





"""simple docstring"""





import functools
import operator

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


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       str	 =		"""sew"""




				def __init__(     self  :Any						,    __lowercase  :int=32						,    __lowercase  :Union[str, Any]=768						,    __lowercase  :List[str]=12						,    __lowercase  :Tuple=12						,    __lowercase  :List[Any]=3072						,    __lowercase  :Optional[Any]=2						,    __lowercase  :Tuple="gelu"						,    __lowercase  :Tuple=0.1						,    __lowercase  :List[str]=0.1						,    __lowercase  :int=0.1						,    __lowercase  :Optional[Any]=0.0						,    __lowercase  :str=0.1						,    __lowercase  :Tuple=0.1						,    __lowercase  :Union[str, Any]=0.02						,    __lowercase  :Optional[int]=1e-5						,    __lowercase  :Union[str, Any]="group"						,    __lowercase  :List[Any]="gelu"						,    __lowercase  :int=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,    __lowercase  :Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,    __lowercase  :Any=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,    __lowercase  :Tuple=False						,    __lowercase  :Optional[Any]=128						,    __lowercase  :List[str]=16						,    __lowercase  :Any=True						,    __lowercase  :Tuple=0.05						,    __lowercase  :Any=10						,    __lowercase  :Optional[Any]=2						,    __lowercase  :Optional[Any]=0.0						,    __lowercase  :Any=10						,    __lowercase  :Optional[Any]=0						,    __lowercase  :Any="mean"						,    __lowercase  :List[Any]=False						,    __lowercase  :List[Any]=False						,    __lowercase  :Dict=256						,    __lowercase  :Optional[int]=0						,    __lowercase  :Optional[Any]=1						,    __lowercase  :List[str]=2						,    **__lowercase  :List[str]						,    ):
											super().__init__(**__lowercase						,    pad_token_id=__lowercase						,    bos_token_id=__lowercase						,    eos_token_id=__lowercase			)
											__lowerCamelCase      :      Dict         =hidden_size
											__lowerCamelCase      :      Any         =feat_extract_norm
											__lowerCamelCase      :      str         =feat_extract_activation
											__lowerCamelCase      :      Dict         =list(__lowercase			)
											__lowerCamelCase      :      Tuple         =list(__lowercase			)
											__lowerCamelCase      :      int         =list(__lowercase			)
											__lowerCamelCase      :      Optional[Any]         =conv_bias
											__lowerCamelCase      :      Tuple         =num_conv_pos_embeddings
											__lowerCamelCase      :      Tuple         =num_conv_pos_embedding_groups
											__lowerCamelCase      :      str         =len(self.conv_dim			)
											__lowerCamelCase      :      str         =num_hidden_layers
											__lowerCamelCase      :      Optional[Any]         =intermediate_size
											__lowerCamelCase      :      str         =squeeze_factor
											__lowerCamelCase      :      Dict         =hidden_act
											__lowerCamelCase      :      List[str]         =num_attention_heads
											__lowerCamelCase      :      List[Any]         =hidden_dropout
											__lowerCamelCase      :      str         =attention_dropout
											__lowerCamelCase      :      Tuple         =activation_dropout
											__lowerCamelCase      :      int         =feat_proj_dropout
											__lowerCamelCase      :      List[str]         =final_dropout
											__lowerCamelCase      :      int         =layerdrop
											__lowerCamelCase      :      str         =layer_norm_eps
											__lowerCamelCase      :      List[Any]         =initializer_range
											__lowerCamelCase      :      Dict         =vocab_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)`,'''
																		    f'but is `len(config.conv_dim) = {len(self.conv_dim			)}`, `len(config.conv_stride)'
																		    f'= {len(self.conv_stride			)}`, `len(config.conv_kernel) = {len(self.conv_kernel			)}`.'			)

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

											# ctc loss
											__lowerCamelCase      :      int         =ctc_loss_reduction
											__lowerCamelCase      :      Tuple         =ctc_zero_infinity

											# sequence classification
											__lowerCamelCase      :      Tuple         =use_weighted_layer_sum
											__lowerCamelCase      :      Dict         =classifier_proj_size




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











"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[Any]	 =		(UnCLIPScheduler,)




				def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
											__lowerCamelCase      :      Optional[int]         ={
											    '''num_train_timesteps''': 1000,
											    '''variance_type''': '''fixed_small_log''',
											    '''clip_sample''': True,
											    '''clip_sample_range''': 1.0,
											    '''prediction_type''': '''epsilon''',
											}

											config.update(**__lowercase			)
											return config




				def 					__lowercase  (     self  :Tuple			):
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase			)




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




				def 					__lowercase  (     self  :List[str]			):
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=__lowercase			)




				def 					__lowercase  (     self  :List[Any]			):
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=__lowercase			)




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




				def 					__lowercase  (     self  :Union[str, Any]			):
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

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




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      str         =self.get_scheduler_config(variance_type='''fixed_small_log'''			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0e-1_0			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0549625			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(variance_type='''learned_range'''			)
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Optional[int]         =0.5

											assert scheduler._get_variance(1						,    predicted_variance=__lowercase			) - -10.1712790 < 1e-5
											assert scheduler._get_variance(487						,    predicted_variance=__lowercase			) - -5.7998052 < 1e-5
											assert scheduler._get_variance(999						,    predicted_variance=__lowercase			) - -0.0010011 < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config()
											__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Tuple         =scheduler.timesteps

											__lowerCamelCase      :      Any         =self.dummy_model()
											__lowerCamelCase      :      Any         =self.dummy_sample_deter
											__lowerCamelCase      :      List[Any]         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Tuple         =model(__lowercase						,    __lowercase			)

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      List[Any]         =pred_prev_sample

											__lowerCamelCase      :      Optional[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 252.2682495			) < 1e-2
											assert abs(result_mean.item() - 0.3284743			) < 1e-3




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      Any         =self.get_scheduler_config()
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											scheduler.set_timesteps(25			)

											__lowerCamelCase      :      Dict         =scheduler.timesteps

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

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    __lowercase			)

																		if i + 1 == timesteps.shape[0]:
																									__lowerCamelCase      :      Any         =None
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =timesteps[i + 1]

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[str]         =scheduler.step(
																		    __lowercase						,    __lowercase						,    __lowercase						,    prev_timestep=__lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      int         =pred_prev_sample

											__lowerCamelCase      :      List[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 258.2044983			) < 1e-2
											assert abs(result_mean.item() - 0.3362038			) < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											pass




				def 					__lowercase  (     self  :Tuple			):
											pass




