


"""simple docstring"""







import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      (VQDiffusionScheduler,)






							def 					lowerCAmelCase     (						self :     Dict		, **lowerCamelCase_ :     Dict				):
													UpperCAmelCase__		=    {
													    '''num_vec_classes''': 4097,
													    '''num_train_timesteps''': 100,
													}

													config.update(**lowerCamelCase_				)
													return config






							def 					lowerCAmelCase     (						self :     Optional[int]		, lowerCamelCase_ :     Union[str, Any]				):
													UpperCAmelCase__		=    4
													UpperCAmelCase__		=    8
													UpperCAmelCase__		=    8

													UpperCAmelCase__		=    torch.randint(0		, lowerCamelCase_		, (batch_size, height * width)				)

													return sample






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													assert False






							def 					lowerCAmelCase     (						self :     Any		, lowerCamelCase_ :     List[Any]				):
													def model(lowerCamelCase_ :     Tuple		, lowerCamelCase_ :     List[Any]		, *lowerCamelCase_ :     Union[str, Any]				):
																			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 					lowerCAmelCase     (						self :     Union[str, Any]				):
													for timesteps in [2, 5, 100, 1000]:
																			self.check_over_configs(num_train_timesteps=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[str]				):
													for num_vec_classes in [5, 100, 1000, 4000]:
																			self.check_over_configs(num_vec_classes=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Union[str, Any]				):
													for t in [0, 50, 99]:
																			self.check_over_forward(time_step=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Dict				):
													pass



"""simple docstring"""







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


__a			 =   logging.get_logger(__name__)

__a			 =   {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """pegasus_x"""
							SCREAMING_SNAKE_CASE										=      ["""past_key_values"""]
							SCREAMING_SNAKE_CASE										=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(						self :     int		, lowerCamelCase_ :     Any=96103		, lowerCamelCase_ :     Union[str, Any]=16384		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     str=4096		, lowerCamelCase_ :     Tuple=16		, lowerCamelCase_ :     Optional[int]=16		, lowerCamelCase_ :     Tuple=4096		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     List[str]=0.0		, lowerCamelCase_ :     int=0.0		, lowerCamelCase_ :     List[Any]=True		, lowerCamelCase_ :     str=True		, lowerCamelCase_ :     int="gelu"		, lowerCamelCase_ :     Optional[Any]=1024		, lowerCamelCase_ :     Optional[int]=0.1		, lowerCamelCase_ :     str=0.0		, lowerCamelCase_ :     Optional[Any]=0.0		, lowerCamelCase_ :     List[str]=0.02		, lowerCamelCase_ :     Optional[Any]=0		, lowerCamelCase_ :     List[str]=True		, lowerCamelCase_ :     List[Any]=0		, lowerCamelCase_ :     str=1		, lowerCamelCase_ :     Optional[Any]=1		, lowerCamelCase_ :     Tuple=32		, lowerCamelCase_ :     Dict=512		, lowerCamelCase_ :     List[Any]=True		, **lowerCamelCase_ :     int		, ):
													UpperCAmelCase__		=    vocab_size
													UpperCAmelCase__		=    max_position_embeddings
													UpperCAmelCase__		=    d_model
													UpperCAmelCase__		=    encoder_ffn_dim
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    encoder_attention_heads
													UpperCAmelCase__		=    decoder_ffn_dim
													UpperCAmelCase__		=    decoder_layers
													UpperCAmelCase__		=    decoder_attention_heads
													UpperCAmelCase__		=    dropout
													UpperCAmelCase__		=    attention_dropout
													UpperCAmelCase__		=    activation_dropout
													UpperCAmelCase__		=    activation_function
													UpperCAmelCase__		=    init_std
													UpperCAmelCase__		=    encoder_layerdrop
													UpperCAmelCase__		=    decoder_layerdrop
													UpperCAmelCase__		=    use_cache
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    scale_embedding  # scale factor will be sqrt(d_model) if True

													UpperCAmelCase__		=    num_global_tokens
													UpperCAmelCase__		=    block_size
													UpperCAmelCase__		=    stagger_local_blocks

													super().__init__(
													    pad_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, is_encoder_decoder=lowerCamelCase_		, decoder_start_token_id=lowerCamelCase_		, forced_eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.encoder_attention_heads






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.d_model