

import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler





class      snake_case_      (					unittest.TestCase				):


							"""simple docstring"""





							__UpperCAmelCase      =(ScoreSdeVeScheduler,)
							__UpperCAmelCase      =()




							@property
							def 							A__	(			self				):
												__lowerCAmelCase       =				4
												__lowerCAmelCase       =				3
												__lowerCAmelCase       =				8
												__lowerCAmelCase       =				8

												__lowerCAmelCase       =				torch.rand((batch_size, num_channels, height, width)				)

												return sample




							@property
							def 							A__	(			self				):
												__lowerCAmelCase       =				4
												__lowerCAmelCase       =				3
												__lowerCAmelCase       =				8
												__lowerCAmelCase       =				8

												__lowerCAmelCase       =				batch_size * num_channels * height * width
												__lowerCAmelCase       =				torch.arange(_lowerCAmelCase				)
												__lowerCAmelCase       =				sample.reshape(_lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase				)
												__lowerCAmelCase       =				sample / num_elems
												__lowerCAmelCase       =				sample.permute(3       ,    0       ,    1       ,    2				)

												return sample




							def 							A__	(			self				):
												def model(_A       ,    _A       ,    *_A				):
																	return sample * t / (t + 1)

												return model




							def 							A__	(			self       ,    **_A				):
												__lowerCAmelCase       =				{
												    """num_train_timesteps""": 2_0_0_0,
												    """snr""": 0.15,
												    """sigma_min""": 0.01,
												    """sigma_max""": 1_3_4_8,
												    """sampling_eps""": 1e-5,
												}

												config.update(**_lowerCAmelCase				)
												return config




							def 							A__	(			self       ,    _A=0       ,    **_A				):
												__lowerCAmelCase       =				dict(self.forward_default_kwargs				)

												for scheduler_class in self.scheduler_classes:
																	__lowerCAmelCase       =				self.dummy_sample
																	__lowerCAmelCase       =				0.1 * sample

																	__lowerCAmelCase       =				self.get_scheduler_config(**_lowerCAmelCase				)
																	__lowerCAmelCase       =				scheduler_class(**_lowerCAmelCase				)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_lowerCAmelCase				)
																						__lowerCAmelCase       =				scheduler_class.from_pretrained(_lowerCAmelCase				)

																	__lowerCAmelCase       =				scheduler.step_pred(
																	    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    generator=torch.manual_seed(0				)       ,    **_lowerCAmelCase				).prev_sample
																	__lowerCAmelCase       =				new_scheduler.step_pred(
																	    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    generator=torch.manual_seed(0				)       ,    **_lowerCAmelCase				).prev_sample

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

																	__lowerCAmelCase       =				scheduler.step_correct(_lowerCAmelCase       ,    _lowerCAmelCase       ,    generator=torch.manual_seed(0				)       ,    **_lowerCAmelCase				).prev_sample
																	__lowerCAmelCase       =				new_scheduler.step_correct(
																	    _lowerCAmelCase       ,    _lowerCAmelCase       ,    generator=torch.manual_seed(0				)       ,    **_lowerCAmelCase				).prev_sample

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




							def 							A__	(			self       ,    _A=0       ,    **_A				):
												__lowerCAmelCase       =				dict(self.forward_default_kwargs				)
												kwargs.update(_lowerCAmelCase				)

												for scheduler_class in self.scheduler_classes:
																	__lowerCAmelCase       =				self.dummy_sample
																	__lowerCAmelCase       =				0.1 * sample

																	__lowerCAmelCase       =				self.get_scheduler_config()
																	__lowerCAmelCase       =				scheduler_class(**_lowerCAmelCase				)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_lowerCAmelCase				)
																						__lowerCAmelCase       =				scheduler_class.from_pretrained(_lowerCAmelCase				)

																	__lowerCAmelCase       =				scheduler.step_pred(
																	    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    generator=torch.manual_seed(0				)       ,    **_lowerCAmelCase				).prev_sample
																	__lowerCAmelCase       =				new_scheduler.step_pred(
																	    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    generator=torch.manual_seed(0				)       ,    **_lowerCAmelCase				).prev_sample

