


"""simple docstring"""


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()



class   _a      (      unittest.TestCase ):
    a_    :				Dict             =    StableDiffusionLDMaDPipeline
    a_    :				int             =    TEXT_TO_IMAGE_PARAMS
    a_    :				Union[str, Any]             =    TEXT_TO_IMAGE_BATCH_PARAMS
    a_    :				Optional[Any]             =    TEXT_TO_IMAGE_IMAGE_PARAMS
    def      _UpperCamelCase       ( self							:					List[Any]				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		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							,					)
          lowerCamelCase__           =		DDIMScheduler(
              beta_start=0.0_00_85							,					beta_end=0.0_12							,					beta_schedule='scaled_linear'							,					clip_sample=SCREAMING_SNAKE_CASE__							,					set_alpha_to_one=SCREAMING_SNAKE_CASE__							,					)
          torch.manual_seed(0				)
          lowerCamelCase__           =		AutoencoderKL(
              block_out_channels=[32, 64]							,					in_channels=6							,					out_channels=6							,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,					latent_channels=4							,					)
          torch.manual_seed(0				)
          lowerCamelCase__           =		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=10_00							,					)
          lowerCamelCase__           =		CLIPTextModel(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

          lowerCamelCase__           =		{
              'unet': unet,
              'scheduler': scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'safety_checker': None,
              'feature_extractor': None,
          }
          return components
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0				):
          if str(SCREAMING_SNAKE_CASE__				).startswith('mps'				):
                lowerCamelCase__           =		torch.manual_seed(SCREAMING_SNAKE_CASE__				)
          else:
                lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'prompt': 'A painting of a squirrel eating a burger',
              'generator': generator,
              'num_inference_steps': 2,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
          }
          return inputs
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		'cpu'  # ensure determinism for the device-dependent torch.Generator

          lowerCamelCase__           =		self.get_dummy_components()
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe.to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth

          lowerCamelCase__           =		rgb[0, -3:, -3:, -1]
          lowerCamelCase__           =		depth[0, -3:, -1]

          assert rgb.shape == (1, 64, 64, 3)
          assert depth.shape == (1, 64, 64)

          lowerCamelCase__           =		np.array(
              [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]				)
          lowerCamelCase__           =		np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]				)

          assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb				).max() < 1e-2
          assert np.abs(image_slice_depth.flatten() - expected_slice_depth				).max() < 1e-2
    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__           =		self.get_dummy_components()
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe.to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		3 * [inputs['prompt']]

          # forward
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth
          lowerCamelCase__           =		rgb_slice_a[0, -3:, -3:, -1]
          lowerCamelCase__           =		depth_slice_a[0, -3:, -1]

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		3 * [inputs.pop('prompt'				)]

          lowerCamelCase__           =		ldmad_pipe.tokenizer(
              SCREAMING_SNAKE_CASE__							,					padding='max_length'							,					max_length=ldmad_pipe.tokenizer.model_max_length							,					truncation=SCREAMING_SNAKE_CASE__							,					return_tensors='pt'							,					)
          lowerCamelCase__           =		text_inputs['input_ids'].to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		ldmad_pipe.text_encoder(SCREAMING_SNAKE_CASE__				)[0]

          lowerCamelCase__           =		prompt_embeds

          # forward
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth
          lowerCamelCase__           =		rgb_slice_a[0, -3:, -3:, -1]
          lowerCamelCase__           =		depth_slice_a[0, -3:, -1]

          assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()				).max() < 1e-4
          assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()				).max() < 1e-4


    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		'cpu'  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__           =		self.get_dummy_components()
          lowerCamelCase__           =		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe.to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		'french fries'
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__							,					negative_prompt=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth
          lowerCamelCase__           =		rgb[0, -3:, -3:, -1]
          lowerCamelCase__           =		depth[0, -3:, -1]

          assert rgb.shape == (1, 64, 64, 3)
          assert depth.shape == (1, 64, 64)

          lowerCamelCase__           =		np.array(
              [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]				)
          lowerCamelCase__           =		np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]				)
          assert np.abs(rgb_slice.flatten() - expected_slice_rgb				).max() < 1e-2
          assert np.abs(depth_slice.flatten() - expected_slice_depth				).max() < 1e-2





@slow
@require_torch_gpu
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					Any				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]="cpu"							,					SCREAMING_SNAKE_CASE__							:					int=torch.floataa							,					SCREAMING_SNAKE_CASE__							:					Tuple=0				):
          lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		np.random.RandomState(SCREAMING_SNAKE_CASE__				).standard_normal((1, 4, 64, 64)				)
          lowerCamelCase__           =		torch.from_numpy(SCREAMING_SNAKE_CASE__				).to(device=SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'prompt': 'a photograph of an astronaut riding a horse',
              'latents': latents,
              'generator': generator,
              'num_inference_steps': 3,
              'guidance_scale': 7.5,
              'output_type': 'numpy',
          }
          return inputs


    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'				)
          lowerCamelCase__           =		ldmad_pipe.to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth
          lowerCamelCase__           =		rgb[0, -3:, -3:, -1].flatten()
          lowerCamelCase__           =		rgb[0, -3:, -1].flatten()

          assert rgb.shape == (1, 5_12, 5_12, 3)
          assert depth.shape == (1, 5_12, 5_12)

          lowerCamelCase__           =		np.array(
              [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]				)
          lowerCamelCase__           =		np.array(
              [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]				)
          assert np.abs(rgb_slice - expected_slice_rgb				).max() < 3e-3
          assert np.abs(depth_slice - expected_slice_depth				).max() < 3e-3





