



'''simple docstring'''

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 	a__     (				_lowercase	):
	__magic_name__		:    str	    =     (DDPMScheduler,)


	def 				lowercase__  (self : Dict,					**__UpperCAmelCase : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= {
					    '''num_train_timesteps''': 1000,
					    '''beta_start''': 0.0001,
					    '''beta_end''': 0.02,
					    '''beta_schedule''': '''linear''',
					    '''variance_type''': '''fixed_small''',
					    '''clip_sample''': True,
					}

					config.update(**__UpperCAmelCase      )
					return config


	def 				lowercase__  (self : Optional[Any]      )     ->			Tuple:

					"""simple docstring"""

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


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

					"""simple docstring"""

					for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],					[0.002, 0.02, 0.2, 2]      ):
									self.check_over_configs(beta_start=__UpperCAmelCase,					beta_end=__UpperCAmelCase      )


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

					"""simple docstring"""

					for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					for variance in ["fixed_small", "fixed_large", "other"]:
									self.check_over_configs(variance_type=__UpperCAmelCase      )


	def 				lowercase__  (self : str      )     ->			List[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Optional[int]      )     ->			List[Any]:

					"""simple docstring"""

					self.check_over_configs(thresholding=__UpperCAmelCase      )
					for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=__UpperCAmelCase,					prediction_type=__UpperCAmelCase,					sample_max_value=__UpperCAmelCase,					)


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

					"""simple docstring"""

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


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

					"""simple docstring"""

					for t in [0, 500, 999]:
									self.check_over_forward(time_step=__UpperCAmelCase      )


	def 				lowercase__  (self : int      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						Dict      					= scheduler_class(**__UpperCAmelCase      )

					assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0979      )      ) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1e-5


	def 				lowercase__  (self : Dict      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Any      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						Dict      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= len(__UpperCAmelCase      )

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

					for t in reversed(range(__UpperCAmelCase      )      ):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE  :						Tuple      					= model(__UpperCAmelCase,					__UpperCAmelCase      )

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE  :						Tuple      					= scheduler.step(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									SCREAMING_SNAKE_CASE  :						str      					= pred_prev_sample

					SCREAMING_SNAKE_CASE  :						Tuple      					= torch.sum(torch.abs(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						str      					= torch.mean(torch.abs(__UpperCAmelCase      )      )

					assert abs(result_sum.item() - 258.9606      ) < 1e-2
					assert abs(result_mean.item() - 0.3372      ) < 1e-3


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_scheduler_config(prediction_type='''v_prediction'''      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= len(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.dummy_model()
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.dummy_sample_deter
					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.manual_seed(0      )

					for t in reversed(range(__UpperCAmelCase      )      ):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE  :						Any      					= model(__UpperCAmelCase,					__UpperCAmelCase      )

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

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= pred_prev_sample

					SCREAMING_SNAKE_CASE  :						Any      					= torch.sum(torch.abs(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.mean(torch.abs(__UpperCAmelCase      )      )

					assert abs(result_sum.item() - 202.0296      ) < 1e-2
					assert abs(result_mean.item() - 0.2631      ) < 1e-3


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						int      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						int      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [100, 87, 50, 1, 0]

					scheduler.set_timesteps(timesteps=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= scheduler.timesteps

					for i, timestep in enumerate(__UpperCAmelCase      ):
									if i == len(__UpperCAmelCase      ) - 1:
													SCREAMING_SNAKE_CASE  :						str      					= -1
									else:
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= timesteps[i + 1]

									SCREAMING_SNAKE_CASE  :						Optional[int]      					= scheduler.previous_timestep(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Any      					= prev_t.item()

									self.assertEqual(__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : int      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= [100, 87, 50, 51, 0]

					with self.assertRaises(__UpperCAmelCase,					msg='''`custom_timesteps` must be in descending order.'''      ):
									scheduler.set_timesteps(timesteps=__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= [100, 87, 50, 1, 0]
					SCREAMING_SNAKE_CASE  :						List[str]      					= len(__UpperCAmelCase      )

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






	def 				lowercase__  (self : Tuple      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						Dict      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [scheduler.config.num_train_timesteps]

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









'''simple docstring'''

import functools
import operator

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


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class 	a__     (				_lowercase	):
	__magic_name__		:    Dict	    =     "wav2vec2"


