





import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class 		__a							(	lowerCamelCase_			,lowerCamelCase_			,lowerCamelCase_			,unittest.TestCase      ):
       __snake_case						:   Dict      =		StableDiffusionControlNetInpaintPipeline
       __snake_case						:   List[Any]      =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       __snake_case						:   List[Any]      =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __snake_case						:   List[Any]      =		frozenset({"""control_image"""}      )  # skip `image` and `mask` for now, only test for control_image
       __snake_case						:   Optional[int]      =		TEXT_TO_IMAGE_IMAGE_PARAMS



       def  A							(     self	:   List[Any]			):
           torch.manual_seed(0			)
           lowerCAmelCase_					:	int						=   UNetaDConditionModel(
               block_out_channels=(32, 64)					,			layers_per_block=2					,			sample_size=32					,			in_channels=9					,			out_channels=4					,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,			cross_attention_dim=32					,			)
           torch.manual_seed(0			)
           lowerCAmelCase_					:	Union[str, Any]						=   ControlNetModel(
               block_out_channels=(32, 64)					,			layers_per_block=2					,			in_channels=4					,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,			cross_attention_dim=32					,			conditioning_embedding_out_channels=(16, 32)					,			)
           torch.manual_seed(0			)
           lowerCAmelCase_					:	Optional[Any]						=   DDIMScheduler(
               beta_start=0.0_0085					,			beta_end=0.012					,			beta_schedule="""scaled_linear"""					,			clip_sample=UpperCAmelCase					,			set_alpha_to_one=UpperCAmelCase					,			)
           torch.manual_seed(0			)
           lowerCAmelCase_					:	Optional[int]						=   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			)
           lowerCAmelCase_					:	Optional[Any]						=   CLIPTextConfig(
               bos_token_id=0					,			eos_token_id=2					,			hidden_size=32					,			intermediate_size=37					,			layer_norm_eps=1e-0_5					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=10_00					,			)
           lowerCAmelCase_					:	int						=   CLIPTextModel(UpperCAmelCase			)
           lowerCAmelCase_					:	Optional[int]						=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

           lowerCAmelCase_					:	List[Any]						=   {
               "unet": unet,
               "controlnet": controlnet,
               "scheduler": scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "safety_checker": None,
               "feature_extractor": None,
           }
           return components



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict					,			UpperCAmelCase	:   Union[str, Any]=0			):







           if str(UpperCAmelCase			).startswith("""mps"""			):
               lowerCAmelCase_					:	Optional[int]						=   torch.manual_seed(UpperCAmelCase			)
           else:
               lowerCAmelCase_					:	str						=   torch.Generator(device=UpperCAmelCase			).manual_seed(UpperCAmelCase			)

           lowerCAmelCase_					:	Dict						=   2
           lowerCAmelCase_					:	List[Any]						=   randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,			generator=UpperCAmelCase					,			device=torch.device(UpperCAmelCase			)					,			)
           lowerCAmelCase_					:	Optional[int]						=   floats_tensor((1, 3, 32, 32)					,			rng=random.Random(UpperCAmelCase			)			).to(UpperCAmelCase			)
           lowerCAmelCase_					:	str						=   init_image.cpu().permute(0					,			2					,			3					,			1			)[0]

           lowerCAmelCase_					:	Union[str, Any]						=   Image.fromarray(np.uinta(UpperCAmelCase			)			).convert("""RGB"""			).resize((64, 64)			)
           lowerCAmelCase_					:	Union[str, Any]						=   Image.fromarray(np.uinta(init_image + 4			)			).convert("""RGB"""			).resize((64, 64)			)

           lowerCAmelCase_					:	Tuple						=   {
               "prompt": "A painting of a squirrel eating a burger",
               "generator": generator,
               "num_inference_steps": 2,
               "guidance_scale": 6.0,
               "output_type": "numpy",
               "image": image,
               "mask_image": mask_image,
               "control_image": control_image,
           }

           return inputs



       def  A							(     self	:   str			):
           return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3			)



       @unittest.skipIf(
           torch_device != """cuda""" or not is_xformers_available()					,			reason="""XFormers attention is only available with CUDA and `xformers` installed"""					,			)
       def  A							(     self	:   List[str]			):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)



       def  A							(     self	:   Optional[Any]			):
           self._test_inference_batch_single_identical(expected_max_diff=2e-3			)






class 		__a							(	lowerCamelCase_      ):
       __snake_case						:   Any      =		StableDiffusionControlNetInpaintPipeline
       __snake_case						:   Optional[int]      =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       __snake_case						:   int      =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __snake_case						:   List[str]      =		frozenset([]      )



