






"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 						A_							(      unittest.TestCase  ):







				def     _snake_case					(							self							:						Tuple	)				->					List[str]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				@property
				def     _snake_case					(							self							:						Tuple	)				->					Optional[int]:
										__magic_name__            =     1
										__magic_name__            =     3
										__magic_name__            =     (3_2, 3_2)

										__magic_name__            =     floats_tensor((batch_size, num_channels) + sizes						,    rng=random.Random(0	)	).to(__lowerCamelCase	)
										return image







				@property
				def     _snake_case					(							self							:						Optional[Any]	)				->					str:
										torch.manual_seed(0	)
										__magic_name__            =     UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)						,    layers_per_block=2						,    sample_size=3_2						,    in_channels=4						,    out_channels=4						,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,    cross_attention_dim=3_2						,    )
										return model







				@property
				def     _snake_case					(							self							:						List[str]	)				->					Dict:
										torch.manual_seed(0	)
										__magic_name__            =     AutoencoderKL(
										    block_out_channels=[3_2, 6_4]						,    in_channels=3						,    out_channels=3						,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,    latent_channels=4						,    )
										return model







				@property
				def     _snake_case					(							self							:						Optional[Any]	)				->					str:
										torch.manual_seed(0	)
										__magic_name__            =     RobertaSeriesConfig(
										    hidden_size=3_2						,    project_dim=3_2						,    intermediate_size=3_7						,    layer_norm_eps=1e-05						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=5_0_0_6						,    )
										return RobertaSeriesModelWithTransformation(__lowerCamelCase	)







				@property
				def     _snake_case					(							self							:						Any	)				->					Any:
										def extract(*__lowerCamelCase							:						Optional[int]						,    **__lowerCamelCase							:						Optional[int]	):

																class 						A_							:







																				def __init__(							self							:						int	)				->					str:
																										__magic_name__            =     torch.ones([0]	)







																				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Any	)				->					Optional[Any]:
																										self.pixel_values.to(__lowerCamelCase	)
																										return self





																return Out()

										return extract







				def     _snake_case					(							self							:						Any	)				->					int:
										__magic_name__            =     "cpu"  # ensure determinism for the device-dependent torch.Generator
										__magic_name__            =     self.dummy_cond_unet
										__magic_name__            =     PNDMScheduler(skip_prk_steps=__lowerCamelCase	)
										__magic_name__            =     self.dummy_vae
										__magic_name__            =     self.dummy_text_encoder
										__magic_name__            =     XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"	)
										__magic_name__            =     7_7

										__magic_name__            =     self.dummy_image.to(__lowerCamelCase	)
										__magic_name__            =     init_image / 2 + 0.5

										# make sure here that pndm scheduler skips prk
										__magic_name__            =     AltDiffusionImgaImgPipeline(
										    unet=__lowerCamelCase						,    scheduler=__lowerCamelCase						,    vae=__lowerCamelCase						,    text_encoder=__lowerCamelCase						,    tokenizer=__lowerCamelCase						,    safety_checker=__lowerCamelCase						,    feature_extractor=self.dummy_extractor						,    )
										__magic_name__            =     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,    do_normalize=__lowerCamelCase	)
										__magic_name__            =     alt_pipe.to(__lowerCamelCase	)
										alt_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

										__magic_name__            =     "A painting of a squirrel eating a burger"
										__magic_name__            =     torch.Generator(device=__lowerCamelCase	).manual_seed(0	)
										__magic_name__            =     alt_pipe(
										    [prompt]						,    generator=__lowerCamelCase						,    guidance_scale=6.0						,    num_inference_steps=2						,    output_type="np"						,    image=__lowerCamelCase						,    )

										__magic_name__            =     output.images

										__magic_name__            =     torch.Generator(device=__lowerCamelCase	).manual_seed(0	)
										__magic_name__            =     alt_pipe(
										    [prompt]						,    generator=__lowerCamelCase						,    guidance_scale=6.0						,    num_inference_steps=2						,    output_type="np"						,    image=__lowerCamelCase						,    return_dict=__lowerCamelCase						,    )[0]

										__magic_name__            =     image[0, -3:, -3:, -1]
										__magic_name__            =     image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 3_2, 3_2, 3)
										__magic_name__            =     np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 5e-3







