





"""simple docstring"""


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[Any]										=  (CMStochasticIterativeScheduler,)
			__lowerCAmelCase     :				Dict										=  10







			def   _a      (      self						,   **_A    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			{
									    """num_train_timesteps""": 2_0_1,
									    """sigma_min""": 0.0_02,
									    """sigma_max""": 80.0,
									}

									config.update(**_A    )
									return config







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			1_0

									UpperCamelCase				:		Union[str, Any]				   =			self.get_scheduler_config()
									UpperCamelCase				:		int				   =			self.scheduler_classes[0](**_A    )

									scheduler.set_timesteps(_A    )

									UpperCamelCase				:		Optional[int]				   =			scheduler.timesteps[0]
									UpperCamelCase				:		List[str]				   =			scheduler.timesteps[1]

									UpperCamelCase				:		str				   =			self.dummy_sample
									UpperCamelCase				:		int				   =			0.1 * sample

									UpperCamelCase				:		str				   =			scheduler.step(_A						,   _A						,   _A    ).prev_sample
									UpperCamelCase				:		Optional[Any]				   =			scheduler.step(_A						,   _A						,   _A    ).prev_sample

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







			def   _a      (      self    ):






									'''simple docstring'''







									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=_A    )







			def   _a      (      self    ):






									'''simple docstring'''







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

									UpperCamelCase				:		str				   =			1
									scheduler.set_timesteps(_A    )
									UpperCamelCase				:		Optional[int]				   =			scheduler.timesteps

									UpperCamelCase				:		Any				   =			torch.manual_seed(0    )

									UpperCamelCase				:		Union[str, Any]				   =			self.dummy_model()
									UpperCamelCase				:		Dict				   =			self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(_A    ):
															# 1. scale model input
															UpperCamelCase				:		Any				   =			scheduler.scale_model_input(_A						,   _A    )

															# 2. predict noise residual
															UpperCamelCase				:		List[str]				   =			model(_A						,   _A    )

															# 3. predict previous sample x_t-1
															UpperCamelCase				:		Optional[int]				   =			scheduler.step(_A						,   _A						,   _A						,   generator=_A    ).prev_sample

															UpperCamelCase				:		Tuple				   =			pred_prev_sample

									UpperCamelCase				:		Tuple				   =			torch.sum(torch.abs(_A    )    )
									UpperCamelCase				:		Optional[int]				   =			torch.mean(torch.abs(_A    )    )

									assert abs(result_sum.item() - 1_92.76_14    ) < 1e-2
									assert abs(result_mean.item() - 0.25_10    ) < 1e-3







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.scheduler_classes[0]
									UpperCamelCase				:		int				   =			self.get_scheduler_config()
									UpperCamelCase				:		List[str]				   =			scheduler_class(**_A    )

									UpperCamelCase				:		int				   =			[1_0_6, 0]
									scheduler.set_timesteps(timesteps=_A    )
									UpperCamelCase				:		int				   =			scheduler.timesteps

									UpperCamelCase				:		Optional[int]				   =			torch.manual_seed(0    )

									UpperCamelCase				:		List[str]				   =			self.dummy_model()
									UpperCamelCase				:		Union[str, Any]				   =			self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
															# 1. scale model input
															UpperCamelCase				:		int				   =			scheduler.scale_model_input(_A						,   _A    )

															# 2. predict noise residual
															UpperCamelCase				:		int				   =			model(_A						,   _A    )

															# 3. predict previous sample x_t-1
															UpperCamelCase				:		str				   =			scheduler.step(_A						,   _A						,   _A						,   generator=_A    ).prev_sample

															UpperCamelCase				:		int				   =			pred_prev_sample

									UpperCamelCase				:		Union[str, Any]				   =			torch.sum(torch.abs(_A    )    )
									UpperCamelCase				:		Dict				   =			torch.mean(torch.abs(_A    )    )

									assert abs(result_sum.item() - 3_47.63_57    ) < 1e-2
									assert abs(result_mean.item() - 0.45_27    ) < 1e-3







			def   _a      (      self    ):






									'''simple docstring'''







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

									UpperCamelCase				:		List[Any]				   =			[3_9, 3_0, 1_2, 1_5, 0]

									with self.assertRaises(_A						,   msg="""`timesteps` must be in descending order."""    ):
															scheduler.set_timesteps(timesteps=_A    )







			def   _a      (      self    ):






