



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class       _lowercase				:





	"""simple docstring"""
	# setable values
	__A		      =				None
	__A		      =				None
	__A		      =				None




	@classmethod
	def 	UpperCamelCase_			(cls						):
						"""simple docstring"""

						return cls()




@dataclass
class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				42
	__A		      =				42
	__A		      =				None




class       _lowercase				(  lowerCAmelCase, lowerCAmelCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return True




	@register_to_config
	def __init__(self						,			lowerCamelCase_ = 2000						,			lowerCamelCase_ = 0.15						,			lowerCamelCase_ = 0.01						,			lowerCamelCase_ = 1348.0						,			lowerCamelCase_ = 1E-5						,			lowerCamelCase_ = 1						,			):
						"""simple docstring"""

						pass




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowerCamelCase_						,			self.config.num_train_timesteps						,			self.config.sigma_min						,			self.config.sigma_max						,			self.config.sampling_eps						,			)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_ = ()						,			lowerCamelCase_ = None						):
						"""simple docstring"""

						a   	=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

						a   	=  jnp.linspace(1						,			lowerCamelCase_						,			lowerCamelCase_						)
						return state.replace(timesteps=lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			):
						"""simple docstring"""

						a   	=  sigma_min if sigma_min is not None else self.config.sigma_min
						a   	=  sigma_max if sigma_max is not None else self.config.sigma_max
						a   	=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											a   	=  self.set_timesteps(lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						)

						a   	=  jnp.exp(jnp.linspace(jnp.log(lowerCamelCase_						)						,			jnp.log(lowerCamelCase_						)						,			lowerCamelCase_						)						)
						a   	=  jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]						)

						return state.replace(discrete_sigmas=lowerCamelCase_						,			sigmas=lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						return jnp.where(timesteps == 0						,			jnp.zeros_like(lowerCamelCase_						)						,			state.discrete_sigmas[timesteps - 1]						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_ = True						,			):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"						)

						a   	=  timestep * jnp.ones(
						    sample.shape[0]						,			)
						a   	=  (timestep * (len(state.timesteps						) - 1)).long()

						a   	=  state.discrete_sigmas[timesteps]
						a   	=  self.get_adjacent_sigma(lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						)
						a   	=  jnp.zeros_like(lowerCamelCase_						)
						a   	=  (sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						a   	=  diffusion.flatten()
						a   	=  broadcast_to_shape_from_left(lowerCamelCase_						,			sample.shape						)
						a   	=  drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						a   	=  random.split(lowerCamelCase_						,			num=1						)
						a   	=  random.normal(key=lowerCamelCase_						,			shape=sample.shape						)
						a   	=  sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						a   	=  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
											return (prev_sample, prev_sample_mean, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_						,			prev_sample_mean=lowerCamelCase_						,			state=lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_ = True						,			):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"						)

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						a   	=  random.split(lowerCamelCase_						,			num=1						)
						a   	=  random.normal(key=lowerCamelCase_						,			shape=sample.shape						)

						# compute step size from the model_output, the noise, and the snr
						a   	=  jnp.linalg.norm(lowerCamelCase_						)
						a   	=  jnp.linalg.norm(lowerCamelCase_						)
						a   	=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						a   	=  step_size * jnp.ones(sample.shape[0]						)

						# compute corrected sample: model_output term and noise term
						a   	=  step_size.flatten()
						a   	=  broadcast_to_shape_from_left(lowerCamelCase_						,			sample.shape						)
						a   	=  sample + step_size * model_output
						a   	=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
											return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_						,			state=lowerCamelCase_						)




	def __len__(self						):
						"""simple docstring"""

						return self.config.num_train_timesteps





from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowercase:					List[str]      =						logging.get_logger(__name__)

_lowercase:					str      =						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase:					Union[str, Any]      =						{
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
    "tokenizer_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"},
}

_lowercase:					int      =						{
    "openai-gpt": 512,
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				VOCAB_FILES_NAMES
	__A		      =				PRETRAINED_VOCAB_FILES_MAP
	__A		      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A		      =				["input_ids", "attention_mask"]
	__A		      =				OpenAIGPTTokenizer




	def __init__(self						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_="<unk>"						,			**lowerCamelCase_						):
						"""simple docstring"""

						super().__init__(lowerCamelCase_						,			lowerCamelCase_						,			tokenizer_file=lowerCamelCase_						,			unk_token=lowerCamelCase_						,			**lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return True




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						):
						"""simple docstring"""

						a   	=  self._tokenizer.model.save(lowerCamelCase_						,			name=lowerCamelCase_						)
						return tuple(lowerCamelCase_						)