       def  A							(     self	:   Union[str, Any]			):
           torch.manual_seed(0			)
           lowerCAmelCase_					:	Optional[int]						=   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					,			)
           torch.manual_seed(0			)
           lowerCAmelCase_					:	Optional[int]						=   ControlNetModel(
               block_out_channels=(32, 64)					,			layers_per_block=2					,			in_channels=4					,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,			cross_attention_dim=32					,			conditioning_embedding_out_channels=(16, 32)					,			)
           torch.manual_seed(0			)
           lowerCAmelCase_					:	Optional[int]						=   DDIMScheduler(
               beta_start=0.0_0085					,			beta_end=0.012					,			beta_schedule="""scaled_linear"""					,			clip_sample=UpperCAmelCase					,			set_alpha_to_one=UpperCAmelCase					,			)
           torch.manual_seed(0			)
           lowerCAmelCase_					:	Dict						=   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			)
           lowerCAmelCase_					:	int						=   CLIPTextConfig(
               bos_token_id=0					,			eos_token_id=2					,			hidden_size=32					,			intermediate_size=37					,			layer_norm_eps=1e-0_5					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=10_00					,			)
           lowerCAmelCase_					:	Any						=   CLIPTextModel(UpperCAmelCase			)
           lowerCAmelCase_					:	str						=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

           lowerCAmelCase_					:	int						=   {
               "unet": unet,
               "controlnet": controlnet,
               "scheduler": scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "safety_checker": None,
               "feature_extractor": None,
           }
           return components






class 		__a							(	lowerCamelCase_			,lowerCamelCase_			,unittest.TestCase      ):
       __snake_case						:   str      =		StableDiffusionControlNetInpaintPipeline
       __snake_case						:   List[Any]      =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       __snake_case						:   Any      =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



       def  A							(     self	:   Tuple			):
           torch.manual_seed(0			)
           lowerCAmelCase_					:	Any						=   UNetaDConditionModel(
               block_out_channels=(32, 64)					,			layers_per_block=2					,			sample_size=32					,			in_channels=9					,			out_channels=4					,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,			cross_attention_dim=32					,			)
           torch.manual_seed(0			)

           def init_weights(UpperCAmelCase	:   Optional[Any]			):
               if isinstance(UpperCAmelCase					,			torch.nn.Convad			):
                   torch.nn.init.normal(m.weight			)
                   m.bias.data.fill_(1.0			)

           lowerCAmelCase_					:	Tuple						=   ControlNetModel(
               block_out_channels=(32, 64)					,			layers_per_block=2					,			in_channels=4					,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,			cross_attention_dim=32					,			conditioning_embedding_out_channels=(16, 32)					,			)
           controlneta.controlnet_down_blocks.apply(UpperCAmelCase			)

           torch.manual_seed(0			)
           lowerCAmelCase_					:	Any						=   ControlNetModel(
               block_out_channels=(32, 64)					,			layers_per_block=2					,			in_channels=4					,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,			cross_attention_dim=32					,			conditioning_embedding_out_channels=(16, 32)					,			)
           controlneta.controlnet_down_blocks.apply(UpperCAmelCase			)

           torch.manual_seed(0			)
           lowerCAmelCase_					:	str						=   DDIMScheduler(
               beta_start=0.0_0085					,			beta_end=0.012					,			beta_schedule="""scaled_linear"""					,			clip_sample=UpperCAmelCase					,			set_alpha_to_one=UpperCAmelCase					,			)
           torch.manual_seed(0			)
           lowerCAmelCase_					:	int						=   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			)
           lowerCAmelCase_					:	Optional[Any]						=   CLIPTextConfig(
               bos_token_id=0					,			eos_token_id=2					,			hidden_size=32					,			intermediate_size=37					,			layer_norm_eps=1e-0_5					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=10_00					,			)
           lowerCAmelCase_					:	List[Any]						=   CLIPTextModel(UpperCAmelCase			)
           lowerCAmelCase_					:	int						=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

           lowerCAmelCase_					:	Tuple						=   MultiControlNetModel([controlneta, controlneta]			)

           lowerCAmelCase_					:	List[str]						=   {
               "unet": unet,
               "controlnet": controlnet,
               "scheduler": scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "safety_checker": None,
               "feature_extractor": None,
           }
           return components



       def  A							(     self	:   Tuple					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Union[str, Any]=0			):







           if str(UpperCAmelCase			).startswith("""mps"""			):
               lowerCAmelCase_					:	int						=   torch.manual_seed(UpperCAmelCase			)
           else:
               lowerCAmelCase_					:	List[str]						=   torch.Generator(device=UpperCAmelCase			).manual_seed(UpperCAmelCase			)

