


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
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_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class        lowercase_				( _SCREAMING_SNAKE_CASE			,unittest.TestCase):






							"""simple docstring"""







							snake_case_        =		PaintByExamplePipeline
							snake_case_        =		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
							snake_case_        =		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							snake_case_        =		frozenset([])  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess


							def 						lowercase__     (   self		):







													"""simple docstring"""


													torch.manual_seed(0		)
													a_					=		UNetaDConditionModel(
													    block_out_channels=(32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=9			,    out_channels=4			,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,    up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,    cross_attention_dim=32			,    )
													a_					=		PNDMScheduler(skip_prk_steps=UpperCamelCase__		)
													torch.manual_seed(0		)
													a_					=		AutoencoderKL(
													    block_out_channels=[32, 64]			,    in_channels=3			,    out_channels=3			,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,    latent_channels=4			,    )
													torch.manual_seed(0		)
													a_					=		CLIPVisionConfig(
													    hidden_size=32			,    projection_dim=32			,    intermediate_size=37			,    layer_norm_eps=1e-0_5			,    num_attention_heads=4			,    num_hidden_layers=5			,    image_size=32			,    patch_size=4			,    )
													a_					=		PaintByExampleImageEncoder(UpperCamelCase__			,    proj_size=32		)
													a_					=		CLIPImageProcessor(crop_size=32			,    size=32		)

													a_					=		{
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """vae""": vae,
													    """image_encoder""": image_encoder,
													    """safety_checker""": None,
													    """feature_extractor""": feature_extractor,
													}
													return components


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		np.array(image.convert("""RGB"""		)		)
													a_					=		image[None].transpose(0			,    3			,    1			,    2		)
													a_					=		torch.from_numpy(UpperCamelCase__		).to(dtype=torch.floataa		) / 1_2_7.5 - 1.0
													return image


							def 						lowercase__     (   self			,    _UpperCAmelCase="cpu"			,    _UpperCAmelCase=0		):







													"""simple docstring"""


													a_					=		floats_tensor((1, 3, 32, 32)			,    rng=random.Random(UpperCamelCase__		)		).to(UpperCamelCase__		)
													a_					=		image.cpu().permute(0			,    2			,    3			,    1		)[0]
													a_					=		Image.fromarray(np.uinta(UpperCamelCase__		)		).convert("""RGB"""		).resize((64, 64)		)
													a_					=		Image.fromarray(np.uinta(image + 4		)		).convert("""RGB"""		).resize((64, 64)		)
													a_					=		Image.fromarray(np.uinta(UpperCamelCase__		)		).convert("""RGB"""		).resize((32, 32)		)

													if str(UpperCamelCase__		).startswith("""mps"""		):
																			a_					=		torch.manual_seed(UpperCamelCase__		)
													else:
																			a_					=		torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)
													a_					=		{
													    """example_image""": example_image,
													    """image""": init_image,
													    """mask_image""": mask_image,
													    """generator""": generator,
													    """num_inference_steps""": 2,
													    """guidance_scale""": 6.0,
													    """output_type""": """numpy""",
													}
													return inputs


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_dummy_components()

													# make sure here that pndm scheduler skips prk
													a_					=		PaintByExamplePipeline(**UpperCamelCase__		)
													a_					=		pipe.to("""cpu"""		)
													pipe.set_progress_bar_config(disable=UpperCamelCase__		)

													a_					=		self.get_dummy_inputs()
													a_					=		pipe(**UpperCamelCase__		)
													a_					=		output.images

													a_					=		image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													a_					=		np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]		)

													assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		"""cpu"""
													a_					=		self.get_dummy_inputs()
													inputs.pop("""mask_image"""		)
													a_					=		self.convert_to_pt(inputs.pop("""image"""		)		)
													a_					=		image.clamp(0			,    1		) / 2

													# make sure here that pndm scheduler skips prk
													a_					=		PaintByExamplePipeline(**self.get_dummy_components()		)
													a_					=		pipe.to(UpperCamelCase__		)
													pipe.set_progress_bar_config(disable=UpperCamelCase__		)

													a_					=		pipe(image=UpperCamelCase__			,    mask_image=mask_image[:, 0]			,    **UpperCamelCase__		)
													a_					=		output.images

													a_					=		image.cpu().permute(0			,    2			,    3			,    1		)[0]
													a_					=		mask_image.cpu().permute(0			,    2			,    3			,    1		)[0]

													a_					=		Image.fromarray(np.uinta(UpperCamelCase__		)		).convert("""RGB"""		)
													a_					=		Image.fromarray(np.uinta(UpperCamelCase__		)		).convert("""RGB"""		)

													a_					=		pipe(**self.get_dummy_inputs()		)
													a_					=		output.images

													assert out_a.shape == (1, 64, 64, 3)
													assert np.abs(out_a.flatten() - out_a.flatten()		).max() < 5e-2






							def 						lowercase__     (   self		):