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

																	__lowerCAmelCase       =				scheduler.step_correct(_lowerCAmelCase       ,    _lowerCAmelCase       ,    generator=torch.manual_seed(0				)       ,    **_lowerCAmelCase				).prev_sample
																	__lowerCAmelCase       =				new_scheduler.step_correct(
																	    _lowerCAmelCase       ,    _lowerCAmelCase       ,    generator=torch.manual_seed(0				)       ,    **_lowerCAmelCase				).prev_sample

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




							def 							A__	(			self				):
												for timesteps in [1_0, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_lowerCAmelCase				)




							def 							A__	(			self				):
												for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]       ,    [1, 1_0_0, 1_0_0_0]				):
																	self.check_over_configs(sigma_min=_lowerCAmelCase       ,    sigma_max=_lowerCAmelCase				)




							def 							A__	(			self				):
												for t in [0.1, 0.5, 0.75]:
																	self.check_over_forward(time_step=_lowerCAmelCase				)




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

												__lowerCAmelCase       =				self.scheduler_classes[0]
												__lowerCAmelCase       =				self.get_scheduler_config()
												__lowerCAmelCase       =				scheduler_class(**_lowerCAmelCase				)

												__lowerCAmelCase       =				3

												__lowerCAmelCase       =				self.dummy_model()
												__lowerCAmelCase       =				self.dummy_sample_deter

												scheduler.set_sigmas(_lowerCAmelCase				)
												scheduler.set_timesteps(_lowerCAmelCase				)
												__lowerCAmelCase       =				torch.manual_seed(0				)

												for i, t in enumerate(scheduler.timesteps				):
																	__lowerCAmelCase       =				scheduler.sigmas[i]

																	for _ in range(scheduler.config.correct_steps				):
																						with torch.no_grad():
																											__lowerCAmelCase       =				model(_lowerCAmelCase       ,    _lowerCAmelCase				)
																						__lowerCAmelCase       =				scheduler.step_correct(_lowerCAmelCase       ,    _lowerCAmelCase       ,    generator=_lowerCAmelCase       ,    **_lowerCAmelCase				).prev_sample

																	with torch.no_grad():
																						__lowerCAmelCase       =				model(_lowerCAmelCase       ,    _lowerCAmelCase				)

																	__lowerCAmelCase       =				scheduler.step_pred(_lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    generator=_lowerCAmelCase       ,    **_lowerCAmelCase				)
																	__lowerCAmelCase       =				output.prev_sample, output.prev_sample_mean

												__lowerCAmelCase       =				torch.sum(torch.abs(_lowerCAmelCase				)				)
												__lowerCAmelCase       =				torch.mean(torch.abs(_lowerCAmelCase				)				)

												assert np.isclose(result_sum.item()       ,    143_7275_8528.0				)
												assert np.isclose(result_mean.item()       ,    1871_4530.0				)




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

												__lowerCAmelCase       =				kwargs.pop('num_inference_steps'       ,    _lowerCAmelCase				)

												for scheduler_class in self.scheduler_classes:
																	__lowerCAmelCase       =				self.get_scheduler_config()
																	__lowerCAmelCase       =				scheduler_class(**_lowerCAmelCase				)

																	__lowerCAmelCase       =				self.dummy_sample
																	__lowerCAmelCase       =				0.1 * sample

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

																	__lowerCAmelCase       =				scheduler.step_pred(_lowerCAmelCase       ,    0       ,    _lowerCAmelCase       ,    generator=torch.manual_seed(0				)       ,    **_lowerCAmelCase				).prev_sample
																	__lowerCAmelCase       =				scheduler.step_pred(_lowerCAmelCase       ,    1       ,    _lowerCAmelCase       ,    generator=torch.manual_seed(0				)       ,    **_lowerCAmelCase				).prev_sample

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







from collections import OrderedDict
from typing import Mapping

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


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

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



