
import warnings

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


__lowerCAmelCase			:     List[Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Optional[int]           =   {
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""xlnet"""
  a__           =		["""mems"""]
  a__           =		{
      """n_token""": """vocab_size""",  # Backward compatibility
      """hidden_size""": """d_model""",
      """num_attention_heads""": """n_head""",
      """num_hidden_layers""": """n_layer""",
  }
  def __init__( self			: int			,							UpperCamelCase__			: List[str]=3_2000			,							UpperCamelCase__			: Optional[int]=1024			,							UpperCamelCase__			: Union[str, Any]=24			,							UpperCamelCase__			: Optional[int]=16			,							UpperCamelCase__			: List[str]=4096			,							UpperCamelCase__			: Union[str, Any]="gelu"			,							UpperCamelCase__			: Tuple=True			,							UpperCamelCase__			: Tuple="bi"			,							UpperCamelCase__			: List[str]=0.02			,							UpperCamelCase__			: List[str]=1E-12			,							UpperCamelCase__			: Tuple=0.1			,							UpperCamelCase__			: Tuple=512			,							UpperCamelCase__			: str=None			,							UpperCamelCase__			: Dict=True			,							UpperCamelCase__			: Optional[int]=False			,							UpperCamelCase__			: List[str]=False			,							UpperCamelCase__			: Any=-1			,							UpperCamelCase__			: Dict=False			,							UpperCamelCase__			: int="last"			,							UpperCamelCase__			: Optional[Any]=True			,							UpperCamelCase__			: List[Any]="tanh"			,							UpperCamelCase__			: Tuple=0.1			,							UpperCamelCase__			: List[str]=5			,							UpperCamelCase__			: Tuple=5			,							UpperCamelCase__			: str=5			,							UpperCamelCase__			: Optional[int]=1			,							UpperCamelCase__			: List[str]=2			,							**UpperCamelCase__			: List[Any]			,							)			->	Any:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       d_model
    __magic_name__				     =       n_layer
    __magic_name__				     =       n_head
    if d_model % n_head != 0:
      raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''    )
    if "d_head" in kwargs:
      if kwargs["d_head"] != d_model // n_head:
        raise ValueError(
            F'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''    )
    __magic_name__				     =       d_model // n_head
    __magic_name__				     =       ff_activation
    __magic_name__				     =       d_inner
    __magic_name__				     =       untie_r
    __magic_name__				     =       attn_type

    __magic_name__				     =       initializer_range
    __magic_name__				     =       layer_norm_eps

    __magic_name__				     =       dropout
    __magic_name__				     =       mem_len
    __magic_name__				     =       reuse_len
    __magic_name__				     =       bi_data
    __magic_name__				     =       clamp_len
    __magic_name__				     =       same_length

    __magic_name__				     =       summary_type
    __magic_name__				     =       summary_use_proj
    __magic_name__				     =       summary_activation
    __magic_name__				     =       summary_last_dropout
    __magic_name__				     =       start_n_top
    __magic_name__				     =       end_n_top

    __magic_name__				     =       bos_token_id
    __magic_name__				     =       pad_token_id
    __magic_name__				     =       eos_token_id

    if "use_cache" in kwargs:
      warnings.warn(
          """The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"""
          """ instead."""			,							UpperCamelCase__			,							)
      __magic_name__				     =       kwargs["""use_cache"""]

    __magic_name__				     =       use_mems_eval
    __magic_name__				     =       use_mems_train
    super().__init__(pad_token_id=UpperCamelCase__			,							bos_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							**UpperCamelCase__    )
  @property
  def 							_lowercase							( self			: int    )			->	Tuple:







    """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 							_lowercase							( self			: Any			,							UpperCamelCase__			: Optional[Any]    )			->	Dict:







    """simple docstring"""






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



'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

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

     assert image.shape == (1, 32, 32, 3)
     _a				:	List[Any]			=   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

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







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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



