





import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


UpperCAmelCase_  							=				logging.get_logger(__name__)

enable_full_determinism()






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Any     	=	UNetaDModel
   a		:							str     	=	"sample"




   @property
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   4
       UpperCamelCase__							:       str      =   3
       UpperCamelCase__							:       Dict      =   (32, 32)

       UpperCamelCase__							:       Optional[int]      =   floats_tensor((batch_size, num_channels) + sizes							).to(__magic_name__							)
       UpperCamelCase__							:       List[str]      =   torch.tensor([10]							).to(__magic_name__							)

       return {"sample": noise, "timestep": time_step}




   @property
   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       return (3, 32, 32)




   @property
   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       return (3, 32, 32)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   {
           '''block_out_channels''': (32, 64),
           '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
           '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
           '''attention_head_dim''': 3,
           '''out_channels''': 3,
           '''in_channels''': 3,
           '''layers_per_block''': 2,
           '''sample_size''': 32,
       }
       UpperCamelCase__							:       Dict      =   self.dummy_input
       return init_dict, inputs_dict






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							str     	=	UNetaDModel
   a		:							Any     	=	"sample"




   @property
   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   4
       UpperCamelCase__							:       List[str]      =   4
       UpperCamelCase__							:       Optional[int]      =   (32, 32)

       UpperCamelCase__							:       Optional[Any]      =   floats_tensor((batch_size, num_channels) + sizes							).to(__magic_name__							)
       UpperCamelCase__							:       Any      =   torch.tensor([10]							).to(__magic_name__							)

       return {"sample": noise, "timestep": time_step}




   @property
   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       return (4, 32, 32)




   @property
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       return (4, 32, 32)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   {
           '''sample_size''': 32,
           '''in_channels''': 4,
           '''out_channels''': 4,
           '''layers_per_block''': 2,
           '''block_out_channels''': (32, 64),
           '''attention_head_dim''': 32,
           '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
           '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
       }
       UpperCamelCase__							:       str      =   self.dummy_input
       return init_dict, inputs_dict




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''',	output_loading_info=__magic_name__							)

       self.assertIsNotNone(__magic_name__							)
       self.assertEqual(len(loading_info['''missing_keys''']							),	0							)

       model.to(__magic_name__							)
       UpperCamelCase__							:       List[str]      =   model(**self.dummy_input							).sample

       assert image is not None, "Make sure output is not None"




   @unittest.skipIf(torch_device != '''cuda''',	'''This test is supposed to run on GPU'''							)
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''',	output_loading_info=__magic_name__							)
       model.to(__magic_name__							)
       UpperCamelCase__							:       List[Any]      =   model(**self.dummy_input							).sample

       assert image is not None, "Make sure output is not None"




   @unittest.skipIf(torch_device != '''cuda''',	'''This test is supposed to run on GPU'''							)
   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
       UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''',	output_loading_info=__magic_name__							)
       model_accelerate.to(__magic_name__							)
       model_accelerate.eval()

       UpperCamelCase__							:       Tuple      =   torch.randn(
           1,	model_accelerate.config.in_channels,	model_accelerate.config.sample_size,	model_accelerate.config.sample_size,	generator=torch.manual_seed(0							),	)
       UpperCamelCase__							:       Union[str, Any]      =   noise.to(__magic_name__							)
       UpperCamelCase__							:       Any      =   torch.tensor([10] * noise.shape[0]							).to(__magic_name__							)

       UpperCamelCase__							:       Optional[Any]      =   model_accelerate(__magic_name__,	__magic_name__							)['''sample''']

       # two models don't need to stay in the device at the same time
       del model_accelerate
       torch.cuda.empty_cache()
       gc.collect()

       UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   UNetaDModel.from_pretrained(
           '''fusing/unet-ldm-dummy-update''',	output_loading_info=__magic_name__,	low_cpu_mem_usage=__magic_name__							)
       model_normal_load.to(__magic_name__							)
       model_normal_load.eval()
       UpperCamelCase__							:       List[str]      =   model_normal_load(__magic_name__,	__magic_name__							)['''sample''']

       assert torch_all_close(__magic_name__,	__magic_name__,	rtol=1E-3							)




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''							)
       model.eval()
       model.to(__magic_name__							)

