


'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
 from transformers.models.rag.configuration_rag import RagConfig
 from transformers.models.rag.tokenization_rag import RagTokenizer


@require_faiss
@require_torch
class        _UpperCAmelCase						(		snake_case			):




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     tempfile.mkdtemp()
       lowercase_	:    int           =     8

       # DPR tok
       lowercase_	:    Optional[int]           =     [
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "[PAD]",
           "[MASK]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       lowercase_	:    Optional[Any]           =     os.path.join(self.tmpdirname  ,     "dpr_tokenizer"			)
       os.makedirs(a  ,     exist_ok=a			)
       lowercase_	:    Any           =     os.path.join(a  ,     DPR_VOCAB_FILES_NAMES["vocab_file"]			)
       with open(self.vocab_file  ,     "w"  ,     encoding="utf-8"			) as vocab_writer:
          vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)

       # BART tok
       lowercase_	:    Optional[int]           =     [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       lowercase_	:    int           =     dict(zip(a  ,     range(len(a			)			)			)			)
       lowercase_	:    str           =     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       lowercase_	:    List[str]           =     {"unk_token": "<unk>"}

       lowercase_	:    List[str]           =     os.path.join(self.tmpdirname  ,     "bart_tokenizer"			)
       os.makedirs(a  ,     exist_ok=a			)
       lowercase_	:    Optional[Any]           =     os.path.join(a  ,     BART_VOCAB_FILES_NAMES["vocab_file"]			)
       lowercase_	:    str           =     os.path.join(a  ,     BART_VOCAB_FILES_NAMES["merges_file"]			)
       with open(self.vocab_file  ,     "w"  ,     encoding="utf-8"			) as fp:
          fp.write(json.dumps(a			) + "\n"			)
       with open(self.merges_file  ,     "w"  ,     encoding="utf-8"			) as fp:
          fp.write("\n".join(a			)			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,     "dpr_tokenizer"			)			)




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,     "bart_tokenizer"			)			)




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       shutil.rmtree(self.tmpdirname			)




    @require_tokenizers
    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     os.path.join(self.tmpdirname  ,     "rag_tokenizer"			)
       lowercase_	:    Optional[int]           =     RagConfig(question_encoder=DPRConfig().to_dict()  ,     generator=BartConfig().to_dict()			)
       lowercase_	:    Optional[int]           =     RagTokenizer(question_encoder=self.get_dpr_tokenizer()  ,     generator=self.get_bart_tokenizer()			)
       rag_config.save_pretrained(a			)
       rag_tokenizer.save_pretrained(a			)
       lowercase_	:    Dict           =     RagTokenizer.from_pretrained(a  ,     config=a			)
       self.assertIsInstance(new_rag_tokenizer.question_encoder  ,     a			)
       self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()  ,     rag_tokenizer.question_encoder.get_vocab()			)
       self.assertIsInstance(new_rag_tokenizer.generator  ,     a			)
       self.assertEqual(new_rag_tokenizer.generator.get_vocab()  ,     rag_tokenizer.generator.get_vocab()			)




    @slow
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Any           =     RagTokenizer.from_pretrained("facebook/rag-token-nq"			)
       lowercase_	:    Union[str, Any]           =     [
           "who got the first nobel prize in physics",
           "when is the next deadpool movie being released",
           "which mode is used for short wave broadcast service",
           "who is the owner of reading football club",
           "when is the next scandal episode coming out",
           "when is the last time the philadelphia won the superbowl",
           "what is the most current adobe flash player version",
           "how many episodes are there in dragon ball z",
           "what is the first step in the evolution of the eye",
           "where is gall bladder situated in human body",
           "what is the main mineral in lithium batteries",
           "who is the president of usa right now",
           "where do the greasers live in the outsiders",
           "panda is a national animal of which country",
           "what is the name of manchester united stadium",
       ]
       lowercase_	:    List[Any]           =     tokenizer(a			)
       self.assertIsNotNone(a			)





    @slow
    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    Dict           =     RagTokenizer.from_pretrained("facebook/rag-sequence-nq"			)
       lowercase_	:    Tuple           =     [
           "who got the first nobel prize in physics",
           "when is the next deadpool movie being released",
           "which mode is used for short wave broadcast service",
           "who is the owner of reading football club",
           "when is the next scandal episode coming out",
           "when is the last time the philadelphia won the superbowl",
           "what is the most current adobe flash player version",
           "how many episodes are there in dragon ball z",
           "what is the first step in the evolution of the eye",
           "where is gall bladder situated in human body",
           "what is the main mineral in lithium batteries",
           "who is the president of usa right now",
           "where do the greasers live in the outsiders",
           "panda is a national animal of which country",
           "what is the name of manchester united stadium",
       ]
       lowercase_	:    int           =     tokenizer(a			)
       self.assertIsNotNone(a			)












