





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


A_        =  logging.get_logger(__name__)

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

A_        =  {
    "vocab_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json",
    },
}

A_        =  {
    "microsoft/mpnet-base": 5_1_2,
}

A_        =  {
    "microsoft/mpnet-base": {"do_lower_case": True},
}

class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					A__								=     VOCAB_FILES_NAMES
					A__								=     PRETRAINED_VOCAB_FILES_MAP
					A__								=     PRETRAINED_INIT_CONFIGURATION
					A__								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__								=     MPNetTokenizer
					A__								=     ["input_ids", "attention_mask"]





					def __init__(			self						,	_lowerCAmelCase=None						,	_lowerCAmelCase=None						,	_lowerCAmelCase=True						,	_lowerCAmelCase="<s>"						,	_lowerCAmelCase="</s>"						,	_lowerCAmelCase="</s>"						,	_lowerCAmelCase="<s>"						,	_lowerCAmelCase="[UNK]"						,	_lowerCAmelCase="<pad>"						,	_lowerCAmelCase="<mask>"						,	_lowerCAmelCase=True						,	_lowerCAmelCase=None						,	**_lowerCAmelCase						,	):
										super().__init__(
										    _lowerCAmelCase						,	tokenizer_file=_lowerCAmelCase						,	do_lower_case=_lowerCAmelCase						,	bos_token=_lowerCAmelCase						,	eos_token=_lowerCAmelCase						,	sep_token=_lowerCAmelCase						,	cls_token=_lowerCAmelCase						,	unk_token=_lowerCAmelCase						,	pad_token=_lowerCAmelCase						,	mask_token=_lowerCAmelCase						,	tokenize_chinese_chars=_lowerCAmelCase						,	strip_accents=_lowerCAmelCase						,	**_lowerCAmelCase						,	)

										lowerCamelCase__      =							json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
										if (
										    pre_tok_state.get("lowercase"						,	_lowerCAmelCase						) != do_lower_case
										    or pre_tok_state.get("strip_accents"						,	_lowerCAmelCase						) != strip_accents
										):
															lowerCamelCase__      =							getattr(_lowerCAmelCase						,	pre_tok_state.pop("type"						)						)
															lowerCamelCase__      =							do_lower_case
															lowerCamelCase__      =							strip_accents
															lowerCamelCase__      =							pre_tok_class(**_lowerCAmelCase						)

										lowerCamelCase__      =							do_lower_case





					@property
					def        __magic_name__			(			self						):
										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        __magic_name__			(			self						,	_lowerCAmelCase						):
										lowerCamelCase__      =							AddedToken(_lowerCAmelCase						,	lstrip=_lowerCAmelCase						,	rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase						,	_lowerCAmelCase						) else value
										lowerCamelCase__      =							value





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=None						):
										lowerCamelCase__      =							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
															return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase = None						):
										lowerCamelCase__      =							[self.sep_token_id]
										lowerCamelCase__      =							[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 + sep + token_ids_a + sep						) * [0]






					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase = None						):
										lowerCamelCase__      =							self._tokenizer.model.save(_lowerCAmelCase						,	name=_lowerCAmelCase						)
										return tuple(_lowerCAmelCase						)








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

import flax
import jax.numpy as jnp
from scipy import integrate

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

@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					A__								=     42

					# setable values
					A__								=     42
					A__								=     42
					A__								=     42
					A__								=     None

					# running values
					A__								=     None





					@classmethod
					def        __magic_name__			(			cls						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										return cls(common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						)

@dataclass
class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					A__								=     42

class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_ ):





					"""simple docstring"""


					A__								=     [e.name for e in FlaxKarrasDiffusionSchedulers]

					A__								=     42





					@property
					def        __magic_name__			(			self						):
										return True





