



'''simple docstring'''




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


a   :			List[Any]					  =    logging.get_logger(__name__)  # pylint: disable=invalid-name

class 			UpperCamelCase_ (							__magic_name__			):





	def __init__(  self		,		A		,		A		,		A		,		A		,		A		,		A		,		A		,		)			->			Optional[Any]:
								super().__init__()
								self.register_modules(
								    vae=A		,		text_encoder=A		,		tokenizer=A		,		unet=A		,		scheduler=A		,		safety_checker=A		,		feature_extractor=A		,		)





	def  _lowercase(  self		,		A = "auto"    )			->			List[Any]:
								if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															UpperCAmelCase     :		Optional[Any]							     =		self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(A    )





	def  _lowercase(  self    )			->			Dict:
								self.enable_attention_slicing(A    )






	@torch.no_grad()
	def __call__(  self		,		A		,		A = 512		,		A = 512		,		A = 50		,		A = 7.5		,		A = None		,		A = 1		,		A = 0.0		,		A = None		,		A = None		,		A = "pil"		,		A = True		,		A = None		,		A = 1		,		A = None		,		**A		,		)			->			List[Any]:

								if isinstance(A		,		A    ):
															UpperCAmelCase     :		List[str]							     =		1
								elif isinstance(A		,		A    ):
															UpperCAmelCase     :		Dict							     =		len(A    )
								else:
															raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(A    )}'''    )

								if height % 8 != 0 or width % 8 != 0:
															raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''    )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(A		,		A    ) or callback_steps <= 0)
								):
															raise ValueError(
															    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
															    f''' {type(A    )}.'''    )

								# get prompt text embeddings
								UpperCAmelCase     :		List[str]							     =		self.tokenizer(
								    A		,		padding="""max_length"""		,		max_length=self.tokenizer.model_max_length		,		return_tensors="""pt"""		,		)
								UpperCAmelCase     :		List[Any]							     =		text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
															UpperCAmelCase     :		int							     =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
															logger.warning(
															    """The following part of your input was truncated because CLIP can only handle sequences up to"""
															    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''    )
															UpperCAmelCase     :		Tuple							     =		text_input_ids[:, : self.tokenizer.model_max_length]

								if text_embeddings is None:
															UpperCAmelCase     :		Union[str, Any]							     =		self.text_encoder(text_input_ids.to(self.device    )    )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		text_embeddings.shape
								UpperCAmelCase     :		List[str]							     =		text_embeddings.repeat(1		,		A		,		1    )
								UpperCAmelCase     :		List[Any]							     =		text_embeddings.view(bs_embed * num_images_per_prompt		,		A		,		-1    )

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								UpperCAmelCase     :		Optional[int]							     =		guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
															UpperCAmelCase     :		List[str]
															if negative_prompt is None:
																						UpperCAmelCase     :		Any							     =		[""""""]
															elif type(A    ) is not type(A    ):
																						raise TypeError(
																						    f'''`negative_prompt` should be the same type to `prompt`, but got {type(A    )} !='''
																						    f''' {type(A    )}.'''    )
															elif isinstance(A		,		A    ):
																						UpperCAmelCase     :		Optional[int]							     =		[negative_prompt]
															elif batch_size != len(A    ):
																						raise ValueError(
																						    f'''`negative_prompt`: {negative_prompt} has batch size {len(A    )}, but `prompt`:'''
																						    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																						    """ the batch size of `prompt`."""    )
															else:
																						UpperCAmelCase     :		Any							     =		negative_prompt

															UpperCAmelCase     :		Dict							     =		text_input_ids.shape[-1]
															UpperCAmelCase     :		List[Any]							     =		self.tokenizer(
															    A		,		padding="""max_length"""		,		max_length=A		,		truncation=A		,		return_tensors="""pt"""		,		)
															UpperCAmelCase     :		Tuple							     =		self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															UpperCAmelCase     :		int							     =		uncond_embeddings.shape[1]
															UpperCAmelCase     :		List[Any]							     =		uncond_embeddings.repeat(A		,		A		,		1    )
															UpperCAmelCase     :		List[str]							     =		uncond_embeddings.view(batch_size * num_images_per_prompt		,		A		,		-1    )

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															UpperCAmelCase     :		List[str]							     =		torch.cat([uncond_embeddings, text_embeddings]    )

