
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class    _UpperCAmelCase(   SCREAMING_SNAKE_CASE__     ):
		lowercase__      =				(UnCLIPScheduler,)



		def 						UpperCAmelCase							( self    ,		**__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  {
							    '''num_train_timesteps''': 10_00,
							    '''variance_type''': '''fixed_small_log''',
							    '''clip_sample''': True,
							    '''clip_sample_range''': 1.0,
							    '''prediction_type''': '''epsilon''',
							}

							config.update(**a_)
							return config



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''



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



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''



							for variance in ["fixed_small_log", "learned_range"]:
												self.check_over_configs(variance_type=a_)



		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''



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



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''



							for clip_sample_range in [1, 5, 10, 20]:
												self.check_over_configs(clip_sample_range=a_)



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''



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



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''



							for time_step in [0, 5_00, 9_99]:
												for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
																	if prev_timestep is not None and prev_timestep >= time_step:
																						continue

																	self.check_over_forward(time_step=a_    ,		prev_timestep=a_)



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.scheduler_classes[0]
							_UpperCamelCase										=  self.get_scheduler_config(variance_type='''fixed_small_log''')
							_UpperCamelCase										=  scheduler_class(**a_)

							assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0_000e-10)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.054_9625)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.999_4987)) < 1e-5



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.scheduler_classes[0]
							_UpperCamelCase										=  self.get_scheduler_config(variance_type='''learned_range''')
							_UpperCamelCase										=  scheduler_class(**a_)

							_UpperCamelCase										=  0.5

							assert scheduler._get_variance(1    ,		predicted_variance=a_) - -10.171_2790 < 1e-5
							assert scheduler._get_variance(4_87    ,		predicted_variance=a_) - -5.799_8052 < 1e-5
							assert scheduler._get_variance(9_99    ,		predicted_variance=a_) - -0.001_0011 < 1e-5



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.scheduler_classes[0]
							_UpperCamelCase										=  self.get_scheduler_config()
							_UpperCamelCase										=  scheduler_class(**a_)

							_UpperCamelCase										=  scheduler.timesteps

							_UpperCamelCase										=  self.dummy_model()
							_UpperCamelCase										=  self.dummy_sample_deter
							_UpperCamelCase										=  torch.manual_seed(0)

							for i, t in enumerate(a_):
												# 1. predict noise residual
												_UpperCamelCase										=  model(a_    ,		a_)

												# 2. predict previous mean of sample x_t-1
												_UpperCamelCase										=  scheduler.step(a_    ,		a_    ,		a_    ,		generator=a_).prev_sample

												_UpperCamelCase										=  pred_prev_sample

							_UpperCamelCase										=  torch.sum(torch.abs(a_))
							_UpperCamelCase										=  torch.mean(torch.abs(a_))

							assert abs(result_sum.item() - 252.268_2495) < 1e-2
							assert abs(result_mean.item() - 0.328_4743) < 1e-3



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.scheduler_classes[0]
							_UpperCamelCase										=  self.get_scheduler_config()
							_UpperCamelCase										=  scheduler_class(**a_)

							scheduler.set_timesteps(25)

							_UpperCamelCase										=  scheduler.timesteps

							_UpperCamelCase										=  self.dummy_model()
							_UpperCamelCase										=  self.dummy_sample_deter
							_UpperCamelCase										=  torch.manual_seed(0)

							for i, t in enumerate(a_):
												# 1. predict noise residual
												_UpperCamelCase										=  model(a_    ,		a_)

												if i + 1 == timesteps.shape[0]:
																	_UpperCamelCase										=  None
												else:
																	_UpperCamelCase										=  timesteps[i + 1]

												# 2. predict previous mean of sample x_t-1
												_UpperCamelCase										=  scheduler.step(
												    a_    ,		a_    ,		a_    ,		prev_timestep=a_    ,		generator=a_).prev_sample

												_UpperCamelCase										=  pred_prev_sample

							_UpperCamelCase										=  torch.sum(torch.abs(a_))
							_UpperCamelCase										=  torch.mean(torch.abs(a_))

							assert abs(result_sum.item() - 258.204_4983) < 1e-2
							assert abs(result_mean.item() - 0.336_2038) < 1e-3



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							pass




		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							pass








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_a														=		logging.get_logger(__name__)

_a														=		{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bert'



		def __init__( self    ,		__a=3_05_22    ,		__a=7_68    ,		__a=12    ,		__a=12    ,		__a=30_72    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=2    ,		__a=0.02    ,		__a=1e-12    ,		__a=0    ,		__a="absolute"    ,		__a=True    ,		__a=None    ,		**__a    ,		)	->	Optional[Any]:






							'''simple docstring'''
							super().__init__(pad_token_id=__a    ,		**__a)

							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  classifier_dropout






class    _UpperCAmelCase(   lowerCamelCase     ):



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							if self.task == "multiple-choice":
												_UpperCamelCase										=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												_UpperCamelCase										=  {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							        ('''token_type_ids''', dynamic_axis),
							    ])






