


import gc
import random
import unittest

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

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class A  (   UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Optional[int]      =UnCLIPImageVariationPipeline
      UpperCamelCase__							:  str      =IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
      UpperCamelCase__							:  List[str]      =IMAGE_VARIATION_BATCH_PARAMS

      UpperCamelCase__							:  Tuple      =[
          'generator',
          'return_dict',
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]
      UpperCamelCase__							:  Optional[int]      =False






      @property
      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             return 32






      @property
      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[Any]:
             """simple docstring"""




             return 32






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             return self.time_input_dim






      @property
      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             return self.time_input_dim * 4






      @property
      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             return 100






      @property
      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Any		=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
             return tokenizer






      @property
      def        lowerCamelCase       (					self						:     Dict )					->						Optional[int]:
             """simple docstring"""




             torch.manual_seed(0 )
             _lowerCamelCase       :		List[Any]		=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-05			,   num_attention_heads=4			,   num_hidden_layers=5			,   pad_token_id=1			,   vocab_size=1000			,   )
             return CLIPTextModelWithProjection(lowercase_ )






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						List[str]:
             """simple docstring"""




             torch.manual_seed(0 )
             _lowerCamelCase       :		Optional[int]		=CLIPVisionConfig(
                 hidden_size=self.text_embedder_hidden_size			,   projection_dim=self.text_embedder_hidden_size			,   num_hidden_layers=5			,   num_attention_heads=4			,   image_size=32			,   intermediate_size=37			,   patch_size=1			,   )
             return CLIPVisionModelWithProjection(lowercase_ )






      @property
      def        lowerCamelCase       (					self						:     Optional[Any] )					->						List[str]:
             """simple docstring"""




             torch.manual_seed(0 )

             _lowerCamelCase       :		Dict		={
                 'clip_embeddings_dim': self.text_embedder_hidden_size,
                 'time_embed_dim': self.time_embed_dim,
                 'cross_attention_dim': self.cross_attention_dim,
             }

             _lowerCamelCase       :		int		=UnCLIPTextProjModel(**lowercase_ )
             return model






      @property
      def        lowerCamelCase       (					self						:     Any )					->						str:
             """simple docstring"""




             torch.manual_seed(0 )

             _lowerCamelCase       :		str		={
                 'sample_size': 32,
                 # RGB in channels
                 'in_channels': 3,
                 # Out channels is double in channels because predicts mean and variance
                 'out_channels': 6,
                 'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
                 'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
                 'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
                 'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
                 'layers_per_block': 1,
                 'cross_attention_dim': self.cross_attention_dim,
                 'attention_head_dim': 4,
                 'resnet_time_scale_shift': 'scale_shift',
                 'class_embed_type': 'identity',
             }

             _lowerCamelCase       :		int		=UNetaDConditionModel(**lowercase_ )
             return model






      @property
      def        lowerCamelCase       (					self						:     List[str] )					->						List[Any]:
             """simple docstring"""




             return {
                 "sample_size": 64,
                 "layers_per_block": 1,
                 "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
                 "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
                 "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                 "in_channels": 6,
                 "out_channels": 3,
             }






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             torch.manual_seed(0 )

             _lowerCamelCase       :		int		=UNetaDModel(**self.dummy_super_res_kwargs )
             return model






      @property
      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             torch.manual_seed(1 )

             _lowerCamelCase       :		List[Any]		=UNetaDModel(**self.dummy_super_res_kwargs )
             return model






      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=self.dummy_decoder
             _lowerCamelCase       :		Any		=self.dummy_text_proj
             _lowerCamelCase       :		str		=self.dummy_text_encoder
             _lowerCamelCase       :		List[Any]		=self.dummy_tokenizer
             _lowerCamelCase       :		Union[str, Any]		=self.dummy_super_res_first
             _lowerCamelCase       :		str		=self.dummy_super_res_last

             _lowerCamelCase       :		List[str]		=UnCLIPScheduler(
                 variance_type='learned_range'			,   prediction_type='epsilon'			,   num_train_timesteps=1000			,   )

             _lowerCamelCase       :		Any		=UnCLIPScheduler(
                 variance_type='fixed_small_log'			,   prediction_type='epsilon'			,   num_train_timesteps=1000			,   )

             _lowerCamelCase       :		Union[str, Any]		=CLIPImageProcessor(crop_size=32			,   size=32 )

             _lowerCamelCase       :		Optional[Any]		=self.dummy_image_encoder

             return {
                 "decoder": decoder,
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "text_proj": text_proj,
                 "feature_extractor": feature_extractor,
                 "image_encoder": image_encoder,
                 "super_res_first": super_res_first,
                 "super_res_last": super_res_last,
                 "decoder_scheduler": decoder_scheduler,
                 "super_res_scheduler": super_res_scheduler,
             }