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								UpperCAmelCase     :		Tuple							     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								UpperCAmelCase     :		Optional[int]							     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
								UpperCAmelCase     :		str							     =		text_embeddings.dtype
								if latents is None:
															if self.device.type == "mps":
																						# randn does not exist on mps
																						UpperCAmelCase     :		Dict							     =		torch.randn(
																						    A		,		generator=A		,		device="""cpu"""		,		dtype=A    ).to(self.device    )
																						UpperCAmelCase     :		int							     =		torch.randn(A		,		generator=A		,		device="""cpu"""		,		dtype=A    ).to(
																						    self.device    )
															else:
																						UpperCAmelCase     :		int							     =		torch.randn(
																						    A		,		generator=A		,		device=self.device		,		dtype=A    )
																						UpperCAmelCase     :		int							     =		torch.randn(A		,		generator=A		,		device=self.device		,		dtype=A    )
								else:
															if latents_reference.shape != latents_shape:
																						raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''    )
															UpperCAmelCase     :		Optional[Any]							     =		latents_reference.to(self.device    )
															UpperCAmelCase     :		Tuple							     =		latents.to(self.device    )

								# This is the key part of the pipeline where we
								# try to ensure that the generated images w/ the same seed
								# but different sizes actually result in similar images
								UpperCAmelCase     :		int							     =		(latents_shape[3] - latents_shape_reference[3]) // 2
								UpperCAmelCase     :		List[str]							     =		(latents_shape[2] - latents_shape_reference[2]) // 2
								UpperCAmelCase     :		Union[str, Any]							     =		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								UpperCAmelCase     :		Union[str, Any]							     =		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								UpperCAmelCase     :		Optional[int]							     =		0 if dx < 0 else dx
								UpperCAmelCase     :		List[str]							     =		0 if dy < 0 else dy
								UpperCAmelCase     :		Union[str, Any]							     =		max(-dx		,		0    )
								UpperCAmelCase     :		List[Any]							     =		max(-dy		,		0    )
								# import pdb
								# pdb.set_trace()
								UpperCAmelCase     :		str							     =		latents_reference[:, :, dy : dy + h, dx : dx + w]

								# set timesteps
								self.scheduler.set_timesteps(A    )

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								UpperCAmelCase     :		Union[str, Any]							     =		self.scheduler.timesteps.to(self.device    )

								# scale the initial noise by the standard deviation required by the scheduler
								UpperCAmelCase     :		Optional[int]							     =		latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								UpperCAmelCase     :		int							     =		"""eta""" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
								UpperCAmelCase     :		Optional[Any]							     =		{}
								if accepts_eta:
															UpperCAmelCase     :		List[str]							     =		eta

								for i, t in enumerate(self.progress_bar(A    )    ):
															# expand the latents if we are doing classifier free guidance
															UpperCAmelCase     :		Optional[Any]							     =		torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
															UpperCAmelCase     :		str							     =		self.scheduler.scale_model_input(A		,		A    )

															# predict the noise residual
															UpperCAmelCase     :		Any							     =		self.unet(A		,		A		,		encoder_hidden_states=A    ).sample

															# perform guidance
															if do_classifier_free_guidance:
																						UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		noise_pred.chunk(2    )
																						UpperCAmelCase     :		List[Any]							     =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# compute the previous noisy sample x_t -> x_t-1
															UpperCAmelCase     :		Dict							     =		self.scheduler.step(A		,		A		,		A		,		**A    ).prev_sample

															# call the callback, if provided
															if callback is not None and i % callback_steps == 0:
																						callback(A		,		A		,		A    )

								UpperCAmelCase     :		Union[str, Any]							     =		1 / 0.1_8_2_1_5 * latents
								UpperCAmelCase     :		Tuple							     =		self.vae.decode(A    ).sample

								UpperCAmelCase     :		Union[str, Any]							     =		(image / 2 + 0.5).clamp(0		,		1    )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								UpperCAmelCase     :		Union[str, Any]							     =		image.cpu().permute(0		,		2		,		3		,		1    ).float().numpy()

								if self.safety_checker is not None:
															UpperCAmelCase     :		int							     =		self.feature_extractor(self.numpy_to_pil(A    )		,		return_tensors="""pt"""    ).to(
															    self.device    )
															UpperCAmelCase     ,     UpperCAmelCase     :		int							     =		self.safety_checker(
															    images=A		,		clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
								else:
															UpperCAmelCase     :		Any							     =		None

								if output_type == "pil":
															UpperCAmelCase     :		int							     =		self.numpy_to_pil(A    )

								if not return_dict:
															return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=A		,		nsfw_content_detected=A    )






'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
a   :			Tuple					  =    {"""LayoutLMv2Config""", """LayoutLMv3Config"""}

@is_pipeline_test
class 			UpperCamelCase_ (							unittest.TestCase			):
	lowercase          = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	lowercase          = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

	if model_mapping is not None:
		lowercase          = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
	if tf_model_mapping is not None:
		lowercase          = {
		    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
		}