													"""simple docstring"""


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





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






							"""simple docstring"""







							def 						lowercase__     (   self		):







													"""simple docstring"""


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






							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/paint_by_example/dog_in_bucket.png"""		)
													a_					=		load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/paint_by_example/mask.png"""		)
													a_					=		load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/paint_by_example/panda.jpg"""		)

													a_					=		PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""		)
													a_					=		pipe.to(UpperCamelCase__		)
													pipe.set_progress_bar_config(disable=UpperCamelCase__		)

													a_					=		torch.manual_seed(321		)
													a_					=		pipe(
													    image=UpperCamelCase__			,    mask_image=UpperCamelCase__			,    example_image=UpperCamelCase__			,    generator=UpperCamelCase__			,    guidance_scale=5.0			,    num_inference_steps=50			,    output_type="""np"""			,    )

													a_					=		output.images

													a_					=		image[0, -3:, -3:, -1]

													assert image.shape == (1, 512, 512, 3)
													a_					=		np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]		)

													assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	A_    :					List[Any]             ="""platform"""

	import jax
	import jax.numpy as jnp

	from transformers import AutoTokenizer
	from transformers.models.mbart.modeling_flax_mbart import (
	    FlaxMBartForConditionalGeneration,
	    FlaxMBartForQuestionAnswering,
	    FlaxMBartForSequenceClassification,
	    FlaxMBartModel,
	    shift_tokens_right,
	)




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    ):



						"""simple docstring"""




						if attention_mask is None:
												a_					=		np.where(input_ids != config.pad_token_id       ,    1       ,    0      )
						if decoder_attention_mask is None:
												a_					=		np.where(decoder_input_ids != config.pad_token_id       ,    1       ,    0      )
						if head_mask is None:
												a_					=		np.ones((config.encoder_layers, config.encoder_attention_heads)      )
						if decoder_head_mask is None:
												a_					=		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
						if cross_attn_head_mask is None:
												a_					=		np.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,
						}





class        lowercase_				( unittest.TestCase):






							"""simple docstring"""







							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase=13			,    _UpperCAmelCase=7			,    _UpperCAmelCase=True			,    _UpperCAmelCase=False			,    _UpperCAmelCase=99			,    _UpperCAmelCase=16			,    _UpperCAmelCase=2			,    _UpperCAmelCase=4			,    _UpperCAmelCase=4			,    _UpperCAmelCase="gelu"			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=32			,    _UpperCAmelCase=2			,    _UpperCAmelCase=1			,    _UpperCAmelCase=0			,    _UpperCAmelCase=2			,    _UpperCAmelCase=0.0_2			,    ):







													"""simple docstring"""


													a_					=		parent
													a_					=		batch_size
													a_					=		seq_length
													a_					=		is_training
													a_					=		use_labels
													a_					=		vocab_size
													a_					=		hidden_size
													a_					=		num_hidden_layers
													a_					=		num_attention_heads
													a_					=		intermediate_size
													a_					=		hidden_act
													a_					=		hidden_dropout_prob
													a_					=		attention_probs_dropout_prob
													a_					=		max_position_embeddings
													a_					=		eos_token_id
													a_					=		pad_token_id
													a_					=		bos_token_id
													a_					=		decoder_start_token_id
													a_					=		initializer_range


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,    self.vocab_size		)			,    3			,    self.vocab_size		)
													a_					=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,    dtype=np.intaa		))			,    -1		)

													a_					=		shift_tokens_right(_UpperCAmelCase			,    1		)

													a_					=		MBartConfig(
													    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_id=self.eos_token_id			,    bos_token_id=self.bos_token_id			,    pad_token_id=self.pad_token_id			,    decoder_start_token_id=self.decoder_start_token_id			,    initializer_range=self.initializer_range			,    use_cache=_UpperCAmelCase			,    )
													a_					=		prepare_mbart_inputs_dict(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
													return config, inputs_dict


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_					=		self.prepare_config_and_inputs()
													return config, inputs_dict