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     str			,   lowercase_						:     str=0			,   lowercase_						:     Dict=True )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Any		=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(lowercase_ ) ).to(lowercase_ )
             if str(lowercase_ ).startswith('mps' ):
                    _lowerCamelCase       :		Dict		=torch.manual_seed(lowercase_ )
             else:
                    _lowerCamelCase       :		Optional[Any]		=torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )

             if pil_image:
                    _lowerCamelCase       :		Any		=input_image * 0.5 + 0.5
                    _lowerCamelCase       :		List[str]		=input_image.clamp(0			,   1 )
                    _lowerCamelCase       :		List[str]		=input_image.cpu().permute(0			,   2			,   3			,   1 ).float().numpy()
                    _lowerCamelCase       :		List[str]		=DiffusionPipeline.numpy_to_pil(lowercase_ )[0]

             return {
                 "image": input_image,
                 "generator": generator,
                 "decoder_num_inference_steps": 2,
                 "super_res_num_inference_steps": 2,
                 "output_type": "np",
             }






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		='cpu'

             _lowerCamelCase       :		Any		=self.get_dummy_components()

             _lowerCamelCase       :		Any		=self.pipeline_class(**lowercase_ )
             _lowerCamelCase       :		List[str]		=pipe.to(lowercase_ )

             pipe.set_progress_bar_config(disable=lowercase_ )

             _lowerCamelCase       :		List[Any]		=self.get_dummy_inputs(lowercase_			,   pil_image=lowercase_ )

             _lowerCamelCase       :		Any		=pipe(**lowercase_ )
             _lowerCamelCase       :		Dict		=output.images

             _lowerCamelCase       :		str		=self.get_dummy_inputs(lowercase_			,   pil_image=lowercase_ )

             _lowerCamelCase       :		Optional[int]		=pipe(
                 **lowercase_			,   return_dict=lowercase_			,   )[0]

             _lowerCamelCase       :		Any		=image[0, -3:, -3:, -1]
             _lowerCamelCase       :		Any		=image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)

             _lowerCamelCase       :		str		=np.array(
                 [
                     0.9997,
                     0.0002,
                     0.9997,
                     0.9997,
                     0.9969,
                     0.0023,
                     0.9997,
                     0.9969,
                     0.9970,
                 ] )

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






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		='cpu'

             _lowerCamelCase       :		Any		=self.get_dummy_components()

             _lowerCamelCase       :		Tuple		=self.pipeline_class(**lowercase_ )
             _lowerCamelCase       :		List[Any]		=pipe.to(lowercase_ )

             pipe.set_progress_bar_config(disable=lowercase_ )

             _lowerCamelCase       :		str		=self.get_dummy_inputs(lowercase_			,   pil_image=lowercase_ )

             _lowerCamelCase       :		List[Any]		=pipe(**lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=output.images

             _lowerCamelCase       :		Union[str, Any]		=self.get_dummy_inputs(lowercase_			,   pil_image=lowercase_ )

             _lowerCamelCase       :		Optional[Any]		=pipe(
                 **lowercase_			,   return_dict=lowercase_			,   )[0]

             _lowerCamelCase       :		List[Any]		=image[0, -3:, -3:, -1]
             _lowerCamelCase       :		Optional[int]		=image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)

             _lowerCamelCase       :		str		=np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )

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






      def        lowerCamelCase       (					self						:     str )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Any		='cpu'

             _lowerCamelCase       :		Optional[Any]		=self.get_dummy_components()

             _lowerCamelCase       :		int		=self.pipeline_class(**lowercase_ )
             _lowerCamelCase       :		Tuple		=pipe.to(lowercase_ )

             pipe.set_progress_bar_config(disable=lowercase_ )

             _lowerCamelCase       :		Union[str, Any]		=self.get_dummy_inputs(lowercase_			,   pil_image=lowercase_ )
             _lowerCamelCase       :		Dict		=[
                 pipeline_inputs['image'],
                 pipeline_inputs['image'],
             ]

             _lowerCamelCase       :		str		=pipe(**lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=output.images

             _lowerCamelCase       :		Optional[int]		=self.get_dummy_inputs(lowercase_			,   pil_image=lowercase_ )
             _lowerCamelCase       :		Optional[int]		=[
                 tuple_pipeline_inputs['image'],
                 tuple_pipeline_inputs['image'],
             ]

             _lowerCamelCase       :		Optional[Any]		=pipe(
                 **lowercase_			,   return_dict=lowercase_			,   )[0]

