



"""simple docstring"""


import math
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  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42


def 					_lowerCAmelCase (  lowercase_						,      lowercase_=0.9_9_9						,      lowercase_="cosine"						,      ):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowercase_		):
																		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(lowercase_		):
																		return math.exp(t * -1_2.0		)

						else:
												raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""		)

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







class  A_     (	SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				1






						@register_to_config
						def __init__(   self					:str  ,    lowercase_					:int = 10_00  ,    lowercase_					:float = 0.0001  ,    lowercase_					:float = 0.02  ,    lowercase_					:str = "linear"  ,    lowercase_					:float = 0.0  ,    lowercase_					:Optional[np.ndarray] = None  ,    lowercase_					:bool = True  ,    )							->     Dict:
												if trained_betas is not None:
																		UpperCAmelCase			 =					torch.from_numpy(lowercase_   )
												elif beta_schedule == "linear":
																		UpperCAmelCase			 =					torch.linspace(lowercase_  ,    lowercase_  ,    lowercase_  ,    dtype=torch.floataa   )
												elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		UpperCAmelCase			 =					(
																		    torch.linspace(beta_start**0.5  ,    beta_end**0.5  ,    lowercase_  ,    dtype=torch.floataa   ) ** 2
																		)
												elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		UpperCAmelCase			 =					betas_for_alpha_bar(lowercase_   )
												elif beta_schedule == "sigmoid":
																		# GeoDiff sigmoid schedule
																		UpperCAmelCase			 =					torch.linspace(-6  ,    6  ,    lowercase_   )
																		UpperCAmelCase			 =					torch.sigmoid(lowercase_   ) * (beta_end - beta_start) + beta_start
												else:
																		raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""   )

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

												UpperCAmelCase			 =					torch.tensor(1.0   )

												# standard deviation of the initial noise distribution
												UpperCAmelCase			 =					1.0

												# setable values
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					torch.from_numpy(np.arange(0  ,    lowercase_   )[::-1].copy()   )

												UpperCAmelCase			 =					eta






						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:Optional[int] = None   )							->     torch.FloatTensor:
												return sample






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:int  ,    lowercase_					:int = 10  ,    lowercase_					:int = 10  ,    lowercase_					:Union[str, torch.device] = None  ,    )							->     Union[str, Any]:
												UpperCAmelCase			 =					min(self.config.num_train_timesteps  ,    lowercase_   )
												UpperCAmelCase			 =					num_inference_steps

												UpperCAmelCase			 =					[]

												UpperCAmelCase			 =					{}
												for j in range(0  ,    num_inference_steps - jump_length  ,    lowercase_   ):
																		UpperCAmelCase			 =					jump_n_sample - 1

												UpperCAmelCase			 =					num_inference_steps
												while t >= 1:
																		UpperCAmelCase			 =					t - 1
																		timesteps.append(lowercase_   )

																		if jumps.get(lowercase_  ,    0   ) > 0:
																								UpperCAmelCase			 =					jumps[t] - 1
																								for _ in range(lowercase_   ):
																														UpperCAmelCase			 =					t + 1
																														timesteps.append(lowercase_   )

												UpperCAmelCase			 =					np.array(lowercase_   ) * (self.config.num_train_timesteps // self.num_inference_steps)
												UpperCAmelCase			 =					torch.from_numpy(lowercase_   ).to(lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:Optional[int]   )							->     Tuple:
												UpperCAmelCase			 =					t - self.config.num_train_timesteps // self.num_inference_steps

												UpperCAmelCase			 =					self.alphas_cumprod[t]
												UpperCAmelCase			 =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
												UpperCAmelCase			 =					1 - alpha_prod_t
												UpperCAmelCase			 =					1 - alpha_prod_t_prev

												# For t > 0, compute predicted variance βt (see formula (6) and (7) from
												# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
												# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
												# variance to pred_sample
												# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
												# without eta.
												# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
												UpperCAmelCase			 =					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

												return variance






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:int  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:Optional[torch.Generator] = None  ,    lowercase_					:bool = True  ,    )							->     Union[RePaintSchedulerOutput, Tuple]:
												UpperCAmelCase			 =					timestep
												UpperCAmelCase			 =					timestep - self.config.num_train_timesteps // self.num_inference_steps

												# 1. compute alphas, betas
												UpperCAmelCase			 =					self.alphas_cumprod[t]
												UpperCAmelCase			 =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
												UpperCAmelCase			 =					1 - alpha_prod_t

												# 2. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
												UpperCAmelCase			 =					(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

												# 3. Clip "predicted x_0"
												if self.config.clip_sample:
																		UpperCAmelCase			 =					torch.clamp(lowercase_  ,    -1  ,    1   )

												# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
												# substitute formula (7) in the algorithm coming from DDPM paper
												# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
												# DDIM schedule gives the same results as DDPM with eta = 1.0
												# Noise is being reused in 7. and 8., but no impact on quality has
												# been observed.

