


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE							    =							"""scheduler_config.json"""


class     __UpperCAmelCase							(       a__							):


     """simple docstring"""
     _lowerCamelCase   =      1
     _lowerCamelCase   =      2
     _lowerCamelCase   =      3
     _lowerCamelCase   =      4
     _lowerCamelCase   =      5







@dataclass
class     __UpperCAmelCase							(       a__							):


     """simple docstring"""
     _lowerCamelCase   =      42







class     __UpperCAmelCase							:


     """simple docstring"""
     _lowerCamelCase   =      SCHEDULER_CONFIG_NAME
     _lowerCamelCase   =      ["""dtype"""]
     _lowerCamelCase   =      []
     _lowerCamelCase   =      True


     @classmethod
     def 	snake_case_  (	cls  ,       __A = None  ,       __A = None  ,       __A=False  ,       **__A  ,       ):
        __a					=  cls.load_config(
            pretrained_model_name_or_path=__a  ,       subfolder=__a  ,       return_unused_kwargs=__a  ,       **__a  ,       )
        __a					=  cls.from_config(__a  ,       return_unused_kwargs=__a  ,       **__a    )

        if hasattr(__a  ,       """create_state"""    ) and getattr(__a  ,       """has_state"""  ,       __a    ):
           __a					=  scheduler.create_state()

        if return_unused_kwargs:
           return scheduler, state, unused_kwargs

        return scheduler, state


     def 	snake_case_  (	self  ,       __A  ,       __A = False  ,       **__A    ):
        self.save_config(save_directory=__a  ,       push_to_hub=__a  ,       **__a    )


     @property
     def 	snake_case_  (	self    ):
        return self._get_compatibles()


     @classmethod
     def 	snake_case_  (	cls    ):
        __a					=  list(set([cls.__name__] + cls._compatibles    )    )
        __a					=  importlib.import_module(__name__.split("""."""    )[0]    )
        __a					=  [
            getattr(__a  ,       __a    ) for c in compatible_classes_str if hasattr(__a  ,       __a    )
        ]
        return compatible_classes







def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   assert len(__lowercase   ) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__lowercase   ) - x.ndim)   )		,				__lowercase   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=0.9_9_9		,				lowerCAmelCase__=jnp.floataa   ):

   def alpha_bar(lowerCAmelCase__   ):
      return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

   __a					=  []
   for i in range(__lowercase   ):
      __a					=  i / num_diffusion_timesteps
      __a					=  (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(__lowercase   ) / alpha_bar(__lowercase   )		,				__lowercase   )   )
   return jnp.array(__lowercase		,				dtype=__lowercase   )


@flax.struct.dataclass
class     __UpperCAmelCase							:


     """simple docstring"""
     _lowerCamelCase   =      42
     _lowerCamelCase   =      42
     _lowerCamelCase   =      42


     @classmethod
     def 	snake_case_  (	cls  ,       __A    ):
        __a					=  scheduler.config

        if config.trained_betas is not None:
           __a					=  jnp.asarray(config.trained_betas  ,       dtype=scheduler.dtype    )
        elif config.beta_schedule == "linear":
           __a					=  jnp.linspace(config.beta_start  ,       config.beta_end  ,       config.num_train_timesteps  ,       dtype=scheduler.dtype    )
        elif config.beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           __a					=  (
               jnp.linspace(
                   config.beta_start**0.5  ,       config.beta_end**0.5  ,       config.num_train_timesteps  ,       dtype=scheduler.dtype    )
               ** 2
           )
        elif config.beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           __a					=  betas_for_alpha_bar(config.num_train_timesteps  ,       dtype=scheduler.dtype    )
        else:
           raise NotImplementedError(
               f'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''    )

        __a					=  1.0 - betas

        __a					=  jnp.cumprod(__a  ,       axis=0    )

        return cls(
            alphas=__a  ,       betas=__a  ,       alphas_cumprod=__a  ,       )







def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  state.alphas_cumprod

   __a					=  alphas_cumprod[timesteps] ** 0.5
   __a					=  sqrt_alpha_prod.flatten()
   __a					=  broadcast_to_shape_from_left(__lowercase		,				original_samples.shape   )

   __a					=  (1 - alphas_cumprod[timesteps]) ** 0.5
   __a					=  sqrt_one_minus_alpha_prod.flatten()
   __a					=  broadcast_to_shape_from_left(__lowercase		,				original_samples.shape   )

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  get_sqrt_alpha_prod(__lowercase		,				__lowercase		,				__lowercase		,				__lowercase   )
   __a					=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  get_sqrt_alpha_prod(__lowercase		,				__lowercase		,				__lowercase		,				__lowercase   )
   __a					=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity









'''simple docstring'''







import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase      :   Any      =       logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase      :   List[Any]      =       argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=3_0522, type=int)
      lowercase      :   str      =       parser.parse_args()

      logger.info(F"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase      :   int      =       pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase      :   List[Any]      =       Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase      :   int      =       [0] * args.vocab_size
      for k, v in counter.items():
            lowercase      :   List[Any]      =       v

      logger.info(F"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