									'''simple docstring'''







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

									UpperCamelCase				:		Any				   =			[3_9, 3_0, 1_2, 1, 0]
									UpperCamelCase				:		Union[str, Any]				   =			len(_A    )

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			self.scheduler_classes[0]
									UpperCamelCase				:		Any				   =			self.get_scheduler_config()
									UpperCamelCase				:		int				   =			scheduler_class(**_A    )

									UpperCamelCase				:		Optional[int]				   =			[scheduler.config.num_train_timesteps]

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







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:       Optional[int]										=	logging.get_logger(__name__)

snake_case							:       Optional[int]										=	{
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'unispeech'




 def __init__(						self   ,    _lowerCamelCase=32   ,    _lowerCamelCase=768   ,    _lowerCamelCase=12   ,    _lowerCamelCase=12   ,    _lowerCamelCase=3072   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=1e-5   ,    _lowerCamelCase="group"   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=(512, 512, 512, 512, 512, 512, 512)   ,    _lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)   ,    _lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)   ,    _lowerCamelCase=False   ,    _lowerCamelCase=128   ,    _lowerCamelCase=16   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=0.05   ,    _lowerCamelCase=10   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=10   ,    _lowerCamelCase=0   ,    _lowerCamelCase=320   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=100   ,    _lowerCamelCase=256   ,    _lowerCamelCase=256   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase="mean"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=False   ,    _lowerCamelCase=256   ,    _lowerCamelCase=80   ,    _lowerCamelCase=0   ,    _lowerCamelCase=1   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.5   ,    **_lowerCamelCase   ,    ):
       super().__init__(**_lowerCamelCase   ,    pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase       )
       a		:Any									=			hidden_size
       a		:str									=			feat_extract_norm
       a		:List[Any]									=			feat_extract_activation
       a		:Tuple									=			list(_lowerCamelCase       )
       a		:Any									=			list(_lowerCamelCase       )
       a		:List[Any]									=			list(_lowerCamelCase       )
       a		:Union[str, Any]									=			conv_bias
       a		:str									=			num_conv_pos_embeddings
       a		:str									=			num_conv_pos_embedding_groups
       a		:Tuple									=			len(self.conv_dim       )
       a		:int									=			num_hidden_layers
       a		:Any									=			intermediate_size
       a		:Optional[Any]									=			hidden_act
       a		:Tuple									=			num_attention_heads
       a		:Any									=			hidden_dropout
       a		:Any									=			attention_dropout
       a		:Optional[Any]									=			activation_dropout
       a		:Optional[Any]									=			feat_proj_dropout
       a		:Any									=			final_dropout
       a		:int									=			layerdrop
       a		:int									=			layer_norm_eps
       a		:Dict									=			initializer_range
       a		:Dict									=			num_ctc_classes
       a		:Optional[Any]									=			vocab_size
       a		:str									=			do_stable_layer_norm
       a		:Tuple									=			use_weighted_layer_sum
       a		:Any									=			classifier_proj_size

       if (
           (len(self.conv_stride       ) != self.num_feat_extract_layers)
           or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
           or (len(self.conv_dim       ) != self.num_feat_extract_layers)
       ):
             raise ValueError(
                 '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                 ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                 F''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
                 F''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       a		:List[Any]									=			apply_spec_augment
       a		:Any									=			mask_time_prob
       a		:Union[str, Any]									=			mask_time_length
       a		:str									=			mask_time_min_masks
       a		:Tuple									=			mask_feature_prob
       a		:Dict									=			mask_feature_length
       a		:int									=			mask_feature_min_masks

       # parameters for pretraining with codevector quantized representations
       a		:Union[str, Any]									=			num_codevectors_per_group
       a		:Dict									=			num_codevector_groups
       a		:List[Any]									=			contrastive_logits_temperature
       a		:Union[str, Any]									=			feat_quantizer_dropout
       a		:Optional[Any]									=			num_negatives
       a		:Tuple									=			codevector_dim
       a		:Optional[Any]									=			proj_codevector_dim
       a		:Union[str, Any]									=			diversity_loss_weight

       # ctc loss
       a		:List[Any]									=			ctc_loss_reduction
       a		:Union[str, Any]									=			ctc_zero_infinity

       # pretraining loss
       a		:int									=			replace_prob




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return functools.reduce(operator.mul   ,    self.conv_stride   ,    1       )





