






"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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


class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				)		->							Tuple:
										super().__init__()

										if safety_checker is None:
														logger.warning(
														    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
														    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
														    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
														    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
														    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
														    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'							)

										self.register_modules(
										    speech_model=lowerCAmelCase_   ,				speech_processor=lowerCAmelCase_   ,				vae=lowerCAmelCase_   ,				text_encoder=lowerCAmelCase_   ,				tokenizer=lowerCAmelCase_   ,				unet=lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				feature_extractor=lowerCAmelCase_   ,				)







						def 			A      (    self   ,				lowerCAmelCase_ = "auto"							)		->							Optional[Any]:
										if slice_size == "auto":
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(lowerCAmelCase_							)







						def 			A      (    self							)		->							int:
										self.enable_attention_slicing(lowerCAmelCase_							)







						@torch.no_grad()
						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=1_6_0_0_0   ,				lowerCAmelCase_ = 5_1_2   ,				lowerCAmelCase_ = 5_1_2   ,				lowerCAmelCase_ = 5_0   ,				lowerCAmelCase_ = 7.5   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = 0.0   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = "pil"   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 1   ,				**lowerCAmelCase_   ,				)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.speech_processor.feature_extractor(
										    lowerCAmelCase_   ,				return_tensors='pt'   ,				sampling_rate=lowerCAmelCase_							).input_features.to(self.device							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.speech_model.generate(lowerCAmelCase_   ,				max_length=4_8_0_0_0_0							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.speech_processor.tokenizer.batch_decode(lowerCAmelCase_   ,				skip_special_tokens=lowerCAmelCase_   ,				normalize=lowerCAmelCase_							)[
										    0
										]

										if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    1
										elif isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		int       		=    len(lowerCAmelCase_							)
										else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_							)}"""							)

										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(lowerCAmelCase_   ,				lowerCAmelCase_							) or callback_steps <= 0)
										):
														raise ValueError(
														    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
														    F""" {type(lowerCAmelCase_							)}."""							)

										# get prompt text embeddings
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.tokenizer(
										    lowerCAmelCase_   ,				padding='max_length'   ,				max_length=self.tokenizer.model_max_length   ,				return_tensors='pt'   ,				)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    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   :		Optional[int]       		=    text_input_ids[:, : self.tokenizer.model_max_length]
										_SCREAMING_SNAKE_CASE   :		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
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    text_embeddings.shape
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    text_embeddings.repeat(1   ,				lowerCAmelCase_   ,				1							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    text_embeddings.view(bs_embed * num_images_per_prompt   ,				lowerCAmelCase_   ,				-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   :		Any       		=    guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
														_SCREAMING_SNAKE_CASE   :		List[str]
														if negative_prompt is None:
																		_SCREAMING_SNAKE_CASE   :		int       		=    [''] * batch_size
														elif type(lowerCAmelCase_							) is not type(lowerCAmelCase_							):
																		raise TypeError(
																		    F"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase_							)} !="""
																		    F""" {type(lowerCAmelCase_							)}."""							)
														elif isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							):
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    [negative_prompt]
														elif batch_size != len(lowerCAmelCase_							):
																		raise ValueError(
																		    F"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase_							)}, 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   :		List[Any]       		=    negative_prompt

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    text_input_ids.shape[-1]
														_SCREAMING_SNAKE_CASE   :		Any       		=    self.tokenizer(
														    lowerCAmelCase_   ,				padding='max_length'   ,				max_length=lowerCAmelCase_   ,				truncation=lowerCAmelCase_   ,				return_tensors='pt'   ,				)
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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   :		Optional[Any]       		=    uncond_embeddings.shape[1]
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    uncond_embeddings.repeat(1   ,				lowerCAmelCase_   ,				1							)
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    uncond_embeddings.view(batch_size * num_images_per_prompt   ,				lowerCAmelCase_   ,				-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   :		Optional[Any]       		=    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   :		Tuple       		=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    text_embeddings.dtype
										if latents is None:
														if self.device.type == "mps":
																		# randn does not exist on mps
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    torch.randn(lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				device='cpu'   ,				dtype=lowerCAmelCase_							).to(
																		    self.device							)
														else:
																		_SCREAMING_SNAKE_CASE   :		int       		=    torch.randn(lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				device=self.device   ,				dtype=lowerCAmelCase_							)
										else:
														if latents.shape != latents_shape:
																		raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""							)
														_SCREAMING_SNAKE_CASE   :		int       		=    latents.to(self.device							)

										# set timesteps
										self.scheduler.set_timesteps(lowerCAmelCase_							)

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.scheduler.timesteps.to(self.device							)

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

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

														# predict the noise residual
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self.unet(lowerCAmelCase_   ,				lowerCAmelCase_   ,				encoder_hidden_states=lowerCAmelCase_							).sample

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

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

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

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    1 / 0.18_215 * latents
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.vae.decode(lowerCAmelCase_							).sample