       UpperCamelCase__							:       Tuple      =   torch.randn(
           1,	model.config.in_channels,	model.config.sample_size,	model.config.sample_size,	generator=torch.manual_seed(0							),	)
       UpperCamelCase__							:       Tuple      =   noise.to(__magic_name__							)
       UpperCamelCase__							:       Optional[int]      =   torch.tensor([10] * noise.shape[0]							).to(__magic_name__							)

       with torch.no_grad():
           UpperCamelCase__							:       Any      =   model(__magic_name__,	__magic_name__							).sample

       UpperCamelCase__							:       str      =   output[0, -1, -3:, -3:].flatten().cpu()
       # fmt: off
       UpperCamelCase__							:       Dict      =   torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]							)
       # fmt: on

       self.assertTrue(torch_all_close(__magic_name__,	__magic_name__,	rtol=1E-3							)							)






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Optional[Any]     	=	UNetaDModel
   a		:							Optional[Any]     	=	"sample"




   @property
   def    UpperCamelCase__						(					self,	__magic_name__=(32, 32)							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   4
       UpperCamelCase__							:       Optional[Any]      =   3

       UpperCamelCase__							:       List[Any]      =   floats_tensor((batch_size, num_channels) + sizes							).to(__magic_name__							)
       UpperCamelCase__							:       Optional[int]      =   torch.tensor(batch_size * [10]							).to(dtype=torch.intaa,	device=__magic_name__							)

       return {"sample": noise, "timestep": time_step}




   @property
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       return (3, 32, 32)




   @property
   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       return (3, 32, 32)




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       int      =   {
           '''block_out_channels''': [32, 64, 64, 64],
           '''in_channels''': 3,
           '''layers_per_block''': 1,
           '''out_channels''': 3,
           '''time_embedding_type''': '''fourier''',
           '''norm_eps''': 1E-6,
           '''mid_block_scale_factor''': math.sqrt(2.0							),
           '''norm_num_groups''': None,
           '''down_block_types''': [
               '''SkipDownBlock2D''',
               '''AttnSkipDownBlock2D''',
               '''SkipDownBlock2D''',
               '''SkipDownBlock2D''',
           ],
           '''up_block_types''': [
               '''SkipUpBlock2D''',
               '''SkipUpBlock2D''',
               '''AttnSkipUpBlock2D''',
               '''SkipUpBlock2D''',
           ],
       }
       UpperCamelCase__							:       List[str]      =   self.dummy_input
       return init_dict, inputs_dict




   @slow
   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       str      =   UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256''',	output_loading_info=__magic_name__							)
       self.assertIsNotNone(__magic_name__							)
       self.assertEqual(len(loading_info['''missing_keys''']							),	0							)

       model.to(__magic_name__							)
       UpperCamelCase__							:       Dict      =   self.dummy_input
       UpperCamelCase__							:       Dict      =   floats_tensor((4, 3) + (256, 256)							).to(__magic_name__							)
       UpperCamelCase__							:       str      =   noise
       UpperCamelCase__							:       str      =   model(**__magic_name__							)

       assert image is not None, "Make sure output is not None"




   @slow
   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''							)
       model.to(__magic_name__							)

       UpperCamelCase__							:       List[str]      =   4
       UpperCamelCase__							:       Dict      =   3
       UpperCamelCase__							:       Any      =   (256, 256)

       UpperCamelCase__							:       Optional[Any]      =   torch.ones((batch_size, num_channels) + sizes							).to(__magic_name__							)
       UpperCamelCase__							:       List[str]      =   torch.tensor(batch_size * [1E-4]							).to(__magic_name__							)

       with torch.no_grad():
           UpperCamelCase__							:       List[str]      =   model(__magic_name__,	__magic_name__							).sample

       UpperCamelCase__							:       int      =   output[0, -3:, -3:, -1].flatten().cpu()
       # fmt: off
       UpperCamelCase__							:       int      =   torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608]							)
       # fmt: on

       self.assertTrue(torch_all_close(__magic_name__,	__magic_name__,	rtol=1E-2							)							)




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''							)
       model.to(__magic_name__							)

       UpperCamelCase__							:       List[Any]      =   4
       UpperCamelCase__							:       Tuple      =   3
       UpperCamelCase__							:       List[Any]      =   (32, 32)