'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCamelCase__ =			'scheduler_config.json'


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   int		       =    1
    __lowerCamelCase:   List[Any]		       =    2
    __lowerCamelCase:   Optional[Any]		       =    3
    __lowerCamelCase:   int		       =    4
    __lowerCamelCase:   Optional[int]		       =    5


@dataclass
class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   jnp.ndarray


class        _UpperCAmelCase						:
    __lowerCamelCase:   List[str]		       =    SCHEDULER_CONFIG_NAME
    __lowerCamelCase:   Optional[int]		       =    ['dtype']
    __lowerCamelCase:   int		       =    []
    __lowerCamelCase:   Dict		       =    True




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Dict[str, Any] = None  ,     a  :       Optional[str] = None  ,     a  :       Union[str, Any]=False  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Any           =     cls.load_config(
           pretrained_model_name_or_path=a  ,     subfolder=a  ,     return_unused_kwargs=a  ,     **a  ,     )
       lowercase_    ,		lowercase_	:    Union[str, Any]           =     cls.from_config(a  ,     return_unused_kwargs=a  ,     **a			)

       if hasattr(a  ,     "create_state"			) and getattr(a  ,     "has_state"  ,     a			):
          lowercase_	:    Tuple           =     scheduler.create_state()

       if return_unused_kwargs:
          return scheduler, state, unused_kwargs

       return scheduler, state




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Union[str, os.PathLike]  ,     a  :       bool = False  ,     **a  :       int			):






       '''simple docstring'''






       self.save_config(save_directory=a  ,     push_to_hub=a  ,     **a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       return self._get_compatibles()





    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     list(set([cls.__name__] + cls._compatibles			)			)
       lowercase_	:    str           =     importlib.import_module(__name__.split("."			)[0]			)
       lowercase_	:    Optional[Any]           =     [
           getattr(a  ,     a			) for c in compatible_classes_str if hasattr(a  ,     a			)
       ]
       return compatible_classes




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   assert len(_UpperCamelCase							) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCamelCase							) - x.ndim)							)						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=0.999						,		_UpperCamelCase=jnp.floataa							):


   """simple docstring"""




   def alpha_bar(_UpperCamelCase							):
      return math.cos((time_step + 0.008) / 1.008 * math.pi / 2							) ** 2

   lowercase_	:    int           =     []
   for i in range(_UpperCamelCase							):
      lowercase_	:    Union[str, Any]           =     i / num_diffusion_timesteps
      lowercase_	:    Dict           =     (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(_UpperCamelCase							) / alpha_bar(_UpperCamelCase							)						,		_UpperCamelCase							)							)
   return jnp.array(_UpperCamelCase						,		dtype=_UpperCamelCase							)


@flax.struct.dataclass
class        _UpperCAmelCase						:
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    Any           =     scheduler.config

       if config.trained_betas is not None:
          lowercase_	:    Union[str, Any]           =     jnp.asarray(config.trained_betas  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "linear":
          lowercase_	:    List[Any]           =     jnp.linspace(config.beta_start  ,     config.beta_end  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase_	:    Tuple           =     (
              jnp.linspace(
                  config.beta_start**0.5  ,     config.beta_end**0.5  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
              ** 2
          )
       elif config.beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase_	:    Union[str, Any]           =     betas_for_alpha_bar(config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       else:
          raise NotImplementedError(
              f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""			)

       lowercase_	:    str           =     1.0 - betas

       lowercase_	:    Dict           =     jnp.cumprod(a  ,     axis=0			)

       return cls(
           alphas=a  ,     betas=a  ,     alphas_cumprod=a  ,     )




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     state.alphas_cumprod

   lowercase_	:    Optional[Any]           =     alphas_cumprod[timesteps] ** 0.5
   lowercase_	:    int           =     sqrt_alpha_prod.flatten()
   lowercase_	:    Union[str, Any]           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   lowercase_	:    Optional[int]           =     (1 - alphas_cumprod[timesteps]) ** 0.5
   lowercase_	:    Union[str, Any]           =     sqrt_one_minus_alpha_prod.flatten()
   lowercase_	:    Any           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    str           =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Any           =     sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity




