove(os.path.join(__SCREAMING_SNAKE_CASE	,	"merges.txt"))

												# load tokenizer without merges file should not throw an error
												A     =   SpeechaTextaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							with tempfile.TemporaryDirectory() as tmp_dirname:
												# save tokenizer and load again
												tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE)
												A     =   SpeechaTextaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							if not self.test_slow_tokenizer:
												return

							A     =   inspect.signature(self.tokenizer_class.__init__)
							A     =   self.get_tokenizer()

							for parameter_name, parameter in signature.parameters.items():
												if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																	self.assertIn(__SCREAMING_SNAKE_CASE	,	tokenizer.init_kwargs)





