



'''simple docstring'''






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left

@flax.struct.dataclass
class 	_lowerCAmelCase     :






		'''simple docstring'''
		snake_case_							= None
		snake_case_							= None
		snake_case_							= None
		@classmethod
		def 						__lowercase      (						cls							:       Optional[int]		)      ->			Union[str, Any]:







									'''simple docstring'''





									return cls()




@dataclass
class 	_lowerCAmelCase     (    lowercase_       ):






		'''simple docstring'''
		snake_case_							= 42
		snake_case_							= 42
		snake_case_							= None




class 	_lowerCAmelCase     (    lowercase_     ,		lowercase_       ):






		'''simple docstring'''
		@property
		def 						__lowercase      (						self							:       List[Any]		)      ->			List[str]:







									'''simple docstring'''





									return True
		@register_to_config
		def __init__(						self							:       int      ,		UpperCamelCase_							:       int = 2_000      ,		UpperCamelCase_							:       str = 0.15      ,		UpperCamelCase_							:       Tuple = 0.01      ,		UpperCamelCase_							:       Dict = 1_348.0      ,		UpperCamelCase_							:       int = 1e-5      ,		UpperCamelCase_							:       Union[str, Any] = 1      ,		)      ->			Dict:







									'''simple docstring'''





									pass
		def 						__lowercase      (						self							:       Any		)      ->			str:







									'''simple docstring'''





									_lowercase							:		List[Any]				=   ScoreSdeVeSchedulerState.create()
									return self.set_sigmas(
									    UpperCAmelCase_      ,		self.config.num_train_timesteps      ,		self.config.sigma_min      ,		self.config.sigma_max      ,		self.config.sampling_eps      ,		)
		def 						__lowercase      (						self							:       Dict      ,		UpperCamelCase_							:       Tuple      ,		UpperCamelCase_							:       str      ,		UpperCamelCase_							:       Tuple = ()      ,		UpperCamelCase_							:       Tuple = None		)      ->			ScoreSdeVeSchedulerState:







									'''simple docstring'''





									_lowercase							:		Union[str, Any]				=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

									_lowercase							:		Union[str, Any]				=   jnp.linspace(1      ,		UpperCAmelCase_      ,		UpperCAmelCase_		)
									return state.replace(timesteps=UpperCAmelCase_		)
		def 						__lowercase      (						self							:       str      ,		UpperCamelCase_							:       List[str]      ,		UpperCamelCase_							:       List[str]      ,		UpperCamelCase_							:       Union[str, Any] = None      ,		UpperCamelCase_							:       int = None      ,		UpperCamelCase_							:       Union[str, Any] = None      ,		)      ->			ScoreSdeVeSchedulerState:







									'''simple docstring'''





									_lowercase							:		Optional[int]				=   sigma_min if sigma_min is not None else self.config.sigma_min
									_lowercase							:		Any				=   sigma_max if sigma_max is not None else self.config.sigma_max
									_lowercase							:		Dict				=   sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if state.timesteps is None:
																_lowercase							:		int				=   self.set_timesteps(UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_		)

									_lowercase							:		Union[str, Any]				=   jnp.exp(jnp.linspace(jnp.log(UpperCAmelCase_		)      ,		jnp.log(UpperCAmelCase_		)      ,		UpperCAmelCase_		)		)
									_lowercase							:		List[Any]				=   jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]		)

									return state.replace(discrete_sigmas=UpperCAmelCase_      ,		sigmas=UpperCAmelCase_		)
		def 						__lowercase      (						self							:       int      ,		UpperCamelCase_							:       Optional[int]      ,		UpperCamelCase_							:       Optional[int]      ,		UpperCamelCase_							:       List[str]		)      ->			Tuple:







									'''simple docstring'''





									return jnp.where(timesteps == 0      ,		jnp.zeros_like(UpperCAmelCase_		)      ,		state.discrete_sigmas[timesteps - 1]		)
		def 						__lowercase      (						self							:       Dict      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       Optional[Any]      ,		UpperCamelCase_							:       Optional[int]      ,		UpperCamelCase_							:       int      ,		UpperCamelCase_							:       List[Any]      ,		UpperCamelCase_							:       Dict = True      ,		)      ->			Union[FlaxSdeVeOutput, Tuple]:







									'''simple docstring'''





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

									_lowercase							:		List[str]				=   timestep * jnp.ones(
									    sample.shape[0]      ,		)
									_lowercase							:		Optional[Any]				=   (timestep * (len(state.timesteps		) - 1)).long()

									_lowercase							:		List[str]				=   state.discrete_sigmas[timesteps]
									_lowercase							:		Optional[int]				=   self.get_adjacent_sigma(UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_		)
									_lowercase							:		List[Any]				=   jnp.zeros_like(UpperCAmelCase_		)
									_lowercase							:		str				=   (sigma**2 - adjacent_sigma**2) ** 0.5

