





"""simple docstring"""





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 		lowercase					:
      _SCREAMING_SNAKE_CASE					=      42

      # setable values
      _SCREAMING_SNAKE_CASE					=      42
      _SCREAMING_SNAKE_CASE					=      42
      _SCREAMING_SNAKE_CASE					=      None





      @classmethod
      def     _snake_case			(       cls      ,						lowercase      ,						lowercase      ,						lowercase )		->     List[Any]:
            return cls(common=lowercase      ,						init_noise_sigma=lowercase      ,						timesteps=lowercase )







@dataclass
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      [e.name for e in FlaxKarrasDiffusionSchedulers]

      _SCREAMING_SNAKE_CASE					=      42





      @property
      def     _snake_case			(       self )		->     Tuple:
            return True





      @register_to_config
      def __init__(       self      ,						lowercase = 1_000      ,						lowercase = 0.0_001      ,						lowercase = 0.02      ,						lowercase = "linear"      ,						lowercase = None      ,						lowercase = "fixed_small"      ,						lowercase = True      ,						lowercase = "epsilon"      ,						lowercase = jnp.floataa      ,						)		->     List[Any]:
            lowerCAmelCase  						=       dtype





      def     _snake_case			(       self      ,						lowercase = None )		->     DDPMSchedulerState:
            if common is None:
                  lowerCAmelCase  						=       CommonSchedulerState.create(self )

            # standard deviation of the initial noise distribution
            lowerCAmelCase  						=       jnp.array(1.0      ,						dtype=self.dtype )

            lowerCAmelCase  						=       jnp.arange(0      ,						self.config.num_train_timesteps ).round()[::-1]

            return DDPMSchedulerState.create(
                common=lowercase      ,						init_noise_sigma=lowercase      ,						timesteps=lowercase      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = None )		->     jnp.ndarray:
            return sample





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = () )		->     DDPMSchedulerState:
            lowerCAmelCase  						=       self.config.num_train_timesteps // num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # rounding to avoid issues when num_inference_step is power of 3
            lowerCAmelCase  						=       (jnp.arange(0      ,						lowercase ) * step_ratio).round()[::-1]

            return state.replace(
                num_inference_steps=lowercase      ,						timesteps=lowercase      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						lowercase=None )		->     Optional[int]:
            lowerCAmelCase  						=       state.common.alphas_cumprod[t]
            lowerCAmelCase  						=       jnp.where(t > 0      ,						state.common.alphas_cumprod[t - 1]      ,						jnp.array(1.0      ,						dtype=self.dtype ) )

            # 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
            lowerCAmelCase  						=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

            if variance_type is None:
                  lowerCAmelCase  						=       self.config.variance_type

            # hacks - were probably added for training stability
            if variance_type == "fixed_small":
                  lowerCAmelCase  						=       jnp.clip(lowercase      ,						a_min=1e-20 )
            # for rl-diffuser https://arxiv.org/abs/2205.09991
            elif variance_type == "fixed_small_log":
                  lowerCAmelCase  						=       jnp.log(jnp.clip(lowercase      ,						a_min=1e-20 ) )
            elif variance_type == "fixed_large":
                  lowerCAmelCase  						=       state.common.betas[t]
            elif variance_type == "fixed_large_log":
                  # Glide max_log
                  lowerCAmelCase  						=       jnp.log(state.common.betas[t] )
            elif variance_type == "learned":
                  return predicted_variance
            elif variance_type == "learned_range":
                  lowerCAmelCase  						=       variance
                  lowerCAmelCase  						=       state.common.betas[t]
                  lowerCAmelCase  						=       (predicted_variance + 1) / 2
                  lowerCAmelCase  						=       frac * max_log + (1 - frac) * min_log

            return variance





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						lowercase = True      ,						)		->     Union[FlaxDDPMSchedulerOutput, Tuple]:
            lowerCAmelCase  						=       timestep

            if key is None:
                  lowerCAmelCase  						=       jax.random.PRNGKey(0 )

            if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
                  lowerCAmelCase						,		lowerCAmelCase  						=       jnp.split(lowercase      ,						sample.shape[1]      ,						axis=1 )
            else:
                  lowerCAmelCase  						=       None

