


'''simple docstring'''



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    (DDIMParallelScheduler,)
	A_				=    (("eta", 0.0), ("num_inference_steps", 50))




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  {
						    'num_train_timesteps': 1000,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'clip_sample': True,
						}

						config.update(**__a							)
						return config




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						__a			:       int					    =  self.scheduler_classes[0]
						__a			:       List[Any]					    =  self.get_scheduler_config(**__a							)
						__a			:       Tuple					    =  scheduler_class(**__a							)

						__a    ,				__a			:       Optional[Any]					    =  10, 0.0

						__a			:       List[str]					    =  self.dummy_model()
						__a			:       Optional[int]					    =  self.dummy_sample_deter

						scheduler.set_timesteps(__a							)

						for t in scheduler.timesteps:
											__a			:       Dict					    =  model(__a						,				__a							)
											__a			:       Tuple					    =  scheduler.step(__a						,				__a						,				__a						,				__a							).prev_sample

						return sample




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for timesteps in [100, 500, 1000]:
											self.check_over_configs(num_train_timesteps=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=__a							)

						__a			:       Tuple					    =  self.scheduler_classes[0]
						__a			:       Optional[Any]					    =  self.get_scheduler_config(steps_offset=1							)
						__a			:       Optional[int]					    =  scheduler_class(**__a							)
						scheduler.set_timesteps(5							)
						assert torch.equal(scheduler.timesteps						,				torch.LongTensor([801, 601, 401, 201, 1]							)							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]						,				[0.002, 0.02, 0.2, 2]							):
											self.check_over_configs(beta_start=__a						,				beta_end=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.check_over_configs(thresholding=__a							)
						for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=__a						,				prediction_type=__a						,				sample_max_value=__a						,				)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for t in [1, 10, 49]:
											self.check_over_forward(time_step=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for t, num_inference_steps in zip([1, 10, 50]						,				[10, 50, 500]							):
											self.check_over_forward(time_step=__a						,				num_inference_steps=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for t, eta in zip([1, 10, 49]						,				[0.0, 0.5, 1.0]							):
											self.check_over_forward(time_step=__a						,				eta=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.scheduler_classes[0]
						__a			:       List[str]					    =  self.get_scheduler_config()
						__a			:       List[Any]					    =  scheduler_class(**__a							)

						assert torch.sum(torch.abs(scheduler._get_variance(0						,				0							) - 0.0							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420						,				400							) - 0.14771							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980						,				960							) - 0.32460							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0						,				0							) - 0.0							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487						,				486							) - 0.00979							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999						,				998							) - 0.02							)							) < 1E-5




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  self.scheduler_classes[0]
						__a			:       Any					    =  self.get_scheduler_config()
						__a			:       int					    =  scheduler_class(**__a							)

						__a    ,				__a			:       int					    =  10, 0.0
						scheduler.set_timesteps(__a							)

						__a			:       Optional[int]					    =  self.dummy_model()
						__a			:       List[Any]					    =  self.dummy_sample_deter
						__a			:       Union[str, Any]					    =  self.dummy_sample_deter + 0.1
						__a			:       List[Any]					    =  self.dummy_sample_deter - 0.1

						__a			:       Dict					    =  samplea.shape[0]
						__a			:       Optional[Any]					    =  torch.stack([samplea, samplea, samplea]						,				dim=0							)
						__a			:       Dict					    =  torch.arange(__a							)[0:3, None].repeat(1						,				__a							)

						__a			:       Dict					    =  model(samples.flatten(0						,				1							)						,				timesteps.flatten(0						,				1							)							)
						__a			:       Union[str, Any]					    =  scheduler.batch_step_no_noise(__a						,				timesteps.flatten(0						,				1							)						,				samples.flatten(0						,				1							)						,				__a							)

						__a			:       Any					    =  torch.sum(torch.abs(__a							)							)
						__a			:       Tuple					    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 1147.7904							) < 1E-2
						assert abs(result_mean.item() - 0.4982							) < 1E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  self.full_loop()