       UpperCamelCase__							:       Any      =   torch.ones((batch_size, num_channels) + sizes							).to(__magic_name__							)
       UpperCamelCase__							:       List[Any]      =   torch.tensor(batch_size * [1E-4]							).to(__magic_name__							)

       with torch.no_grad():
           UpperCamelCase__							:       List[str]      =   model(__magic_name__,	__magic_name__							).sample

       UpperCamelCase__							:       List[Any]      =   output[0, -3:, -3:, -1].flatten().cpu()
       # fmt: off
       UpperCamelCase__							:       List[str]      =   torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]							)
       # fmt: on

       self.assertTrue(torch_all_close(__magic_name__,	__magic_name__,	rtol=1E-2							)							)




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       # not required for this model
       pass








import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
  import os

  import jax.numpy as jnp
  from jax import jit

  from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
  from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

  UpperCAmelCase_  							=				'0.12'  # assumed parallelism: 8


if is_torch_available():
  import torch




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Optional[Any]=None   )   ->			Union[str, Any]:
    if rng is None:
        UpperCamelCase__							:       Tuple      =   random.Random()

    UpperCamelCase__							:       List[str]      =   1
    for dim in shape:
        total_dims *= dim

    UpperCamelCase__							:       Tuple      =   []
    for _ in range(__UpperCAmelCase   ):
        values.append(rng.randint(0	,     vocab_size - 1   )   )

    UpperCamelCase__							:       Any      =   np.array(__UpperCAmelCase	,     dtype=jnp.intaa   ).reshape(__UpperCAmelCase   )

    return output




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[int]=None   )   ->			Any:
    UpperCamelCase__							:       Any      =   ids_tensor(__UpperCAmelCase	,     vocab_size=2	,     rng=__UpperCAmelCase   )
    # make sure that at least one token is attended to for each batch
    UpperCamelCase__							:       Tuple      =   1
    return attn_mask






@require_flax
class 					lowercase__ :

   '''simple docstring'''


   a		:							Optional[int]     	=	None
   a		:							Union[str, Any]     	=	()




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   self.model_tester.prepare_config_and_inputs_for_common()

       # cut to half length & take max batch_size 3
       UpperCamelCase__							:       str      =   2
       UpperCamelCase__							:       Dict      =   inputs['''input_ids'''].shape[-1] // 2
       UpperCamelCase__							:       int      =   inputs['''input_ids'''][:max_batch_size, :sequence_length]

       UpperCamelCase__							:       Tuple      =   jnp.ones_like(__magic_name__							)
       UpperCamelCase__							:       Any      =   attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       UpperCamelCase__							:       List[str]      =   input_ids.shape[-1] + 5
       if config.eos_token_id is not None and config.pad_token_id is None:
           # hack to allow generate for models such as GPT2 as is done in `generate()`
           UpperCamelCase__							:       Tuple      =   config.eos_token_id
       return config, input_ids, attention_mask, max_length




   @is_pt_flax_cross_test
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Optional[int]      =   False
       UpperCamelCase__							:       Optional[int]      =   max_length
       UpperCamelCase__							:       Dict      =   0

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       str      =   model_class(__magic_name__							)

           UpperCamelCase__							:       int      =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
           UpperCamelCase__							:       List[str]      =   getattr(__magic_name__,	__magic_name__							)
           UpperCamelCase__							:       List[Any]      =   pt_model_class(__magic_name__							).eval()
           UpperCamelCase__							:       int      =   load_flax_weights_in_pytorch_model(__magic_name__,	flax_model.params							)

           UpperCamelCase__							:       Optional[int]      =   flax_model.generate(__magic_name__							).sequences
           UpperCamelCase__							:       Union[str, Any]      =   pt_model.generate(torch.tensor(__magic_name__,	dtype=torch.long							)							)

           if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
               UpperCamelCase__							:       Optional[Any]      =   flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

           self.assertListEqual(pt_generation_outputs.numpy().tolist(),	flax_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Dict      =   False
       UpperCamelCase__							:       List[str]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Dict      =   model_class(__magic_name__							)