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		20
													a_					=		model_class_name(_UpperCAmelCase		)

													a_					=		model.encode(inputs_dict["""input_ids"""]		)

													a_			,   a_					=		(
													    inputs_dict["""decoder_input_ids"""],
													    inputs_dict["""decoder_attention_mask"""],
													)

													a_					=		model.init_cache(decoder_input_ids.shape[0]			,    _UpperCAmelCase			,    _UpperCAmelCase		)
													a_					=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,    dtype="""i4"""		)

													a_					=		jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )
													a_					=		model.decode(
													    decoder_input_ids[:, :-1]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )

													a_					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
													a_					=		model.decode(
													    decoder_input_ids[:, -1:]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=outputs_cache.past_key_values			,    decoder_position_ids=_UpperCAmelCase			,    )

													a_					=		model.decode(_UpperCAmelCase			,    _UpperCAmelCase		)

													a_					=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
													self.parent.assertTrue(diff < 1e-3			,    msg=f"Max diff is {diff}"		)






							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		20
													a_					=		model_class_name(_UpperCAmelCase		)

													a_					=		model.encode(inputs_dict["""input_ids"""]		)

													a_			,   a_					=		(
													    inputs_dict["""decoder_input_ids"""],
													    inputs_dict["""decoder_attention_mask"""],
													)

													a_					=		jnp.concatenate(
													    [
													        decoder_attention_mask,
													        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
													    ]			,    axis=-1			,    )

													a_					=		model.init_cache(decoder_input_ids.shape[0]			,    _UpperCAmelCase			,    _UpperCAmelCase		)
													a_					=		jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )

													a_					=		model.decode(
													    decoder_input_ids[:, :-1]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )
													a_					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
													a_					=		model.decode(
													    decoder_input_ids[:, -1:]			,    _UpperCAmelCase			,    past_key_values=outputs_cache.past_key_values			,    decoder_attention_mask=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )

													a_					=		model.decode(_UpperCAmelCase			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase		)

													a_					=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
													self.parent.assertTrue(diff < 1e-3			,    msg=f"Max diff is {diff}"		)





@require_flax
class        lowercase_				( unittest.TestCase):






							"""simple docstring"""







							snake_case_        =		99


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		np.array(
													    [
													        [71, 82, 18, 33, 46, 91, 2],
													        [68, 34, 26, 58, 30, 82, 2],
													        [5, 97, 17, 39, 94, 40, 2],
													        [76, 83, 94, 25, 70, 78, 2],
													        [87, 59, 41, 35, 48, 66, 2],
													        [55, 13, 16, 58, 5, 2, 1],  # note padding
													        [64, 27, 31, 51, 12, 75, 2],
													        [52, 64, 86, 17, 83, 39, 2],
													        [48, 61, 9, 24, 71, 82, 2],
													        [26, 1, 60, 48, 22, 13, 2],
													        [21, 5, 62, 28, 14, 76, 2],
													        [45, 98, 37, 86, 59, 48, 2],
													        [70, 70, 50, 9, 28, 0, 2],
													    ]			,    dtype=np.intaa			,    )

													a_					=		input_ids.shape[0]
													a_					=		MBartConfig(
													    vocab_size=self.vocab_size			,    d_model=24			,    encoder_layers=2			,    decoder_layers=2			,    encoder_attention_heads=2			,    decoder_attention_heads=2			,    encoder_ffn_dim=32			,    decoder_ffn_dim=32			,    max_position_embeddings=48			,    eos_token_id=2			,    pad_token_id=1			,    bos_token_id=0			,    )
													return config, input_ids, batch_size


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_			,   a_					=		self._get_config_and_data()
													a_					=		FlaxMBartForSequenceClassification(_UpperCAmelCase		)
													a_					=		model(input_ids=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
													a_					=		(batch_size, config.num_labels)
													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_			,   a_					=		self._get_config_and_data()
													a_					=		FlaxMBartForQuestionAnswering(_UpperCAmelCase		)
													a_					=		model(input_ids=_UpperCAmelCase		)

													self.assertEqual(outputs["""start_logits"""].shape			,    input_ids.shape		)
													self.assertEqual(outputs["""end_logits"""].shape			,    input_ids.shape		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_			,   a_					=		self._get_config_and_data()
													a_					=		FlaxMBartForConditionalGeneration(_UpperCAmelCase		)
													a_					=		lm_model(input_ids=_UpperCAmelCase		)
													a_					=		(batch_size, input_ids.shape[1], config.vocab_size)
													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		MBartConfig(
													    vocab_size=self.vocab_size			,    d_model=14			,    encoder_layers=2			,    decoder_layers=2			,    encoder_attention_heads=2			,    decoder_attention_heads=2			,    encoder_ffn_dim=8			,    decoder_ffn_dim=8			,    max_position_embeddings=48			,    )
													a_					=		FlaxMBartForConditionalGeneration(_UpperCAmelCase		)
													a_					=		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,    dtype=np.intaa		)
													a_					=		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,    dtype=np.intaa		)
													a_					=		lm_model(input_ids=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
													a_					=		(*summary.shape, config.vocab_size)
													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)