@nightly
@require_torch_gpu
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					Any				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]="cpu"							,					SCREAMING_SNAKE_CASE__							:					Dict=torch.floataa							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0				):
          lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		np.random.RandomState(SCREAMING_SNAKE_CASE__				).standard_normal((1, 4, 64, 64)				)
          lowerCamelCase__           =		torch.from_numpy(SCREAMING_SNAKE_CASE__				).to(device=SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'prompt': 'a photograph of an astronaut riding a horse',
              'latents': latents,
              'generator': generator,
              'num_inference_steps': 50,
              'guidance_scale': 7.5,
              'output_type': 'numpy',
          }
          return inputs
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'				).to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth

          lowerCamelCase__           =		0.49_55_86
          lowerCamelCase__           =		0.33_79_55_15
          lowerCamelCase__           =		1_12.4_85_18
          lowerCamelCase__           =		98.48_97_46
          assert np.abs(expected_rgb_mean - rgb.mean()				) < 1e-3
          assert np.abs(expected_rgb_std - rgb.std()				) < 1e-3
          assert np.abs(expected_depth_mean - depth.mean()				) < 1e-3
          assert np.abs(expected_depth_std - depth.std()				) < 1e-3


    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'				).to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth

          lowerCamelCase__           =		0.4_19_41_27
          lowerCamelCase__           =		0.35_37_55_86
          lowerCamelCase__           =		0.5_63_85_02
          lowerCamelCase__           =		0.34_68_61_03

          assert rgb.shape == (1, 5_12, 5_12, 3)
          assert depth.shape == (1, 5_12, 5_12, 1)
          assert np.abs(expected_rgb_mean - rgb.mean()				) < 1e-3
          assert np.abs(expected_rgb_std - rgb.std()				) < 1e-3
          assert np.abs(expected_depth_mean - depth.mean()				) < 1e-3
          assert np.abs(expected_depth_std - depth.std()				) < 1e-3







"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_snake_case          =							[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

_snake_case          =							{
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
_snake_case          =							{f"""funnel-transformer/{name}""": 512 for name in _model_names}
_snake_case          =							{f"""funnel-transformer/{name}""": {"do_lower_case": True} for name in _model_names}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				List[str]             =    VOCAB_FILES_NAMES
    a_    :				List[str]             =    PRETRAINED_VOCAB_FILES_MAP
    a_    :				Optional[int]             =    PRETRAINED_INIT_CONFIGURATION
    a_    :				List[str]             =    FunnelTokenizer
    a_    :				Dict             =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a_    :				int             =    2
    def __init__( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=None							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=None							,					SCREAMING_SNAKE_CASE__							:					int=True							,					SCREAMING_SNAKE_CASE__							:					Any="<unk>"							,					SCREAMING_SNAKE_CASE__							:					List[Any]="<sep>"							,					SCREAMING_SNAKE_CASE__							:					int="<pad>"							,					SCREAMING_SNAKE_CASE__							:					Tuple="<cls>"							,					SCREAMING_SNAKE_CASE__							:					Tuple="<mask>"							,					SCREAMING_SNAKE_CASE__							:					Any="<s>"							,					SCREAMING_SNAKE_CASE__							:					Tuple="</s>"							,					SCREAMING_SNAKE_CASE__							:					str=True							,					SCREAMING_SNAKE_CASE__							:					Any=True							,					SCREAMING_SNAKE_CASE__							:					str=None							,					SCREAMING_SNAKE_CASE__							:					int="##"							,					**SCREAMING_SNAKE_CASE__							:					Any							,					):
          super().__init__(
              SCREAMING_SNAKE_CASE__							,					tokenizer_file=SCREAMING_SNAKE_CASE__							,					do_lower_case=SCREAMING_SNAKE_CASE__							,					unk_token=SCREAMING_SNAKE_CASE__							,					sep_token=SCREAMING_SNAKE_CASE__							,					pad_token=SCREAMING_SNAKE_CASE__							,					cls_token=SCREAMING_SNAKE_CASE__							,					mask_token=SCREAMING_SNAKE_CASE__							,					bos_token=SCREAMING_SNAKE_CASE__							,					eos_token=SCREAMING_SNAKE_CASE__							,					clean_text=SCREAMING_SNAKE_CASE__							,					tokenize_chinese_chars=SCREAMING_SNAKE_CASE__							,					strip_accents=SCREAMING_SNAKE_CASE__							,					wordpieces_prefix=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__							,					)

          lowerCamelCase__           =		json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
          if (
              normalizer_state.get('lowercase'							,					SCREAMING_SNAKE_CASE__				) != do_lower_case
              or normalizer_state.get('strip_accents'							,					SCREAMING_SNAKE_CASE__				) != strip_accents
              or normalizer_state.get('handle_chinese_chars'							,					SCREAMING_SNAKE_CASE__				) != tokenize_chinese_chars
          ):
                lowerCamelCase__           =		getattr(SCREAMING_SNAKE_CASE__							,					normalizer_state.pop('type'				)				)
                lowerCamelCase__           =		do_lower_case
                lowerCamelCase__           =		strip_accents
                lowerCamelCase__           =		tokenize_chinese_chars
                lowerCamelCase__           =		normalizer_class(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		do_lower_case
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=None				):
          lowerCamelCase__           =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                output += token_ids_a + [self.sep_token_id]

          return output
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					List[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[List[int]] = None				):
          lowerCamelCase__           =		[self.sep_token_id]
          lowerCamelCase__           =		[self.cls_token_id]
          if token_ids_a is None:
                return len(cls				) * [self.cls_token_type_id] + len(token_ids_a + sep				) * [0]
          return len(cls				) * [self.cls_token_type_id] + len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]


    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = None				):
          lowerCamelCase__           =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE__							,					name=SCREAMING_SNAKE_CASE__				)
          return tuple(SCREAMING_SNAKE_CASE__				)