				@unittest.skipIf(torch_device != "cuda"						,    "This test requires a GPU"	)
				def     _snake_case					(							self							:						Tuple	)				->					Optional[int]:
										__magic_name__            =     self.dummy_cond_unet
										__magic_name__            =     PNDMScheduler(skip_prk_steps=__lowerCamelCase	)
										__magic_name__            =     self.dummy_vae
										__magic_name__            =     self.dummy_text_encoder
										__magic_name__            =     XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"	)
										__magic_name__            =     7_7

										__magic_name__            =     self.dummy_image.to(__lowerCamelCase	)

										# put models in fp16
										__magic_name__            =     unet.half()
										__magic_name__            =     vae.half()
										__magic_name__            =     bert.half()

										# make sure here that pndm scheduler skips prk
										__magic_name__            =     AltDiffusionImgaImgPipeline(
										    unet=__lowerCamelCase						,    scheduler=__lowerCamelCase						,    vae=__lowerCamelCase						,    text_encoder=__lowerCamelCase						,    tokenizer=__lowerCamelCase						,    safety_checker=__lowerCamelCase						,    feature_extractor=self.dummy_extractor						,    )
										__magic_name__            =     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,    do_normalize=__lowerCamelCase	)
										__magic_name__            =     alt_pipe.to(__lowerCamelCase	)
										alt_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

										__magic_name__            =     "A painting of a squirrel eating a burger"
										__magic_name__            =     torch.manual_seed(0	)
										__magic_name__            =     alt_pipe(
										    [prompt]						,    generator=__lowerCamelCase						,    num_inference_steps=2						,    output_type="np"						,    image=__lowerCamelCase						,    ).images

										assert image.shape == (1, 3_2, 3_2, 3)







				@unittest.skipIf(torch_device != "cuda"						,    "This test requires a GPU"	)
				def     _snake_case					(							self							:						Any	)				->					Tuple:
										__magic_name__            =     load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"	)
										# resize to resolution that is divisible by 8 but not 16 or 32
										__magic_name__            =     init_image.resize((7_6_0, 5_0_4)	)

										__magic_name__            =     "BAAI/AltDiffusion"
										__magic_name__            =     AltDiffusionImgaImgPipeline.from_pretrained(
										    __lowerCamelCase						,    safety_checker=__lowerCamelCase						,    )
										pipe.to(__lowerCamelCase	)
										pipe.set_progress_bar_config(disable=__lowerCamelCase	)
										pipe.enable_attention_slicing()

										__magic_name__            =     "A fantasy landscape, trending on artstation"

										__magic_name__            =     torch.manual_seed(0	)
										__magic_name__            =     pipe(
										    prompt=__lowerCamelCase						,    image=__lowerCamelCase						,    strength=0.75						,    guidance_scale=7.5						,    generator=__lowerCamelCase						,    output_type="np"						,    )
										__magic_name__            =     output.images[0]

										__magic_name__            =     image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

										assert image.shape == (5_0_4, 7_6_0, 3)
										__magic_name__            =     np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]	)

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





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







				def     _snake_case					(							self							:						Optional[Any]	)				->					Union[str, Any]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def     _snake_case					(							self							:						Optional[int]	)				->					List[Any]:
										__magic_name__            =     load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"	)
										__magic_name__            =     init_image.resize((7_6_8, 5_1_2)	)
										__magic_name__            =     load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"	)

										__magic_name__            =     "BAAI/AltDiffusion"
										__magic_name__            =     AltDiffusionImgaImgPipeline.from_pretrained(
										    __lowerCamelCase						,    safety_checker=__lowerCamelCase						,    )
										pipe.to(__lowerCamelCase	)
										pipe.set_progress_bar_config(disable=__lowerCamelCase	)
										pipe.enable_attention_slicing()

										__magic_name__            =     "A fantasy landscape, trending on artstation"

										__magic_name__            =     torch.manual_seed(0	)
										__magic_name__            =     pipe(
										    prompt=__lowerCamelCase						,    image=__lowerCamelCase						,    strength=0.75						,    guidance_scale=7.5						,    generator=__lowerCamelCase						,    output_type="np"						,    )
										__magic_name__            =     output.images[0]

										assert image.shape == (5_1_2, 7_6_8, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image	).max() < 1e-2