									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
									# also equation 47 shows the analog from SDE models to ancestral sampling methods
									_lowercase							:		Optional[int]				=   diffusion.flatten()
									_lowercase							:		List[Any]				=   broadcast_to_shape_from_left(UpperCAmelCase_      ,		sample.shape		)
									_lowercase							:		List[Any]				=   drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									_lowercase							:		Dict				=   random.split(UpperCAmelCase_      ,		num=1		)
									_lowercase							:		Tuple				=   random.normal(key=UpperCAmelCase_      ,		shape=sample.shape		)
									_lowercase							:		str				=   sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									_lowercase							:		List[Any]				=   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

									if not return_dict:
																return (prev_sample, prev_sample_mean, state)

									return FlaxSdeVeOutput(prev_sample=UpperCAmelCase_      ,		prev_sample_mean=UpperCAmelCase_      ,		state=UpperCAmelCase_		)
		def 						__lowercase      (						self							:       Optional[int]      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       List[str]      ,		UpperCamelCase_							:       Optional[Any]      ,		UpperCamelCase_							:       Union[str, Any]      ,		UpperCamelCase_							:       Any = True      ,		)      ->			Union[FlaxSdeVeOutput, Tuple]:







									'''simple docstring'''





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

									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
									# sample noise for correction
									_lowercase							:		Optional[int]				=   random.split(UpperCAmelCase_      ,		num=1		)
									_lowercase							:		Optional[Any]				=   random.normal(key=UpperCAmelCase_      ,		shape=sample.shape		)

									# compute step size from the model_output, the noise, and the snr
									_lowercase							:		Union[str, Any]				=   jnp.linalg.norm(UpperCAmelCase_		)
									_lowercase							:		int				=   jnp.linalg.norm(UpperCAmelCase_		)
									_lowercase							:		Tuple				=   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
									_lowercase							:		List[Any]				=   step_size * jnp.ones(sample.shape[0]		)

									# compute corrected sample: model_output term and noise term
									_lowercase							:		List[Any]				=   step_size.flatten()
									_lowercase							:		Dict				=   broadcast_to_shape_from_left(UpperCAmelCase_      ,		sample.shape		)
									_lowercase							:		Optional[int]				=   sample + step_size * model_output
									_lowercase							:		Optional[Any]				=   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

									if not return_dict:
																return (prev_sample, state)

									return FlaxSdeVeOutput(prev_sample=UpperCAmelCase_      ,		state=UpperCAmelCase_		)





		def __len__(						self							:       Dict		)      ->			Tuple:







									'''simple docstring'''





									return self.config.num_train_timesteps











"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase					=     logging.get_logger(__name__)

UpperCamelCase					=     {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   UpperCamelCase__     	=							"""squeezebert"""





   def __init__(       self					, UpperCAmelCase_=30_522					, UpperCAmelCase_=768					, UpperCAmelCase_=12					, UpperCAmelCase_=12					, UpperCAmelCase_=3_072					, UpperCAmelCase_="gelu"					, UpperCAmelCase_=0.1					, UpperCAmelCase_=0.1					, UpperCAmelCase_=512					, UpperCAmelCase_=2					, UpperCAmelCase_=0.02					, UpperCAmelCase_=1E-1_2					, UpperCAmelCase_=0					, UpperCAmelCase_=768					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, UpperCAmelCase_=1					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, **UpperCAmelCase_					, )			->   int:




          '''simple docstring'''




          super().__init__(pad_token_id=UpperCAmelCase_					, **UpperCAmelCase_)

          lowercase__:							Union[str, Any]         =     vocab_size
          lowercase__:							int         =     hidden_size
          lowercase__:							int         =     num_hidden_layers
          lowercase__:							List[str]         =     num_attention_heads
          lowercase__:							Any         =     hidden_act
          lowercase__:							List[str]         =     intermediate_size
          lowercase__:							Any         =     hidden_dropout_prob
          lowercase__:							List[Any]         =     attention_probs_dropout_prob
          lowercase__:							Optional[int]         =     max_position_embeddings
          lowercase__:							List[Any]         =     type_vocab_size
          lowercase__:							Union[str, Any]         =     initializer_range
          lowercase__:							str         =     layer_norm_eps
          lowercase__:							Optional[Any]         =     embedding_size
          lowercase__:							Optional[int]         =     q_groups
          lowercase__:							List[str]         =     k_groups
          lowercase__:							Union[str, Any]         =     v_groups
          lowercase__:							str         =     post_attention_groups
          lowercase__:							Optional[int]         =     intermediate_groups
          lowercase__:							List[Any]         =     output_groups







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   @property
   def __lowercase							(       self)			->   Mapping[str, Mapping[int, str]]:




          '''simple docstring'''




          if self.task == "multiple-choice":
                 lowercase__:							Optional[int]         =     {0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase__:							Optional[Any]         =     {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ])