            # 1. compute alphas, betas
            lowerCAmelCase  						=       state.common.alphas_cumprod[t]
            lowerCAmelCase  						=       jnp.where(t > 0      ,						state.common.alphas_cumprod[t - 1]      ,						jnp.array(1.0      ,						dtype=self.dtype ) )
            lowerCAmelCase  						=       1 - alpha_prod_t
            lowerCAmelCase  						=       1 - alpha_prod_t_prev

            # 2. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
            if self.config.prediction_type == "epsilon":
                  lowerCAmelCase  						=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            elif self.config.prediction_type == "sample":
                  lowerCAmelCase  						=       model_output
            elif self.config.prediction_type == "v_prediction":
                  lowerCAmelCase  						=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            else:
                  raise ValueError(
                      f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
                      """ for the FlaxDDPMScheduler.""" )

            # 3. Clip "predicted x_0"
            if self.config.clip_sample:
                  lowerCAmelCase  						=       jnp.clip(lowercase      ,						-1      ,						1 )

            # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            lowerCAmelCase  						=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
            lowerCAmelCase  						=       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

            # 5. Compute predicted previous sample µ_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            lowerCAmelCase  						=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

            # 6. Add noise
            def random_variance():
                  lowerCAmelCase  						=       jax.random.split(lowercase      ,						num=1 )
                  lowerCAmelCase  						=       jax.random.normal(lowercase      ,						shape=model_output.shape      ,						dtype=self.dtype )
                  return (self._get_variance(lowercase      ,						lowercase      ,						predicted_variance=lowercase ) ** 0.5) * noise

            lowerCAmelCase  						=       jnp.where(t > 0      ,						random_variance()      ,						jnp.zeros(model_output.shape      ,						dtype=self.dtype ) )

            lowerCAmelCase  						=       pred_prev_sample + variance

            if not return_dict:
                  return (pred_prev_sample, state)

            return FlaxDDPMSchedulerOutput(prev_sample=lowercase      ,						state=lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     jnp.ndarray:
            return add_noise_common(state.common      ,						lowercase      ,						lowercase      ,						lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     jnp.ndarray:
            return get_velocity_common(state.common      ,						lowercase      ,						lowercase      ,						lowercase )





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








"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_fnet import FNetTokenizer
else:
   _A			:       Union[str, Any]   		=       None

_A			:       str   		=       logging.get_logger(__name__)
_A			:       Tuple   		=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A			:       Optional[Any]   		=       {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

_A			:       Optional[Any]   		=       {
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

_A			:       List[str]   		=       """▁"""

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """token_type_ids"""]
   __lowerCAmelCase            =				FNetTokenizer
   def __init__(			self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=True	,    _a="<unk>"	,    _a="[SEP]"	,    _a="<pad>"	,    _a="[CLS]"	,    _a="[MASK]"	,    **_a	,    ):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          lowercase    :				int       		=							(
              AddedToken(_a	,    lstrip=_a	,    rstrip=_a	,    normalized=_a				)
              if isinstance(_a	,    _a				)
              else mask_token
          )

          super().__init__(
              _a	,    tokenizer_file=_a	,    do_lower_case=_a	,    remove_space=_a	,    keep_accents=_a	,    unk_token=_a	,    sep_token=_a	,    pad_token=_a	,    cls_token=_a	,    mask_token=_a	,    **_a	,    )

          lowercase    :				Dict       		=							do_lower_case
          lowercase    :				Union[str, Any]       		=							remove_space
          lowercase    :				Any       		=							keep_accents
          lowercase    :				List[Any]       		=							vocab_file
          lowercase    :				Union[str, Any]       		=							False if not self.vocab_file else True
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[Any]       		=							[self.sep_token_id]
          lowercase    :				str       		=							[self.cls_token_id]
          if token_ids_a is None:
                 return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Any       		=							[self.sep_token_id]
          lowercase    :				Dict       		=							[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 = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				Optional[Any]       		=							os.path.join(
              _a	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

          if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				):
                 copyfile(self.vocab_file	,    _a				)

          return (out_vocab_file,)


