
"""simple docstring"""

import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	KandinskyVaaPriorEmbaEmbPipeline
	lowercase_        =	['prompt', 'image']
	lowercase_        =	['prompt', 'image']
	lowercase_        =	[
	    'num_images_per_prompt',
	    'strength',
	    'generator',
	    'num_inference_steps',
	    'latents',
	    'negative_prompt',
	    'guidance_scale',
	    'output_type',
	    'return_dict',
	]
	lowercase_        =	False




	@property
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			return 32




	@property
	def 			_UpperCamelCase    ( self					)		->							str:
			return 32




	@property
	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			return self.time_input_dim




	@property
	def 			_UpperCamelCase    ( self					)		->							str:
			return self.time_input_dim * 4




	@property
	def 			_UpperCamelCase    ( self					)		->							List[str]:
			return 100




	@property
	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			str		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)
			return tokenizer




	@property
	def 			_UpperCamelCase    ( self					)		->							str:
			torch.manual_seed(0					)
			lowerCamelCase     :			List[str]		=    CLIPTextConfig(
			    bos_token_id=0		, eos_token_id=2		, hidden_size=self.text_embedder_hidden_size		, projection_dim=self.text_embedder_hidden_size		, intermediate_size=37		, layer_norm_eps=1E-0_5		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1000		, )
			return CLIPTextModelWithProjection(UpperCAmelCase_					)




	@property
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			torch.manual_seed(0					)

			lowerCamelCase     :			Union[str, Any]		=    {
			    'num_attention_heads': 2,
			    'attention_head_dim': 12,
			    'embedding_dim': self.text_embedder_hidden_size,
			    'num_layers': 1,
			}

			lowerCamelCase     :			List[str]		=    PriorTransformer(**UpperCAmelCase_					)
			# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
			lowerCamelCase     :			List[str]		=    nn.Parameter(torch.ones(model.clip_std.shape					)					)
			return model




	@property
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			torch.manual_seed(0					)
			lowerCamelCase     :			List[str]		=    CLIPVisionConfig(
			    hidden_size=self.text_embedder_hidden_size		, image_size=224		, projection_dim=self.text_embedder_hidden_size		, intermediate_size=37		, num_attention_heads=4		, num_channels=3		, num_hidden_layers=5		, patch_size=14		, )

			lowerCamelCase     :			str		=    CLIPVisionModelWithProjection(UpperCAmelCase_					)
			return model




	@property
	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Union[str, Any]		=    CLIPImageProcessor(
			    crop_size=224		, do_center_crop=UpperCAmelCase_		, do_normalize=UpperCAmelCase_		, do_resize=UpperCAmelCase_		, image_mean=[0.48145466, 0.4578275, 0.40821073]		, image_std=[0.26862954, 0.26130258, 0.27577711]		, resample=3		, size=224		, )

			return image_processor




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Tuple		=    self.dummy_prior
			lowerCamelCase     :			Optional[int]		=    self.dummy_image_encoder
			lowerCamelCase     :			Optional[Any]		=    self.dummy_text_encoder
			lowerCamelCase     :			Optional[int]		=    self.dummy_tokenizer
			lowerCamelCase     :			str		=    self.dummy_image_processor

			lowerCamelCase     :			Dict		=    UnCLIPScheduler(
			    variance_type='fixed_small_log'		, prediction_type='sample'		, num_train_timesteps=1000		, clip_sample=UpperCAmelCase_		, clip_sample_range=10.0		, )

			lowerCamelCase     :			str		=    {
			    'prior': prior,
			    'image_encoder': image_encoder,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'scheduler': scheduler,
			    'image_processor': image_processor,
			}

			return components




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_=0					)		->							List[str]:
			if str(UpperCAmelCase_					).startswith('mps'					):
					lowerCamelCase     :			List[str]		=    torch.manual_seed(UpperCAmelCase_					)
			else:
					lowerCamelCase     :			Union[str, Any]		=    torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)

			lowerCamelCase     :			Union[str, Any]		=    floats_tensor((1, 3, 64, 64)		, rng=random.Random(UpperCAmelCase_					)					).to(UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    image.cpu().permute(0		, 2		, 3		, 1					)[0]
			lowerCamelCase     :			Tuple		=    Image.fromarray(np.uinta(UpperCAmelCase_					)					).convert('RGB'					).resize((256, 256)					)

			lowerCamelCase     :			List[Any]		=    {
			    'prompt': 'horse',
			    'image': init_image,
			    'strength': 0.5,
			    'generator': generator,
			    'guidance_scale': 4.0,
			    'num_inference_steps': 2,
			    'output_type': 'np',
			}
			return inputs




