





'''simple docstring'''



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 							__lowercase    ( lowerCAmelCase__	, lowerCAmelCase__  ):
	a      :			int     =    1


	@register_to_config
	def __init__(self       ,_lowerCamelCase=2000       ,_lowerCamelCase=0.1       ,_lowerCamelCase=20       ,_lowerCamelCase=1E-3	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							None
				__lowercase			  =							None
				__lowercase			  =							None


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							torch.linspace(1       ,self.config.sampling_eps       ,_lowerCamelCase       ,device=_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							List[Any]:







				'''simple docstring'''





				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
				__lowercase			  =							(
				    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
				)
				__lowercase			  =							torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff	)	)
				__lowercase			  =							std.flatten()
				while len(std.shape	) < len(score.shape	):
							__lowercase			  =							std.unsqueeze(-1	)
				__lowercase			  =							-score / std

				# compute
				__lowercase			  =							-1.0 / len(self.timesteps	)

				__lowercase			  =							self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
				__lowercase			  =							beta_t.flatten()
				while len(beta_t.shape	) < len(x.shape	):
							__lowercase			  =							beta_t.unsqueeze(-1	)
				__lowercase			  =							-0.5 * beta_t * x

				__lowercase			  =							torch.sqrt(_lowerCamelCase	)
				__lowercase			  =							drift - diffusion**2 * score
				__lowercase			  =							x + drift * dt

				# add noise
				__lowercase			  =							randn_tensor(x.shape       ,layout=x.layout       ,generator=_lowerCamelCase       ,device=x.device       ,dtype=x.dtype	)
				__lowercase			  =							x_mean + diffusion * math.sqrt(-dt	) * noise

				return x, x_mean


	def __len__(self	)							->							Optional[int]:







				'''simple docstring'''





				return self.config.num_train_timesteps












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''MvpTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MvpForCausalLM''',
	    '''MvpForConditionalGeneration''',
	    '''MvpForQuestionAnswering''',
	    '''MvpForSequenceClassification''',
	    '''MvpModel''',
	    '''MvpPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





