

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  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







        A__          =   tempfile.mkdtemp()
        A__          =   8

        # DPR tok
        A__          =   [
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        A__          =   os.path.join(self.tmpdirname,'dpr_tokenizer'		)
        os.makedirs(lowercase_,exist_ok=lowercase_		)
        A__          =   os.path.join(lowercase_,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__          =   [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        A__          =   dict(zip(lowercase_,range(len(lowercase_		)		)		)		)
        A__          =   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        A__          =   {'unk_token': '<unk>'}

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


    def 						snake_case__     (      self :       List[str]		)->    DPRQuestionEncoderTokenizer:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       List[Any]		)->    BartTokenizer:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       List[str]		)->    List[str]:





        '''simple docstring'''







        shutil.rmtree(self.tmpdirname		)


    @require_tokenizers
    def 						snake_case__     (      self :       str		)->    Any:





        '''simple docstring'''







        A__          =   os.path.join(self.tmpdirname,'rag_tokenizer'		)
        A__          =   RagConfig(question_encoder=DPRConfig().to_dict(),generator=BartConfig().to_dict()		)
        A__          =   RagTokenizer(question_encoder=self.get_dpr_tokenizer(),generator=self.get_bart_tokenizer()		)
        rag_config.save_pretrained(lowercase_		)
        rag_tokenizer.save_pretrained(lowercase_		)
        A__          =   RagTokenizer.from_pretrained(lowercase_,config=lowercase_		)
        self.assertIsInstance(new_rag_tokenizer.question_encoder,lowercase_		)
        self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab(),rag_tokenizer.question_encoder.get_vocab()		)
        self.assertIsInstance(new_rag_tokenizer.generator,lowercase_		)
        self.assertEqual(new_rag_tokenizer.generator.get_vocab(),rag_tokenizer.generator.get_vocab()		)


    @slow
    def 						snake_case__     (      self :       Optional[int]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   RagTokenizer.from_pretrained('facebook/rag-token-nq'		)
        A__          =   [
            '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__          =   tokenizer(lowercase_		)
        self.assertIsNotNone(lowercase_		)


    @slow
    def 						snake_case__     (      self :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   RagTokenizer.from_pretrained('facebook/rag-sequence-nq'		)
        A__          =   [
            '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__          =   tokenizer(lowercase_		)
        self.assertIsNotNone(lowercase_		)



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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     None





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: str							,A					: float = 0.02							,A					: float = 1_00							,A					: float = 1.0_07							,A					: float = 80							,A					: float = 0.05							,A					: float = 50							,):
					# standard deviation of the initial noise distribution
					__A   			= sigma_max

					# setable values
					__A   			= None
					__A   			= None
					__A   			= None  # sigma(t_i)






	def 				UpperCamelCase_  (       self					: str							,A					: torch.FloatTensor							,A					: Optional[int] = None ):
					return sample






	def 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: Union[str, torch.device] = None ):
					__A   			= num_inference_steps
					__A   			= np.arange(0							,self.num_inference_steps )[::-1].copy()
					__A   			= torch.from_numpy(A ).to(A )
					__A   			= [
					    (
					        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   			= torch.tensor(A							,dtype=torch.floataa							,device=A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: torch.FloatTensor							,A					: float							,A					: Optional[torch.Generator] = None ):
					if self.config.s_min <= sigma <= self.config.s_max:
									__A   			= min(self.config.s_churn / self.num_inference_steps							,2**0.5 - 1 )
					else:
									__A   			= 0

					# sample eps ~ N(0, S_noise^2 * I)
					__A   			= self.config.s_noise * randn_tensor(sample.shape							,generator=A ).to(sample.device )
					__A   			= sigma + gamma * sigma
					__A   			= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

					return sample_hat, sigma_hat






	def 				UpperCamelCase_  (       self					: Dict							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_hat + sigma_hat * model_output
					__A   			= (sample_hat - pred_original_sample) / sigma_hat
					__A   			= 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 				UpperCamelCase_  (       self					: Optional[int]							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_prev + sigma_prev * model_output
					__A   			= (sample_prev - pred_original_sample) / sigma_prev
					__A   			= 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 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: str ):
					raise NotImplementedError()