             _lowerCamelCase       :		Optional[int]		=image[0, -3:, -3:, -1]
             _lowerCamelCase       :		List[str]		=image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (2, 64, 64, 3)

             _lowerCamelCase       :		Dict		=np.array(
                 [
                     0.9997,
                     0.9989,
                     0.0008,
                     0.0021,
                     0.9960,
                     0.0018,
                     0.0014,
                     0.0002,
                     0.9933,
                 ] )

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






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=torch.device('cpu' )




             class A  :
                   UpperCamelCase__							:  Any      =1




             _lowerCamelCase       :		List[str]		=self.get_dummy_components()

             _lowerCamelCase       :		Optional[int]		=self.pipeline_class(**lowercase_ )
             _lowerCamelCase       :		str		=pipe.to(lowercase_ )

             pipe.set_progress_bar_config(disable=lowercase_ )

             _lowerCamelCase       :		List[str]		=torch.Generator(device=lowercase_ ).manual_seed(0 )
             _lowerCamelCase       :		List[Any]		=pipe.decoder.dtype
             _lowerCamelCase       :		int		=1

             _lowerCamelCase       :		Optional[Any]		=(
                 batch_size,
                 pipe.decoder.config.in_channels,
                 pipe.decoder.config.sample_size,
                 pipe.decoder.config.sample_size,
             )
             _lowerCamelCase       :		Any		=pipe.prepare_latents(
                 lowercase_			,   dtype=lowercase_			,   device=lowercase_			,   generator=lowercase_			,   latents=lowercase_			,   scheduler=DummyScheduler() )

             _lowerCamelCase       :		Any		=(
                 batch_size,
                 pipe.super_res_first.config.in_channels // 2,
                 pipe.super_res_first.config.sample_size,
                 pipe.super_res_first.config.sample_size,
             )
             _lowerCamelCase       :		Tuple		=pipe.prepare_latents(
                 lowercase_			,   dtype=lowercase_			,   device=lowercase_			,   generator=lowercase_			,   latents=lowercase_			,   scheduler=DummyScheduler() )

             _lowerCamelCase       :		str		=self.get_dummy_inputs(lowercase_			,   pil_image=lowercase_ )

             _lowerCamelCase       :		str		=pipe(
                 **lowercase_			,   decoder_latents=lowercase_			,   super_res_latents=lowercase_ ).images

             _lowerCamelCase       :		Dict		=self.get_dummy_inputs(lowercase_			,   pil_image=lowercase_ )
             # Don't pass image, instead pass embedding
             _lowerCamelCase       :		List[str]		=pipeline_inputs.pop('image' )
             _lowerCamelCase       :		Dict		=pipe.image_encoder(lowercase_ ).image_embeds

             _lowerCamelCase       :		Any		=pipe(
                 **lowercase_			,   decoder_latents=lowercase_			,   super_res_latents=lowercase_			,   image_embeddings=lowercase_			,   ).images

             # make sure passing text embeddings manually is identical
             assert np.abs(img_out_a - img_out_a ).max() < 1E-4






      @skip_mps
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=torch_device == 'cpu'

             # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
             _lowerCamelCase       :		List[str]		=1E-2

             self._test_attention_slicing_forward_pass(
                 test_max_difference=lowercase_			,   expected_max_diff=lowercase_ )






      @skip_mps
      def        lowerCamelCase       (					self						:     int )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		str		=torch_device == 'cpu'
             _lowerCamelCase       :		Union[str, Any]		=True
             _lowerCamelCase       :		List[Any]		=[
                 'decoder_num_inference_steps',
                 'super_res_num_inference_steps',
             ]

             self._test_inference_batch_single_identical(
                 test_max_difference=lowercase_			,   relax_max_difference=lowercase_			,   additional_params_copy_to_batched_inputs=lowercase_			,   )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=[
                 'decoder_num_inference_steps',
                 'super_res_num_inference_steps',
             ]

             if torch_device == "mps":
                    # TODO: MPS errors with larger batch sizes
                    _lowerCamelCase       :		int		=[2, 3]
                    self._test_inference_batch_consistent(
                        batch_sizes=lowercase_			,   additional_params_copy_to_batched_inputs=lowercase_			,   )
             else:
                    self._test_inference_batch_consistent(
                        additional_params_copy_to_batched_inputs=lowercase_ )






      @skip_mps
      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             return super().test_dict_tuple_outputs_equivalent()






      @skip_mps
      def        lowerCamelCase       (					self						:     int )					->						List[str]:
             """simple docstring"""




             return super().test_save_load_local()






      @skip_mps
      def        lowerCamelCase       (					self						:     Optional[Any] )					->						List[str]:
             """simple docstring"""




             return super().test_save_load_optional_components()




