



'''simple docstring'''




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		List[str]		      = (DDIMParallelScheduler,)
	__SCREAMING_SNAKE_CASE       :		Any		      = (('eta', 0.0), ('num_inference_steps', 50))


	def    _a     (self	,				**lowercase							):
				A_     :						Union[str, Any]     =					{
				    """num_train_timesteps""": 1000,
				    """beta_start""": 0.00_01,
				    """beta_end""": 0.02,
				    """beta_schedule""": """linear""",
				    """clip_sample""": True,
				}

				config.update(**lowercase							)
				return config


	def    _a     (self	,				**lowercase							):
				A_     :						List[Any]     =					self.scheduler_classes[0]
				A_     :						int     =					self.get_scheduler_config(**lowercase							)
				A_     :						List[str]     =					scheduler_class(**lowercase							)

				A_,   A_     :						int     =					10, 0.0

				A_     :						Optional[int]     =					self.dummy_model()
				A_     :						Any     =					self.dummy_sample_deter

				scheduler.set_timesteps(lowercase							)

				for t in scheduler.timesteps:
							A_     :						Dict     =					model(lowercase	,				lowercase							)
							A_     :						int     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				lowercase							).prev_sample

				return sample


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


	def    _a     (self							):
				for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=lowercase							)

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


	def    _a     (self							):
				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]	,				[0.0_02, 0.02, 0.2, 2]							):
							self.check_over_configs(beta_start=lowercase	,				beta_end=lowercase							)


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


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


	def    _a     (self							):
				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=lowercase							)


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


	def    _a     (self							):
				for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=lowercase							)


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


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


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


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


	def    _a     (self							):
				A_     :						int     =					self.scheduler_classes[0]
				A_     :						str     =					self.get_scheduler_config()
				A_     :						Any     =					scheduler_class(**lowercase							)

				assert torch.sum(torch.abs(scheduler._get_variance(0	,				0							) - 0.0							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(420	,				400							) - 0.1_47_71							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(980	,				960							) - 0.3_24_60							)							) < 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							) - 0.0_09_79							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(999	,				998							) - 0.02							)							) < 1E-5


	def    _a     (self							):
				A_     :						Optional[Any]     =					self.scheduler_classes[0]
				A_     :						Optional[int]     =					self.get_scheduler_config()
				A_     :						Any     =					scheduler_class(**lowercase							)

				A_,   A_     :						Optional[int]     =					10, 0.0
				scheduler.set_timesteps(lowercase							)

				A_     :						Any     =					self.dummy_model()
				A_     :						Dict     =					self.dummy_sample_deter
				A_     :						List[str]     =					self.dummy_sample_deter + 0.1
				A_     :						Any     =					self.dummy_sample_deter - 0.1

				A_     :						Optional[Any]     =					samplea.shape[0]
				A_     :						Optional[int]     =					torch.stack([samplea, samplea, samplea]	,				dim=0							)
				A_     :						str     =					torch.arange(lowercase							)[0:3, None].repeat(1	,				lowercase							)

				A_     :						Tuple     =					model(samples.flatten(0	,				1							)	,				timesteps.flatten(0	,				1							)							)
				A_     :						int     =					scheduler.batch_step_no_noise(lowercase	,				timesteps.flatten(0	,				1							)	,				samples.flatten(0	,				1							)	,				lowercase							)

				A_     :						int     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Union[str, Any]     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 11_47.79_04							) < 1E-2
				assert abs(result_mean.item() - 0.49_82							) < 1E-3


	def    _a     (self							):
				A_     :						Tuple     =					self.full_loop()

				A_     :						int     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Union[str, Any]     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 1_72.00_67							) < 1E-2
				assert abs(result_mean.item() - 0.22_39_67							) < 1E-3


	def    _a     (self							):
				A_     :						Union[str, Any]     =					self.full_loop(prediction_type="""v_prediction"""							)

				A_     :						Optional[Any]     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Optional[Any]     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 52.53_02							) < 1E-2
				assert abs(result_mean.item() - 0.06_84							) < 1E-3


	def    _a     (self							):
				# We specify different beta, so that the first alpha is 0.99
				A_     :						str     =					self.full_loop(set_alpha_to_one=lowercase	,				beta_start=0.01							)
				A_     :						int     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Tuple     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 1_49.82_95							) < 1E-2
				assert abs(result_mean.item() - 0.19_51							) < 1E-3


	def    _a     (self							):
				# We specify different beta, so that the first alpha is 0.99
				A_     :						List[str]     =					self.full_loop(set_alpha_to_one=lowercase	,				beta_start=0.01							)
				A_     :						List[Any]     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Any     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 1_49.07_84							) < 1E-2
				assert abs(result_mean.item() - 0.19_41							) < 1E-3




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase							:Any       = logging.get_logger(__name__)

lowerCamelCase							:List[Any]       = {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 'camembert'


	def __init__(self	,				lowercase=30522	,				lowercase=768	,				lowercase=12	,				lowercase=12	,				lowercase=3072	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=512	,				lowercase=2	,				lowercase=0.02	,				lowercase=1E-12	,				lowercase=1	,				lowercase=0	,				lowercase=2	,				lowercase="absolute"	,				lowercase=True	,				lowercase=None	,				**lowercase	,				):
				super().__init__(pad_token_id=lowercase	,				bos_token_id=lowercase	,				eos_token_id=lowercase	,				**lowercase							)

				A_     :						List[Any]     =					vocab_size
				A_     :						int     =					hidden_size
				A_     :						Dict     =					num_hidden_layers
				A_     :						Dict     =					num_attention_heads
				A_     :						Optional[Any]     =					hidden_act
				A_     :						str     =					intermediate_size
				A_     :						int     =					hidden_dropout_prob
				A_     :						List[Any]     =					attention_probs_dropout_prob
				A_     :						Optional[Any]     =					max_position_embeddings
				A_     :						Optional[int]     =					type_vocab_size
				A_     :						int     =					initializer_range
				A_     :						str     =					layer_norm_eps
				A_     :						int     =					position_embedding_type
				A_     :						Dict     =					use_cache
				A_     :						Any     =					classifier_dropout







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	@property
	def    _a     (self							):
				if self.task == "multiple-choice":
							A_     :						Optional[Any]     =					{0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
							A_     :						int     =					{0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				    ]							)