

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 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	torch.FloatTensor
    __lowerCamelCase						:	torch.FloatTensor
    __lowerCamelCase						:	Optional[torch.FloatTensor]														=					None



class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	__snake_case					):
    __lowerCamelCase						:	Tuple														=					2





    @register_to_config
    def __init__(				self						,       a = 0.02						,       a = 100						,       a = 1.007						,       a = 80						,       a = 0.05						,       a = 50						,       ):
         # standard deviation of the initial noise distribution
         lowercase__   :       str				=	sigma_max

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





    def 				snake_case_    (				self						,       a						,       a = None):
         return sample





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Dict				=	num_inference_steps
         lowercase__   :       Dict				=	np.arange(0						,       self.num_inference_steps)[::-1].copy()
         lowercase__   :       List[Any]				=	torch.from_numpy(a).to(a)
         lowercase__   :       Optional[int]				=	[
             (
                 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
         ]
         lowercase__   :       List[Any]				=	torch.tensor(a						,       dtype=torch.floataa						,       device=a)





    def 				snake_case_    (				self						,       a						,       a						,       a = None):
         if self.config.s_min <= sigma <= self.config.s_max:
              lowercase__   :       Optional[int]				=	min(self.config.s_churn / self.num_inference_steps						,       2**0.5 - 1)
         else:
              lowercase__   :       Optional[int]				=	0

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

         return sample_hat, sigma_hat





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a = True						,       ):
         lowercase__   :       str				=	sample_hat + sigma_hat * model_output
         lowercase__   :       int				=	(sample_hat - pred_original_sample) / sigma_hat
         lowercase__   :       Optional[Any]				=	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 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a = True						,       ):
         lowercase__   :       List[str]				=	sample_prev + sigma_prev * model_output
         lowercase__   :       Optional[int]				=	(sample_prev - pred_original_sample) / sigma_prev
         lowercase__   :       str				=	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 				snake_case_    (				self						,       a						,       a						,       a):
         raise NotImplementedError()




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

snake_case_        =			{
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	Any														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	str														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					["""input_ids""", """attention_mask"""]
    __lowerCamelCase						:	str														=					GPTaTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=None						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a=False						,       **a						,       ):
         super().__init__(
             a						,       a						,       tokenizer_file=a						,       unk_token=a						,       bos_token=a						,       eos_token=a						,       add_prefix_space=a						,       **a						,       )

         lowercase__   :       int				=	kwargs.pop('add_bos_token'						,       a)

         lowercase__   :       List[str]				=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'						,       a) != add_prefix_space:
              lowercase__   :       Optional[Any]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       List[Any]				=	add_prefix_space
              lowercase__   :       str				=	pre_tok_class(**a)

         lowercase__   :       Tuple				=	add_prefix_space





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Tuple				=	kwargs.get('is_split_into_words'						,       a)
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Optional[Any]				=	kwargs.get('is_split_into_words'						,       a)

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Any				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	[]
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(a						,       add_special_tokens=a) + [self.eos_token_id])

         if len(a) > self.model_max_length:
              lowercase__   :       Union[str, Any]				=	input_ids[-self.model_max_length :]
         return input_ids

