


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

import numpy as np
import torch

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


snake_case			:      str 							=   logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					torch.FloatTensor
							SCREAMING_SNAKE_CASE   :					Optional[torch.FloatTensor]        =						None







def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=0.9_9_9   ,UpperCAmelCase__="cosine"   ,):

				"""simple docstring"""






				if alpha_transform_type == "cosine":

								def alpha_bar_fn(UpperCAmelCase__							):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2							) ** 2

				elif alpha_transform_type == "exp":

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

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

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



class    __lowercase  (       UpperCamelCase      ,     UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Tuple        =						[e.name for e in KarrasDiffusionSchedulers]
							SCREAMING_SNAKE_CASE   :					str        =						1





							@register_to_config
							def __init__(    self				, A_ = 1000				, A_ = 0.0001				, A_ = 0.02				, A_ = "linear"				, A_ = None				, A_ = "epsilon"				, A_ = "linear"				, A_ = False				, A_ = "linspace"				, A_ = 0				, )->	Optional[int]:
											if trained_betas is not None:
															_SCREAMING_SNAKE_CASE      = torch.tensor(A_				, dtype=torch.floataa	)
											elif beta_schedule == "linear":
															_SCREAMING_SNAKE_CASE      = torch.linspace(A_				, A_				, A_				, dtype=torch.floataa	)
											elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															_SCREAMING_SNAKE_CASE      = (
															    torch.linspace(beta_start**0.5				, beta_end**0.5				, A_				, dtype=torch.floataa	) ** 2
															)
											elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															_SCREAMING_SNAKE_CASE      = betas_for_alpha_bar(A_	)
											else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

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

											_SCREAMING_SNAKE_CASE      = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
											_SCREAMING_SNAKE_CASE      = np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
											_SCREAMING_SNAKE_CASE      = torch.from_numpy(A_	)

											# setable values
											_SCREAMING_SNAKE_CASE      = None
											_SCREAMING_SNAKE_CASE      = np.linspace(0				, num_train_timesteps - 1				, A_				, dtype=A_	)[::-1].copy()
											_SCREAMING_SNAKE_CASE      = torch.from_numpy(A_	)
											_SCREAMING_SNAKE_CASE      = False
											_SCREAMING_SNAKE_CASE      = use_karras_sigmas





							@property
							def 		__magic_name__			(    self	)->	Union[str, Any]:
											# standard deviation of the initial noise distribution
											if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5





							def 		__magic_name__			(    self				, A_				, A_	)->	torch.FloatTensor:
											if isinstance(A_				, torch.Tensor	):
															_SCREAMING_SNAKE_CASE      = timestep.to(self.timesteps.device	)
											_SCREAMING_SNAKE_CASE      = (self.timesteps == timestep).nonzero().item()
											_SCREAMING_SNAKE_CASE      = self.sigmas[step_index]

											_SCREAMING_SNAKE_CASE      = sample / ((sigma**2 + 1) ** 0.5)

											_SCREAMING_SNAKE_CASE      = True
											return sample





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Dict:
											_SCREAMING_SNAKE_CASE      = num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
															_SCREAMING_SNAKE_CASE      = np.linspace(0				, self.config.num_train_timesteps - 1				, A_				, dtype=A_	)[
															    ::-1
															].copy()
											elif self.config.timestep_spacing == "leading":
															_SCREAMING_SNAKE_CASE      = self.config.num_train_timesteps // self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															_SCREAMING_SNAKE_CASE      = (np.arange(0				, A_	) * step_ratio).round()[::-1].copy().astype(A_	)
															timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
															_SCREAMING_SNAKE_CASE      = self.config.num_train_timesteps / self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															_SCREAMING_SNAKE_CASE      = (np.arange(self.config.num_train_timesteps				, 0				, -step_ratio	)).round().copy().astype(A_	)
															timesteps -= 1
											else:
															raise ValueError(
															    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

