import argparse
from pathlib import Path

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




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A,  __A = None,  __A = None,  __A = None,  )     ->				str:







      '''simple docstring'''


      if config_name_or_path is None:
            UpperCAmelCase__								=  "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

      if generator_tokenizer_name_or_path is None:
            UpperCAmelCase__								=  generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            UpperCAmelCase__								=  question_encoder_name_or_path

      UpperCAmelCase__								=  RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

      # Save model.
      UpperCAmelCase__								=  RagConfig.from_pretrained(__A							)
      UpperCAmelCase__								=  AutoConfig.from_pretrained(__A							)
      UpperCAmelCase__								=  AutoConfig.from_pretrained(__A							)

      UpperCAmelCase__								=  gen_config
      UpperCAmelCase__								=  question_encoder_config

      UpperCAmelCase__								=  model_class.from_pretrained_question_encoder_generator(
          __A,  __A,  config=__A							)
      rag_model.save_pretrained(__A							)

      # Sanity check.
      model_class.from_pretrained(__A							)

      # Save tokenizers.
      UpperCAmelCase__								=  AutoTokenizer.from_pretrained(__A							)
      gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"							)
      UpperCAmelCase__								=  AutoTokenizer.from_pretrained(__A							)
      question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"							)


if __name__ == "__main__":
      UpperCamelCase__											=					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``'
          ),
      )

      UpperCamelCase__											=					parser.parse_args()

      UpperCamelCase__											=					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,
      )




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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : torch.FloatTensor
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None




def    lowerCAmelCase_ (     __A,  __A=0.999,  __A="cosine",  )     ->				Tuple:







      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(__A							):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(__A							):
                  return math.exp(t * -12.0							)

      else:
            raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""							)

      UpperCAmelCase__								=  []
      for i in range(__A							):
            UpperCAmelCase__								=  i / num_diffusion_timesteps
            UpperCAmelCase__								=  (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(__A							) / alpha_bar_fn(__A							),  __A							)							)
      return torch.tensor(__A,  dtype=torch.floataa							)







class      A							(				UpperCAmelCase_		,				UpperCAmelCase_     ):


   @register_to_config
   def __init__(self     :						List[str]       ,  __UpperCAmelCase     :						int = 1_0_0_0       ,  __UpperCAmelCase     :						str = "fixed_small_log"       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Optional[float] = 1.0       ,  __UpperCAmelCase     :						str = "epsilon"       ,  __UpperCAmelCase     :						str = "squaredcos_cap_v2"       ,  )						->				Optional[int]:
         """simple docstring"""



         if beta_schedule != "squaredcos_cap_v2":
               raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"		)

         UpperCAmelCase__								=  betas_for_alpha_bar(__UpperCAmelCase		)

         UpperCAmelCase__								=  1.0 - self.betas
         UpperCAmelCase__								=  torch.cumprod(self.alphas       ,  dim=0		)
         UpperCAmelCase__								=  torch.tensor(1.0		)

         # standard deviation of the initial noise distribution
         UpperCAmelCase__								=  1.0

         # setable values
         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  torch.from_numpy(np.arange(0       ,  __UpperCAmelCase		)[::-1].copy()		)

         UpperCAmelCase__								=  variance_type


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						Optional[int] = None		)						->				torch.FloatTensor:
         """simple docstring"""



         return sample


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Union[str, torch.device] = None		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  num_inference_steps
         UpperCAmelCase__								=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         UpperCAmelCase__								=  (np.arange(0       ,  __UpperCAmelCase		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
         UpperCAmelCase__								=  torch.from_numpy(__UpperCAmelCase		).to(__UpperCAmelCase		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						Optional[int]=None       ,  __UpperCAmelCase     :						Tuple=None       ,  __UpperCAmelCase     :						List[str]=None		)						->				Tuple:
         """simple docstring"""



         if prev_timestep is None:
               UpperCAmelCase__								=  t - 1

         UpperCAmelCase__								=  self.alphas_cumprod[t]
         UpperCAmelCase__								=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         UpperCAmelCase__								=  1 - alpha_prod_t
         UpperCAmelCase__								=  1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               UpperCAmelCase__								=  self.betas[t]
         else:
               UpperCAmelCase__								=  1 - alpha_prod_t / alpha_prod_t_prev

