





# 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      A_	:
	# setable values
	UpperCAmelCase__ =      None
	UpperCAmelCase__ =      None
	UpperCAmelCase__ =      None
	@classmethod
	def 					_lowercase    (   cls		):






				'''simple docstring'''







				return cls()

@dataclass
class      A_	(a_      ):
	UpperCAmelCase__ =      42
	UpperCAmelCase__ =      42
	UpperCAmelCase__ =      None

class      A_	(a_					,							a_      ):
	@property
	def 					_lowercase    (   self		):






				'''simple docstring'''







				return True
	@register_to_config
	def __init__(   self							, _A = 2_0_0_0							, _A = 0.15							, _A = 0.01							, _A = 13_48.0							, _A = 1E-5							, _A = 1							, ):






				'''simple docstring'''







				pass
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ScoreSdeVeSchedulerState.create()
				return self.set_sigmas(
				    _A							, self.config.num_train_timesteps							, self.config.sigma_min							, self.config.sigma_max							, self.config.sampling_eps							, )
	def 					_lowercase    (   self							, _A							, _A							, _A = ()							, _A = None		):






				'''simple docstring'''







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

				UpperCAmelCase       		=    jnp.linspace(1							, _A							, _A		)
				return state.replace(timesteps=_A		)
	def 					_lowercase    (   self							, _A							, _A							, _A = None							, _A = None							, _A = None							, ):






				'''simple docstring'''







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

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

				return state.replace(discrete_sigmas=_A							, sigmas=_A		)
	def 					_lowercase    (   self							, _A							, _A							, _A		):






				'''simple docstring'''







				return jnp.where(timesteps == 0							, jnp.zeros_like(_A		)							, state.discrete_sigmas[timesteps - 1]		)
	def 					_lowercase    (   self							, _A							, _A							, _A							, _A							, _A							, _A = 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'''		)

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

				UpperCAmelCase       		=    state.discrete_sigmas[timesteps]
				UpperCAmelCase       		=    self.get_adjacent_sigma(_A							, _A							, _A		)
				UpperCAmelCase       		=    jnp.zeros_like(_A		)
				UpperCAmelCase       		=    (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
				UpperCAmelCase       		=    diffusion.flatten()
				UpperCAmelCase       		=    broadcast_to_shape_from_left(_A							, sample.shape		)
				UpperCAmelCase       		=    drift - diffusion**2 * model_output

				#  equation 6: sample noise for the diffusion term of
				UpperCAmelCase       		=    random.split(_A							, num=1		)
				UpperCAmelCase       		=    random.normal(key=_A							, shape=sample.shape		)
				UpperCAmelCase       		=    sample - drift  # subtract because `dt` is a small negative timestep
				# TODO is the variable diffusion the correct scaling term for the noise?
				UpperCAmelCase       		=    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=_A							, prev_sample_mean=_A							, state=_A		)
	def 					_lowercase    (   self							, _A							, _A							, _A							, _A							, _A = 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
				UpperCAmelCase       		=    random.split(_A							, num=1		)
				UpperCAmelCase       		=    random.normal(key=_A							, shape=sample.shape		)

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

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

				if not return_dict:
							return (prev_sample, state)

				return FlaxSdeVeOutput(prev_sample=_A							, state=_A		)







	def __len__(   self		):






				'''simple docstring'''







				return self.config.num_train_timesteps










import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file.jsonl'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_utf16_encoded.jsonl'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''			)
			with open(UpperCamelCase__      ,      '''w'''      ,      encoding='''utf-16'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_with_list_of_dicts.json'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        [
            {"col_1": -1},
            {"col_1": 1, "col_2": 2},
            {"col_1": 10, "col_2": 20}
        ]
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    int:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_with_list_of_dicts_field.json'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {
            "field1": 1,
            "field2": "aabb",
            "field3": [
                {"col_1": -1},
                {"col_1": 1, "col_2": 2},
                {"col_1": 10, "col_2": 20}
            ]
        }
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''      ,      [
        ('''jsonl_file''', {}),
        ('''jsonl_file_utf16_encoded''', {'''encoding''': '''utf-16'''}),
        ('''json_file_with_list_of_dicts''', {}),
        ('''json_file_with_list_of_dicts_field''', {'''field''': '''field3'''}),
    ]      ,      )
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Dict:







			'''simple docstring'''

			UpperCAmelCase       		=    Json(**UpperCamelCase__			)
			UpperCAmelCase       		=    json._generate_tables([[request.getfixturevalue(UpperCamelCase__			)]]			)
			UpperCAmelCase       		=    pa.concat_tables([table for _, table in generator]			)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}






@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''      ,      [
        (
            '''jsonl_file''',
            {'''features''': Features({'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			)},
        ),
        (
            '''json_file_with_list_of_dicts''',
            {'''features''': Features({'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			)},
        ),
        (
            '''json_file_with_list_of_dicts_field''',
            {
                '''field''': '''field3''',
                '''features''': Features(
                    {'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			),
            },
        ),
    ]      ,      )
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    Json(**UpperCamelCase__			)
			UpperCAmelCase       		=    json._generate_tables([[request.getfixturevalue(UpperCamelCase__			)]]			)
			UpperCAmelCase       		=    pa.concat_tables([table for _, table in generator]			)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}



