



"""simple docstring"""






import gc
import random
import unittest

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

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

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


enable_full_determinism()






class 	__snake_case	(     _lowercase  ,					_lowercase  ,					_lowercase  ,					unittest.TestCase):
  snake_case__    :       List[Any]        =						StableDiffusionInstructPixaPixPipeline
  snake_case__    :       Any        =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
  snake_case__    :       Dict        =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  snake_case__    :       Any        =						IMAGE_TO_IMAGE_IMAGE_PARAMS
  snake_case__    :       Optional[Any]        =						IMAGE_TO_IMAGE_IMAGE_PARAMS




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         torch.manual_seed(0		)
         _lowerCamelCase						:    Optional[Any]         =			UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)						, layers_per_block=2						, sample_size=3_2						, in_channels=8						, out_channels=4						, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						, cross_attention_dim=3_2						, )
         _lowerCamelCase						:    str         =			PNDMScheduler(skip_prk_steps=__lowerCAmelCase		)
         torch.manual_seed(0		)
         _lowerCamelCase						:    int         =			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						, )
         torch.manual_seed(0		)
         _lowerCamelCase						:    Optional[Any]         =			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						, )
         _lowerCamelCase						:    Tuple         =			CLIPTextModel(__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Any=0		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			floats_tensor((1, 3, 3_2, 3_2)						, rng=random.Random(__lowerCAmelCase		)		).to(__lowerCAmelCase		)
         _lowerCamelCase						:    List[Any]         =			image.cpu().permute(0						, 2						, 3						, 1		)[0]
         _lowerCamelCase						:    List[Any]         =			Image.fromarray(np.uinta(__lowerCAmelCase		)		).convert('''RGB'''		)
         if str(__lowerCAmelCase		).startswith('''mps'''		):
                _lowerCamelCase						:    str         =			torch.manual_seed(__lowerCAmelCase		)
         else:
                _lowerCamelCase						:    List[Any]         =			torch.Generator(device=__lowerCAmelCase		).manual_seed(__lowerCAmelCase		)
         _lowerCamelCase						:    Any         =			{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
             '''image_guidance_scale''': 1,
             '''output_type''': '''numpy''',
         }
         return inputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowerCamelCase						:    Optional[int]         =			self.get_dummy_components()
         _lowerCamelCase						:    Union[str, Any]         =			StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase		)
         _lowerCamelCase						:    Dict         =			sd_pipe.to(__lowerCAmelCase		)
         sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:    int         =			self.get_dummy_inputs(__lowerCAmelCase		)
         _lowerCamelCase						:    Dict         =			sd_pipe(**__lowerCAmelCase		).images
         _lowerCamelCase						:    Dict         =			image[0, -3:, -3:, -1]
         assert image.shape == (1, 3_2, 3_2, 3)
         _lowerCamelCase						:    Optional[Any]         =			np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






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

         _lowerCamelCase						:    int         =			self.get_dummy_inputs(__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			'''french fries'''
         _lowerCamelCase						:    Optional[Any]         =			sd_pipe(**__lowerCAmelCase						, negative_prompt=__lowerCAmelCase		)
         _lowerCamelCase						:    List[Any]         =			output.images
         _lowerCamelCase						:    List[str]         =			image[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         _lowerCamelCase						:    Optional[Any]         =			np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowerCamelCase						:    Any         =			self.get_dummy_components()
         _lowerCamelCase						:    List[str]         =			StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase		)
         _lowerCamelCase						:    List[Any]         =			sd_pipe.to(__lowerCAmelCase		)
         sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:    Union[str, Any]         =			self.get_dummy_inputs(__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			[inputs['''prompt''']] * 2

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

         _lowerCamelCase						:    Dict         =			sd_pipe(**__lowerCAmelCase		).images
         _lowerCamelCase						:    Optional[Any]         =			image[-1, -3:, -3:, -1]

         assert image.shape == (2, 3_2, 3_2, 3)
         _lowerCamelCase						:    List[str]         =			np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowerCamelCase						:    int         =			self.get_dummy_components()
         _lowerCamelCase						:    List[Any]         =			EulerAncestralDiscreteScheduler(
             beta_start=0.0_00_85						, beta_end=0.0_12						, beta_schedule='''scaled_linear'''		)
         _lowerCamelCase						:    Optional[int]         =			StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			sd_pipe.to(__lowerCAmelCase		)
         sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[int]         =			self.get_dummy_inputs(__lowerCAmelCase		)
         _lowerCamelCase						:    Tuple         =			sd_pipe(**__lowerCAmelCase		).images
         _lowerCamelCase						:    List[str]         =			image[0, -3:, -3:, -1]

