





"""simple docstring"""




from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a		:				str  			=    logging.get_logger(__name__)


a		:				Dict  			=    {
    '''nielsr/canine-s''': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
a		:				int  			=    1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
a		:				Dict  			=    0

a		:				Optional[Any]  			=    0xE_000
a		:				Tuple  			=    0xE_001
a		:				str  			=    0xE_002
a		:				str  			=    0xE_003
a		:				Tuple  			=    0xE_004

# Maps special codepoints to human-readable names.
a		:				Dict[int, str]  			=    {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
a		:				Dict[str, int]  			=    {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}


class  __UpperCamelCase       (		__a		):
       lowerCamelCase     :      Tuple         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






       def __init__(     self   ,      lowerCAmelCase__=chr(snake_case__   )   ,      lowerCAmelCase__=chr(snake_case__   )   ,      lowerCAmelCase__=chr(snake_case__   )   ,      lowerCAmelCase__=chr(snake_case__   )   ,      lowerCAmelCase__=chr(snake_case__   )   ,      lowerCAmelCase__=chr(snake_case__   )   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=2048   ,      **lowerCAmelCase__   ,      )			->				Any:
        a   :	int			=       AddedToken(snake_case__   ,      lstrip=snake_case__   ,      rstrip=snake_case__   ) if isinstance(snake_case__   ,      snake_case__   ) else bos_token
        a   :	Dict			=       AddedToken(snake_case__   ,      lstrip=snake_case__   ,      rstrip=snake_case__   ) if isinstance(snake_case__   ,      snake_case__   ) else eos_token
        a   :	List[str]			=       AddedToken(snake_case__   ,      lstrip=snake_case__   ,      rstrip=snake_case__   ) if isinstance(snake_case__   ,      snake_case__   ) else sep_token
        a   :	Dict			=       AddedToken(snake_case__   ,      lstrip=snake_case__   ,      rstrip=snake_case__   ) if isinstance(snake_case__   ,      snake_case__   ) else cls_token
        a   :	List[str]			=       AddedToken(snake_case__   ,      lstrip=snake_case__   ,      rstrip=snake_case__   ) if isinstance(snake_case__   ,      snake_case__   ) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        a   :	int			=       AddedToken(snake_case__   ,      lstrip=snake_case__   ,      rstrip=snake_case__   ) if isinstance(snake_case__   ,      snake_case__   ) else mask_token

        super().__init__(
            bos_token=snake_case__   ,      eos_token=snake_case__   ,      sep_token=snake_case__   ,      cls_token=snake_case__   ,      pad_token=snake_case__   ,      mask_token=snake_case__   ,      add_prefix_space=snake_case__   ,      model_max_length=snake_case__   ,      **snake_case__   ,      )

        # Creates a mapping for looking up the IDs of special symbols.
        a   :	Union[str, Any]			=       {}
        for codepoint, name in SPECIAL_CODEPOINTS.items():
         a   :	int			=       codepoint

        # Creates a mapping for looking up the string forms of special symbol IDs.
        a   :	List[str]			=       {
            codepoint: name for name, codepoint in self._special_codepoints.items()
        }

        a   :	int			=       UNICODE_VOCAB_SIZE
        a   :	List[Any]			=       len(self._special_codepoints   )






       @property
       def 					__a     (     self   )			->				List[Any]:
        return self._unicode_vocab_size






       def 					__a     (     self   ,      lowerCAmelCase__   )			->				int:
        return list(snake_case__   )






       def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
        try:
         return ord(snake_case__   )
        except TypeError:
         raise ValueError(f"""invalid token: '{token}'"""   )






       def 					__a     (     self   ,      lowerCAmelCase__   )			->				List[str]:
        try:
         if index in SPECIAL_CODEPOINTS:
          return SPECIAL_CODEPOINTS[index]
         return chr(snake_case__   )
        except TypeError:
         raise ValueError(f"""invalid id: {index}"""   )






       def 					__a     (     self   ,      lowerCAmelCase__   )			->				int:
        return "".join(snake_case__   )






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[str]:
        a   :	List[Any]			=       [self.sep_token_id]
        a   :	Any			=       [self.cls_token_id]

        a   :	Optional[int]			=       cls + token_ids_a + sep
        if token_ids_a is not None:
         result += token_ids_a + sep
        return result






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   )			->				Union[str, Any]:
        if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=snake_case__   ,      token_ids_a=snake_case__   ,      already_has_special_tokens=snake_case__   )

        a   :	Dict			=       [1] + ([0] * len(snake_case__   )) + [1]
        if token_ids_a is not None:
         result += ([0] * len(snake_case__   )) + [1]
        return result






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Dict:
        a   :	Tuple			=       [self.sep_token_id]
        a   :	Union[str, Any]			=       [self.cls_token_id]

        a   :	List[Any]			=       len(cls + token_ids_a + sep   ) * [0]
        if token_ids_a is not None:
         result += len(token_ids_a + sep   ) * [1]
        return result






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				str:
        return ()









"""simple docstring"""




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       (		a__		):
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      Optional[torch.FloatTensor]         =None


class  __UpperCamelCase       (		a__	, a__		):
							lowerCamelCase     :      Tuple         =2






							@register_to_config
							def __init__(     self   ,      lowerCAmelCase__ = 0.02   ,      lowerCAmelCase__ = 100   ,      lowerCAmelCase__ = 1.007   ,      lowerCAmelCase__ = 80   ,      lowerCAmelCase__ = 0.05   ,      lowerCAmelCase__ = 50   ,      )			->				Union[str, Any]:
								# 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   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				torch.FloatTensor:
								return sample






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[str]:
								a   :	List[Any]			=       num_inference_steps
								a   :	List[str]			=       np.arange(0   ,      self.num_inference_steps   )[::-1].copy()
								a   :	int			=       torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								a   :	List[str]			=       [
								    (
								        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   :	Any			=       torch.tensor(lowerCAmelCase__   ,      dtype=torch.floataa   ,      device=lowerCAmelCase__   )






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

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

								return sample_hat, sigma_hat






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Union[str, Any]			=       sample_hat + sigma_hat * model_output
								a   :	Tuple			=       (sample_hat - pred_original_sample) / sigma_hat
								a   :	List[Any]			=       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
									return (sample_prev, derivative)

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






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Optional[int]			=       sample_prev + sigma_prev * model_output
								a   :	str			=       (sample_prev - pred_original_sample) / sigma_prev
								a   :	Dict			=       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=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
								raise NotImplementedError()


