

"""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     __UpperCamelCase				:
		# setable values
		SCREAMING_SNAKE_CASE              =					None
		SCREAMING_SNAKE_CASE              =					None
		SCREAMING_SNAKE_CASE              =					None


		@classmethod
		def       SCREAMING_SNAKE_CASE__     (cls				:	int):
							return cls()





@dataclass
class     __UpperCamelCase				(     _A		):
		SCREAMING_SNAKE_CASE              =					42
		SCREAMING_SNAKE_CASE              =					42
		SCREAMING_SNAKE_CASE              =					None





class     __UpperCamelCase				(     _A  ,	_A		):


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							return True


		@register_to_config
		def __init__(self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	int = 2_0_0_0  ,  __SCREAMING_SNAKE_CASE				:	float = 0.1_5  ,  __SCREAMING_SNAKE_CASE				:	float = 0.0_1  ,  __SCREAMING_SNAKE_CASE				:	float = 1_3_4_8.0  ,  __SCREAMING_SNAKE_CASE				:	float = 1E-5  ,  __SCREAMING_SNAKE_CASE				:	int = 1  ,  ):
							pass


		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
							A    =      ScoreSdeVeSchedulerState.create()
							return self.set_sigmas(
							    __SCREAMING_SNAKE_CASE  ,  self.config.num_train_timesteps  ,  self.config.sigma_min  ,  self.config.sigma_max  ,  self.config.sampling_eps  ,  )


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	ScoreSdeVeSchedulerState  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Tuple = ()  ,  __SCREAMING_SNAKE_CASE				:	float = None):
							A    =      sampling_eps if sampling_eps is not None else self.config.sampling_eps

							A    =      jnp.linspace(1  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							return state.replace(timesteps=__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	ScoreSdeVeSchedulerState  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	float = None  ,  __SCREAMING_SNAKE_CASE				:	float = None  ,  __SCREAMING_SNAKE_CASE				:	float = None  ,  ):
							A    =      sigma_min if sigma_min is not None else self.config.sigma_min
							A    =      sigma_max if sigma_max is not None else self.config.sigma_max
							A    =      sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if state.timesteps is None:
												A    =      self.set_timesteps(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)

							A    =      jnp.exp(jnp.linspace(jnp.log(__SCREAMING_SNAKE_CASE)  ,  jnp.log(__SCREAMING_SNAKE_CASE)  ,  __SCREAMING_SNAKE_CASE))
							A    =      jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])

							return state.replace(discrete_sigmas=__SCREAMING_SNAKE_CASE  ,  sigmas=__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							return jnp.where(timesteps == 0  ,  jnp.zeros_like(__SCREAMING_SNAKE_CASE)  ,  state.discrete_sigmas[timesteps - 1])


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	ScoreSdeVeSchedulerState  ,  __SCREAMING_SNAKE_CASE				:	jnp.ndarray  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	jnp.ndarray  ,  __SCREAMING_SNAKE_CASE				:	random.KeyArray  ,  __SCREAMING_SNAKE_CASE				:	bool = True  ,  ):
							if state.timesteps is None:
												raise ValueError(
												    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

							A    =      timestep * jnp.ones(
							    sample.shape[0]  ,  )
							A    =      (timestep * (len(state.timesteps) - 1)).long()

							A    =      state.discrete_sigmas[timesteps]
							A    =      self.get_adjacent_sigma(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
							A    =      jnp.zeros_like(__SCREAMING_SNAKE_CASE)
							A    =      (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
							A    =      diffusion.flatten()
							A    =      broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE  ,  sample.shape)
							A    =      drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							A    =      random.split(__SCREAMING_SNAKE_CASE  ,  num=1)
							A    =      random.normal(key=__SCREAMING_SNAKE_CASE  ,  shape=sample.shape)
							A    =      sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							A    =      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=__SCREAMING_SNAKE_CASE  ,  prev_sample_mean=__SCREAMING_SNAKE_CASE  ,  state=__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	ScoreSdeVeSchedulerState  ,  __SCREAMING_SNAKE_CASE				:	jnp.ndarray  ,  __SCREAMING_SNAKE_CASE				:	jnp.ndarray  ,  __SCREAMING_SNAKE_CASE				:	random.KeyArray  ,  __SCREAMING_SNAKE_CASE				:	bool = True  ,  ):
							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
							A    =      random.split(__SCREAMING_SNAKE_CASE  ,  num=1)
							A    =      random.normal(key=__SCREAMING_SNAKE_CASE  ,  shape=sample.shape)