         # 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__								=  beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
               UpperCAmelCase__								=  self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
               UpperCAmelCase__								=  torch.log(torch.clamp(__UpperCAmelCase       ,  min=1E-20		)		)
               UpperCAmelCase__								=  torch.exp(0.5 * variance		)
         elif variance_type == "learned_range":
               # NOTE difference with DDPM scheduler
               UpperCAmelCase__								=  variance.log()
               UpperCAmelCase__								=  beta.log()

               UpperCAmelCase__								=  (predicted_variance + 1) / 2
               UpperCAmelCase__								=  frac * max_log + (1 - frac) * min_log

         return variance


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						Optional[int] = None       ,  __UpperCAmelCase     :						List[str]=None       ,  __UpperCAmelCase     :						bool = True       ,  )						->				Union[UnCLIPSchedulerOutput, Tuple]:
         """simple docstring"""



         UpperCAmelCase__								=  timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
               UpperCAmelCase__		,      UpperCAmelCase__								=  torch.split(__UpperCAmelCase       ,  sample.shape[1]       ,  dim=1		)
         else:
               UpperCAmelCase__								=  None

         # 1. compute alphas, betas
         if prev_timestep is None:
               UpperCAmelCase__								=  t - 1

         UpperCAmelCase__								=  self.alphas_cumprod[t]
         UpperCAmelCase__								=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         UpperCAmelCase__								=  1 - alpha_prod_t
         UpperCAmelCase__								=  1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               UpperCAmelCase__								=  self.betas[t]
               UpperCAmelCase__								=  self.alphas[t]
         else:
               UpperCAmelCase__								=  1 - alpha_prod_t / alpha_prod_t_prev
               UpperCAmelCase__								=  1 - beta

         # 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__								=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
               UpperCAmelCase__								=  model_output
         else:
               raise ValueError(
                   f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
                   " for the UnCLIPScheduler."		)

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
               UpperCAmelCase__								=  torch.clamp(
                   __UpperCAmelCase       ,  -self.config.clip_sample_range       ,  self.config.clip_sample_range		)

         # 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__								=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         UpperCAmelCase__								=  alpha ** 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__								=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         UpperCAmelCase__								=  0
         if t > 0:
               UpperCAmelCase__								=  randn_tensor(
                   model_output.shape       ,  dtype=model_output.dtype       ,  generator=__UpperCAmelCase       ,  device=model_output.device		)

               UpperCAmelCase__								=  self._get_variance(
                   __UpperCAmelCase       ,  predicted_variance=__UpperCAmelCase       ,  prev_timestep=__UpperCAmelCase       ,  )

               if self.variance_type == "fixed_small_log":
                     UpperCAmelCase__								=  variance
               elif self.variance_type == "learned_range":
                     UpperCAmelCase__								=  (0.5 * variance).exp()
               else:
                     raise ValueError(
                         f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                         " for the UnCLIPScheduler."		)

               UpperCAmelCase__								=  variance * variance_noise

         UpperCAmelCase__								=  pred_prev_sample + variance

         if not return_dict:
               return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=__UpperCAmelCase       ,  pred_original_sample=__UpperCAmelCase		)





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						torch.IntTensor       ,  )						->				torch.FloatTensor:
         """simple docstring"""



         UpperCAmelCase__								=  self.alphas_cumprod.to(device=original_samples.device       ,  dtype=original_samples.dtype		)
         UpperCAmelCase__								=  timesteps.to(original_samples.device		)

         UpperCAmelCase__								=  alphas_cumprod[timesteps] ** 0.5
         UpperCAmelCase__								=  sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape		) < len(original_samples.shape		):
               UpperCAmelCase__								=  sqrt_alpha_prod.unsqueeze(-1		)

         UpperCAmelCase__								=  (1 - alphas_cumprod[timesteps]) ** 0.5
         UpperCAmelCase__								=  sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape		) < len(original_samples.shape		):
               UpperCAmelCase__								=  sqrt_one_minus_alpha_prod.unsqueeze(-1		)

         UpperCAmelCase__								=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples



