





"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   str    =(PNDMScheduler,)
							lowerCamelCase  :   Any    =(("""num_inference_steps""", 50),)






							def        __a  (  self ,   **lowerCAmelCase__ )      ->					Union[str, Any]:
								a :						int      =							{
								    "num_train_timesteps": 1000,
								    "beta_start": 0.0_001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								}

								config.update(**lowerCAmelCase__ )
								return config






							def        __a  (  self ,   lowerCAmelCase__=0 ,   **lowerCAmelCase__ )      ->					Dict:
								a :						Union[str, Any]      =							dict(self.forward_default_kwargs )
								a :						Union[str, Any]      =							kwargs.pop("num_inference_steps" ,   lowerCAmelCase__ )
								a :						str      =							self.dummy_sample
								a :						str      =							0.1 * sample
								a :						Optional[Any]      =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
									a :						Dict      =							self.get_scheduler_config(**lowerCAmelCase__ )
									a :						Dict      =							scheduler_class(**lowerCAmelCase__ )
									scheduler.set_timesteps(lowerCAmelCase__ )
									# copy over dummy past residuals
									a :						Dict      =							dummy_past_residuals[:]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase__ )
										a :						Any      =							scheduler_class.from_pretrained(lowerCAmelCase__ )
										new_scheduler.set_timesteps(lowerCAmelCase__ )
										# copy over dummy past residuals
										a :						Optional[int]      =							dummy_past_residuals[:]

									a :						Tuple      =							scheduler.step_prk(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
									a :						str      =							new_scheduler.step_prk(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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

									a :						List[Any]      =							scheduler.step_plms(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
									a :						Union[str, Any]      =							new_scheduler.step_plms(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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






							def        __a  (  self )      ->					Any:
								pass






							def        __a  (  self ,   lowerCAmelCase__=0 ,   **lowerCAmelCase__ )      ->					Any:
								a :						int      =							dict(self.forward_default_kwargs )
								a :						Optional[int]      =							kwargs.pop("num_inference_steps" ,   lowerCAmelCase__ )
								a :						List[str]      =							self.dummy_sample
								a :						List[Any]      =							0.1 * sample
								a :						List[str]      =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
									a :						str      =							self.get_scheduler_config()
									a :						Dict      =							scheduler_class(**lowerCAmelCase__ )
									scheduler.set_timesteps(lowerCAmelCase__ )

									# copy over dummy past residuals (must be after setting timesteps)
									a :						Optional[int]      =							dummy_past_residuals[:]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase__ )
										a :						List[str]      =							scheduler_class.from_pretrained(lowerCAmelCase__ )
										# copy over dummy past residuals
										new_scheduler.set_timesteps(lowerCAmelCase__ )

										# copy over dummy past residual (must be after setting timesteps)
										a :						Tuple      =							dummy_past_residuals[:]

									a :						List[str]      =							scheduler.step_prk(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
									a :						Tuple      =							new_scheduler.step_prk(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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

									a :						List[Any]      =							scheduler.step_plms(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
									a :						List[Any]      =							new_scheduler.step_plms(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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






							def        __a  (  self ,   **lowerCAmelCase__ )      ->					Optional[Any]:
								a :						Any      =							self.scheduler_classes[0]
								a :						List[str]      =							self.get_scheduler_config(**lowerCAmelCase__ )
								a :						List[str]      =							scheduler_class(**lowerCAmelCase__ )

								a :						Dict      =							10
								a :						Optional[int]      =							self.dummy_model()
								a :						str      =							self.dummy_sample_deter
								scheduler.set_timesteps(lowerCAmelCase__ )

								for i, t in enumerate(scheduler.prk_timesteps ):
									a :						Any      =							model(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						List[Any]      =							scheduler.step_prk(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps ):
									a :						Any      =							model(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						List[str]      =							scheduler.step_plms(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ).prev_sample

								return sample






							def        __a  (  self )      ->					Optional[int]:
								a :						Dict      =							dict(self.forward_default_kwargs )