           UpperCamelCase__							:       List[Any]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       int      =   jit(model.generate							)
           UpperCamelCase__							:       Optional[Any]      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Union[str, Any]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Optional[int]      =   True
       UpperCamelCase__							:       List[str]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Dict      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Dict      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Optional[Any]      =   jit(model.generate							)
           UpperCamelCase__							:       Optional[int]      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       List[str]      =   False
       UpperCamelCase__							:       Dict      =   max_length
       UpperCamelCase__							:       str      =   2

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Optional[int]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       List[Any]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       str      =   jit(model.generate							)
           UpperCamelCase__							:       int      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Any      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Tuple      =   False
       UpperCamelCase__							:       Union[str, Any]      =   max_length
       UpperCamelCase__							:       Optional[Any]      =   2
       UpperCamelCase__							:       List[Any]      =   2

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       List[str]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       List[str]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[0],	input_ids.shape[0] * config.num_return_sequences							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       List[str]      =   True
       UpperCamelCase__							:       Any      =   max_length
       UpperCamelCase__							:       Union[str, Any]      =   0.8
       UpperCamelCase__							:       Dict      =   10
       UpperCamelCase__							:       Any      =   0.3
       UpperCamelCase__							:       str      =   1
       UpperCamelCase__							:       Union[str, Any]      =   8
       UpperCamelCase__							:       Union[str, Any]      =   9

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       int      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Union[str, Any]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Any      =   jit(model.generate							)
           UpperCamelCase__							:       List[Any]      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Any      =   max_length
       UpperCamelCase__							:       str      =   1
       UpperCamelCase__							:       Dict      =   8
       UpperCamelCase__							:       List[Any]      =   9

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Optional[int]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Dict      =   jit(model.generate							)
           UpperCamelCase__							:       int      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       int      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Dict      =   max_length
       UpperCamelCase__							:       List[Any]      =   2
       UpperCamelCase__							:       Dict      =   1
       UpperCamelCase__							:       int      =   8
       UpperCamelCase__							:       List[Any]      =   9

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Union[str, Any]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Optional[Any]      =   jit(model.generate							)
           UpperCamelCase__							:       Any      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self._get_input_ids_and_config()

       # pad attention mask on the left
       UpperCamelCase__							:       Optional[int]      =   attention_mask.at[(0, 0)].set(0							)

       UpperCamelCase__							:       Tuple      =   False
       UpperCamelCase__							:       str      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       int      =   model_class(__magic_name__							)

           UpperCamelCase__							:       List[Any]      =   model.generate(__magic_name__,	attention_mask=__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       str      =   jit(model.generate							)
           UpperCamelCase__							:       Optional[Any]      =   jit_generate(__magic_name__,	attention_mask=__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self._get_input_ids_and_config()

       # pad attention mask on the left
       UpperCamelCase__							:       Any      =   attention_mask.at[(0, 0)].set(0							)

       UpperCamelCase__							:       Any      =   True
       UpperCamelCase__							:       Union[str, Any]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Optional[Any]      =   model.generate(__magic_name__,	attention_mask=__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Union[str, Any]      =   jit(model.generate							)
           UpperCamelCase__							:       str      =   jit_generate(__magic_name__,	attention_mask=__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()

       # pad attention mask on the left
       UpperCamelCase__							:       Union[str, Any]      =   attention_mask.at[(0, 0)].set(0							)

       UpperCamelCase__							:       Optional[Any]      =   2
       UpperCamelCase__							:       List[str]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       int      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Any      =   model.generate(__magic_name__,	attention_mask=__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Tuple      =   jit(model.generate							)
           UpperCamelCase__							:       int      =   jit_generate(__magic_name__,	attention_mask=__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)






@require_flax
class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''							)
       UpperCamelCase__							:       Any      =   FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''							)

       UpperCamelCase__							:       int      =   '''Hello world'''
       UpperCamelCase__							:       Tuple      =   tokenizer(__magic_name__,	return_tensors='''np'''							).input_ids

       # typos are quickly detected (the correct argument is `do_sample`)
       with self.assertRaisesRegex(__magic_name__,	'''do_samples'''							):
           model.generate(__magic_name__,	do_samples=__magic_name__							)

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(__magic_name__,	'''foo'''							):
           UpperCamelCase__							:       Optional[Any]      =   {'''foo''': '''bar'''}
           model.generate(__magic_name__,	**__magic_name__							)