							# compute step size from the model_output, the noise, and the snr
							A    =      jnp.linalg.norm(__SCREAMING_SNAKE_CASE)
							A    =      jnp.linalg.norm(__SCREAMING_SNAKE_CASE)
							A    =      (self.config.snr * noise_norm / grad_norm) ** 2 * 2
							A    =      step_size * jnp.ones(sample.shape[0])

							# compute corrected sample: model_output term and noise term
							A    =      step_size.flatten()
							A    =      broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE  ,  sample.shape)
							A    =      sample + step_size * model_output
							A    =      prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
												return (prev_sample, state)

							return FlaxSdeVeOutput(prev_sample=__SCREAMING_SNAKE_CASE  ,  state=__SCREAMING_SNAKE_CASE)


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








"""simple docstring"""




from typing import Mapping

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


__A  :					List[str]												=	logging.get_logger(__name__)





class     __UpperCamelCase				(     _A		):
		SCREAMING_SNAKE_CASE              =					"mt5"
		SCREAMING_SNAKE_CASE              =					["past_key_values"]


		def __init__(self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=2_5_0_1_1_2  ,  __SCREAMING_SNAKE_CASE				:	int=5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Tuple=6_4  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1_0_2_4  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=8  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple=6  ,  __SCREAMING_SNAKE_CASE				:	int=3_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1_2_8  ,  __SCREAMING_SNAKE_CASE				:	Tuple=0.1  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1E-6  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]="gated-gelu"  ,  __SCREAMING_SNAKE_CASE				:	List[str]=True  ,  __SCREAMING_SNAKE_CASE				:	str=True  ,  __SCREAMING_SNAKE_CASE				:	int="T5Tokenizer"  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=False  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=0  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  **__SCREAMING_SNAKE_CASE				:	str  ,  ):
							super().__init__(
							    is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,  tokenizer_class=__SCREAMING_SNAKE_CASE  ,  tie_word_embeddings=__SCREAMING_SNAKE_CASE  ,  pad_token_id=__SCREAMING_SNAKE_CASE  ,  eos_token_id=__SCREAMING_SNAKE_CASE  ,  decoder_start_token_id=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE  ,  )
							A    =      vocab_size
							A    =      d_model
							A    =      d_kv
							A    =      d_ff
							A    =      num_layers
							A    =      (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							A    =      num_heads
							A    =      relative_attention_num_buckets
							A    =      relative_attention_max_distance
							A    =      dropout_rate
							A    =      layer_norm_epsilon
							A    =      initializer_factor
							A    =      feed_forward_proj
							A    =      use_cache

							A    =      self.feed_forward_proj.split("-")
							A    =      act_info[-1]
							A    =      act_info[0] == "gated"

							if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 2:
												raise ValueError(
												    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
												    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
												    "'gated-gelu' or 'relu'")

							# for backwards compatibility
							if feed_forward_proj == "gated-gelu":
												A    =      "gelu_new"


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							return self.d_model


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							return self.num_heads


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							return self.num_layers





class     __UpperCamelCase				(     _A		):


		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def       SCREAMING_SNAKE_CASE__     (self				:	int):
							A    =      {
							    "input_ids": {0: "batch", 1: "encoder_sequence"},
							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
							}
							if self.use_past:
												A    =      "past_encoder_sequence + sequence"
												A    =      {0: "batch"}
												A    =      {0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
												A    =      {0: "batch", 1: "decoder_sequence"}
												A    =      {0: "batch", 1: "decoder_sequence"}

							if self.use_past:
												self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE  ,  direction="inputs")

							return common_inputs


		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							return 1_3


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	int):
							return 5E-4