								a :						Any      =							kwargs.pop("num_inference_steps" ,   lowerCAmelCase__ )

								for scheduler_class in self.scheduler_classes:
									a :						Optional[int]      =							self.get_scheduler_config()
									a :						Optional[Any]      =							scheduler_class(**lowerCAmelCase__ )

									a :						List[str]      =							self.dummy_sample
									a :						str      =							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" ):
										a :						Optional[Any]      =							num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									a :						Any      =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
									a :						List[Any]      =							dummy_past_residuals[:]

									a :						Tuple      =							scheduler.step_prk(lowerCAmelCase__ ,   0 ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
									a :						int      =							scheduler.step_prk(lowerCAmelCase__ ,   1 ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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

									a :						Optional[int]      =							scheduler.step_plms(lowerCAmelCase__ ,   0 ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
									a :						Union[str, Any]      =							scheduler.step_plms(lowerCAmelCase__ ,   1 ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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






							def        __a  (  self )      ->					Optional[Any]:
								for timesteps in [100, 1000]:
									self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )






							def        __a  (  self )      ->					Dict:
								for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=lowerCAmelCase__ )

								a :						Optional[Any]      =							self.scheduler_classes[0]
								a :						Optional[Any]      =							self.get_scheduler_config(steps_offset=1 )
								a :						Any      =							scheduler_class(**lowerCAmelCase__ )
								scheduler.set_timesteps(10 )
								assert torch.equal(
								    scheduler.timesteps ,   torch.LongTensor(
								        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) ,   )






							def        __a  (  self )      ->					Any:
								for beta_start, beta_end in zip([0.0_001, 0.001] ,   [0.002, 0.02] ):
									self.check_over_configs(beta_start=lowerCAmelCase__ ,   beta_end=lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[Any]:
								for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[int]:
								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCAmelCase__ )






							def        __a  (  self )      ->					Tuple:
								for t in [1, 5, 10]:
									self.check_over_forward(time_step=lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[int]:
								for t, num_inference_steps in zip([1, 5, 10] ,   [10, 50, 100] ):
									self.check_over_forward(num_inference_steps=lowerCAmelCase__ )






							def        __a  (  self )      ->					Dict:
								# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
								a :						Any      =							27

								for scheduler_class in self.scheduler_classes:
									a :						List[Any]      =							self.dummy_sample
									a :						Union[str, Any]      =							0.1 * sample

									a :						str      =							self.get_scheduler_config()
									a :						Union[str, Any]      =							scheduler_class(**lowerCAmelCase__ )

									scheduler.set_timesteps(lowerCAmelCase__ )

									# before power of 3 fix, would error on first step, so we only need to do two
									for i, t in enumerate(scheduler.prk_timesteps[:2] ):
										a :						Any      =							scheduler.step_prk(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ).prev_sample






							def        __a  (  self )      ->					Any:
								with self.assertRaises(lowerCAmelCase__ ):
									a :						Tuple      =							self.scheduler_classes[0]
									a :						Union[str, Any]      =							self.get_scheduler_config()
									a :						Optional[int]      =							scheduler_class(**lowerCAmelCase__ )

									scheduler.step_plms(self.dummy_sample ,   1 ,   self.dummy_sample ).prev_sample






							def        __a  (  self )      ->					Any:
								a :						Any      =							self.full_loop()
								a :						Optional[Any]      =							torch.sum(torch.abs(lowerCAmelCase__ ) )
								a :						Dict      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_sum.item() - 198.1_318 ) < 1E-2
								assert abs(result_mean.item() - 0.2_580 ) < 1E-3






							def        __a  (  self )      ->					str:
								a :						Union[str, Any]      =							self.full_loop(prediction_type="v_prediction" )
								a :						int      =							torch.sum(torch.abs(lowerCAmelCase__ ) )
								a :						int      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_sum.item() - 67.3_986 ) < 1E-2
								assert abs(result_mean.item() - 0.0_878 ) < 1E-3






