

'''simple docstring'''



# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class 		lowerCAmelCase__     :



 """simple docstring"""



 lowerCAmelCase__						      =   42
 lowerCAmelCase__						      =   42

 # setable values
 lowerCAmelCase__						      =   42
 lowerCAmelCase__						      =   42
 lowerCAmelCase__						      =   None
 @classmethod
 def    UpperCAmelCase__	(	cls      : Dict      ,							__SCREAMING_SNAKE_CASE      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							)						->				List[Any]:


      """simple docstring"""




      return cls(
          common=snake_case_      ,							final_alpha_cumprod=snake_case_      ,							init_noise_sigma=snake_case_      ,							timesteps=snake_case_      ,							)







@dataclass
class 		lowerCAmelCase__     (   UpperCamelCase__      ):



 """simple docstring"""



 lowerCAmelCase__						      =   42







class 		lowerCAmelCase__     (   UpperCamelCase__				,      UpperCamelCase__      ):



 """simple docstring"""



 lowerCAmelCase__						      =   [e.name for e in FlaxKarrasDiffusionSchedulers]

 lowerCAmelCase__						      =   42
 @property
 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				Tuple:


      """simple docstring"""




      return True
 @register_to_config
 def __init__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : int = 1_000      ,							__SCREAMING_SNAKE_CASE      : Dict = 0.0001      ,							__SCREAMING_SNAKE_CASE      : Optional[Any] = 0.02      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = "linear"      ,							__SCREAMING_SNAKE_CASE      : Any = None      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = True      ,							__SCREAMING_SNAKE_CASE      : List[Any] = 0      ,							__SCREAMING_SNAKE_CASE      : str = "epsilon"      ,							__SCREAMING_SNAKE_CASE      : List[str] = jnp.floataa      ,							)						->				int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   dtype
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = None    )						->				DDIMSchedulerState:


      """simple docstring"""




      if common is None:
           __SCREAMING_SNAKE_CASE						     =   CommonSchedulerState.create(self    )

      # At every step in ddim, we are looking into the previous alphas_cumprod
      # For the final step, there is no previous alphas_cumprod because we are already at 0
      # `set_alpha_to_one` decides whether we set this parameter simply to one or
      # whether we use the final alpha of the "non-previous" one.
      __SCREAMING_SNAKE_CASE						     =   (
          jnp.array(1.0      ,							dtype=self.dtype    ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
      )

      # standard deviation of the initial noise distribution
      __SCREAMING_SNAKE_CASE						     =   jnp.array(1.0      ,							dtype=self.dtype    )

      __SCREAMING_SNAKE_CASE						     =   jnp.arange(0      ,							self.config.num_train_timesteps    ).round()[::-1]

      return DDIMSchedulerState.create(
          common=snake_case_      ,							final_alpha_cumprod=snake_case_      ,							init_noise_sigma=snake_case_      ,							timesteps=snake_case_      ,							)
 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Optional[Any] = None    )						->				jnp.ndarray:


      """simple docstring"""




      return sample
 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : int      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = ()    )						->				DDIMSchedulerState:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      __SCREAMING_SNAKE_CASE						     =   (jnp.arange(0      ,							snake_case_    ) * step_ratio).round()[::-1] + self.config.steps_offset

      return state.replace(
          num_inference_steps=snake_case_      ,							timesteps=snake_case_      ,							)
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Tuple      ,							__SCREAMING_SNAKE_CASE      : int      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   state.common.alphas_cumprod[timestep]
      __SCREAMING_SNAKE_CASE						     =   jnp.where(
          prev_timestep >= 0      ,							state.common.alphas_cumprod[prev_timestep]      ,							state.final_alpha_cumprod    )
      __SCREAMING_SNAKE_CASE						     =   1 - alpha_prod_t
      __SCREAMING_SNAKE_CASE						     =   1 - alpha_prod_t_prev

      __SCREAMING_SNAKE_CASE						     =   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance
 def    UpperCAmelCase__	(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : List[str] = 0.0      ,							__SCREAMING_SNAKE_CASE      : int = True      ,							)						->				Union[FlaxDDIMSchedulerOutput, Tuple]:


      """simple docstring"""




      if state.num_inference_steps is None:
           raise ValueError(
               """Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler"""    )

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      __SCREAMING_SNAKE_CASE						     =   timestep - self.config.num_train_timesteps // state.num_inference_steps

      __SCREAMING_SNAKE_CASE						     =   state.common.alphas_cumprod
      __SCREAMING_SNAKE_CASE						     =   state.final_alpha_cumprod