"""simple docstring"""

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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
			lowercase    			=       '''platform'''

			import jax
			import jax.numpy as jnp

			from transformers import BlenderbotTokenizer
			from transformers.models.blenderbot.modeling_flax_blenderbot import (
			    FlaxBlenderbotForConditionalGeneration,
			    FlaxBlenderbotModel,
			    shift_tokens_right,
			)






def _lowerCAmelCase	(  __lowerCamelCase:List[Any] ,						__lowerCamelCase:int ,						__lowerCamelCase:List[Any]=None ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:List[str]=None ,						__lowerCamelCase:Optional[int]=None ,						__lowerCamelCase:Optional[int]=None ,						):

						'''simple docstring'''






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

class 						A_							:







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[Any]=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						Dict=False						,    __lowerCamelCase							:						Optional[int]=9_9						,    __lowerCamelCase							:						Tuple=1_6						,    __lowerCamelCase							:						Any=2						,    __lowerCamelCase							:						int=4						,    __lowerCamelCase							:						List[Any]=4						,    __lowerCamelCase							:						List[str]="gelu"						,    __lowerCamelCase							:						Union[str, Any]=0.1						,    __lowerCamelCase							:						Optional[int]=0.1						,    __lowerCamelCase							:						List[str]=3_2						,    __lowerCamelCase							:						int=2						,    __lowerCamelCase							:						Any=1						,    __lowerCamelCase							:						int=0						,    __lowerCamelCase							:						Tuple=0.02						,    )				->					List[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     eos_token_id
										__magic_name__            =     pad_token_id
										__magic_name__            =     bos_token_id
										__magic_name__            =     initializer_range







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

										__magic_name__            =     shift_tokens_right(__lowerCamelCase						,    1						,    2	)

										__magic_name__            =     BlenderbotConfig(
										    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						,    initializer_range=self.initializer_range						,    use_cache=__lowerCamelCase						,    )
										__magic_name__            =     prepare_blenderbot_inputs_dict(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
										return config, inputs_dict







				def     _snake_case					(							self							:						Optional[Any]	)				->					Any:
										__magic_name__      ,    __magic_name__            =     self.prepare_config_and_inputs()
										return config, inputs_dict







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[Any]	)				->					Union[str, Any]:
										__magic_name__            =     2_0
										__magic_name__            =     model_class_name(__lowerCamelCase	)

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

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

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

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

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

										__magic_name__            =     model.decode(__lowerCamelCase						,    __lowerCamelCase	)

										__magic_name__            =     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     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Tuple	)				->					int:
										__magic_name__            =     2_0
										__magic_name__            =     model_class_name(__lowerCamelCase	)

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

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

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

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

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

										__magic_name__            =     model.decode(__lowerCamelCase						,    __lowerCamelCase						,    decoder_attention_mask=__lowerCamelCase	)

										__magic_name__            =     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 						A_							(      unittest.TestCase  ):
				UpperCAmelCase__        =				9_9







				def     _snake_case					(							self							:						Dict	)				->					Dict:
										__magic_name__            =     np.array(
										    [
										        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
										        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
										        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
										        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
										        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
										        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
										        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
										        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
										        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
										        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
										        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
										        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
										        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
										    ]						,    dtype=np.intaa						,    )

										__magic_name__            =     input_ids.shape[0]
										__magic_name__            =     BlenderbotConfig(
										    vocab_size=self.vocab_size						,    d_model=2_4						,    encoder_layers=2						,    decoder_layers=2						,    encoder_attention_heads=2						,    decoder_attention_heads=2						,    encoder_ffn_dim=3_2						,    decoder_ffn_dim=3_2						,    max_position_embeddings=4_8						,    eos_token_id=2						,    pad_token_id=1						,    bos_token_id=0						,    )
										return config, input_ids, batch_size