							def        __a  (  self )      ->					int:
								# We specify different beta, so that the first alpha is 0.99
								a :						Union[str, Any]      =							self.full_loop(set_alpha_to_one=lowerCAmelCase__ ,   beta_start=0.01 )
								a :						Optional[int]      =							torch.sum(torch.abs(lowerCAmelCase__ ) )
								a :						Optional[int]      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_sum.item() - 230.0_399 ) < 1E-2
								assert abs(result_mean.item() - 0.2_995 ) < 1E-3






							def        __a  (  self )      ->					int:
								# We specify different beta, so that the first alpha is 0.99
								a :						Dict      =							self.full_loop(set_alpha_to_one=lowerCAmelCase__ ,   beta_start=0.01 )
								a :						Any      =							torch.sum(torch.abs(lowerCAmelCase__ ) )
								a :						Optional[int]      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_sum.item() - 186.9_482 ) < 1E-2
								assert abs(result_mean.item() - 0.2_434 ) < 1E-3









"""simple docstring"""




import functools
import operator

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


a		:					List[Any]	      =     logging.get_logger(__name__)

a		:					List[Any]	      =     {
    '''microsoft/unispeech-sat-base-100h-libri-ft''': (
        '''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Tuple    ="""unispeech-sat"""






							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__=(512, 512, 512, 512, 1500) ,   lowerCAmelCase__=(5, 3, 3, 1, 1) ,   lowerCAmelCase__=(1, 2, 3, 1, 1) ,   lowerCAmelCase__=512 ,   lowerCAmelCase__=0 ,   lowerCAmelCase__=1 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=504 ,   **lowerCAmelCase__ ,   )      ->					List[str]:
								super().__init__(**lowerCAmelCase__ ,   pad_token_id=lowerCAmelCase__ ,   bos_token_id=lowerCAmelCase__ ,   eos_token_id=lowerCAmelCase__ )
								a :						Any      =							hidden_size
								a :						Tuple      =							feat_extract_norm
								a :						Tuple      =							feat_extract_activation
								a :						Dict      =							list(lowerCAmelCase__ )
								a :						int      =							list(lowerCAmelCase__ )
								a :						Optional[Any]      =							list(lowerCAmelCase__ )
								a :						int      =							conv_bias
								a :						str      =							num_conv_pos_embeddings
								a :						Dict      =							num_conv_pos_embedding_groups
								a :						Optional[int]      =							len(self.conv_dim )
								a :						int      =							num_hidden_layers
								a :						Any      =							intermediate_size
								a :						Any      =							hidden_act
								a :						List[Any]      =							num_attention_heads
								a :						Any      =							hidden_dropout
								a :						Union[str, Any]      =							attention_dropout
								a :						Tuple      =							activation_dropout
								a :						Dict      =							feat_proj_dropout
								a :						Optional[Any]      =							final_dropout
								a :						Union[str, Any]      =							layerdrop
								a :						str      =							layer_norm_eps
								a :						Optional[int]      =							initializer_range
								a :						Optional[int]      =							vocab_size
								a :						str      =							num_clusters
								a :						Any      =							do_stable_layer_norm
								a :						str      =							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
								a :						List[Any]      =							apply_spec_augment
								a :						int      =							mask_time_prob
								a :						Optional[int]      =							mask_time_length
								a :						Dict      =							mask_time_min_masks
								a :						Optional[int]      =							mask_feature_prob
								a :						List[str]      =							mask_feature_length
								a :						Dict      =							mask_feature_min_masks

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

								# ctc loss
								a :						Dict      =							ctc_loss_reduction
								a :						Optional[Any]      =							ctc_zero_infinity

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

								# XVector-specific parameters. Feel free to ignore for other classes.
								a :						Union[str, Any]      =							list(lowerCAmelCase__ )
								a :						List[str]      =							list(lowerCAmelCase__ )
								a :						Tuple      =							list(lowerCAmelCase__ )
								a :						Optional[int]      =							xvector_output_dim






							@property
							def        __a  (  self )      ->					Any:
								return functools.reduce(operator.mul ,   self.conv_stride ,   1 )


