





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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       A__     :
      _UpperCAmelCase			:CommonSchedulerState

      # setable values
      _UpperCAmelCase			:jnp.ndarray
      _UpperCAmelCase			:jnp.ndarray
      _UpperCAmelCase			:Optional[int]							=     None



      @classmethod
      def  __UpperCamelCase(				cls ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       return cls(common=A_ ,							init_noise_sigma=A_ ,							timesteps=A_     )



@dataclass
class       A__     ( __snake_case    ):
      _UpperCAmelCase			:DDPMSchedulerState



class       A__     ( __snake_case ,     __snake_case    ):
      _UpperCAmelCase			:Dict							=     [e.name for e in FlaxKarrasDiffusionSchedulers]

      _UpperCAmelCase			:jnp.dtype



      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return True



      @register_to_config
      def __init__(				self ,							A_ = 1000 ,							A_ = 0.00_01 ,							A_ = 0.02 ,							A_ = "linear" ,							A_ = None ,							A_ = "fixed_small" ,							A_ = True ,							A_ = "epsilon" ,							A_ = jnp.floataa ,							):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     dtype



      def  __UpperCamelCase(				self ,							A_ = None     ):
       '''simple docstring'''




       if common is None:
        UpperCamelCase			:      List[Any] 			=     CommonSchedulerState.create(self     )

       # standard deviation of the initial noise distribution
       UpperCamelCase			:      List[Any] 			=     jnp.array(1.0 ,							dtype=self.dtype     )

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

       return DDPMSchedulerState.create(
           common=A_ ,							init_noise_sigma=A_ ,							timesteps=A_ ,							)



      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       return sample



      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ = ()     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     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
       UpperCamelCase			:      Union[str, Any] 			=     (jnp.arange(0 ,							A_     ) * step_ratio).round()[::-1]

       return state.replace(
           num_inference_steps=A_ ,							timesteps=A_ ,							)



      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_=None ,							A_=None     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     state.common.alphas_cumprod[t]
       UpperCamelCase			:      List[str] 			=     jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype     )     )

       # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
       # and sample from it to get previous sample
       # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
       UpperCamelCase			:      str 			=     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
        UpperCamelCase			:      str 			=     self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small":
        UpperCamelCase			:      str 			=     jnp.clip(A_ ,							a_min=1e-20     )
       # for rl-diffuser https://arxiv.org/abs/2205.09991
       elif variance_type == "fixed_small_log":
        UpperCamelCase			:      Optional[Any] 			=     jnp.log(jnp.clip(A_ ,							a_min=1e-20     )     )
       elif variance_type == "fixed_large":
        UpperCamelCase			:      Tuple 			=     state.common.betas[t]
       elif variance_type == "fixed_large_log":
        # Glide max_log
        UpperCamelCase			:      Optional[int] 			=     jnp.log(state.common.betas[t]     )
       elif variance_type == "learned":
        return predicted_variance
       elif variance_type == "learned_range":
        UpperCamelCase			:      Optional[int] 			=     variance
        UpperCamelCase			:      Dict 			=     state.common.betas[t]
        UpperCamelCase			:      Dict 			=     (predicted_variance + 1) / 2
        UpperCamelCase			:      List[Any] 			=     frac * max_log + (1 - frac) * min_log

       return variance



      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ = None ,							A_ = True ,							):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     timestep

       if key is None:
        UpperCamelCase			:      str 			=     jax.random.PRNGKey(0     )

       if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
        UpperCamelCase			:      List[str] 			=     jnp.split(A_ ,							sample.shape[1] ,							axis=1     )
       else:
        UpperCamelCase			:      int 			=     None

       # 1. compute alphas, betas
       UpperCamelCase			:      Tuple 			=     state.common.alphas_cumprod[t]
       UpperCamelCase			:      List[str] 			=     jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype     )     )
       UpperCamelCase			:      Union[str, Any] 			=     1 - alpha_prod_t
       UpperCamelCase			:      Tuple 			=     1 - alpha_prod_t_prev

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if self.config.prediction_type == "epsilon":
        UpperCamelCase			:      List[Any] 			=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
        UpperCamelCase			:      List[Any] 			=     model_output
       elif self.config.prediction_type == "v_prediction":
        UpperCamelCase			:      Tuple 			=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
       else:
        raise ValueError(
            F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
            " for the FlaxDDPMScheduler."     )