      # 2. compute alphas, betas
      __SCREAMING_SNAKE_CASE						     =   alphas_cumprod[timestep]
      __SCREAMING_SNAKE_CASE						     =   jnp.where(prev_timestep >= 0      ,							alphas_cumprod[prev_timestep]      ,							snake_case_    )

      __SCREAMING_SNAKE_CASE						     =   1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      if self.config.prediction_type == "epsilon":
           __SCREAMING_SNAKE_CASE						     =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           __SCREAMING_SNAKE_CASE						     =   model_output
      elif self.config.prediction_type == "sample":
           __SCREAMING_SNAKE_CASE						     =   model_output
           __SCREAMING_SNAKE_CASE						     =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
           __SCREAMING_SNAKE_CASE						     =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           __SCREAMING_SNAKE_CASE						     =   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
      else:
           raise ValueError(
               f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
               """ `v_prediction`"""    )

      # 4. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      __SCREAMING_SNAKE_CASE						     =   self._get_variance(snake_case_      ,							snake_case_      ,							snake_case_    )
      __SCREAMING_SNAKE_CASE						     =   eta * variance ** 0.5

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __SCREAMING_SNAKE_CASE						     =   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

      # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __SCREAMING_SNAKE_CASE						     =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
           return (prev_sample, state)

      return FlaxDDIMSchedulerOutput(prev_sample=snake_case_      ,							state=snake_case_    )
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Any      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							)						->				jnp.ndarray:


      """simple docstring"""




      return add_noise_common(state.common      ,							snake_case_      ,							snake_case_      ,							snake_case_    )
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : int      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							)						->				jnp.ndarray:


      """simple docstring"""




      return get_velocity_common(state.common      ,							snake_case_      ,							snake_case_      ,							snake_case_    )


 def __len__(	self      : List[Any]    )						->				str:


      """simple docstring"""




      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


UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "mt5"
 lowerCAmelCase__						      =   ["past_key_values"]
 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=250_112      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=512      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=64      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=1_024      ,							__SCREAMING_SNAKE_CASE      : List[str]=8      ,							__SCREAMING_SNAKE_CASE      : Any=None      ,							__SCREAMING_SNAKE_CASE      : List[str]=6      ,							__SCREAMING_SNAKE_CASE      : Any=32      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=128      ,							__SCREAMING_SNAKE_CASE      : List[str]=0.1      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=1E-6      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=1.0      ,							__SCREAMING_SNAKE_CASE      : Tuple="gated-gelu"      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple="T5Tokenizer"      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=False      ,							__SCREAMING_SNAKE_CASE      : Tuple=0      ,							__SCREAMING_SNAKE_CASE      : str=1      ,							__SCREAMING_SNAKE_CASE      : Dict=0      ,							**__SCREAMING_SNAKE_CASE      : str      ,							)						->				List[Any]:


      """simple docstring"""




      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      ,							)
      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   d_model
      __SCREAMING_SNAKE_CASE						     =   d_kv
      __SCREAMING_SNAKE_CASE						     =   d_ff
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   relative_attention_num_buckets
      __SCREAMING_SNAKE_CASE						     =   relative_attention_max_distance
      __SCREAMING_SNAKE_CASE						     =   dropout_rate
      __SCREAMING_SNAKE_CASE						     =   layer_norm_epsilon
      __SCREAMING_SNAKE_CASE						     =   initializer_factor
      __SCREAMING_SNAKE_CASE						     =   feed_forward_proj
      __SCREAMING_SNAKE_CASE						     =   use_cache

      __SCREAMING_SNAKE_CASE						     =   self.feed_forward_proj.split("""-"""    )
      __SCREAMING_SNAKE_CASE						     =   act_info[-1]
      __SCREAMING_SNAKE_CASE						     =   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":
           __SCREAMING_SNAKE_CASE						     =   """gelu_new"""
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      return self.d_model
 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				int:


      """simple docstring"""




      return self.num_heads


 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Union[str, Any]:


      """simple docstring"""




      return self.num_layers







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
          """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
      }
      if self.use_past:
           __SCREAMING_SNAKE_CASE						     =   """past_encoder_sequence + sequence"""
           __SCREAMING_SNAKE_CASE						     =   {0: """batch"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}
           __SCREAMING_SNAKE_CASE						     =   {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    UpperCAmelCase__	(	self      : Optional[int]    )						->				int:


      """simple docstring"""




      return 13


 @property
 def    UpperCAmelCase__	(	self      : Tuple    )						->				float:


      """simple docstring"""




      return 5E-4