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    (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   :		Tuple       		=    image.cpu().permute(0   ,				2   ,				3   ,				1							).float().numpy()

										if output_type == "pil":
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self.numpy_to_pil(lowerCAmelCase_							)

										if not return_dict:
														return image

										return StableDiffusionPipelineOutput(images=lowerCAmelCase_   ,				nsfw_content_detected=lowerCAmelCase_							)









"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, 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, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				MBartConfig
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				{}
						SCREAMING_SNAKE_CASE_:					Tuple 					=				'gelu'







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=1_3   ,				lowerCAmelCase_=7   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=9_9   ,				lowerCAmelCase_=3_2   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=4   ,				lowerCAmelCase_=3_7   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=2_0   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=0   ,				)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    parent
										_SCREAMING_SNAKE_CASE   :		str       		=    batch_size
										_SCREAMING_SNAKE_CASE   :		str       		=    seq_length
										_SCREAMING_SNAKE_CASE   :		Dict       		=    is_training
										_SCREAMING_SNAKE_CASE   :		int       		=    use_labels
										_SCREAMING_SNAKE_CASE   :		str       		=    vocab_size
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    hidden_size
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_hidden_layers
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_attention_heads
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    intermediate_size
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		int       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    max_position_embeddings
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    eos_token_id
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    pad_token_id
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    bos_token_id







						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    ids_tensor([self.batch_size, self.seq_length - 1]   ,				self.vocab_size							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)   ,				1							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tf.concat([input_ids, eos_tensor]   ,				axis=1							)

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

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    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   :		Any       		=    prepare_mbart_inputs_dict(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										return config, inputs_dict







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							int:
										_SCREAMING_SNAKE_CASE   :		int       		=    TFMBartModel(config=lowerCAmelCase_							).get_decoder()
										_SCREAMING_SNAKE_CASE   :		int       		=    inputs_dict['input_ids']

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    input_ids[:1, :]
										_SCREAMING_SNAKE_CASE   :		Any       		=    inputs_dict['attention_mask'][:1, :]
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    inputs_dict['head_mask']
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1

										# first forward pass
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    model(lowerCAmelCase_   ,				attention_mask=lowerCAmelCase_   ,				head_mask=lowerCAmelCase_   ,				use_cache=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    outputs.to_tuple()
										_SCREAMING_SNAKE_CASE   :		str       		=    past_key_values[1]







def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						):
				if attention_mask is None:
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.cast(tf.math.not_equal(lowerCamelCase,						config.pad_token_id       ),						tf.inta       )
				if decoder_attention_mask is None:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    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   :		Dict       		=    tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
				if decoder_head_mask is None:
								_SCREAMING_SNAKE_CASE   :		int       		=    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
				if cross_attn_head_mask is None:
								_SCREAMING_SNAKE_CASE   :		int       		=    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 _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					List[str] 					=				(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
						SCREAMING_SNAKE_CASE_:					Dict 					=				(TFMBartForConditionalGeneration,) if is_tf_available() else ()
						SCREAMING_SNAKE_CASE_:					Tuple 					=				(
						    {
						        'conversational': TFMBartForConditionalGeneration,
						        'feature-extraction': TFMBartModel,
						        'summarization': TFMBartForConditionalGeneration,
						        'text2text-generation': TFMBartForConditionalGeneration,
						        'translation': TFMBartForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						SCREAMING_SNAKE_CASE_:					Any 					=				True
						SCREAMING_SNAKE_CASE_:					int 					=				False
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				False







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							List[Any]:
										if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
														# Exception encountered when calling layer '...'
														return True

										return False







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    TFMBartModelTester(self							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    ConfigTester(self   ,				config_class=lowerCAmelCase_							)







						def 			A      (    self							)		->							List[Any]:
										self.config_tester.run_common_tests()







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_							)







@require_sentencepiece
@require_tokenizers
@require_tf
class _lowerCAmelCase						(    unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					str 					=				[
						    ' UN Chief Says There Is No Military Solution in Syria',
						]
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				[
						    'Şeful ONU declară că nu există o soluţie militară în Siria',
						]
						SCREAMING_SNAKE_CASE_:					Dict 					=				'facebook/mbart-large-en-ro'







						@cached_property
						def 			A      (    self							)		->							Union[str, Any]:
										return AutoTokenizer.from_pretrained(self.model_name							)







						@cached_property
						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
										return model







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.translate_src_text(**lowerCAmelCase_							)
										self.assertListEqual(self.expected_text   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.tokenizer(self.src_text   ,				**lowerCAmelCase_   ,				return_tensors='tf'							)
										_SCREAMING_SNAKE_CASE   :		int       		=    self.model.generate(
										    model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2							)
										_SCREAMING_SNAKE_CASE   :		int       		=    self.tokenizer.batch_decode(lowerCAmelCase_   ,				skip_special_tokens=lowerCAmelCase_							)
										return generated_words







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