							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,    dtype=np.intaa		)
													a_					=		shift_tokens_right(_UpperCAmelCase			,    1		)
													a_					=		np.equal(_UpperCAmelCase			,    1		).astype(np.floataa		).sum()
													a_					=		np.equal(_UpperCAmelCase			,    1		).astype(np.floataa		).sum()
													self.assertEqual(shifted.shape			,    input_ids.shape		)
													self.assertEqual(_UpperCAmelCase			,    n_pad_before - 1		)
													self.assertTrue(np.equal(shifted[:, 0]			,    2		).all()		)





@require_flax
class        lowercase_				( UpperCamelCase__			,unittest.TestCase			,UpperCamelCase__):






							"""simple docstring"""







							snake_case_        =		True
							snake_case_        =		(
							    (
							        FlaxMBartModel,
							        FlaxMBartForConditionalGeneration,
							        FlaxMBartForSequenceClassification,
							        FlaxMBartForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							snake_case_        =		(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		FlaxMBartModelTester(self		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_					=		self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_					=		self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_					=		self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__		):
																									a_					=		self._prepare_for_class(_UpperCAmelCase			,    _UpperCAmelCase		)
																									a_					=		model_class(_UpperCAmelCase		)

																									@jax.jit
																									def encode_jitted(_UpperCAmelCase			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
																															return model.encode(input_ids=_UpperCAmelCase			,    attention_mask=_UpperCAmelCase		)

																									with self.subTest("""JIT Enabled"""		):
																															a_					=		encode_jitted(**_UpperCAmelCase		).to_tuple()

																									with self.subTest("""JIT Disabled"""		):
																															with jax.disable_jit():
																																					a_					=		encode_jitted(**_UpperCAmelCase		).to_tuple()

																									self.assertEqual(len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)		)
																									for jitted_output, output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
																															self.assertEqual(jitted_output.shape			,    output.shape		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_					=		self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__		):
																									a_					=		model_class(_UpperCAmelCase		)
																									a_					=		model.encode(inputs_dict["""input_ids"""]			,    inputs_dict["""attention_mask"""]		)

																									a_					=		{
																									    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																									    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																									    """encoder_outputs""": encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
																															return model.decode(
																															    decoder_input_ids=_UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    encoder_outputs=_UpperCAmelCase			,    )

																									with self.subTest("""JIT Enabled"""		):
																															a_					=		decode_jitted(**_UpperCAmelCase		).to_tuple()

																									with self.subTest("""JIT Disabled"""		):
																															with jax.disable_jit():
																																					a_					=		decode_jitted(**_UpperCAmelCase		).to_tuple()

																									self.assertEqual(len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)		)
																									for jitted_output, output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
																															self.assertEqual(jitted_output.shape			,    output.shape		)






							@slow
							def 						lowercase__     (   self		):







													"""simple docstring"""


													for model_class_name in self.all_model_classes:
																			a_					=		model_class_name.from_pretrained("""facebook/mbart-large-cc25"""			,    from_pt=_UpperCAmelCase		)
																			# FlaxMBartForSequenceClassification expects eos token in input_ids
																			a_					=		np.ones((1, 1)		) * model.config.eos_token_id
																			a_					=		model(_UpperCAmelCase		)
																			self.assertIsNotNone(_UpperCAmelCase		)





@require_flax
@require_sentencepiece
@require_tokenizers
class        lowercase_				( unittest.TestCase):






							"""simple docstring"""







							snake_case_        =		[
							    ''' UN Chief Says There Is No Military Solution in Syria''',
							]
							snake_case_        =		[
							    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
							]
							snake_case_        =		'''facebook/mbart-large-en-ro'''


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													return AutoTokenizer.from_pretrained(self.model_name		)


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name			,    from_pt=_UpperCAmelCase		)
													return model


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													a_					=		self.translate_src_text(**_UpperCAmelCase		)
													self.assertListEqual(self.expected_text			,    _UpperCAmelCase		)


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													a_					=		self.tokenizer(self.src_text			,    **_UpperCAmelCase			,    return_tensors="""np"""		)
													a_					=		self.model.generate(
													    model_inputs.input_ids			,    attention_mask=model_inputs.attention_mask			,    decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]			,    early_stopping=_UpperCAmelCase			,    num_beams=2			,    ).sequences
													a_					=		self.tokenizer.batch_decode(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase		)
													return generated_words






							@slow
							def 						lowercase__     (   self		):







													"""simple docstring"""


													self._assert_generated_batch_equal_expected()