

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


_SCREAMING_SNAKE_CASE					:  Tuple             =      logging.get_logger(__name__)

enable_full_determinism()






class 	UpperCAmelCase__		(	A__						,					A__						,					unittest.TestCase	):






  """simple docstring"""


  a									= UNetaDModel
  a									= "sample"






  @property
  def 				lowercase_		(							self						:		Optional[int] )		->							str:
       SCREAMING_SNAKE_CASE__   =   4
       SCREAMING_SNAKE_CASE__   =   3
       SCREAMING_SNAKE_CASE__   =   (32, 32)

       SCREAMING_SNAKE_CASE__   =   floats_tensor((batch_size, num_channels) + sizes ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   torch.tensor([10] ).to(__lowerCamelCase )

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






  @property
  def 				lowercase_		(							self						:		List[Any] )		->							Any:
       return (3, 32, 32)






  @property
  def 				lowercase_		(							self						:		int )		->							Dict:
       return (3, 32, 32)







  def 				lowercase_		(							self						:		Any )		->							Any:
       SCREAMING_SNAKE_CASE__   =   {
           '''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,
       }
       SCREAMING_SNAKE_CASE__   =   self.dummy_input
       return init_dict, inputs_dict






class 	UpperCAmelCase__		(	A__						,					A__						,					unittest.TestCase	):






  """simple docstring"""


  a									= UNetaDModel
  a									= "sample"






  @property
  def 				lowercase_		(							self						:		Any )		->							str:
       SCREAMING_SNAKE_CASE__   =   4
       SCREAMING_SNAKE_CASE__   =   4
       SCREAMING_SNAKE_CASE__   =   (32, 32)

       SCREAMING_SNAKE_CASE__   =   floats_tensor((batch_size, num_channels) + sizes ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   torch.tensor([10] ).to(__lowerCamelCase )

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






  @property
  def 				lowercase_		(							self						:		Dict )		->							int:
       return (4, 32, 32)






  @property
  def 				lowercase_		(							self						:		int )		->							List[Any]:
       return (4, 32, 32)






  def 				lowercase_		(							self						:		Tuple )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   {
           '''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'''),
       }
       SCREAMING_SNAKE_CASE__   =   self.dummy_input
       return init_dict, inputs_dict






  def 				lowercase_		(							self						:		Union[str, Any] )		->							Dict:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''     ,       output_loading_info=__lowerCamelCase )

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

       model.to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   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 				lowercase_		(							self						:		Any )		->							List[Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''     ,       output_loading_info=__lowerCamelCase )
       model.to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   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 				lowercase_		(							self						:		Optional[Any] )		->							Any:
       # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''     ,       output_loading_info=__lowerCamelCase )
       model_accelerate.to(__lowerCamelCase )
       model_accelerate.eval()

       SCREAMING_SNAKE_CASE__   =   torch.randn(
           1     ,       model_accelerate.config.in_channels     ,       model_accelerate.config.sample_size     ,       model_accelerate.config.sample_size     ,       generator=torch.manual_seed(0 )     ,       )
       SCREAMING_SNAKE_CASE__   =   noise.to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   torch.tensor([10] * noise.shape[0] ).to(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   model_accelerate(__lowerCamelCase     ,       __lowerCamelCase )['''sample''']

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

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   UNetaDModel.from_pretrained(
           '''fusing/unet-ldm-dummy-update'''     ,       output_loading_info=__lowerCamelCase     ,       low_cpu_mem_usage=__lowerCamelCase )
       model_normal_load.to(__lowerCamelCase )
       model_normal_load.eval()
       SCREAMING_SNAKE_CASE__   =   model_normal_load(__lowerCamelCase     ,       __lowerCamelCase )['''sample''']

       assert torch_all_close(__lowerCamelCase     ,       __lowerCamelCase     ,       rtol=1e-3 )







  def 				lowercase_		(							self						:		Optional[int] )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' )
       model.eval()
       model.to(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   torch.randn(
           1     ,       model.config.in_channels     ,       model.config.sample_size     ,       model.config.sample_size     ,       generator=torch.manual_seed(0 )     ,       )
       SCREAMING_SNAKE_CASE__   =   noise.to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   torch.tensor([10] * noise.shape[0] ).to(__lowerCamelCase )

       with torch.no_grad():
            SCREAMING_SNAKE_CASE__   =   model(__lowerCamelCase     ,       __lowerCamelCase ).sample

       SCREAMING_SNAKE_CASE__   =   output[0, -1, -3:, -3:].flatten().cpu()
       # fmt: off
       SCREAMING_SNAKE_CASE__   =   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(__lowerCamelCase     ,       __lowerCamelCase     ,       rtol=1e-3 ) )






class 	UpperCAmelCase__		(	A__						,					A__						,					unittest.TestCase	):






  """simple docstring"""


  a									= UNetaDModel
  a									= "sample"






  @property
  def 				lowercase_		(							self						:		Any     ,       __lowerCamelCase						:		Any=(32, 32) )		->							str:
       SCREAMING_SNAKE_CASE__   =   4
       SCREAMING_SNAKE_CASE__   =   3

       SCREAMING_SNAKE_CASE__   =   floats_tensor((batch_size, num_channels) + sizes ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   torch.tensor(batch_size * [10] ).to(dtype=torch.intaa     ,       device=__lowerCamelCase )

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






  @property
  def 				lowercase_		(							self						:		Union[str, Any] )		->							Any:
       return (3, 32, 32)






  @property
  def 				lowercase_		(							self						:		str )		->							Union[str, Any]:
       return (3, 32, 32)






  def 				lowercase_		(							self						:		Any )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   {
           '''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''',
           ],
       }
       SCREAMING_SNAKE_CASE__   =   self.dummy_input
       return init_dict, inputs_dict






