
"""simple docstring"""







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 				A__ (			_lowerCamelCase ,  _lowerCamelCase):
		A_		: List[str]            =			1

		@register_to_config
		def __init__(   self ,	_SCREAMING_SNAKE_CASE=20_00 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=20 ,	_SCREAMING_SNAKE_CASE=1E-3				):
			__lowerCAmelCase    :		int		    =   None
			__lowerCAmelCase    :		Optional[int]		    =   None
			__lowerCAmelCase    :		Tuple		    =   None

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		Optional[Any]		    =   torch.linspace(1 ,	self.config.sampling_eps ,	_SCREAMING_SNAKE_CASE ,	device=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=None				):
			if self.timesteps is None:
				raise ValueError(
				    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'				)

			# TODO(Patrick) better comments + non-PyTorch
			# postprocess model score
			__lowerCAmelCase    :		Dict		    =   (
			    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
			)
			__lowerCAmelCase    :		List[str]		    =   torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
			__lowerCAmelCase    :		Optional[int]		    =   std.flatten()
			while len(std.shape				) < len(score.shape				):
				__lowerCAmelCase    :		Optional[int]		    =   std.unsqueeze(-1				)
			__lowerCAmelCase    :		Union[str, Any]		    =   -score / std

			# compute
			__lowerCAmelCase    :		int		    =   -1.0 / len(self.timesteps				)

			__lowerCAmelCase    :		str		    =   self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
			__lowerCAmelCase    :		Tuple		    =   beta_t.flatten()
			while len(beta_t.shape				) < len(x.shape				):
				__lowerCAmelCase    :		Dict		    =   beta_t.unsqueeze(-1				)
			__lowerCAmelCase    :		List[Any]		    =   -0.5 * beta_t * x

			__lowerCAmelCase    :		Union[str, Any]		    =   torch.sqrt(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   drift - diffusion**2 * score
			__lowerCAmelCase    :		List[str]		    =   x + drift * dt

			# add noise
			__lowerCAmelCase    :		int		    =   randn_tensor(x.shape ,	layout=x.layout ,	generator=_SCREAMING_SNAKE_CASE ,	device=x.device ,	dtype=x.dtype				)
			__lowerCAmelCase    :		Optional[int]		    =   x_mean + diffusion * math.sqrt(-dt				) * noise

			return x, x_mean







		def __len__(   self				):
			return self.config.num_train_timesteps

"""simple docstring"""







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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			'mctct'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=80_65 ,	_SCREAMING_SNAKE_CASE=15_36 ,	_SCREAMING_SNAKE_CASE=36 ,	_SCREAMING_SNAKE_CASE=61_44 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=3_84 ,	_SCREAMING_SNAKE_CASE=9_20 ,	_SCREAMING_SNAKE_CASE=1E-5 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE="relu" ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=(7,) ,	_SCREAMING_SNAKE_CASE=(3,) ,	_SCREAMING_SNAKE_CASE=80 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE="sum" ,	_SCREAMING_SNAKE_CASE=False ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE ,	pad_token_id=_SCREAMING_SNAKE_CASE ,	bos_token_id=_SCREAMING_SNAKE_CASE ,	eos_token_id=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   vocab_size
			__lowerCAmelCase    :		Any		    =   hidden_size
			__lowerCAmelCase    :		int		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   intermediate_size
			__lowerCAmelCase    :		Optional[int]		    =   num_attention_heads
			__lowerCAmelCase    :		str		    =   attention_head_dim
			__lowerCAmelCase    :		int		    =   max_position_embeddings
			__lowerCAmelCase    :		List[Any]		    =   layer_norm_eps
			__lowerCAmelCase    :		Tuple		    =   layerdrop
			__lowerCAmelCase    :		Dict		    =   hidden_act
			__lowerCAmelCase    :		Dict		    =   initializer_range
			__lowerCAmelCase    :		Any		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		List[str]		    =   pad_token_id
			__lowerCAmelCase    :		Union[str, Any]		    =   bos_token_id
			__lowerCAmelCase    :		int		    =   eos_token_id
			__lowerCAmelCase    :		Optional[int]		    =   conv_glu_dim
			__lowerCAmelCase    :		List[Any]		    =   conv_dropout
			__lowerCAmelCase    :		Tuple		    =   num_conv_layers
			__lowerCAmelCase    :		Tuple		    =   input_feat_per_channel
			__lowerCAmelCase    :		Optional[int]		    =   input_channels
			__lowerCAmelCase    :		str		    =   conv_channels
			__lowerCAmelCase    :		str		    =   ctc_loss_reduction
			__lowerCAmelCase    :		Tuple		    =   ctc_zero_infinity

			# prevents config testing fail with exporting to json
			__lowerCAmelCase    :		List[str]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   list(_SCREAMING_SNAKE_CASE				)

			if len(self.conv_kernel				) != self.num_conv_layers:
				raise ValueError(
				    'Configuration for convolutional module is incorrect. '
				    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
				    f"but is `len(config.conv_kernel) = {len(self.conv_kernel				)}`, "
				    f"`config.num_conv_layers = {self.num_conv_layers}`."				)