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Dict:
								UpperCAmelCase     :		Union[str, Any]							     =		ZeroShotClassificationPipeline(
								    model=A		,		tokenizer=A		,		candidate_labels=["""polics""", """health"""]    )
								return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]





	def  _lowercase(  self		,		A		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		Dict							     =		classifier("""Who are you voting for in 2020?"""		,		candidate_labels="""politics"""    )
								self.assertEqual(A		,		{"""sequence""": ANY(A    ), """labels""": [ANY(A    )], """scores""": [ANY(A    )]}    )

								# No kwarg
								UpperCAmelCase     :		Optional[int]							     =		classifier("""Who are you voting for in 2020?"""		,		["""politics"""]    )
								self.assertEqual(A		,		{"""sequence""": ANY(A    ), """labels""": [ANY(A    )], """scores""": [ANY(A    )]}    )

								UpperCAmelCase     :		str							     =		classifier("""Who are you voting for in 2020?"""		,		candidate_labels=["""politics"""]    )
								self.assertEqual(A		,		{"""sequence""": ANY(A    ), """labels""": [ANY(A    )], """scores""": [ANY(A    )]}    )

								UpperCAmelCase     :		List[Any]							     =		classifier("""Who are you voting for in 2020?"""		,		candidate_labels="""politics, public health"""    )
								self.assertEqual(
								    A		,		{"""sequence""": ANY(A    ), """labels""": [ANY(A    ), ANY(A    )], """scores""": [ANY(A    ), ANY(A    )]}    )
								self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]    )    )		,		1.0    )

								UpperCAmelCase     :		Optional[int]							     =		classifier("""Who are you voting for in 2020?"""		,		candidate_labels=["""politics""", """public health"""]    )
								self.assertEqual(
								    A		,		{"""sequence""": ANY(A    ), """labels""": [ANY(A    ), ANY(A    )], """scores""": [ANY(A    ), ANY(A    )]}    )
								self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]    )    )		,		1.0    )

								UpperCAmelCase     :		Dict							     =		classifier(
								    """Who are you voting for in 2020?"""		,		candidate_labels="""politics"""		,		hypothesis_template="""This text is about {}"""    )
								self.assertEqual(A		,		{"""sequence""": ANY(A    ), """labels""": [ANY(A    )], """scores""": [ANY(A    )]}    )

								# https://github.com/huggingface/transformers/issues/13846
								UpperCAmelCase     :		str							     =		classifier(["""I am happy"""]		,		["""positive""", """negative"""]    )
								self.assertEqual(
								    A		,		[
								        {"""sequence""": ANY(A    ), """labels""": [ANY(A    ), ANY(A    )], """scores""": [ANY(A    ), ANY(A    )]}
								        for i in range(1    )
								    ]		,		)
								UpperCAmelCase     :		List[str]							     =		classifier(["""I am happy""", """I am sad"""]		,		["""positive""", """negative"""]    )
								self.assertEqual(
								    A		,		[
								        {"""sequence""": ANY(A    ), """labels""": [ANY(A    ), ANY(A    )], """scores""": [ANY(A    ), ANY(A    )]}
								        for i in range(2    )
								    ]		,		)

								with self.assertRaises(A    ):
															classifier(""""""		,		candidate_labels="""politics"""    )

								with self.assertRaises(A    ):
															classifier(A		,		candidate_labels="""politics"""    )

								with self.assertRaises(A    ):
															classifier("""Who are you voting for in 2020?"""		,		candidate_labels=""""""    )

								with self.assertRaises(A    ):
															classifier("""Who are you voting for in 2020?"""		,		candidate_labels=A    )

								with self.assertRaises(A    ):
															classifier(
															    """Who are you voting for in 2020?"""		,		candidate_labels="""politics"""		,		hypothesis_template="""Not formatting template"""		,		)

								with self.assertRaises(A    ):
															classifier(
															    """Who are you voting for in 2020?"""		,		candidate_labels="""politics"""		,		hypothesis_template=A		,		)

								self.run_entailment_id(A    )





	def  _lowercase(  self		,		A    )			->			Any:
								UpperCAmelCase     :		Tuple							     =		zero_shot_classifier.model.config
								UpperCAmelCase     :		Union[str, Any]							     =		config.labelaid
								UpperCAmelCase     :		Tuple							     =		zero_shot_classifier.entailment_id

								UpperCAmelCase     :		Any							     =		{"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
								self.assertEqual(zero_shot_classifier.entailment_id		,		-1    )

								UpperCAmelCase     :		Optional[Any]							     =		{"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
								self.assertEqual(zero_shot_classifier.entailment_id		,		0    )

								UpperCAmelCase     :		Any							     =		{"""ENTAIL""": 0, """NON-ENTAIL""": 1}
								self.assertEqual(zero_shot_classifier.entailment_id		,		0    )

								UpperCAmelCase     :		List[str]							     =		{"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
								self.assertEqual(zero_shot_classifier.entailment_id		,		2    )