  @slow
  def 				lowercase_		(							self						:		Union[str, Any] )		->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''     ,       output_loading_info=__lowerCamelCase )
       self.assertIsNotNone(__lowerCamelCase )
       self.assertEqual(len(loading_info['''missing_keys'''] )     ,       0 )

       model.to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   self.dummy_input
       SCREAMING_SNAKE_CASE__   =   floats_tensor((4, 3) + (256, 256) ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   noise
       SCREAMING_SNAKE_CASE__   =   model(**__lowerCamelCase )

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






  @slow
  def 				lowercase_		(							self						:		Optional[Any] )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256''' )
       model.to(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   4
       SCREAMING_SNAKE_CASE__   =   3
       SCREAMING_SNAKE_CASE__   =   (256, 256)

       SCREAMING_SNAKE_CASE__   =   torch.ones((batch_size, num_channels) + sizes ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   torch.tensor(batch_size * [1e-4] ).to(__lowerCamelCase )

       with torch.no_grad():
            SCREAMING_SNAKE_CASE__   =   model(__lowerCamelCase     ,       __lowerCamelCase ).sample

       SCREAMING_SNAKE_CASE__   =   output[0, -3:, -3:, -1].flatten().cpu()
       # fmt: off
       SCREAMING_SNAKE_CASE__   =   torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608] )
       # fmt: on

       self.assertTrue(torch_all_close(__lowerCamelCase     ,       __lowerCamelCase     ,       rtol=1e-2 ) )






  def 				lowercase_		(							self						:		Optional[Any] )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update''' )
       model.to(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   4
       SCREAMING_SNAKE_CASE__   =   3
       SCREAMING_SNAKE_CASE__   =   (32, 32)

       SCREAMING_SNAKE_CASE__   =   torch.ones((batch_size, num_channels) + sizes ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   torch.tensor(batch_size * [1e-4] ).to(__lowerCamelCase )

       with torch.no_grad():
            SCREAMING_SNAKE_CASE__   =   model(__lowerCamelCase     ,       __lowerCamelCase ).sample

       SCREAMING_SNAKE_CASE__   =   output[0, -3:, -3:, -1].flatten().cpu()
       # fmt: off
       SCREAMING_SNAKE_CASE__   =   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(__lowerCamelCase     ,       __lowerCamelCase     ,       rtol=1e-2 ) )







  def 				lowercase_		(							self						:		int )		->							Any:
       # not required for this model
       pass





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


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

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






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "transfo-xl"
  a									= ["mems"]
  a									= {
      "n_token": "vocab_size",
      "hidden_size": "d_model",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Any     ,       __lowerCamelCase						:		int=26_7735     ,       __lowerCamelCase						:		Any=[2_0000, 4_0000, 20_0000]     ,       __lowerCamelCase						:		Dict=1024     ,       __lowerCamelCase						:		Optional[int]=1024     ,       __lowerCamelCase						:		List[Any]=16     ,       __lowerCamelCase						:		Union[str, Any]=64     ,       __lowerCamelCase						:		Dict=4096     ,       __lowerCamelCase						:		int=4     ,       __lowerCamelCase						:		Dict=False     ,       __lowerCamelCase						:		Tuple=18     ,       __lowerCamelCase						:		Optional[int]=1600     ,       __lowerCamelCase						:		str=1000     ,       __lowerCamelCase						:		Any=True     ,       __lowerCamelCase						:		Dict=True     ,       __lowerCamelCase						:		List[Any]=0     ,       __lowerCamelCase						:		int=-1     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		List[Any]=0.1     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		str="normal"     ,       __lowerCamelCase						:		List[str]=0.01     ,       __lowerCamelCase						:		Any=0.01     ,       __lowerCamelCase						:		List[str]=0.02     ,       __lowerCamelCase						:		List[str]=1e-5     ,       __lowerCamelCase						:		Union[str, Any]=0     ,       **__lowerCamelCase						:		int     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   []
       self.cutoffs.extend(__lowerCamelCase )
       if proj_share_all_but_first:
            SCREAMING_SNAKE_CASE__   =   [False] + [True] * len(self.cutoffs )
       else:
            SCREAMING_SNAKE_CASE__   =   [False] + [False] * len(self.cutoffs )
       SCREAMING_SNAKE_CASE__   =   d_model
       SCREAMING_SNAKE_CASE__   =   d_embed
       SCREAMING_SNAKE_CASE__   =   d_head
       SCREAMING_SNAKE_CASE__   =   d_inner
       SCREAMING_SNAKE_CASE__   =   div_val
       SCREAMING_SNAKE_CASE__   =   pre_lnorm
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   mem_len
       SCREAMING_SNAKE_CASE__   =   same_length
       SCREAMING_SNAKE_CASE__   =   attn_type
       SCREAMING_SNAKE_CASE__   =   clamp_len
       SCREAMING_SNAKE_CASE__   =   sample_softmax
       SCREAMING_SNAKE_CASE__   =   adaptive
       SCREAMING_SNAKE_CASE__   =   dropout
       SCREAMING_SNAKE_CASE__   =   dropatt
       SCREAMING_SNAKE_CASE__   =   untie_r
       SCREAMING_SNAKE_CASE__   =   init
       SCREAMING_SNAKE_CASE__   =   init_range
       SCREAMING_SNAKE_CASE__   =   proj_init_std
       SCREAMING_SNAKE_CASE__   =   init_std
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       super().__init__(eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )






  @property
  def 				lowercase_		(							self						:		str )		->							Dict:
       # Message copied from Transformer-XL documentation
       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 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Any )		->							List[Any]:
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