           lowerCAmelCase_					:	Optional[Any]						=   2

           lowerCAmelCase_					:	Optional[Any]						=   [
               randn_tensor(
                   (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,			generator=UpperCAmelCase					,			device=torch.device(UpperCAmelCase			)					,			),
               randn_tensor(
                   (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,			generator=UpperCAmelCase					,			device=torch.device(UpperCAmelCase			)					,			),
           ]
           lowerCAmelCase_					:	Optional[int]						=   floats_tensor((1, 3, 32, 32)					,			rng=random.Random(UpperCAmelCase			)			).to(UpperCAmelCase			)
           lowerCAmelCase_					:	List[Any]						=   init_image.cpu().permute(0					,			2					,			3					,			1			)[0]

           lowerCAmelCase_					:	List[Any]						=   Image.fromarray(np.uinta(UpperCAmelCase			)			).convert("""RGB"""			).resize((64, 64)			)
           lowerCAmelCase_					:	str						=   Image.fromarray(np.uinta(init_image + 4			)			).convert("""RGB"""			).resize((64, 64)			)

           lowerCAmelCase_					:	str						=   {
               "prompt": "A painting of a squirrel eating a burger",
               "generator": generator,
               "num_inference_steps": 2,
               "guidance_scale": 6.0,
               "output_type": "numpy",
               "image": image,
               "mask_image": mask_image,
               "control_image": control_image,
           }

           return inputs



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Optional[Any]						=   self.get_dummy_components()
           lowerCAmelCase_					:	int						=   self.pipeline_class(**UpperCAmelCase			)
           pipe.to(UpperCAmelCase			)

           lowerCAmelCase_					:	List[Any]						=   10.0
           lowerCAmelCase_					:	str						=   4

           lowerCAmelCase_					:	Tuple						=   self.get_dummy_inputs(UpperCAmelCase			)
           lowerCAmelCase_					:	Optional[int]						=   steps
           lowerCAmelCase_					:	Any						=   scale
           lowerCAmelCase_					:	Union[str, Any]						=   pipe(**UpperCAmelCase			)[0]

           lowerCAmelCase_					:	str						=   self.get_dummy_inputs(UpperCAmelCase			)
           lowerCAmelCase_					:	Optional[Any]						=   steps
           lowerCAmelCase_					:	Optional[Any]						=   scale
           lowerCAmelCase_					:	Dict						=   pipe(**UpperCAmelCase					,			control_guidance_start=0.1					,			control_guidance_end=0.2			)[0]

           lowerCAmelCase_					:	Union[str, Any]						=   self.get_dummy_inputs(UpperCAmelCase			)
           lowerCAmelCase_					:	Optional[int]						=   steps
           lowerCAmelCase_					:	Optional[int]						=   scale
           lowerCAmelCase_					:	List[str]						=   pipe(**UpperCAmelCase					,			control_guidance_start=[0.1, 0.3]					,			control_guidance_end=[0.2, 0.7]			)[0]

           lowerCAmelCase_					:	Union[str, Any]						=   self.get_dummy_inputs(UpperCAmelCase			)
           lowerCAmelCase_					:	Dict						=   steps
           lowerCAmelCase_					:	Optional[int]						=   scale
           lowerCAmelCase_					:	Union[str, Any]						=   pipe(**UpperCAmelCase					,			control_guidance_start=0.4					,			control_guidance_end=[0.5, 0.8]			)[0]

           # make sure that all outputs are different
           assert np.sum(np.abs(output_a - output_a			)			) > 1e-3
           assert np.sum(np.abs(output_a - output_a			)			) > 1e-3
           assert np.sum(np.abs(output_a - output_a			)			) > 1e-3



       def  A							(     self	:   List[str]			):
           return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3			)



       @unittest.skipIf(
           torch_device != """cuda""" or not is_xformers_available()					,			reason="""XFormers attention is only available with CUDA and `xformers` installed"""					,			)
       def  A							(     self	:   List[str]			):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)



       def  A							(     self	:   Tuple			):
           self._test_inference_batch_single_identical(expected_max_diff=2e-3			)



       def  A							(     self	:   Optional[int]			):
           lowerCAmelCase_					:	List[str]						=   self.get_dummy_components()
           lowerCAmelCase_					:	Union[str, Any]						=   self.pipeline_class(**UpperCAmelCase			)
           pipe.to(UpperCAmelCase			)
           pipe.set_progress_bar_config(disable=UpperCAmelCase			)
           with tempfile.TemporaryDirectory() as tmpdir:
               try:
                   # save_pretrained is not implemented for Multi-ControlNet
                   pipe.save_pretrained(UpperCAmelCase			)
               except NotImplementedError:
                   pass