												# 5. Add noise
												UpperCAmelCase			 =					model_output.device
												UpperCAmelCase			 =					randn_tensor(model_output.shape  ,    generator=lowercase_  ,    device=lowercase_  ,    dtype=model_output.dtype   )
												UpperCAmelCase			 =					self.eta * self._get_variance(lowercase_   ) ** 0.5

												UpperCAmelCase			 =					0
												if t > 0 and self.eta > 0:
																		UpperCAmelCase			 =					std_dev_t * noise

												# 6. compute "direction pointing to x_t" of formula (12)
												# from https://arxiv.org/pdf/2010.02502.pdf
												UpperCAmelCase			 =					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

												# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												UpperCAmelCase			 =					alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

												# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
												UpperCAmelCase			 =					(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

												# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
												UpperCAmelCase			 =					mask * prev_known_part + (1.0 - mask) * prev_unknown_part

												if not return_dict:
																		return (
																		    pred_prev_sample,
																		    pred_original_sample,
																		)

												return RePaintSchedulerOutput(prev_sample=lowercase_  ,    pred_original_sample=lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[int]  ,    lowercase_					:Dict=None   )							->     Optional[Any]:
												UpperCAmelCase			 =					self.config.num_train_timesteps // self.num_inference_steps

												for i in range(lowercase_   ):
																		UpperCAmelCase			 =					self.betas[timestep + i]
																		if sample.device.type == "mps":
																								# randn does not work reproducibly on mps
																								UpperCAmelCase			 =					randn_tensor(sample.shape  ,    dtype=sample.dtype  ,    generator=lowercase_   )
																								UpperCAmelCase			 =					noise.to(sample.device   )
																		else:
																								UpperCAmelCase			 =					randn_tensor(sample.shape  ,    generator=lowercase_  ,    device=sample.device  ,    dtype=sample.dtype   )

																		# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
																		UpperCAmelCase			 =					(1 - beta) ** 0.5 * sample + beta**0.5 * noise

												return sample






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:torch.IntTensor  ,    )							->     torch.FloatTensor:
												raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'   )






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










"""simple docstring"""


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_								=			logging.get_logger(__name__)

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

snake_case_								=			{
    """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_								=			{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

snake_case_								=			{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """attention_mask""", """token_type_ids"""]
						__UpperCamelCase													=				DebertaTokenizer






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

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

												UpperCAmelCase			 =					add_prefix_space






						@property
						def       UpperCAmelCase__	(   self					:str   )							->     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       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:Any   )							->     Any:
												UpperCAmelCase			 =					AddedToken(lowercase_  ,    lstrip=lowercase_  ,    rstrip=lowercase_   ) if isinstance(lowercase_  ,    lowercase_   ) else value
												UpperCAmelCase			 =					value






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												UpperCAmelCase			 =					[self.cls_token_id]
												UpperCAmelCase			 =					[self.sep_token_id]
												return cls + token_ids_a + sep + token_ids_a + sep






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.sep_token_id]
												UpperCAmelCase			 =					[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       UpperCAmelCase__	(   self					:int  ,    *lowercase_					:str  ,    **lowercase_					:Optional[Any]   )							->     BatchEncoding:
												UpperCAmelCase			 =					kwargs.get('is_split_into_words'  ,    lowercase_   )
												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(*lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    *lowercase_					:Dict  ,    **lowercase_					:Any   )							->     BatchEncoding:
												UpperCAmelCase			 =					kwargs.get('is_split_into_words'  ,    lowercase_   )

												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(*lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												UpperCAmelCase			 =					self._tokenizer.model.save(lowercase_  ,    name=lowercase_   )
												return tuple(lowercase_   )






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:"Conversation"   )							->     List[int]:
												UpperCAmelCase			 =					[]
												for is_user, text in conversation.iter_texts():
																		input_ids.extend(self.encode(lowercase_  ,    add_special_tokens=lowercase_   ) + [self.eos_token_id]   )

												if len(lowercase_   ) > self.model_max_length:
																		UpperCAmelCase			 =					input_ids[-self.model_max_length :]
												return input_ids





