





'''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


lowercase_					= logging.get_logger(__name__)  # pylint: disable=invalid-name


class 					a_     (				snake_case_       ):

    '''simple docstring'''


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


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


    def 			snake_case_(		self       )		->   List[Any]:
        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[str]:

        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			1
        elif isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			self.tokenizer(
            A ,		padding="""max_length""" ,		max_length=self.tokenizer.model_max_length ,		return_tensors="""pt""" ,		)
        _SCREAMING_SNAKE_CASE									=			text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
            _SCREAMING_SNAKE_CASE									=			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}'       )
            _SCREAMING_SNAKE_CASE									=			text_input_ids[:, : self.tokenizer.model_max_length]

        if text_embeddings is None:
            _SCREAMING_SNAKE_CASE									=			self.text_encoder(text_input_ids.to(self.device       )       )[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			text_embeddings.shape
        _SCREAMING_SNAKE_CASE									=			text_embeddings.repeat(1 ,		A ,		1       )
        _SCREAMING_SNAKE_CASE									=			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.
        _SCREAMING_SNAKE_CASE									=			guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            _SCREAMING_SNAKE_CASE									=			42
            if negative_prompt is None:
                _SCREAMING_SNAKE_CASE									=			[""""""]
            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       ):
                _SCREAMING_SNAKE_CASE									=			[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:
                _SCREAMING_SNAKE_CASE									=			negative_prompt

            _SCREAMING_SNAKE_CASE									=			text_input_ids.shape[-1]
            _SCREAMING_SNAKE_CASE									=			self.tokenizer(
                A ,		padding="""max_length""" ,		max_length=A ,		truncation=A ,		return_tensors="""pt""" ,		)
            _SCREAMING_SNAKE_CASE									=			self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            _SCREAMING_SNAKE_CASE									=			uncond_embeddings.shape[1]
            _SCREAMING_SNAKE_CASE									=			uncond_embeddings.repeat(A ,		A ,		1       )
            _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			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`.
        _SCREAMING_SNAKE_CASE									=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        _SCREAMING_SNAKE_CASE									=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
        _SCREAMING_SNAKE_CASE									=			text_embeddings.dtype
        if latents is None:
            if self.device.type == "mps":
                # randn does not exist on mps
                _SCREAMING_SNAKE_CASE									=			torch.randn(
                    A ,		generator=A ,		device="""cpu""" ,		dtype=A       ).to(self.device       )
                _SCREAMING_SNAKE_CASE									=			torch.randn(A ,		generator=A ,		device="""cpu""" ,		dtype=A       ).to(
                    self.device       )
            else:
                _SCREAMING_SNAKE_CASE									=			torch.randn(
                    A ,		generator=A ,		device=self.device ,		dtype=A       )
                _SCREAMING_SNAKE_CASE									=			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}'       )
            _SCREAMING_SNAKE_CASE									=			latents_reference.to(self.device       )
            _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			(latents_shape[3] - latents_shape_reference[3]) // 2
        _SCREAMING_SNAKE_CASE									=			(latents_shape[2] - latents_shape_reference[2]) // 2
        _SCREAMING_SNAKE_CASE									=			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
        _SCREAMING_SNAKE_CASE									=			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
        _SCREAMING_SNAKE_CASE									=			0 if dx < 0 else dx
        _SCREAMING_SNAKE_CASE									=			0 if dy < 0 else dy
        _SCREAMING_SNAKE_CASE									=			max(-dx ,		0       )
        _SCREAMING_SNAKE_CASE									=			max(-dy ,		0       )
        # import pdb
        # pdb.set_trace()
        _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			self.scheduler.timesteps.to(self.device       )

        # scale the initial noise by the standard deviation required by the scheduler
        _SCREAMING_SNAKE_CASE									=			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]
        _SCREAMING_SNAKE_CASE									=			"""eta""" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
        _SCREAMING_SNAKE_CASE									=			{}
        if accepts_eta:
            _SCREAMING_SNAKE_CASE									=			eta

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

            # predict the noise residual
            _SCREAMING_SNAKE_CASE									=			self.unet(A ,		A ,		encoder_hidden_states=A       ).sample

            # perform guidance
            if do_classifier_free_guidance:
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.chunk(2       )
                _SCREAMING_SNAKE_CASE									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            _SCREAMING_SNAKE_CASE									=			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       )

        _SCREAMING_SNAKE_CASE									=			1 / 0.1_8215 * latents
        _SCREAMING_SNAKE_CASE									=			self.vae.decode(A       ).sample

        _SCREAMING_SNAKE_CASE									=			(image / 2 + 0.5).clamp(0 ,		1       )

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

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

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

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=A ,		nsfw_content_detected=A       )










'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = PegasusConfig
    UpperCamelCase     = {}
    UpperCamelCase     = '''gelu'''


    def __init__(		self ,		A ,		A=13 ,		A=7 ,		A=True ,		A=False ,		A=99 ,		A=32 ,		A=2 ,		A=4 ,		A=37 ,		A=0.1 ,		A=0.1 ,		A=40 ,		A=2 ,		A=1 ,		A=0 ,		)		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			seq_length
        _SCREAMING_SNAKE_CASE									=			is_training
        _SCREAMING_SNAKE_CASE									=			use_labels
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			intermediate_size

        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			eos_token_id
        _SCREAMING_SNAKE_CASE									=			pad_token_id
        _SCREAMING_SNAKE_CASE									=			bos_token_id


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size, self.seq_length - 1] ,		self.vocab_size       )
        _SCREAMING_SNAKE_CASE									=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       ) ,		1       )
        _SCREAMING_SNAKE_CASE									=			tf.concat([input_ids, eos_tensor] ,		axis=1       )