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



       def  A							(     self	:   int			):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



       def  A							(     self	:   Any			):
           lowerCAmelCase_					:	Union[str, Any]						=   ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""			)

           lowerCAmelCase_					:	List[Any]						=   StableDiffusionControlNetInpaintPipeline.from_pretrained(
               """runwayml/stable-diffusion-inpainting"""					,			safety_checker=UpperCAmelCase					,			controlnet=UpperCAmelCase			)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=UpperCAmelCase			)

           lowerCAmelCase_					:	Optional[int]						=   torch.Generator(device="""cpu"""			).manual_seed(0			)
           lowerCAmelCase_					:	int						=   load_image(
               """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""			).resize((5_12, 5_12)			)

           lowerCAmelCase_					:	int						=   load_image(
               """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
               """/stable_diffusion_inpaint/input_bench_mask.png"""			).resize((5_12, 5_12)			)

           lowerCAmelCase_					:	Tuple						=   "pitch black hole"

           lowerCAmelCase_					:	Optional[int]						=   load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""			).resize((5_12, 5_12)			)

           lowerCAmelCase_					:	Optional[Any]						=   pipe(
               UpperCAmelCase					,			image=UpperCAmelCase					,			mask_image=UpperCAmelCase					,			control_image=UpperCAmelCase					,			generator=UpperCAmelCase					,			output_type="""np"""					,			num_inference_steps=3					,			)

           lowerCAmelCase_					:	Optional[Any]						=   output.images[0]

           assert image.shape == (5_12, 5_12, 3)

           lowerCAmelCase_					:	Union[str, Any]						=   load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"""			)

           assert np.abs(expected_image - image			).max() < 9e-2



       def  A							(     self	:   Tuple			):
           lowerCAmelCase_					:	int						=   ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""			)

           lowerCAmelCase_					:	Tuple						=   StableDiffusionControlNetInpaintPipeline.from_pretrained(
               """runwayml/stable-diffusion-v1-5"""					,			safety_checker=UpperCAmelCase					,			controlnet=UpperCAmelCase			)
           lowerCAmelCase_					:	Tuple						=   DDIMScheduler.from_config(pipe.scheduler.config			)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=UpperCAmelCase			)

           lowerCAmelCase_					:	List[str]						=   torch.Generator(device="""cpu"""			).manual_seed(33			)

           lowerCAmelCase_					:	Any						=   load_image(
               """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""			)
           lowerCAmelCase_					:	List[str]						=   init_image.resize((5_12, 5_12)			)

           lowerCAmelCase_					:	Optional[Any]						=   load_image(
               """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""			)
           lowerCAmelCase_					:	Any						=   mask_image.resize((5_12, 5_12)			)

           lowerCAmelCase_					:	int						=   "a handsome man with ray-ban sunglasses"

           def make_inpaint_condition(UpperCAmelCase	:   str					,			UpperCAmelCase	:   str			):
               lowerCAmelCase_					:	Optional[int]						=   np.array(image.convert("""RGB"""			)			).astype(np.floataa			) / 2_55.0
               lowerCAmelCase_					:	int						=   np.array(image_mask.convert("""L"""			)			).astype(np.floataa			) / 2_55.0

               assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
               lowerCAmelCase_					:	Union[str, Any]						=   -1.0  # set as masked pixel
               lowerCAmelCase_					:	List[str]						=   np.expand_dims(UpperCAmelCase					,			0			).transpose(0					,			3					,			1					,			2			)
               lowerCAmelCase_					:	Optional[Any]						=   torch.from_numpy(UpperCAmelCase			)
               return image

           lowerCAmelCase_					:	Any						=   make_inpaint_condition(UpperCAmelCase					,			UpperCAmelCase			)

           lowerCAmelCase_					:	List[Any]						=   pipe(
               UpperCAmelCase					,			image=UpperCAmelCase					,			mask_image=UpperCAmelCase					,			control_image=UpperCAmelCase					,			guidance_scale=9.0					,			eta=1.0					,			generator=UpperCAmelCase					,			num_inference_steps=20					,			output_type="""np"""					,			)
           lowerCAmelCase_					:	Optional[Any]						=   output.images[0]

           assert image.shape == (5_12, 5_12, 3)

           lowerCAmelCase_					:	Tuple						=   load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""			)

           assert np.abs(expected_image - image			).max() < 9e-2









import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 		__a							(	unittest.TestCase      ):



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""			)



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	List[str]						=   self.tokenizer_ab
           lowerCAmelCase_					:	Any						=   """ I am a small frog."""
           lowerCAmelCase_					:	Union[str, Any]						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Dict						=   self.rust_tokenizer_ab
           lowerCAmelCase_					:	str						=   """ I am a small frog."""
           lowerCAmelCase_					:	Tuple						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   str			):
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]



       def  A							(     self	:   List[str]			):
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]