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
        UpperCamelCase			:      Dict 			=     jnp.clip(A_ ,							-1 ,							1     )

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       UpperCamelCase			:      int 			=     (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
       UpperCamelCase			:      Union[str, Any] 			=     state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       UpperCamelCase			:      Any 			=     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       def random_variance():
        UpperCamelCase			:      Dict 			=     jax.random.split(A_ ,							num=1     )
        UpperCamelCase			:      List[Any] 			=     jax.random.normal(A_ ,							shape=model_output.shape ,							dtype=self.dtype     )
        return (self._get_variance(A_ ,							A_ ,							predicted_variance=A_     ) ** 0.5) * noise

       UpperCamelCase			:      Tuple 			=     jnp.where(t > 0 ,							random_variance() ,							jnp.zeros(model_output.shape ,							dtype=self.dtype     )     )

       UpperCamelCase			:      str 			=     pred_prev_sample + variance

       if not return_dict:
        return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=A_ ,							state=A_     )



      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							):
       '''simple docstring'''




       return add_noise_common(state.common ,							A_ ,							A_ ,							A_     )



      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							):
       '''simple docstring'''




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



      def __len__(				self     ):
       '''simple docstring'''




       return self.config.num_train_timesteps







import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase = None							,     _lowerCAmelCase = None							,     _lowerCAmelCase = None							,     )		->				str:
 if config_name_or_path is None:
  UpperCamelCase			:      Dict 			=     "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

 if generator_tokenizer_name_or_path is None:
  UpperCamelCase			:      Tuple 			=     generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  UpperCamelCase			:      Tuple 			=     question_encoder_name_or_path

 UpperCamelCase			:      Any 			=     RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

 # Save model.
 UpperCamelCase			:      Optional[Any] 			=     RagConfig.from_pretrained(_lowerCAmelCase  )
 UpperCamelCase			:      Union[str, Any] 			=     AutoConfig.from_pretrained(_lowerCAmelCase  )
 UpperCamelCase			:      Tuple 			=     AutoConfig.from_pretrained(_lowerCAmelCase  )

 UpperCamelCase			:      int 			=     gen_config
 UpperCamelCase			:      Dict 			=     question_encoder_config

 UpperCamelCase			:      Tuple 			=     model_class.from_pretrained_question_encoder_generator(
     _lowerCAmelCase							,     _lowerCAmelCase							,     config=_lowerCAmelCase  )
 rag_model.save_pretrained(_lowerCAmelCase  )

 # Sanity check.
 model_class.from_pretrained(_lowerCAmelCase  )

 # Save tokenizers.
 UpperCamelCase			:      Optional[Any] 			=     AutoTokenizer.from_pretrained(_lowerCAmelCase  )
 gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"  )
 UpperCamelCase			:      Union[str, Any] 			=     AutoTokenizer.from_pretrained(_lowerCAmelCase  )
 question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"  )


if __name__ == "__main__":
    __lowerCamelCase :  Tuple										=  argparse.ArgumentParser()
    parser.add_argument(
        """--model_type""",
        choices=["""rag_sequence""", """rag_token"""],
        required=True,
        type=str,
        help="""RAG model type: rag_sequence, rag_token""",
    )
    parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
    parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
    parser.add_argument(
        """--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
    )

    parser.add_argument(
        """--generator_tokenizer_name_or_path""",
        type=str,
        help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
    )
    parser.add_argument(
        """--question_encoder_tokenizer_name_or_path""",
        type=str,
        help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
    )
    parser.add_argument(
        """--config_name_or_path""",
        type=str,
        help=(
            """Identifier of the model config to use, if not provided, resolves to a base config for a given"""
            """ ``model_type``"""
        ),
    )

    __lowerCamelCase :  Dict										=  parser.parse_args()

    __lowerCamelCase :  Dict										=  Path(args.dest)
    dest_dir.mkdir(exist_ok=True)

    consolidate(
        args.model_type,
        args.generator_name_or_path,
        args.question_encoder_name_or_path,
        dest_dir,
        args.config_name_or_path,
        args.generator_tokenizer_name_or_path,
        args.question_encoder_tokenizer_name_or_path,
    )