						__a			:       Optional[Any]					    =  torch.sum(torch.abs(__a							)							)
						__a			:       str					    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 172.0067							) < 1E-2
						assert abs(result_mean.item() - 0.223967							) < 1E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  self.full_loop(prediction_type='v_prediction'							)

						__a			:       List[str]					    =  torch.sum(torch.abs(__a							)							)
						__a			:       str					    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 52.5302							) < 1E-2
						assert abs(result_mean.item() - 0.0684							) < 1E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.full_loop(set_alpha_to_one=__a						,				beta_start=0.01							)
						__a			:       List[str]					    =  torch.sum(torch.abs(__a							)							)
						__a			:       Union[str, Any]					    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 149.8295							) < 1E-2
						assert abs(result_mean.item() - 0.1951							) < 1E-3





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.full_loop(set_alpha_to_one=__a						,				beta_start=0.01							)
						__a			:       Optional[Any]					    =  torch.sum(torch.abs(__a							)							)
						__a			:       List[str]					    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 149.0784							) < 1E-2
						assert abs(result_mean.item() - 0.1941							) < 1E-3







'''simple docstring'''



from typing import Mapping

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


__lowercase	:						Dict       =	logging.get_logger(__name__)

__lowercase	:						Optional[Any]       =	{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "umt5"
	A_				=    ["past_key_values"]




	def __init__(   self						,				__a=25_0112						,				__a=512						,				__a=64						,				__a=1024						,				__a=8						,				__a=None						,				__a=6						,				__a=32						,				__a=128						,				__a=0.1						,				__a=1E-6						,				__a=1.0						,				__a="gated-gelu"						,				__a=True						,				__a=True						,				__a="T5Tokenizer"						,				__a=True						,				__a=0						,				__a=1						,				__a=0						,				**__a						,				):

						'''simple docstring'''





						super().__init__(
						    is_encoder_decoder=__a						,				tokenizer_class=__a						,				tie_word_embeddings=__a						,				pad_token_id=__a						,				eos_token_id=__a						,				decoder_start_token_id=__a						,				**__a						,				)
						__a			:       Any					    =  vocab_size
						__a			:       Any					    =  d_model
						__a			:       str					    =  d_kv
						__a			:       Dict					    =  d_ff
						__a			:       Union[str, Any]					    =  num_layers
						__a			:       int					    =  (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__a			:       Optional[int]					    =  num_heads
						__a			:       Tuple					    =  relative_attention_num_buckets
						__a			:       Optional[Any]					    =  relative_attention_max_distance
						__a			:       Optional[int]					    =  dropout_rate
						__a			:       List[Any]					    =  layer_norm_epsilon
						__a			:       int					    =  initializer_factor
						__a			:       Union[str, Any]					    =  feed_forward_proj
						__a			:       Any					    =  use_cache

						__a			:       List[Any]					    =  self.feed_forward_proj.split('-'							)
						__a			:       Dict					    =  act_info[-1]
						__a			:       Dict					    =  act_info[0] == 'gated'

						if len(__a							) > 1 and act_info[0] != "gated" or len(__a							) > 2:
											raise ValueError(
											    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\''							)

						if feed_forward_proj == "gated-gelu":
											__a			:       Optional[int]					    =  'gelu_new'




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.d_model




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_heads





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_layers





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  {
						    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
						    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
						}
						if self.use_past:
											__a			:       Dict					    =  'past_encoder_sequence + sequence'
											__a			:       Tuple					    =  {0: 'batch'}
											__a			:       Tuple					    =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
											__a			:       List[Any]					    =  {0: 'batch', 1: 'decoder_sequence'}
											__a			:       int					    =  {0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
											self.fill_with_past_key_values_(__a						,				direction='inputs'							)

						return common_inputs




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 13





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 5E-4