	def __init__(self : Tuple,					__UpperCAmelCase : Tuple=32,					__UpperCAmelCase : int=768,					__UpperCAmelCase : Optional[Any]=12,					__UpperCAmelCase : Any=12,					__UpperCAmelCase : Any=3072,					__UpperCAmelCase : Optional[int]="gelu",					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : List[str]=0.1,					__UpperCAmelCase : List[Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.0,					__UpperCAmelCase : str=0.0,					__UpperCAmelCase : Optional[Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.02,					__UpperCAmelCase : Dict=1e-5,					__UpperCAmelCase : List[str]="group",					__UpperCAmelCase : str="gelu",					__UpperCAmelCase : str=(512, 512, 512, 512, 512, 512, 512),					__UpperCAmelCase : str=(5, 2, 2, 2, 2, 2, 2),					__UpperCAmelCase : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2),					__UpperCAmelCase : Any=False,					__UpperCAmelCase : Optional[Any]=128,					__UpperCAmelCase : Tuple=16,					__UpperCAmelCase : str=False,					__UpperCAmelCase : int=True,					__UpperCAmelCase : Tuple=0.05,					__UpperCAmelCase : List[Any]=10,					__UpperCAmelCase : Union[str, Any]=2,					__UpperCAmelCase : Optional[Any]=0.0,					__UpperCAmelCase : List[Any]=10,					__UpperCAmelCase : Dict=0,					__UpperCAmelCase : Any=320,					__UpperCAmelCase : int=2,					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : Tuple=100,					__UpperCAmelCase : Any=256,					__UpperCAmelCase : Tuple=256,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : Any="sum",					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : str=False,					__UpperCAmelCase : str=256,					__UpperCAmelCase : Union[str, Any]=(512, 512, 512, 512, 1500),					__UpperCAmelCase : Optional[int]=(5, 3, 3, 1, 1),					__UpperCAmelCase : Dict=(1, 2, 3, 1, 1),					__UpperCAmelCase : List[str]=512,					__UpperCAmelCase : Union[str, Any]=0,					__UpperCAmelCase : List[Any]=1,					__UpperCAmelCase : List[str]=2,					__UpperCAmelCase : List[Any]=False,					__UpperCAmelCase : str=3,					__UpperCAmelCase : Tuple=2,					__UpperCAmelCase : str=3,					__UpperCAmelCase : int=None,					__UpperCAmelCase : List[Any]=None,					**__UpperCAmelCase : int,					)     ->			Tuple:

					"""simple docstring"""

					super().__init__(**__UpperCAmelCase,					pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= feat_extract_norm
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= feat_extract_activation
					SCREAMING_SNAKE_CASE  :						Dict      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= conv_bias
					SCREAMING_SNAKE_CASE  :						Dict      					= num_conv_pos_embeddings
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_conv_pos_embedding_groups
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= len(self.conv_dim      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						str      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_act
					SCREAMING_SNAKE_CASE  :						str      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						int      					= hidden_dropout
					SCREAMING_SNAKE_CASE  :						Any      					= attention_dropout
					SCREAMING_SNAKE_CASE  :						int      					= activation_dropout
					SCREAMING_SNAKE_CASE  :						int      					= feat_proj_dropout
					SCREAMING_SNAKE_CASE  :						List[str]      					= final_dropout
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= layerdrop
					SCREAMING_SNAKE_CASE  :						Tuple      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						Dict      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= do_stable_layer_norm
					SCREAMING_SNAKE_CASE  :						Dict      					= use_weighted_layer_sum

					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
					SCREAMING_SNAKE_CASE  :						str      					= apply_spec_augment
					SCREAMING_SNAKE_CASE  :						Dict      					= mask_time_prob
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= mask_time_length
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= mask_time_min_masks
					SCREAMING_SNAKE_CASE  :						Dict      					= mask_feature_prob
					SCREAMING_SNAKE_CASE  :						Tuple      					= mask_feature_length
					SCREAMING_SNAKE_CASE  :						Tuple      					= mask_feature_min_masks

					# parameters for pretraining with codevector quantized representations
					SCREAMING_SNAKE_CASE  :						Any      					= num_codevectors_per_group
					SCREAMING_SNAKE_CASE  :						int      					= num_codevector_groups
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= contrastive_logits_temperature
					SCREAMING_SNAKE_CASE  :						List[Any]      					= feat_quantizer_dropout
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= num_negatives
					SCREAMING_SNAKE_CASE  :						Any      					= codevector_dim
					SCREAMING_SNAKE_CASE  :						str      					= proj_codevector_dim
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= diversity_loss_weight

					# ctc loss
					SCREAMING_SNAKE_CASE  :						Tuple      					= ctc_loss_reduction
					SCREAMING_SNAKE_CASE  :						Any      					= ctc_zero_infinity

					# adapter
					SCREAMING_SNAKE_CASE  :						int      					= add_adapter
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= adapter_kernel_size
					SCREAMING_SNAKE_CASE  :						int      					= adapter_stride
					SCREAMING_SNAKE_CASE  :						List[str]      					= num_adapter_layers
					SCREAMING_SNAKE_CASE  :						Tuple      					= output_hidden_size or hidden_size
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= adapter_attn_dim

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						Dict      					= classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						int      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= xvector_output_dim






	@property
	def 				lowercase__  (self : Tuple      )     ->			Optional[Any]:

					"""simple docstring"""

					return functools.reduce(operator.mul,					self.conv_stride,					1      )




