




"""simple docstring"""

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    List[str]         =   (DDPMScheduler,)







				def 					a_  (							self   :					Optional[int]      ,     **_lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Any	   =		{
					    '''num_train_timesteps''': 10_00,
					    '''beta_start''': 0.0001,
					    '''beta_end''': 0.02,
					    '''beta_schedule''': '''linear''',
					    '''variance_type''': '''fixed_small''',
					    '''clip_sample''': True,
					}

					config.update(**_lowerCamelCase       )
					return config







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					for timesteps in [1, 5, 1_00, 10_00]:
						self.check_over_configs(num_train_timesteps=_lowerCamelCase       )







				def 					a_  (							self   :					List[str]       ):



					"""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=_lowerCamelCase      ,     beta_end=_lowerCamelCase       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					for variance in ["fixed_small", "fixed_large", "other"]:
						self.check_over_configs(variance_type=_lowerCamelCase       )







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					for t in [0, 5_00, 9_99]:
						self.check_over_forward(time_step=_lowerCamelCase       )







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					A_  :				Tuple	   =		self.scheduler_classes[0]
					A_  :				List[str]	   =		self.get_scheduler_config()
					A_  :				Dict	   =		scheduler_class(**_lowerCamelCase       )

					assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_87       ) - 0.00979       )       ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99       ) - 0.02       )       ) < 1E-5







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				Tuple	   =		self.scheduler_classes[0]
					A_  :				Optional[int]	   =		self.get_scheduler_config()
					A_  :				Dict	   =		scheduler_class(**_lowerCamelCase       )

					A_  :				Tuple	   =		len(_lowerCamelCase       )

					A_  :				Optional[int]	   =		self.dummy_model()
					A_  :				Optional[Any]	   =		self.dummy_sample_deter
					A_  :				List[str]	   =		torch.manual_seed(0       )

					for t in reversed(range(_lowerCamelCase       )       ):
						# 1. predict noise residual
						A_  :				Optional[int]	   =		model(_lowerCamelCase      ,     _lowerCamelCase       )

						# 2. predict previous mean of sample x_t-1
						A_  :				str	   =		scheduler.step(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     generator=_lowerCamelCase       ).prev_sample

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						A_  :				int	   =		pred_prev_sample

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

					assert abs(result_sum.item() - 258.9606       ) < 1E-2
					assert abs(result_mean.item() - 0.3372       ) < 1E-3







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				List[str]	   =		self.scheduler_classes[0]
					A_  :				List[Any]	   =		self.get_scheduler_config(prediction_type='''v_prediction'''       )
					A_  :				Tuple	   =		scheduler_class(**_lowerCamelCase       )

					A_  :				Optional[Any]	   =		len(_lowerCamelCase       )

					A_  :				Optional[int]	   =		self.dummy_model()
					A_  :				Optional[Any]	   =		self.dummy_sample_deter
					A_  :				Union[str, Any]	   =		torch.manual_seed(0       )

					for t in reversed(range(_lowerCamelCase       )       ):
						# 1. predict noise residual
						A_  :				List[str]	   =		model(_lowerCamelCase      ,     _lowerCamelCase       )

						# 2. predict previous mean of sample x_t-1
						A_  :				Union[str, Any]	   =		scheduler.step(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     generator=_lowerCamelCase       ).prev_sample

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						A_  :				Any	   =		pred_prev_sample

					A_  :				Any	   =		torch.sum(torch.abs(_lowerCamelCase       )       )
					A_  :				Tuple	   =		torch.mean(torch.abs(_lowerCamelCase       )       )

					assert abs(result_sum.item() - 202.0296       ) < 1E-2
					assert abs(result_mean.item() - 0.2631       ) < 1E-3







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		self.scheduler_classes[0]
					A_  :				Union[str, Any]	   =		self.get_scheduler_config()
					A_  :				Union[str, Any]	   =		scheduler_class(**_lowerCamelCase       )

					A_  :				Dict	   =		[1_00, 87, 50, 1, 0]

					scheduler.set_timesteps(timesteps=_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		scheduler.timesteps

					for i, timestep in enumerate(_lowerCamelCase       ):
						if i == len(_lowerCamelCase       ) - 1:
							A_  :				List[Any]	   =		-1
						else:
							A_  :				Tuple	   =		timesteps[i + 1]

						A_  :				Dict	   =		scheduler.previous_timestep(_lowerCamelCase       )
						A_  :				Any	   =		prev_t.item()

						self.assertEqual(_lowerCamelCase      ,     _lowerCamelCase       )







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		self.scheduler_classes[0]
					A_  :				List[Any]	   =		self.get_scheduler_config()
					A_  :				Any	   =		scheduler_class(**_lowerCamelCase       )

					A_  :				Optional[int]	   =		[1_00, 87, 50, 51, 0]

					with self.assertRaises(_lowerCamelCase      ,     msg='''`custom_timesteps` must be in descending order.'''       ):
						scheduler.set_timesteps(timesteps=_lowerCamelCase       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		self.scheduler_classes[0]
					A_  :				str	   =		self.get_scheduler_config()
					A_  :				str	   =		scheduler_class(**_lowerCamelCase       )

					A_  :				List[Any]	   =		[1_00, 87, 50, 1, 0]
					A_  :				Any	   =		len(_lowerCamelCase       )

					with self.assertRaises(_lowerCamelCase      ,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''       ):
						scheduler.set_timesteps(num_inference_steps=_lowerCamelCase      ,     timesteps=_lowerCamelCase       )







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		self.scheduler_classes[0]
					A_  :				Optional[int]	   =		self.get_scheduler_config()
					A_  :				Union[str, Any]	   =		scheduler_class(**_lowerCamelCase       )

					A_  :				Dict	   =		[scheduler.config.num_train_timesteps]

					with self.assertRaises(
					    _lowerCamelCase      ,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''      ,     ):
						scheduler.set_timesteps(timesteps=_lowerCamelCase       )











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase  :			List[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Tuple        =       {
    '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        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   """camembert"""







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[Any]=3_05_22      ,     _lowerCamelCase   :					List[str]=7_68      ,     _lowerCamelCase   :					Optional[Any]=12      ,     _lowerCamelCase   :					List[str]=12      ,     _lowerCamelCase   :					Any=30_72      ,     _lowerCamelCase   :					List[str]="gelu"      ,     _lowerCamelCase   :					Optional[Any]=0.1      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					str=2      ,     _lowerCamelCase   :					Optional[Any]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					str=1      ,     _lowerCamelCase   :					Union[str, Any]=0      ,     _lowerCamelCase   :					Optional[int]=2      ,     _lowerCamelCase   :					List[str]="absolute"      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					List[Any]=None      ,     **_lowerCamelCase   :					Union[str, Any]      ,     ):



					"""simple docstring"""

					super().__init__(pad_token_id=_lowerCamelCase      ,     bos_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase       )

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

class        lowercase      (    __UpperCAmelCase):







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					if self.task == "multiple-choice":
						A_  :				Union[str, Any]	   =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
						A_  :				Dict	   =		{0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					    ]       )