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






      def        lowerCamelCase       (					self						:     int )					->						Dict:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Tuple )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png' )
             _lowerCamelCase       :		str		=load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/unclip/karlo_v1_alpha_cat_variation_fp16.npy' )

             _lowerCamelCase       :		int		=UnCLIPImageVariationPipeline.from_pretrained(
                 'kakaobrain/karlo-v1-alpha-image-variations'			,   torch_dtype=torch.floataa )
             _lowerCamelCase       :		Dict		=pipeline.to(lowercase_ )
             pipeline.set_progress_bar_config(disable=lowercase_ )

             _lowerCamelCase       :		Optional[Any]		=torch.Generator(device='cpu' ).manual_seed(0 )
             _lowerCamelCase       :		int		=pipeline(
                 lowercase_			,   generator=lowercase_			,   output_type='np'			,   )

             _lowerCamelCase       :		Any		=output.images[0]

             assert image.shape == (256, 256, 3)

             assert_mean_pixel_difference(lowercase_			,   lowercase_			,   15 )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase											=       logging.get_logger(__name__)

lowerCamelCase											=       {
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Dict      ='transfo-xl'
      UpperCamelCase__							:  int      =['mems']
      UpperCamelCase__							:  List[str]      ={
          'n_token': 'vocab_size',
          'hidden_size': 'd_model',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }






      def __init__(					self						:     Tuple			,   lowercase_						:     List[Any]=26_7735			,   lowercase_						:     List[Any]=[2_0000, 4_0000, 20_0000]			,   lowercase_						:     Tuple=1024			,   lowercase_						:     Optional[int]=1024			,   lowercase_						:     str=16			,   lowercase_						:     int=64			,   lowercase_						:     Any=4096			,   lowercase_						:     List[Any]=4			,   lowercase_						:     List[Any]=False			,   lowercase_						:     List[str]=18			,   lowercase_						:     Tuple=1600			,   lowercase_						:     List[str]=1000			,   lowercase_						:     Any=True			,   lowercase_						:     str=True			,   lowercase_						:     Optional[int]=0			,   lowercase_						:     List[Any]=-1			,   lowercase_						:     Optional[int]=True			,   lowercase_						:     Any=0.1			,   lowercase_						:     Tuple=0.0			,   lowercase_						:     str=True			,   lowercase_						:     Union[str, Any]="normal"			,   lowercase_						:     Dict=0.01			,   lowercase_						:     Optional[Any]=0.01			,   lowercase_						:     List[Any]=0.02			,   lowercase_						:     List[Any]=1E-5			,   lowercase_						:     Tuple=0			,   **lowercase_						:     Tuple			,   )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=vocab_size
             _lowerCamelCase       :		Optional[Any]		=[]
             self.cutoffs.extend(lowercase_ )
             if proj_share_all_but_first:
                    _lowerCamelCase       :		List[str]		=[False] + [True] * len(self.cutoffs )
             else:
                    _lowerCamelCase       :		str		=[False] + [False] * len(self.cutoffs )
             _lowerCamelCase       :		str		=d_model
             _lowerCamelCase       :		str		=d_embed
             _lowerCamelCase       :		str		=d_head
             _lowerCamelCase       :		Dict		=d_inner
             _lowerCamelCase       :		Union[str, Any]		=div_val
             _lowerCamelCase       :		Dict		=pre_lnorm
             _lowerCamelCase       :		Tuple		=n_layer
             _lowerCamelCase       :		Optional[Any]		=n_head
             _lowerCamelCase       :		List[Any]		=mem_len
             _lowerCamelCase       :		List[Any]		=same_length
             _lowerCamelCase       :		Optional[int]		=attn_type
             _lowerCamelCase       :		List[str]		=clamp_len
             _lowerCamelCase       :		int		=sample_softmax
             _lowerCamelCase       :		List[Any]		=adaptive
             _lowerCamelCase       :		List[str]		=dropout
             _lowerCamelCase       :		Any		=dropatt
             _lowerCamelCase       :		Dict		=untie_r
             _lowerCamelCase       :		List[Any]		=init
             _lowerCamelCase       :		List[str]		=init_range
             _lowerCamelCase       :		int		=proj_init_std
             _lowerCamelCase       :		Optional[int]		=init_std
             _lowerCamelCase       :		Tuple		=layer_norm_epsilon
             super().__init__(eos_token_id=lowercase_			,   **lowercase_ )






      @property
      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
             return -1






      @max_position_embeddings.setter
      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     int )					->						List[str]:
             """simple docstring"""




             raise NotImplementedError(
                 F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


