
from collections import OrderedDict
from typing import Mapping

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


lowercase__	:str										=					logging.get_logger(__name__)

lowercase__	:List[str]										=					{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class        lowercase	(      __SCREAMING_SNAKE_CASE	):
							lowercase_ : Any       ='''ibert'''




							def __init__(       self     ,A__=3_0_5_2_2     ,A__=7_6_8     ,A__=1_2     ,A__=1_2     ,A__=3_0_7_2     ,A__="gelu"     ,A__=0.1     ,A__=0.1     ,A__=5_1_2     ,A__=2     ,A__=0.02     ,A__=1E-12     ,A__=1     ,A__=0     ,A__=2     ,A__="absolute"     ,A__=False     ,A__="none"     ,**A__     ,):
								super().__init__(pad_token_id=__snake_case     ,bos_token_id=__snake_case     ,eos_token_id=__snake_case     ,**__snake_case)

								lowercase					=		vocab_size
								lowercase					=		hidden_size
								lowercase					=		num_hidden_layers
								lowercase					=		num_attention_heads
								lowercase					=		hidden_act
								lowercase					=		intermediate_size
								lowercase					=		hidden_dropout_prob
								lowercase					=		attention_probs_dropout_prob
								lowercase					=		max_position_embeddings
								lowercase					=		type_vocab_size
								lowercase					=		initializer_range
								lowercase					=		layer_norm_eps
								lowercase					=		position_embedding_type
								lowercase					=		quant_mode
								lowercase					=		force_dequant







class        lowercase	(      __SCREAMING_SNAKE_CASE	):




							@property
							def 				A__   (       self):
								if self.task == "multiple-choice":
									lowercase					=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
									lowercase					=		{0: '''batch''', 1: '''sequence'''}
								return OrderedDict(
								    [
								        ('''input_ids''', dynamic_axis),
								        ('''attention_mask''', dynamic_axis),
								    ])






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       =(UniPCMultistepScheduler,)
							lowercase_ : Tuple       =(('''num_inference_steps''', 25),)




							def 				A__   (       self     ,**A__):
												lowercase					=		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''solver_order''': 2,
												    '''solver_type''': '''bh2''',
												}

												config.update(**A__)
												return config




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)
																	# copy over dummy past residuals
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(A__)
																						lowercase					=		scheduler_class.from_pretrained(A__)
																						new_scheduler.set_timesteps(A__)
																						# copy over dummy past residuals
																						lowercase					=		dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowercase			,		lowercase					=		sample, sample
																	for t in range(A__     ,time_step + scheduler.config.solver_order + 1):
																						lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																						lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

																						assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)

																	# copy over dummy past residuals (must be after setting timesteps)
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(A__)
																						lowercase					=		scheduler_class.from_pretrained(A__)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(A__)

																						# copy over dummy past residual (must be after setting timesteps)
																						lowercase					=		dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

																	assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"




							def 				A__   (       self     ,A__=None     ,**A__):
												if scheduler is None:
																	lowercase					=		self.scheduler_classes[0]
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(**A__)
												lowercase					=		scheduler_class(**A__)

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter
												scheduler.set_timesteps(A__)

												for i, t in enumerate(scheduler.timesteps):
																	lowercase					=		model(A__     ,A__)
																	lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

												return sample




							def 				A__   (       self):
												lowercase					=		dict(self.forward_default_kwargs)

												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)

																	lowercase					=		self.dummy_sample
																	lowercase					=		0.1 * sample

																	if num_inference_steps is not None and hasattr(A__     ,'''set_timesteps'''):
																						scheduler.set_timesteps(A__)
																	elif num_inference_steps is not None and not hasattr(A__     ,'''set_timesteps'''):
																						lowercase					=		num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	lowercase					=		scheduler.timesteps[5]
																	lowercase					=		scheduler.timesteps[6]

																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

																	self.assertEqual(output_a.shape     ,sample.shape)
																	self.assertEqual(output_a.shape     ,output_a.shape)




							def 				A__   (       self):
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												lowercase					=		UniPCMultistepScheduler(**self.get_scheduler_config())
												lowercase					=		self.full_loop(scheduler=A__)
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3

												lowercase					=		DPMSolverSinglestepScheduler.from_config(scheduler.config)
												lowercase					=		DEISMultistepScheduler.from_config(scheduler.config)
												lowercase					=		DPMSolverMultistepScheduler.from_config(scheduler.config)
												lowercase					=		UniPCMultistepScheduler.from_config(scheduler.config)

												lowercase					=		self.full_loop(scheduler=A__)
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=A__)




							def 				A__   (       self):
												self.check_over_configs(thresholding=A__)
												for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=A__     ,prediction_type=A__     ,sample_max_value=A__     ,solver_order=A__     ,solver_type=A__     ,)




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




							def 				A__   (       self):
												for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																											self.check_over_configs(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											lowercase					=		self.full_loop(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											assert not torch.isnan(A__).any(), "Samples have nan numbers"




							def 				A__   (       self):
												self.check_over_configs(lower_order_final=A__)
												self.check_over_configs(lower_order_final=A__)




							def 				A__   (       self):
												for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=A__     ,time_step=0)




							def 				A__   (       self):
												lowercase					=		self.full_loop()
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.full_loop(prediction_type='''v_prediction''')
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.1014) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(thresholding=A__     ,dynamic_thresholding_ratio=0)
												lowercase					=		scheduler_class(**A__)

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter.half()
												scheduler.set_timesteps(A__)

												for i, t in enumerate(scheduler.timesteps):
																	lowercase					=		model(A__     ,A__)
																	lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

												assert sample.dtype == torch.floataa





							def 				A__   (       self     ,**A__):
												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps)
																	assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps