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			Dict		=    'cpu'

			lowerCamelCase     :			int		=    self.get_dummy_components()

			lowerCamelCase     :			Optional[Any]		=    self.pipeline_class(**UpperCAmelCase_					)
			lowerCamelCase     :			Optional[Any]		=    pipe.to(UpperCAmelCase_					)

			pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    pipe(**self.get_dummy_inputs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Union[str, Any]		=    output.image_embeds

			lowerCamelCase     :			List[Any]		=    pipe(
			    **self.get_dummy_inputs(UpperCAmelCase_					)		, return_dict=UpperCAmelCase_		, )[0]

			lowerCamelCase     :			List[Any]		=    image[0, -10:]
			lowerCamelCase     :			Optional[int]		=    image_from_tuple[0, -10:]

			assert image.shape == (1, 32)

			lowerCamelCase     :			List[str]		=    np.array(
			    [
			        0.1071284,
			        1.3330271,
			        0.61260223,
			        -0.6691065,
			        -0.3846852,
			        -1.0303661,
			        0.22716111,
			        0.03348901,
			        0.30040675,
			        -0.24805029,
			    ]					)

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




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			List[str]		=    torch_device == 'cpu'
			lowerCamelCase     :			List[Any]		=    True
			lowerCamelCase     :			List[Any]		=    False

			self._test_inference_batch_single_identical(
			    test_max_difference=UpperCAmelCase_		, relax_max_difference=UpperCAmelCase_		, test_mean_pixel_difference=UpperCAmelCase_		, )




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase     :			Union[str, Any]		=    torch_device == 'cpu'
			lowerCamelCase     :			Optional[int]		=    False

			self._test_attention_slicing_forward_pass(
			    test_max_difference=UpperCAmelCase_		, test_mean_pixel_difference=UpperCAmelCase_		, )








"""simple docstring"""

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            = '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 		UpperCAmelCase					(		a_,						a_,						a_=None,						a_=None,						a_=None,						a_=None,						a_=None,						a_=None,						):





		'''simple docstring'''


		if attention_mask is None:
				lowerCamelCase     :			Dict		=    np.where(input_ids != config.pad_token_id,						1,						0			)
		if decoder_attention_mask is None:
				lowerCamelCase     :			Dict		=    np.where(decoder_input_ids != config.pad_token_id,						1,						0			)
		if head_mask is None:
				lowerCamelCase     :			List[Any]		=    np.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				lowerCamelCase     :			Optional[Any]		=    np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				lowerCamelCase     :			Any		=    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		):




	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_=0.02		, )		->							Union[str, Any]:
			lowerCamelCase     :			Any		=    parent
			lowerCamelCase     :			Dict		=    batch_size
			lowerCamelCase     :			str		=    seq_length
			lowerCamelCase     :			Dict		=    is_training
			lowerCamelCase     :			List[Any]		=    use_labels
			lowerCamelCase     :			Dict		=    vocab_size
			lowerCamelCase     :			List[str]		=    hidden_size
			lowerCamelCase     :			Tuple		=    num_hidden_layers
			lowerCamelCase     :			Union[str, Any]		=    num_attention_heads
			lowerCamelCase     :			int		=    intermediate_size
			lowerCamelCase     :			str		=    hidden_act
			lowerCamelCase     :			str		=    hidden_dropout_prob
			lowerCamelCase     :			str		=    attention_probs_dropout_prob
			lowerCamelCase     :			List[str]		=    max_position_embeddings
			lowerCamelCase     :			int		=    eos_token_id
			lowerCamelCase     :			Optional[int]		=    pad_token_id
			lowerCamelCase     :			Dict		=    bos_token_id
			lowerCamelCase     :			List[Any]		=    decoder_start_token_id
			lowerCamelCase     :			Dict		=    initializer_range