				def     _snake_case					(							self							:						Optional[Any]	)				->					Optional[int]:
										__magic_name__      ,    __magic_name__      ,    __magic_name__            =     self._get_config_and_data()
										__magic_name__            =     FlaxBlenderbotForConditionalGeneration(__lowerCamelCase	)
										__magic_name__            =     lm_model(input_ids=__lowerCamelCase	)
										__magic_name__            =     (batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["logits"].shape						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										__magic_name__            =     BlenderbotConfig(
										    vocab_size=self.vocab_size						,    d_model=1_4						,    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=4_8						,    )
										__magic_name__            =     FlaxBlenderbotForConditionalGeneration(__lowerCamelCase	)
										__magic_name__            =     np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]						,    dtype=np.intaa	)
										__magic_name__            =     np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]						,    dtype=np.intaa	)
										__magic_name__            =     lm_model(input_ids=__lowerCamelCase						,    decoder_input_ids=__lowerCamelCase	)
										__magic_name__            =     (*summary.shape, config.vocab_size)
										self.assertEqual(outputs["logits"].shape						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[Any]:
										__magic_name__            =     np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]						,    dtype=np.intaa	)
										__magic_name__            =     shift_tokens_right(__lowerCamelCase						,    1						,    2	)
										__magic_name__            =     np.equal(__lowerCamelCase						,    1	).astype(np.floataa	).sum()
										__magic_name__            =     np.equal(__lowerCamelCase						,    1	).astype(np.floataa	).sum()
										self.assertEqual(shifted.shape						,    input_ids.shape	)
										self.assertEqual(__lowerCamelCase						,    n_pad_before - 1	)
										self.assertTrue(np.equal(shifted[:, 0]						,    2	).all()	)





@require_flax
class 						A_							(      snake_case_					,							unittest.TestCase					,							snake_case_  ):
				UpperCAmelCase__        =				True
				UpperCAmelCase__        =				(
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				UpperCAmelCase__        =				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										__magic_name__            =     FlaxBlenderbotModelTester(self	)







				def     _snake_case					(							self							:						int	)				->					Optional[Any]:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]	)				->					Union[str, Any]:
										__magic_name__      ,    __magic_name__            =     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(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Dict:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__	):
																						__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)
																						__magic_name__            =     model_class(__lowerCamelCase	)

																						@jax.jit
																						def encode_jitted(__lowerCamelCase							:						Dict						,    __lowerCamelCase							:						str=None						,    **__lowerCamelCase							:						List[str]	):
																												return model.encode(input_ids=__lowerCamelCase						,    attention_mask=__lowerCamelCase	)

																						with self.subTest("JIT Enabled"	):
																												__magic_name__            =     encode_jitted(**__lowerCamelCase	).to_tuple()

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

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







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

																						__magic_name__            =     {
																						    "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(__lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Dict	):
																												return model.decode(
																												    decoder_input_ids=__lowerCamelCase						,    decoder_attention_mask=__lowerCamelCase						,    encoder_outputs=__lowerCamelCase						,    )

																						with self.subTest("JIT Enabled"	):
																												__magic_name__            =     decode_jitted(**__lowerCamelCase	).to_tuple()

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

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







				@slow
				def     _snake_case					(							self							:						int	)				->					int:
										for model_class_name in self.all_model_classes:
																__magic_name__            =     model_class_name.from_pretrained("facebook/blenderbot-400M-distill"	)
																# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																__magic_name__            =     np.ones((1, 1)	) * model.config.eos_token_id
																__magic_name__            =     model(__lowerCamelCase	)
																self.assertIsNotNone(__lowerCamelCase	)







				@unittest.skipUnless(jax_device != "cpu"						,    "3B test too slow on CPU."	)
				@slow
				def     _snake_case					(							self							:						int	)				->					List[Any]:
										__magic_name__            =     {"num_beams": 1, "early_stopping": True, "min_length": 1_5, "max_length": 2_5}
										__magic_name__            =     {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

										__magic_name__            =     FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"						,    from_pt=__lowerCamelCase	)
										__magic_name__            =     BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"	)

										__magic_name__            =     ["Sam"]
										__magic_name__            =     tokenizer(__lowerCamelCase						,    return_tensors="jax"	)

										__magic_name__            =     model.generate(**__lowerCamelCase						,    **__lowerCamelCase	)
										__magic_name__            =     "Sam is a great name. It means \"sun\" in Gaelic."

										__magic_name__            =     tokenizer.batch_decode(__lowerCamelCase						,    **__lowerCamelCase	)
										assert generated_txt[0].strip() == tgt_text