											_SCREAMING_SNAKE_CASE      = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
											_SCREAMING_SNAKE_CASE      = np.log(A_	)

											if self.config.interpolation_type == "linear":
															_SCREAMING_SNAKE_CASE      = np.interp(A_				, np.arange(0				, len(A_	)	)				, A_	)
											elif self.config.interpolation_type == "log_linear":
															_SCREAMING_SNAKE_CASE      = torch.linspace(np.log(sigmas[-1]	)				, np.log(sigmas[0]	)				, num_inference_steps + 1	).exp()
											else:
															raise ValueError(
															    F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
															    ' \'linear\' or \'log_linear\''	)

											if self.use_karras_sigmas:
															_SCREAMING_SNAKE_CASE      = self._convert_to_karras(in_sigmas=A_				, num_inference_steps=self.num_inference_steps	)
															_SCREAMING_SNAKE_CASE      = np.array([self._sigma_to_t(A_				, A_	) for sigma in sigmas]	)

											_SCREAMING_SNAKE_CASE      = np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
											_SCREAMING_SNAKE_CASE      = torch.from_numpy(A_	).to(device=A_	)
											if str(A_	).startswith('mps'	):
															# mps does not support float64
															_SCREAMING_SNAKE_CASE      = torch.from_numpy(A_	).to(A_				, dtype=torch.floataa	)
											else:
															_SCREAMING_SNAKE_CASE      = torch.from_numpy(A_	).to(device=A_	)





							def 		__magic_name__			(    self				, A_				, A_	)->	Any:
											# get log sigma
											_SCREAMING_SNAKE_CASE      = np.log(A_	)

											# get distribution
											_SCREAMING_SNAKE_CASE      = log_sigma - log_sigmas[:, np.newaxis]

											# get sigmas range
											_SCREAMING_SNAKE_CASE      = np.cumsum((dists >= 0)				, axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
											_SCREAMING_SNAKE_CASE      = low_idx + 1

											_SCREAMING_SNAKE_CASE      = log_sigmas[low_idx]
											_SCREAMING_SNAKE_CASE      = log_sigmas[high_idx]

											# interpolate sigmas
											_SCREAMING_SNAKE_CASE      = (low - log_sigma) / (low - high)
											_SCREAMING_SNAKE_CASE      = np.clip(A_				, 0				, 1	)

											# transform interpolation to time range
											_SCREAMING_SNAKE_CASE      = (1 - w) * low_idx + w * high_idx
											_SCREAMING_SNAKE_CASE      = t.reshape(sigma.shape	)
											return t





							def 		__magic_name__			(    self				, A_				, A_	)->	torch.FloatTensor:
											_SCREAMING_SNAKE_CASE      = in_sigmas[-1].item()
											_SCREAMING_SNAKE_CASE      = in_sigmas[0].item()

											_SCREAMING_SNAKE_CASE      = 7.0  # 7.0 is the value used in the paper
											_SCREAMING_SNAKE_CASE      = np.linspace(0				, 1				, A_	)
											_SCREAMING_SNAKE_CASE      = sigma_min ** (1 / rho)
											_SCREAMING_SNAKE_CASE      = sigma_max ** (1 / rho)
											_SCREAMING_SNAKE_CASE      = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_ = 0.0				, A_ = 0.0				, A_ = float('inf'	)				, A_ = 1.0				, A_ = None				, A_ = True				, )->	Union[EulerDiscreteSchedulerOutput, Tuple]:

											if (
											    isinstance(A_				, A_	)
											    or isinstance(A_				, torch.IntTensor	)
											    or isinstance(A_				, torch.LongTensor	)
											):
															raise ValueError(
															    (
															        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
															        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
															        ' one of the `scheduler.timesteps` as a timestep.'
															    )				, )

											if not self.is_scale_input_called:
															logger.warning(
															    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
															    'See `StableDiffusionPipeline` for a usage example.'	)

											if isinstance(A_				, torch.Tensor	):
															_SCREAMING_SNAKE_CASE      = timestep.to(self.timesteps.device	)

											_SCREAMING_SNAKE_CASE      = (self.timesteps == timestep).nonzero().item()
											_SCREAMING_SNAKE_CASE      = self.sigmas[step_index]

											_SCREAMING_SNAKE_CASE      = min(s_churn / (len(self.sigmas	) - 1)				, 2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0

											_SCREAMING_SNAKE_CASE      = randn_tensor(
											    model_output.shape				, dtype=model_output.dtype				, device=model_output.device				, generator=A_	)