         _lowerCamelCase						:    Any         =			[round(__lowerCAmelCase						, 4		) for x in image_slice.flatten().tolist()]
         print(''','''.join([str(__lowerCAmelCase		) for x in slice]		)		)

         assert image.shape == (1, 3_2, 3_2, 3)
         _lowerCamelCase						:    Optional[Any]         =			np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         super().test_inference_batch_single_identical(expected_max_diff=3E-3		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			self.get_dummy_components()
         _lowerCamelCase						:    Optional[int]         =			StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase		)
         _lowerCamelCase						:    Union[str, Any]         =			VaeImageProcessor(do_resize=__lowerCAmelCase						, do_normalize=__lowerCAmelCase		)
         _lowerCamelCase						:    int         =			pipe.to(__lowerCAmelCase		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

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

         _lowerCamelCase						:    Dict         =			components['''vae''']
         _lowerCamelCase						:    Dict         =			self.get_dummy_inputs_by_type(__lowerCAmelCase						, input_image_type='''pt'''		)

         for image_param in self.image_latents_params:
                if image_param in inputs.keys():
                       _lowerCamelCase						:    Optional[int]         =			vae.encode(inputs[image_param]		).latent_dist.mode()

         _lowerCamelCase						:    Any         =			pipe(**__lowerCAmelCase		)[0]

         _lowerCamelCase						:    List[Any]         =			np.abs(out - out_latents_inputs		).max()
         self.assertLess(__lowerCAmelCase						, 1E-4						, '''passing latents as image input generate different result from passing image'''		)






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						List[str]=0		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			torch.manual_seed(__lowerCAmelCase		)
         _lowerCamelCase						:    List[Any]         =			load_image(
             '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''		)
         _lowerCamelCase						:    str         =			{
             '''prompt''': '''turn him into a cyborg''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 7.5,
             '''image_guidance_scale''': 1.0,
             '''output_type''': '''numpy''',
         }
         return inputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''						, safety_checker=__lowerCAmelCase		)
         pipe.to(__lowerCAmelCase		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)
         pipe.enable_attention_slicing()

         _lowerCamelCase						:    Optional[Any]         =			self.get_inputs()
         _lowerCamelCase						:    int         =			pipe(**__lowerCAmelCase		).images
         _lowerCamelCase						:    Optional[int]         =			image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCamelCase						:    Union[str, Any]         =			np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''						, safety_checker=__lowerCAmelCase		)
         _lowerCamelCase						:    Dict         =			LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
         pipe.to(__lowerCAmelCase		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)
         pipe.enable_attention_slicing()

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCamelCase						:    List[Any]         =			np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''						, safety_checker=__lowerCAmelCase		)
         _lowerCamelCase						:    Any         =			DDIMScheduler.from_config(pipe.scheduler.config		)
         pipe.to(__lowerCAmelCase		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)
         pipe.enable_attention_slicing()

         _lowerCamelCase						:    Optional[Any]         =			self.get_inputs()
         _lowerCamelCase						:    Optional[int]         =			pipe(**__lowerCAmelCase		).images
         _lowerCamelCase						:    Optional[int]         =			image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCamelCase						:    List[Any]         =			np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			0

         def callback_fn(__lowerCAmelCase					:						int						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						torch.FloatTensor		) -> None:
                _lowerCamelCase						:    Optional[Any]         =			True
                nonlocal number_of_steps
                number_of_steps += 1
                if step == 1:
                       _lowerCamelCase						:    List[str]         =			latents.detach().cpu().numpy()
                       assert latents.shape == (1, 4, 6_4, 6_4)
                       _lowerCamelCase						:    int         =			latents[0, -3:, -3:, -1]
                       _lowerCamelCase						:    Any         =			np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]		)

                       assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5E-2
                elif step == 2:
                       _lowerCamelCase						:    int         =			latents.detach().cpu().numpy()
                       assert latents.shape == (1, 4, 6_4, 6_4)
                       _lowerCamelCase						:    Dict         =			latents[0, -3:, -3:, -1]
                       _lowerCamelCase						:    Optional[Any]         =			np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]		)

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

         _lowerCamelCase						:    Dict         =			False

         _lowerCamelCase						:    List[str]         =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''						, safety_checker=__lowerCAmelCase						, torch_dtype=torch.floataa		)
         _lowerCamelCase						:    Dict         =			pipe.to(__lowerCAmelCase		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)
         pipe.enable_attention_slicing()