        _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size       )

        _SCREAMING_SNAKE_CASE									=			self.config_cls(
            vocab_size=self.vocab_size ,		d_model=self.hidden_size ,		encoder_layers=self.num_hidden_layers ,		decoder_layers=self.num_hidden_layers ,		encoder_attention_heads=self.num_attention_heads ,		decoder_attention_heads=self.num_attention_heads ,		encoder_ffn_dim=self.intermediate_size ,		decoder_ffn_dim=self.intermediate_size ,		dropout=self.hidden_dropout_prob ,		attention_dropout=self.attention_probs_dropout_prob ,		max_position_embeddings=self.max_position_embeddings ,		eos_token_ids=[2] ,		bos_token_id=self.bos_token_id ,		pad_token_id=self.pad_token_id ,		decoder_start_token_id=self.pad_token_id ,		**self.config_updates ,		)
        _SCREAMING_SNAKE_CASE									=			prepare_pegasus_inputs_dict(A ,		A ,		A       )
        return config, inputs_dict





    def 			snake_case_(		self ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			TFPegasusModel(config=A       ).get_decoder()
        _SCREAMING_SNAKE_CASE									=			inputs_dict["""input_ids"""]

        _SCREAMING_SNAKE_CASE									=			input_ids[:1, :]
        _SCREAMING_SNAKE_CASE									=			inputs_dict["""attention_mask"""][:1, :]
        _SCREAMING_SNAKE_CASE									=			inputs_dict["""head_mask"""]
        _SCREAMING_SNAKE_CASE									=			1

        # first forward pass
        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		head_mask=A ,		use_cache=A       )

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        _SCREAMING_SNAKE_CASE									=			ids_tensor((self.batch_size, 3) ,		config.vocab_size       )
        _SCREAMING_SNAKE_CASE									=			tf.cast(ids_tensor((self.batch_size, 3) ,		2       ) ,		tf.inta       )

        # append to next input_ids and
        _SCREAMING_SNAKE_CASE									=			tf.concat([input_ids, next_tokens] ,		axis=-1       )
        _SCREAMING_SNAKE_CASE									=			tf.concat([attention_mask, next_attn_mask] ,		axis=-1       )

        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A       )[0]
        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		past_key_values=A       )[0]

        self.parent.assertEqual(next_tokens.shape[1] ,		output_from_past.shape[1]       )

        # select random slice
        _SCREAMING_SNAKE_CASE									=			int(ids_tensor((1,) ,		output_from_past.shape[-1]       )       )
        _SCREAMING_SNAKE_CASE									=			output_from_no_past[:, -3:, random_slice_idx]
        _SCREAMING_SNAKE_CASE									=			output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(A ,		A ,		rtol=1e-3       )





def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    str					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    int=None					,       __lowerCamelCase :    Dict=None					,       __lowerCamelCase :    Union[str, Any]=None					,       __lowerCamelCase :    str=None					,       __lowerCamelCase :    List[Any]=None					,       )						->int:
    if attention_mask is None:
        _SCREAMING_SNAKE_CASE									=			tf.cast(tf.math.not_equal(__lowerCamelCase					,       config.pad_token_id    )					,       tf.inta    )
    if decoder_attention_mask is None:
        _SCREAMING_SNAKE_CASE									=			tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape					,       dtype=tf.inta    ),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,       config.pad_token_id    )					,       tf.inta    ),
            ]					,       axis=-1					,       )
    if head_mask is None:
        _SCREAMING_SNAKE_CASE									=			tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
    if decoder_head_mask is None:
        _SCREAMING_SNAKE_CASE									=			tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
    if cross_attn_head_mask is None:
        _SCREAMING_SNAKE_CASE									=			tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }


@require_tf
class 					a_     (				snake_case_      ,    snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
    UpperCamelCase     = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    UpperCamelCase     = (
        {
            '''conversational''': TFPegasusForConditionalGeneration,
            '''feature-extraction''': TFPegasusModel,
            '''summarization''': TFPegasusForConditionalGeneration,
            '''text2text-generation''': TFPegasusForConditionalGeneration,
            '''translation''': TFPegasusForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    UpperCamelCase     = True
    UpperCamelCase     = False
    UpperCamelCase     = False


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			TFPegasusModelTester(self       )
        _SCREAMING_SNAKE_CASE									=			ConfigTester(self ,		config_class=A       )


    def 			snake_case_(		self       )		->   List[str]:
        self.config_tester.run_common_tests()





    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*A       )




@require_sentencepiece
@require_tokenizers
@require_tf
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = [
        ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
        ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
    ]
    UpperCamelCase     = [
        '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
        ''' reduce the risk of wildfires.''',
        '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
    ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
    UpperCamelCase     = '''google/pegasus-xsum'''


    @cached_property
    def 			snake_case_(		self       )		->   List[str]:
        return AutoTokenizer.from_pretrained(self.model_name       )


    @cached_property
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
        return model


    def 			snake_case_(		self ,		**A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.translate_src_text(**A       )
        assert self.expected_text == generated_words


    def 			snake_case_(		self ,		**A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.tokenizer(self.src_text ,		**A ,		padding=A ,		return_tensors="""tf"""       )
        _SCREAMING_SNAKE_CASE									=			self.model.generate(
            model_inputs.input_ids ,		attention_mask=model_inputs.attention_mask ,		num_beams=2 ,		use_cache=A ,		)
        _SCREAMING_SNAKE_CASE									=			self.tokenizer.batch_decode(generated_ids.numpy() ,		skip_special_tokens=A       )
        return generated_words





    @slow
    def 			snake_case_(		self       )		->   Any:
        self._assert_generated_batch_equal_expected()