					@register_to_config
					def __init__(			self						,	_lowerCAmelCase = 1000						,	_lowerCAmelCase = 0.0_001						,	_lowerCAmelCase = 0.02						,	_lowerCAmelCase = "linear"						,	_lowerCAmelCase = None						,	_lowerCAmelCase = "epsilon"						,	_lowerCAmelCase = jnp.floataa						,	):
										lowerCamelCase__      =							dtype





					def        __magic_name__			(			self						,	_lowerCAmelCase = None						):
										if common is None:
															lowerCamelCase__      =							CommonSchedulerState.create(self						)

										lowerCamelCase__      =							jnp.arange(0						,	self.config.num_train_timesteps						).round()[::-1]
										lowerCamelCase__      =							((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

										# standard deviation of the initial noise distribution
										lowerCamelCase__      =							sigmas.max()

										return LMSDiscreteSchedulerState.create(
										    common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										((lowerCamelCase__)     ,		)      =							jnp.where(state.timesteps == timestep						,	size=1						)
										lowerCamelCase__      =							step_index[0]

										lowerCamelCase__      =							state.sigmas[step_index]
										lowerCamelCase__      =							sample / ((sigma**2 + 1) ** 0.5)
										return sample





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):

										def lms_derivative(_lowerCAmelCase						):
															lowerCamelCase__      =							1.0
															for k in range(_lowerCAmelCase						):
																				if current_order == k:
																									continue
																				prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
															return prod

										lowerCamelCase__      =							integrate.quad(_lowerCAmelCase						,	state.sigmas[t]						,	state.sigmas[t + 1]						,	epsrel=1E-4						)[0]

										return integrated_coeff





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = ()						):
										lowerCamelCase__      =							jnp.linspace(self.config.num_train_timesteps - 1						,	0						,	_lowerCAmelCase						,	dtype=self.dtype						)

										lowerCamelCase__      =							jnp.floor(_lowerCAmelCase						).astype(jnp.intaa						)
										lowerCamelCase__      =							jnp.ceil(_lowerCAmelCase						).astype(jnp.intaa						)

										lowerCamelCase__      =							jnp.mod(_lowerCAmelCase						,	1.0						)

										lowerCamelCase__      =							((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
										lowerCamelCase__      =							(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
										lowerCamelCase__      =							jnp.concatenate([sigmas, jnp.array([0.0]						,	dtype=self.dtype						)]						)

										lowerCamelCase__      =							timesteps.astype(jnp.intaa						)

										# initial running values
										lowerCamelCase__      =							jnp.zeros((0,) + shape						,	dtype=self.dtype						)

										return state.replace(
										    timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	num_inference_steps=_lowerCAmelCase						,	derivatives=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = 4						,	_lowerCAmelCase = True						,	):
										if state.num_inference_steps is None:
															raise ValueError(
															    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"						)

										lowerCamelCase__      =							state.sigmas[timestep]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
															lowerCamelCase__      =							sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															lowerCamelCase__      =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
															raise ValueError(
															    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

										# 2. Convert to an ODE derivative
										lowerCamelCase__      =							(sample - pred_original_sample) / sigma
										lowerCamelCase__      =							state.replace(derivatives=jnp.append(state.derivatives						,	_lowerCAmelCase						)						)
										if len(state.derivatives						) > order:
															lowerCamelCase__      =							state.replace(derivatives=jnp.delete(state.derivatives						,	0						)						)

										# 3. Compute linear multistep coefficients
										lowerCamelCase__      =							min(timestep + 1						,	_lowerCAmelCase						)
										lowerCamelCase__      =							[self.get_lms_coefficient(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						) for curr_order in range(_lowerCAmelCase						)]

										# 4. Compute previous sample based on the derivatives path
										lowerCamelCase__      =							sample + sum(
										    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase						,	reversed(state.derivatives						)						)						)

										if not return_dict:
															return (prev_sample, state)

										return FlaxLMSSchedulerOutput(prev_sample=_lowerCAmelCase						,	state=_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							state.sigmas[timesteps].flatten()
										lowerCamelCase__      =							broadcast_to_shape_from_left(_lowerCAmelCase						,	noise.shape						)

										lowerCamelCase__      =							original_samples + noise * sigma

										return noisy_samples






					def __len__(			self						):
										return self.config.num_train_timesteps

