




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
class 		lowercase							(     UpperCamelCase__	):
     _a											=		4_2
     _a											=		4_2
     _a											=		None







class 		lowercase							(     UpperCamelCase__,UpperCamelCase__	):
     _a											=		2

     @register_to_config
     def __init__(						self	,  _a = 0.02	,  _a = 100	,  _a = 1.007	,  _a = 80	,  _a = 0.05	,  _a = 50	,  )		->							str:
           # standard deviation of the initial noise distribution
           _A							:  Tuple    =		sigma_max

           # setable values
           _A							:  int    =		None
           _A							:  np.IntTensor    =		None
           _A							:  torch.FloatTensor    =		None  # sigma(t_i)

     def       a__			(						self	,  _a	,  _a = None       )		->							torch.FloatTensor:
           return sample

     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  List[Any]    =		num_inference_steps
           _A							:  int    =		np.arange(0	,  self.num_inference_steps       )[::-1].copy()
           _A							:  Tuple    =		torch.from_numpy(_a       ).to(_a       )
           _A							:  Tuple    =		[
               (
                   self.config.sigma_max**2
                   * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
               )
               for i in self.timesteps
           ]
           _A							:  List[str]    =		torch.tensor(_a	,  dtype=torch.floataa	,  device=_a       )

     def       a__			(						self	,  _a	,  _a	,  _a = None       )		->							Tuple[torch.FloatTensor, float]:
           if self.config.s_min <= sigma <= self.config.s_max:
                 _A							:  Tuple    =		min(self.config.s_churn / self.num_inference_steps	,  2**0.5 - 1       )
           else:
                 _A							:  str    =		0

           # sample eps ~ N(0, S_noise^2 * I)
           _A							:  Optional[Any]    =		self.config.s_noise * randn_tensor(sample.shape	,  generator=_a       ).to(sample.device       )
           _A							:  Union[str, Any]    =		sigma + gamma * sigma
           _A							:  str    =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

           return sample_hat, sigma_hat

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a = True	,  )		->							Union[KarrasVeOutput, Tuple]:
           _A							:  Any    =		sample_hat + sigma_hat * model_output
           _A							:  Optional[Any]    =		(sample_hat - pred_original_sample) / sigma_hat
           _A							:  Optional[int]    =		sample_hat + (sigma_prev - sigma_hat) * derivative

           if not return_dict:
                 return (sample_prev, derivative)

           return KarrasVeOutput(
               prev_sample=_a	,  derivative=_a	,  pred_original_sample=_a       )

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a = True	,  )		->							Union[KarrasVeOutput, Tuple]:
           _A							:  Union[str, Any]    =		sample_prev + sigma_prev * model_output
           _A							:  List[str]    =		(sample_prev - pred_original_sample) / sigma_prev
           _A							:  Union[str, Any]    =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

           if not return_dict:
                 return (sample_prev, derivative)

           return KarrasVeOutput(
               prev_sample=_a	,  derivative=_a	,  pred_original_sample=_a       )



     def       a__			(						self	,  _a	,  _a	,  _a       )		->							Dict:
           raise NotImplementedError()







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 		lowercase							(     UpperCamelCase__	):

     def       a__			(						self       )		->							int:
           _A							:  int    =		tempfile.mkdtemp()
           _A							:  Union[str, Any]    =		8

           # DPR tok
           _A							:  List[str]    =		[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           _A							:  List[str]    =		os.path.join(self.tmpdirname	,  """dpr_tokenizer"""       )
           os.makedirs(_a	,  exist_ok=_a       )
           _A							:  str    =		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
           _A							:  Dict    =		[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           _A							:  Optional[Any]    =		dict(zip(_a	,  range(len(_a       )       )       )       )
           _A							:  Tuple    =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           _A							:  Dict    =		{"""unk_token""": """<unk>"""}

           _A							:  Optional[Any]    =		os.path.join(self.tmpdirname	,  """bart_tokenizer"""       )
           os.makedirs(_a	,  exist_ok=_a       )
           _A							:  str    =		os.path.join(_a	,  BART_VOCAB_FILES_NAMES["""vocab_file"""]       )
           _A							:  List[Any]    =		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       a__			(						self       )		->							DPRQuestionEncoderTokenizer:
           return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,  """dpr_tokenizer"""       )       )

     def       a__			(						self       )		->							BartTokenizer:
           return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,  """bart_tokenizer"""       )       )

     def       a__			(						self       )		->							Optional[Any]:
           shutil.rmtree(self.tmpdirname       )

     @require_tokenizers
     def       a__			(						self       )		->							str:
           _A							:  Optional[Any]    =		os.path.join(self.tmpdirname	,  """rag_tokenizer"""       )
           _A							:  int    =		RagConfig(question_encoder=DPRConfig().to_dict()	,  generator=BartConfig().to_dict()       )
           _A							:  Any    =		RagTokenizer(question_encoder=self.get_dpr_tokenizer()	,  generator=self.get_bart_tokenizer()       )
           rag_config.save_pretrained(_a       )
           rag_tokenizer.save_pretrained(_a       )
           _A							:  Optional[Any]    =		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       a__			(						self       )		->							str:
           _A							:  Union[str, Any]    =		RagTokenizer.from_pretrained("""facebook/rag-token-nq"""       )
           _A							:  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""",
           ]
           _A							:  Tuple    =		tokenizer(_a       )
           self.assertIsNotNone(_a       )



     @slow
     def       a__			(						self       )		->							Dict:
           _A							:  Dict    =		RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""       )
           _A							:  str    =		[
               """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""",
           ]
           _A							:  Optional[Any]    =		tokenizer(_a       )
           self.assertIsNotNone(_a       )

