



import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class 				a_    (						_snake_case							):
						UpperCamelCase__ :		Union[str, Any]										=(VQDiffusionScheduler,)

						def       __a			(       self      :Tuple  ,		**_lowercase      :int)						->   Tuple:
												UpperCAmelCase_					=	{
												    '''num_vec_classes''': 4097,
												    '''num_train_timesteps''': 100,
												}

												config.update(**a__)
												return config

						def       __a			(       self      :Tuple  ,		_lowercase      :Optional[int])						->   Tuple:
												UpperCAmelCase_					=	4
												UpperCAmelCase_					=	8
												UpperCAmelCase_					=	8

												UpperCAmelCase_					=	torch.randint(0  ,		a__  ,		(batch_size, height * width))

												return sample

						@property
						def       __a			(       self      :str)						->   Tuple:
												assert False

						def       __a			(       self      :str  ,		_lowercase      :int)						->   List[str]:
												def model(_lowercase      :Any  ,		_lowercase      :List[str]  ,		*_lowercase      :Dict):
																		UpperCAmelCase_				, UpperCAmelCase_					=	sample.shape
																		UpperCAmelCase_					=	torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels))
																		UpperCAmelCase_					=	F.log_softmax(logits.double()  ,		dim=1).float()
																		return return_value

												return model

						def       __a			(       self      :Any)						->   List[Any]:
												for timesteps in [2, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=a__)

						def       __a			(       self      :List[str])						->   Tuple:
												for num_vec_classes in [5, 100, 1000, 4000]:
																		self.check_over_configs(num_vec_classes=a__)

						def       __a			(       self      :Optional[Any])						->   Union[str, Any]:
												for t in [0, 50, 99]:
																		self.check_over_forward(time_step=a__)




						def       __a			(       self      :Optional[Any])						->   Tuple:
												pass












"""simple docstring"""






from typing import Callable, Optional, Union

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


lowerCAmelCase_      =				logging.get_logger(__name__)

lowerCAmelCase_      =				{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	"""prophetnet"""
       lowerCAmelCase__    			=	["""past_key_values"""]
       lowerCAmelCase__    			=	{
           """num_attention_heads""": """num_encoder_attention_heads""",
       }






       def __init__( self     ,	a__ = 0.1     ,	a__ = "gelu"     ,	a__ = 3_05_22     ,	a__ = 10_24     ,	a__ = 40_96     ,	a__ = 12     ,	a__ = 16     ,	a__ = 40_96     ,	a__ = 12     ,	a__ = 16     ,	a__ = 0.1     ,	a__ = 0.1     ,	a__ = 5_12     ,	a__ = 0.02     ,	a__ = True     ,	a__ = True     ,	a__ = 0     ,	a__ = 2     ,	a__ = 32     ,	a__ = 1_28     ,	a__ = False     ,	a__ = 0.0     ,	a__ = True     ,	a__ = 0     ,	a__ = 1     ,	a__ = 2     ,	**a__     ,	):
              _UpperCAmelCase			     =  vocab_size
              _UpperCAmelCase			     =  hidden_size
              _UpperCAmelCase			     =  encoder_ffn_dim
              _UpperCAmelCase			     =  num_encoder_layers
              _UpperCAmelCase			     =  num_encoder_attention_heads
              _UpperCAmelCase			     =  decoder_ffn_dim
              _UpperCAmelCase			     =  num_decoder_layers
              _UpperCAmelCase			     =  num_decoder_attention_heads
              _UpperCAmelCase			     =  max_position_embeddings
              _UpperCAmelCase			     =  init_std  # Normal(0, this parameter)
              _UpperCAmelCase			     =  activation_function

              # parameters for prophetnet
              _UpperCAmelCase			     =  ngram
              _UpperCAmelCase			     =  num_buckets
              _UpperCAmelCase			     =  relative_max_distance
              _UpperCAmelCase			     =  disable_ngram_loss
              _UpperCAmelCase			     =  eps

              # 3 Types of Dropout
              _UpperCAmelCase			     =  attention_dropout
              _UpperCAmelCase			     =  activation_dropout
              _UpperCAmelCase			     =  dropout

              _UpperCAmelCase			     =  use_cache

              super().__init__(
                  pad_token_id=a__     ,	bos_token_id=a__     ,	eos_token_id=a__     ,	is_encoder_decoder=a__     ,	add_cross_attention=a__     ,	decoder_start_token_id=a__     ,	**a__     ,	)






       @property
       def 				__A			( self  ):
              return self.num_encoder_layers + self.num_decoder_layers






       @num_hidden_layers.setter
       def 				__A			( self     ,	a__  ):
              raise NotImplementedError(
                  'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
                  ' `num_decoder_layers`.'  )