								UpperCAmelCase     :		Tuple							     =		original_labelaid
								self.assertEqual(A		,		zero_shot_classifier.entailment_id    )





	@require_torch
	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     :		int							     =		pipeline(
								    """zero-shot-classification"""		,		model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""		,		framework="""pt"""		,		)
								# There was a regression in 4.10 for this
								# Adding a test so we don't make the mistake again.
								# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
								zero_shot_classifier(
								    """Who are you voting for in 2020?""" * 100		,		candidate_labels=["""politics""", """public health""", """science"""]    )





	@require_torch
	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		Optional[int]							     =		pipeline(
								    """zero-shot-classification"""		,		model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""		,		framework="""pt"""		,		)
								UpperCAmelCase     :		Union[str, Any]							     =		zero_shot_classifier(
								    """Who are you voting for in 2020?"""		,		candidate_labels=["""politics""", """public health""", """science"""]    )

								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """sequence""": """Who are you voting for in 2020?""",
								        """labels""": ["""science""", """public health""", """politics"""],
								        """scores""": [0.3_3_3, 0.3_3_3, 0.3_3_3],
								    }		,		)





	@require_tf
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		pipeline(
								    """zero-shot-classification"""		,		model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""		,		framework="""tf"""		,		)
								UpperCAmelCase     :		List[Any]							     =		zero_shot_classifier(
								    """Who are you voting for in 2020?"""		,		candidate_labels=["""politics""", """public health""", """science"""]    )

								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """sequence""": """Who are you voting for in 2020?""",
								        """labels""": ["""science""", """public health""", """politics"""],
								        """scores""": [0.3_3_3, 0.3_3_3, 0.3_3_3],
								    }		,		)





	@slow
	@require_torch
	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		Optional[int]							     =		pipeline("""zero-shot-classification"""		,		model="""roberta-large-mnli"""		,		framework="""pt"""    )
								UpperCAmelCase     :		Optional[int]							     =		zero_shot_classifier(
								    """Who are you voting for in 2020?"""		,		candidate_labels=["""politics""", """public health""", """science"""]    )

								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """sequence""": """Who are you voting for in 2020?""",
								        """labels""": ["""politics""", """public health""", """science"""],
								        """scores""": [0.9_7_6, 0.0_1_5, 0.0_0_9],
								    }		,		)
								UpperCAmelCase     :		str							     =		zero_shot_classifier(
								    """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
								    """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
								    """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
								    """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
								    """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
								    """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
								    """ English-to-German translation task, improving over the existing best results, including ensembles by"""
								    """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
								    """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
								    """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
								    """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
								    """ large and limited training data."""		,		candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]		,		multi_label=A		,		)
								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """sequence""": (
								            """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
								            """ networks in an encoder-decoder configuration. The best performing models also connect the"""
								            """ encoder and decoder through an attention mechanism. We propose a new simple network"""
								            """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
								            """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
								            """ superior in quality while being more parallelizable and requiring significantly less time to"""
								            """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
								            """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
								            """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
								            """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
								            """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
								            """ other tasks by applying it successfully to English constituency parsing both with large and"""
								            """ limited training data."""
								        ),
								        """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
								        """scores""": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
								    }		,		)






	@slow
	@require_tf
	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		int							     =		pipeline("""zero-shot-classification"""		,		model="""roberta-large-mnli"""		,		framework="""tf"""    )
								UpperCAmelCase     :		Tuple							     =		zero_shot_classifier(
								    """Who are you voting for in 2020?"""		,		candidate_labels=["""politics""", """public health""", """science"""]    )

								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """sequence""": """Who are you voting for in 2020?""",
								        """labels""": ["""politics""", """public health""", """science"""],
								        """scores""": [0.9_7_6, 0.0_1_5, 0.0_0_9],
								    }		,		)
								UpperCAmelCase     :		Any							     =		zero_shot_classifier(
								    """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
								    """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
								    """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
								    """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
								    """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
								    """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
								    """ English-to-German translation task, improving over the existing best results, including ensembles by"""
								    """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
								    """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
								    """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
								    """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
								    """ large and limited training data."""		,		candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]		,		multi_label=A		,		)
								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """sequence""": (
								            """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
								            """ networks in an encoder-decoder configuration. The best performing models also connect the"""
								            """ encoder and decoder through an attention mechanism. We propose a new simple network"""
								            """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
								            """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
								            """ superior in quality while being more parallelizable and requiring significantly less time to"""
								            """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
								            """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
								            """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
								            """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
								            """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
								            """ other tasks by applying it successfully to English constituency parsing both with large and"""
								            """ limited training data."""
								        ),
								        """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
								        """scores""": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
								    }		,		)