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			List[Any]		=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size					)		, 3		, self.vocab_size					)
			lowerCamelCase     :			List[Any]		=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		, dtype=np.intaa					))		, -1					)

			lowerCamelCase     :			int		=    shift_tokens_right(UpperCAmelCase_		, 1					)

			lowerCamelCase     :			Dict		=    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_		, )
			lowerCamelCase     :			List[str]		=    prepare_mbart_inputs_dict(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
			return config, inputs_dict




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase	,    lowerCamelCase     :			str		=    self.prepare_config_and_inputs()
			return config, inputs_dict




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Any:
			lowerCamelCase     :			List[str]		=    20
			lowerCamelCase     :			Optional[int]		=    model_class_name(UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    model.encode(inputs_dict['input_ids']					)

			lowerCamelCase	,    lowerCamelCase     :			List[str]		=    (
			    inputs_dict['decoder_input_ids'],
			    inputs_dict['decoder_attention_mask'],
			)

			lowerCamelCase     :			str		=    model.init_cache(decoder_input_ids.shape[0]		, UpperCAmelCase_		, UpperCAmelCase_					)
			lowerCamelCase     :			Dict		=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		, dtype='i4'					)

			lowerCamelCase     :			Tuple		=    jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]		, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		, )
			lowerCamelCase     :			int		=    model.decode(
			    decoder_input_ids[:, :-1]		, UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_		, past_key_values=UpperCAmelCase_		, decoder_position_ids=UpperCAmelCase_		, )

			lowerCamelCase     :			Optional[int]		=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		, dtype='i4'					)
			lowerCamelCase     :			Union[str, Any]		=    model.decode(
			    decoder_input_ids[:, -1:]		, UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_		, past_key_values=outputs_cache.past_key_values		, decoder_position_ids=UpperCAmelCase_		, )

			lowerCamelCase     :			Optional[Any]		=    model.decode(UpperCAmelCase_		, UpperCAmelCase_					)

			lowerCamelCase     :			Tuple		=    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 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Union[str, Any]:
			lowerCamelCase     :			int		=    20
			lowerCamelCase     :			Tuple		=    model_class_name(UpperCAmelCase_					)

			lowerCamelCase     :			Optional[Any]		=    model.encode(inputs_dict['input_ids']					)

			lowerCamelCase	,    lowerCamelCase     :			Dict		=    (
			    inputs_dict['decoder_input_ids'],
			    inputs_dict['decoder_attention_mask'],
			)

			lowerCamelCase     :			Optional[Any]		=    jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
			    ]		, axis=-1		, )

			lowerCamelCase     :			Optional[Any]		=    model.init_cache(decoder_input_ids.shape[0]		, UpperCAmelCase_		, UpperCAmelCase_					)
			lowerCamelCase     :			Tuple		=    jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]		, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		, )

			lowerCamelCase     :			int		=    model.decode(
			    decoder_input_ids[:, :-1]		, UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_		, past_key_values=UpperCAmelCase_		, decoder_position_ids=UpperCAmelCase_		, )
			lowerCamelCase     :			int		=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		, dtype='i4'					)
			lowerCamelCase     :			int		=    model.decode(
			    decoder_input_ids[:, -1:]		, UpperCAmelCase_		, past_key_values=outputs_cache.past_key_values		, decoder_attention_mask=UpperCAmelCase_		, decoder_position_ids=UpperCAmelCase_		, )

			lowerCamelCase     :			List[Any]		=    model.decode(UpperCAmelCase_		, UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_					)

			lowerCamelCase     :			Tuple		=    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		):
	lowercase_        =	9_9




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			Optional[int]		=    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		, )

			lowerCamelCase     :			List[Any]		=    input_ids.shape[0]
			lowerCamelCase     :			Optional[Any]		=    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 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			List[Any]		=    self._get_config_and_data()
			lowerCamelCase     :			str		=    FlaxMBartForSequenceClassification(UpperCAmelCase_					)
			lowerCamelCase     :			Tuple		=    model(input_ids=UpperCAmelCase_		, decoder_input_ids=UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    (batch_size, config.num_labels)
			self.assertEqual(outputs['logits'].shape		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			str		=    self._get_config_and_data()
			lowerCamelCase     :			Any		=    FlaxMBartForQuestionAnswering(UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    model(input_ids=UpperCAmelCase_					)

