
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							torch.FloatTensor
     UpperCAmelCase__    :							Optional[torch.FloatTensor]									=		None



def 						lowercase_      (       A__			,		A__=0.999			,		A__="cosine"			,		)							->							Any:
    """simple docstring"""

    if alpha_transform_type == "cosine":

        def alpha_bar_fn(A__		):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

    elif alpha_transform_type == "exp":

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

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

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







class 					lowerCamelCase		(  A_							,			A_  ):
     UpperCAmelCase__    :							str									=		1





     @register_to_config
     def __init__(self				:   Dict    ,   _A				:   int = 1_0_0_0    ,   _A				:   float = 0.00_01    ,   _A				:   float = 0.02    ,   _A				:   str = "linear"    ,   _A				:   Optional[Union[np.ndarray, List[float]]] = None    ,   _A				:   bool = True    ,   _A				:   bool = True    ,   _A				:   int = 0    ,   _A				:   str = "epsilon"    ,   _A				:   float = 1.0    ,   **_A				:   Union[str, Any]    ,   )						->						Union[str, Any]:
         if kwargs.get("set_alpha_to_one"    ,   _A       ) is not None:
             snake_case								= (
                 "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
             )
             deprecate("set_alpha_to_one"    ,   "1.0.0"    ,   _A    ,   standard_warn=_A       )
             snake_case								= kwargs["set_alpha_to_one"]
         if trained_betas is not None:
             snake_case								= torch.tensor(_A    ,   dtype=torch.floataa       )
         elif beta_schedule == "linear":
             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.
             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
             snake_case								= betas_for_alpha_bar(_A       )
         else:
             raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

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

         # At every step in inverted ddim, we are looking into the next alphas_cumprod
         # For the final step, there is no next alphas_cumprod, and the index is out of bounds
         # `set_alpha_to_zero` decides whether we set this parameter simply to zero
         # in this case, self.step() just output the predicted noise
         # or whether we use the final alpha of the "non-previous" one.
         snake_case								= torch.tensor(0.0       ) if set_alpha_to_zero else self.alphas_cumprod[-1]

         # standard deviation of the initial noise distribution
         snake_case								= 1.0

         # setable values
         snake_case								= None
         snake_case								= torch.from_numpy(np.arange(0    ,   _A       ).copy().astype(np.intaa       )       )





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





     def 		UpperCAmelCase(self				:   str    ,   _A				:   int    ,   _A				:   Union[str, torch.device] = None       )						->						Union[str, Any]:

         if num_inference_steps > self.config.num_train_timesteps:
             raise ValueError(
                 f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                 f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                 f' maximal {self.config.num_train_timesteps} timesteps.'       )

         snake_case								= num_inference_steps
         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
         snake_case								= (np.arange(0    ,   _A       ) * step_ratio).round().copy().astype(np.intaa       )
         snake_case								= torch.from_numpy(_A       ).to(_A       )
         self.timesteps += self.config.steps_offset





     def 		UpperCAmelCase(self				:   str    ,   _A				:   torch.FloatTensor    ,   _A				:   int    ,   _A				:   torch.FloatTensor    ,   _A				:   float = 0.0    ,   _A				:   bool = False    ,   _A				:   Optional[torch.FloatTensor] = None    ,   _A				:   bool = True    ,   )						->						Union[DDIMSchedulerOutput, Tuple]:
         # 1. get previous step value (=t+1)
         snake_case								= timestep + self.config.num_train_timesteps // self.num_inference_steps

         # 2. compute alphas, betas
         # change original implementation to exactly match noise levels for analogous forward process
         snake_case								= self.alphas_cumprod[timestep]
         snake_case								= (
             self.alphas_cumprod[prev_timestep]
             if prev_timestep < self.config.num_train_timesteps
             else self.final_alpha_cumprod
         )

         snake_case								= 1 - alpha_prod_t

         # 3. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         if self.config.prediction_type == "epsilon":
             snake_case								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             snake_case								= model_output
         elif self.config.prediction_type == "sample":
             snake_case								= model_output
             snake_case								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
         elif self.config.prediction_type == "v_prediction":
             snake_case								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
             snake_case								= (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
         else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                 " `v_prediction`"       )

         # 4. Clip or threshold "predicted x_0"
         if self.config.clip_sample:
             snake_case								= pred_original_sample.clamp(
                 -self.config.clip_sample_range    ,   self.config.clip_sample_range       )

         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         snake_case								= (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

         # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         snake_case								= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

         if not return_dict:
             return (prev_sample, pred_original_sample)
         return DDIMSchedulerOutput(prev_sample=_A    ,   pred_original_sample=_A       )





     def __len__(self				:   int       )						->						Tuple:
         return self.config.num_train_timesteps



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_A   =			logging.get_logger(__name__)

_A   =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_A   =			{
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

_A   =			{"mobilebert-uncased": 5_12}


_A   =			{}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Any									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							Optional[Any]									=		MobileBertTokenizer





     def __init__(self				:   Any    ,   _A				:   str=None    ,   _A				:   str=None    ,   _A				:   Union[str, Any]=True    ,   _A				:   Optional[Any]="[UNK]"    ,   _A				:   int="[SEP]"    ,   _A				:   Dict="[PAD]"    ,   _A				:   int="[CLS]"    ,   _A				:   Union[str, Any]="[MASK]"    ,   _A				:   Any=True    ,   _A				:   Dict=None    ,   **_A				:   List[str]    ,   )						->						List[str]:
         super().__init__(
             _A    ,   tokenizer_file=_A    ,   do_lower_case=_A    ,   unk_token=_A    ,   sep_token=_A    ,   pad_token=_A    ,   cls_token=_A    ,   mask_token=_A    ,   tokenize_chinese_chars=_A    ,   strip_accents=_A    ,   **_A    ,   )

         snake_case								= json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get("lowercase"    ,   _A       ) != do_lower_case
             or normalizer_state.get("strip_accents"    ,   _A       ) != strip_accents
             or normalizer_state.get("handle_chinese_chars"    ,   _A       ) != tokenize_chinese_chars
         ):
             snake_case								= getattr(_A    ,   normalizer_state.pop("type"       )       )
             snake_case								= do_lower_case
             snake_case								= strip_accents
             snake_case								= tokenize_chinese_chars
             snake_case								= normalizer_class(**_A       )

         snake_case								= do_lower_case





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   Union[str, Any]    ,   _A				:   Dict=None       )						->						Optional[Any]:
         snake_case								= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

         return output





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:
         snake_case								= [self.sep_token_id]
         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 		UpperCAmelCase(self				:   int    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple[str]:
         snake_case								= self._tokenizer.model.save(_A    ,   name=_A       )
         return tuple(_A       )