         _lowerCamelCase						:    int         =			self.get_inputs()
         pipe(**__lowerCAmelCase						, callback=__lowerCAmelCase						, callback_steps=1		)
         assert callback_fn.has_been_called
         assert number_of_steps == 3




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         _lowerCamelCase						:    int         =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''						, safety_checker=__lowerCAmelCase						, torch_dtype=torch.floataa		)
         _lowerCamelCase						:    Union[str, Any]         =			pipe.to(__lowerCAmelCase		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)
         pipe.enable_attention_slicing(1		)
         pipe.enable_sequential_cpu_offload()

         _lowerCamelCase						:    Tuple         =			self.get_inputs()
         _lowerCamelCase						:    Union[str, Any]         =			pipe(**__lowerCAmelCase		)

         _lowerCamelCase						:    Union[str, Any]         =			torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 1_0**9




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         _lowerCamelCase						:    List[str]         =			inputs['''image'''].resize((5_0_4, 5_0_4)		)

         _lowerCamelCase						:    List[Any]         =			'''timbrooks/instruct-pix2pix'''
         _lowerCamelCase						:    List[str]         =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
             __lowerCAmelCase						, safety_checker=__lowerCAmelCase						, )
         pipe.to(__lowerCAmelCase		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)
         pipe.enable_attention_slicing()

         _lowerCamelCase						:    int         =			pipe(**__lowerCAmelCase		)
         _lowerCamelCase						:    Union[str, Any]         =			output.images[0]

         _lowerCamelCase						:    Optional[int]         =			image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert image.shape == (5_0_4, 5_0_4, 3)
         _lowerCamelCase						:    str         =			np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]		)

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







"""simple docstring"""






import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class 	__snake_case	(     _lowercase  ,					unittest.TestCase):
  snake_case__    :       Optional[int]        =						BlenderbotSmallTokenizer
  snake_case__    :       List[str]        =						False




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         super().setUp()

         _lowerCamelCase						:    str         =			['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
         _lowerCamelCase						:    Any         =			dict(zip(__lowerCAmelCase						, range(len(__lowerCAmelCase		)		)		)		)

         _lowerCamelCase						:    Any         =			['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
         _lowerCamelCase						:    List[str]         =			{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

         _lowerCamelCase						:    Dict         =			os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['''vocab_file''']		)
         _lowerCamelCase						:    List[str]         =			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(__lowerCAmelCase		) + '''\n'''		)
         with open(self.merges_file						, '''w'''						, encoding='''utf-8'''		) as fp:
                fp.write('''\n'''.join(__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, **__lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         kwargs.update(self.special_tokens_map		)
         return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname						, **__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			'''adapt act apte'''
         _lowerCamelCase						:    Tuple         =			'''adapt act apte'''
         return input_text, output_text




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			BlenderbotSmallTokenizer(self.vocab_file						, self.merges_file						, **self.special_tokens_map		)
         _lowerCamelCase						:    int         =			'''adapt act apte'''
         _lowerCamelCase						:    Optional[Any]         =			['''adapt''', '''act''', '''ap@@''', '''te''']
         _lowerCamelCase						:    Optional[Any]         =			tokenizer.tokenize(__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						, __lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

         _lowerCamelCase						:    Dict         =			[0, 1, 2, 3, 4, 5]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''		)
         assert tok('''sam'''		).input_ids == [1_3_8_4]
         _lowerCamelCase						:    List[str]         =			'''I am a small frog.'''
         _lowerCamelCase						:    str         =			tok([src_text]						, padding=__lowerCAmelCase						, truncation=__lowerCAmelCase		)['''input_ids''']
         _lowerCamelCase						:    Any         =			tok.batch_decode(__lowerCAmelCase						, skip_special_tokens=__lowerCAmelCase						, clean_up_tokenization_spaces=__lowerCAmelCase		)[0]
         assert src_text != decoded  # I wish it did!
         assert decoded == "i am a small frog ."




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''		)
         _lowerCamelCase						:    Optional[Any]         =			'''I am a small frog .'''
         _lowerCamelCase						:    str         =			'''.'''
         _lowerCamelCase						:    str         =			tok(__lowerCAmelCase		)['''input_ids''']
         _lowerCamelCase						:    Dict         =			tok(__lowerCAmelCase		)['''input_ids''']

         assert encoded[-1] == encoded_dot[0]