											_SCREAMING_SNAKE_CASE      = noise * s_noise
											_SCREAMING_SNAKE_CASE      = sigma * (gamma + 1)

											if gamma > 0:
															_SCREAMING_SNAKE_CASE      = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											# NOTE: "original_sample" should not be an expected prediction_type but is left in for
											# backwards compatibility
											if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
															_SCREAMING_SNAKE_CASE      = model_output
											elif self.config.prediction_type == "epsilon":
															_SCREAMING_SNAKE_CASE      = sample - sigma_hat * model_output
											elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															_SCREAMING_SNAKE_CASE      = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											else:
															raise ValueError(
															    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''	)

											# 2. Convert to an ODE derivative
											_SCREAMING_SNAKE_CASE      = (sample - pred_original_sample) / sigma_hat

											_SCREAMING_SNAKE_CASE      = self.sigmas[step_index + 1] - sigma_hat

											_SCREAMING_SNAKE_CASE      = sample + derivative * dt

											if not return_dict:
															return (prev_sample,)

											return EulerDiscreteSchedulerOutput(prev_sample=A_				, pred_original_sample=A_	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, )->	torch.FloatTensor:
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											_SCREAMING_SNAKE_CASE      = self.sigmas.to(device=original_samples.device				, dtype=original_samples.dtype	)
											if original_samples.device.type == "mps" and torch.is_floating_point(A_	):
															# mps does not support float64
															_SCREAMING_SNAKE_CASE      = self.timesteps.to(original_samples.device				, dtype=torch.floataa	)
															_SCREAMING_SNAKE_CASE      = timesteps.to(original_samples.device				, dtype=torch.floataa	)
											else:
															_SCREAMING_SNAKE_CASE      = self.timesteps.to(original_samples.device	)
															_SCREAMING_SNAKE_CASE      = timesteps.to(original_samples.device	)

											_SCREAMING_SNAKE_CASE      = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

											_SCREAMING_SNAKE_CASE      = sigmas[step_indices].flatten()
											while len(sigma.shape	) < len(original_samples.shape	):
															_SCREAMING_SNAKE_CASE      = sigma.unsqueeze(-1	)

											_SCREAMING_SNAKE_CASE      = original_samples + noise * sigma
											return noisy_samples





							def __len__(    self	)->	List[str]:
											return self.config.num_train_timesteps





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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


snake_case			:      Dict 							=   logging.get_logger(__name__)

snake_case			:      List[str] 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case			:      Any 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Optional[int] 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Union[str, Any] 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[int]        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[str]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						["input_ids", "attention_mask", "token_type_ids"]
							SCREAMING_SNAKE_CASE   :					Any        =						DebertaTokenizer





							def __init__(    self				, A_=None				, A_=None				, A_=None				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, **A_				, )->	List[str]:
											super().__init__(
											    A_				, A_				, tokenizer_file=A_				, errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, **A_				, )
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_bos_token'				, A_	)

											_SCREAMING_SNAKE_CASE      = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
											if pre_tok_state.get('add_prefix_space'				, A_	) != add_prefix_space:
															_SCREAMING_SNAKE_CASE      = getattr(A_				, pre_tok_state.pop('type'	)	)
															_SCREAMING_SNAKE_CASE      = add_prefix_space
															_SCREAMING_SNAKE_CASE      = pre_tok_class(**A_	)

											_SCREAMING_SNAKE_CASE      = add_prefix_space





							@property
							def 		__magic_name__			(    self	)->	str:
											if self._mask_token is None:
															if self.verbose:
																			logger.error('Using mask_token, but it is not set yet.'	)
															return None
											return str(self._mask_token	)





							@mask_token.setter
							def 		__magic_name__			(    self				, A_	)->	int:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else value
											_SCREAMING_SNAKE_CASE      = value





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep	) * [0]
											return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





							def 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
											_SCREAMING_SNAKE_CASE      = 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 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
											_SCREAMING_SNAKE_CASE      = 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 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											_SCREAMING_SNAKE_CASE      = self._tokenizer.model.save(A_				, name=A_	)
											return tuple(A_	)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											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:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