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




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    self._get_config_and_data()
			lowerCamelCase     :			List[str]		=    FlaxMBartForConditionalGeneration(UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    lm_model(input_ids=UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    (batch_size, input_ids.shape[1], config.vocab_size)
			self.assertEqual(outputs['logits'].shape		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			Optional[Any]		=    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		, )
			lowerCamelCase     :			int		=    FlaxMBartForConditionalGeneration(UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		, dtype=np.intaa					)
			lowerCamelCase     :			Any		=    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		, dtype=np.intaa					)
			lowerCamelCase     :			List[Any]		=    lm_model(input_ids=UpperCAmelCase_		, decoder_input_ids=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    (*summary.shape, config.vocab_size)
			self.assertEqual(outputs['logits'].shape		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			Optional[int]		=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		, dtype=np.intaa					)
			lowerCamelCase     :			List[Any]		=    shift_tokens_right(UpperCAmelCase_		, 1					)
			lowerCamelCase     :			str		=    np.equal(UpperCAmelCase_		, 1					).astype(np.floataa					).sum()
			lowerCamelCase     :			Any		=    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		):
	lowercase_        =	True
	lowercase_        =	(
	    (
	        FlaxMBartModel,
	        FlaxMBartForConditionalGeneration,
	        FlaxMBartForSequenceClassification,
	        FlaxMBartForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	lowercase_        =	(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Any		=    FlaxMBartModelTester(self					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase	,    lowerCamelCase     :			int		=    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 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase	,    lowerCamelCase     :			str		=    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 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase	,    lowerCamelCase     :			Tuple		=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__					):
							lowerCamelCase     :			Union[str, Any]		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_					)
							lowerCamelCase     :			Dict		=    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'					):
									lowerCamelCase     :			Dict		=    encode_jitted(**UpperCAmelCase_					).to_tuple()

							with self.subTest('JIT Disabled'					):
									with jax.disable_jit():
											lowerCamelCase     :			List[Any]		=    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 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase	,    lowerCamelCase     :			Union[str, Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__					):
							lowerCamelCase     :			int		=    model_class(UpperCAmelCase_					)
							lowerCamelCase     :			Any		=    model.encode(inputs_dict['input_ids']		, inputs_dict['attention_mask']					)

							lowerCamelCase     :			Optional[int]		=    {
							    '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'					):
									lowerCamelCase     :			int		=    decode_jitted(**UpperCAmelCase_					).to_tuple()

							with self.subTest('JIT Disabled'					):
									with jax.disable_jit():
											lowerCamelCase     :			Dict		=    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 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			for model_class_name in self.all_model_classes:
					lowerCamelCase     :			List[Any]		=    model_class_name.from_pretrained('facebook/mbart-large-cc25'		, from_pt=UpperCAmelCase_					)
					# FlaxMBartForSequenceClassification expects eos token in input_ids
					lowerCamelCase     :			str		=    np.ones((1, 1)					) * model.config.eos_token_id
					lowerCamelCase     :			int		=    model(UpperCAmelCase_					)
					self.assertIsNotNone(UpperCAmelCase_					)






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




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




	@cached_property
	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			Optional[int]		=    FlaxMBartForConditionalGeneration.from_pretrained(self.model_name		, from_pt=UpperCAmelCase_					)
			return model




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							int:
			lowerCamelCase     :			List[str]		=    self.translate_src_text(**UpperCAmelCase_					)
			self.assertListEqual(self.expected_text		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							Dict:
			lowerCamelCase     :			Optional[int]		=    self.tokenizer(self.src_text		, **UpperCAmelCase_		, return_tensors='np'					)
			lowerCamelCase     :			List[Any]		=    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
			lowerCamelCase     :			str		=    self.tokenizer.batch_decode(UpperCAmelCase_		, skip_special_tokens=UpperCAmelCase_					)
			return generated_words




	@slow
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			self._assert_generated_batch_equal_expected()






