





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__lowerCAmelCase			:Any	 =     logging.get_logger(__name__)

__lowerCAmelCase			:Dict	 =     {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}






class 							_a(     UpperCAmelCase__     ):
		lowerCamelCase__  :List[Any] 					=		"plbart"
		lowerCamelCase__  :int 					=		["past_key_values"]
		lowerCamelCase__  :str 					=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







		def __init__(     self			,    __snake_case=5_0_0_0_5			,    __snake_case=1_0_2_4			,    __snake_case=6			,    __snake_case=3_0_7_2			,    __snake_case=1_2			,    __snake_case=6			,    __snake_case=3_0_7_2			,    __snake_case=1_2			,    __snake_case=0.0			,    __snake_case=0.0			,    __snake_case=True			,    __snake_case=True			,    __snake_case="gelu"			,    __snake_case=7_6_8			,    __snake_case=0.1			,    __snake_case=0.1			,    __snake_case=0.0			,    __snake_case=0.02			,    __snake_case=0.0			,    __snake_case=True			,    __snake_case=1			,    __snake_case=0			,    __snake_case=2			,    __snake_case=2			,    **__snake_case			,    )   ->   Union[str, Any]:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= vocab_size
							_snake_case							:       Optional[int]												= max_position_embeddings
							_snake_case							:       int												= d_model
							_snake_case							:       Union[str, Any]												= encoder_ffn_dim
							_snake_case							:       List[Any]												= encoder_layers
							_snake_case							:       Optional[Any]												= encoder_attention_heads
							_snake_case							:       Dict												= decoder_ffn_dim
							_snake_case							:       List[Any]												= decoder_layers
							_snake_case							:       Any												= decoder_attention_heads
							_snake_case							:       Dict												= dropout
							_snake_case							:       List[Any]												= attention_dropout
							_snake_case							:       Union[str, Any]												= activation_dropout
							_snake_case							:       Optional[Any]												= activation_function
							_snake_case							:       Union[str, Any]												= init_std
							_snake_case							:       Dict												= encoder_layerdrop
							_snake_case							:       Optional[int]												= decoder_layerdrop
							_snake_case							:       Tuple												= classifier_dropout
							_snake_case							:       List[str]												= use_cache
							_snake_case							:       Any												= encoder_layers
							_snake_case							:       Optional[int]												= scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=snake_case__			,    bos_token_id=snake_case__			,    eos_token_id=snake_case__			,    is_encoder_decoder=snake_case__			,    forced_eos_token_id=snake_case__			,    **snake_case__			,    )






class 							_a(     UpperCAmelCase__     ):







		@property
		def 							lowercase							(     self					)   ->   Tuple:


							'''simple docstring'''

							return OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "sequence"}),
							        ("attention_mask", {0: "batch", 1: "sequence"}),
							    ]					)







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


							'''simple docstring'''

							if self.use_past:
												return OrderedDict(
												    [
												        ("last_hidden_state", {0: "batch", 1: "sequence"}),
												        ("past_keys", {0: "batch", 2: "sequence"}),
												        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
												    ]					)
							else:
												return OrderedDict(
												    [
												        ("last_hidden_state", {0: "batch", 1: "sequence"}),
												        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
												    ]					)





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class        __lowerCAmelCase					(			UpperCAmelCase__					):
			snake_case_ :					List[str]						=	(VQDiffusionScheduler,)







			def UpperCamelCase     (			self       :     Dict       ,       **snake_case__       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	{
									    "num_vec_classes": 4_097,
									    "num_train_timesteps": 100,
									}

									config.update(**snake_case__ )
									return config







			def UpperCamelCase     (			self       :     Optional[int]       ,       snake_case__       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	4
									_UpperCAmelCase						=	8
									_UpperCAmelCase						=	8

									_UpperCAmelCase						=	torch.randint(0       ,       snake_case__       ,       (batch_size, height * width) )

									return sample







			@property
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									assert False







			def UpperCamelCase     (			self       :     List[str]       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


									def model(snake_case__       :     Dict       ,       snake_case__       :     Tuple       ,       *snake_case__       :     str ):
															_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 UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for timesteps in [2, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for num_vec_classes in [5, 100, 1_000, 4_000]:
															self.check_over_configs(num_vec_classes=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									for t in [0, 50, 99]:
															self.check_over_forward(time_step=snake_case__ )







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									pass




