






import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil






@pytest.fixture
def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    tmp_path / '''file.csv'''
				lowercase__     				=    textwrap.dedent(
				    '''\
        header1,header2
        1,2
        10,20
        '''       )
				with open(lowerCamelCase_	,     '''w'''       ) as f:
								f.write(lowerCamelCase_       )
				return str(lowerCamelCase_       )






@pytest.fixture
def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    tmp_path / '''malformed_file.csv'''
				lowercase__     				=    textwrap.dedent(
				    '''\
        header1,header2
        1,2
        10,20,
        '''       )
				with open(lowerCamelCase_	,     '''w'''       ) as f:
								f.write(lowerCamelCase_       )
				return str(lowerCamelCase_       )






@pytest.fixture
def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    tmp_path / '''csv_with_image.csv'''
				lowercase__     				=    textwrap.dedent(
				    F"""\
        image
        {image_file}
        """       )
				with open(lowerCamelCase_	,     '''w'''       ) as f:
								f.write(lowerCamelCase_       )
				return str(lowerCamelCase_       )






@pytest.fixture
def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    tmp_path / '''csv_with_label.csv'''
				lowercase__     				=    textwrap.dedent(
				    '''\
        label
        good
        bad
        good
        '''       )
				with open(lowerCamelCase_	,     '''w'''       ) as f:
								f.write(lowerCamelCase_       )
				return str(lowerCamelCase_       )






@pytest.fixture
def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    tmp_path / '''csv_with_int_list.csv'''
				lowercase__     				=    textwrap.dedent(
				    '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''       )
				with open(lowerCamelCase_	,     '''w'''       ) as f:
								f.write(lowerCamelCase_       )
				return str(lowerCamelCase_       )






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    Csv()
				lowercase__     				=    csv._generate_tables([[csv_file, malformed_csv_file]]       )
				with pytest.raises(lowerCamelCase_	,     match='''Error tokenizing data'''       ):
								for _ in generator:
												pass
				assert any(
				    record.levelname == '''ERROR'''
				    and '''Failed to read file''' in record.message
				    and os.path.basename(lowerCamelCase_       ) in record.message
				    for record in caplog.records       )






@require_pil
def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				with open(lowerCamelCase_	,     encoding='''utf-8'''       ) as f:
								lowercase__     				=    f.read().splitlines()[1]
				lowercase__     				=    Csv(encoding='''utf-8'''	,     features=Features({'''image''': Image()}       )       )
				lowercase__     				=    csv._generate_tables([[csv_file_with_image]]       )
				lowercase__     				=    pa.concat_tables([table for _, table in generator]       )
				assert pa_table.schema.field('''image'''       ).type == Image()()
				lowercase__     				=    pa_table.to_pydict()['''image''']
				assert generated_content == [{"path": image_file, "bytes": None}]






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				with open(lowerCamelCase_	,     encoding='''utf-8'''       ) as f:
								lowercase__     				=    f.read().splitlines()[1:]
				lowercase__     				=    Csv(encoding='''utf-8'''	,     features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']       )}       )       )
				lowercase__     				=    csv._generate_tables([[csv_file_with_label]]       )
				lowercase__     				=    pa.concat_tables([table for _, table in generator]       )
				assert pa_table.schema.field('''label'''       ).type == ClassLabel(names=['''good''', '''bad''']       )()
				lowercase__     				=    pa_table.to_pydict()['''label''']
				assert generated_content == [ClassLabel(names=['''good''', '''bad''']       ).straint(lowerCamelCase_       ) for label in labels]






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    Csv(encoding='''utf-8'''	,     sep=''','''	,     converters={'''int_list''': lambda lowerCamelCase_       : [int(lowerCamelCase_       ) for i in x.split()]}       )
				lowercase__     				=    csv._generate_tables([[csv_file_with_int_list]]       )
				lowercase__     				=    pa.concat_tables([table for _, table in generator]       )
				assert pa.types.is_list(pa_table.schema.field('''int_list'''       ).type       )
				lowercase__     				=    pa_table.to_pydict()['''int_list''']
				assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]












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

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= 1




				@register_to_config
				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					int = 2_000,						lowerCamelCase		:					float = 0.15,						lowerCamelCase		:					float = 0.01,						lowerCamelCase		:					float = 1348.0,						lowerCamelCase		:					float = 1E-5,						lowerCamelCase		:					int = 1,						):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__     				=    sigma_max

								# setable values
								lowercase__     				=    None

								self.set_sigmas(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					Union[str, torch.device] = None						):

								'''simple docstring'''
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

								lowercase__     				=    torch.linspace(1,						lowerCamelCase,						lowerCamelCase,						device=lowerCamelCase						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None						):

								'''simple docstring'''
								lowercase__     				=    sigma_min if sigma_min is not None else self.config.sigma_min
								lowercase__     				=    sigma_max if sigma_max is not None else self.config.sigma_max
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
												self.set_timesteps(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								lowercase__     				=    torch.exp(torch.linspace(math.log(lowerCamelCase						),						math.log(lowerCamelCase						),						lowerCamelCase						)						)
								lowercase__     				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''simple docstring'''
								return torch.where(
								    timesteps == 0,						torch.zeros_like(t.to(timesteps.device						)						),						self.discrete_sigmas[timesteps - 1].to(timesteps.device						),						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

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

								lowercase__     				=    timestep * torch.ones(
								    sample.shape[0],						device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								lowercase__     				=    (timestep * (len(self.timesteps						) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								lowercase__     				=    timesteps.to(self.discrete_sigmas.device						)

								lowercase__     				=    self.discrete_sigmas[timesteps].to(sample.device						)
								lowercase__     				=    self.get_adjacent_sigma(lowerCamelCase,						lowerCamelCase						).to(sample.device						)
								lowercase__     				=    torch.zeros_like(lowerCamelCase						)
								lowercase__     				=    (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
								lowercase__     				=    diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
												lowercase__     				=    diffusion.unsqueeze(-1						)
								lowercase__     				=    drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								lowercase__     				=    randn_tensor(
								    sample.shape,						layout=sample.layout,						generator=lowerCamelCase,						device=sample.device,						dtype=sample.dtype						)
								lowercase__     				=    sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								lowercase__     				=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
												return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowerCamelCase,						prev_sample_mean=lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.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
								lowercase__     				=    randn_tensor(sample.shape,						layout=sample.layout,						generator=lowerCamelCase						).to(sample.device						)

								# compute step size from the model_output, the noise, and the snr
								lowercase__     				=    torch.norm(model_output.reshape(model_output.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    torch.norm(noise.reshape(noise.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								lowercase__     				=    step_size * torch.ones(sample.shape[0]						).to(sample.device						)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								lowercase__     				=    step_size.flatten()
								while len(step_size.shape						) < len(sample.shape						):
												lowercase__     				=    step_size.unsqueeze(-1						)
								lowercase__     				=    sample + step_size * model_output
								lowercase__     				=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__     				=    timesteps.to(original_samples.device						)
								lowercase__     				=    self.discrete_sigmas.to(original_samples.device						)[timesteps]
								lowercase__     				=    (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowerCamelCase						) * sigmas[:, None, None, None]
								)
								lowercase__     				=    noise + original_samples
								return noisy_samples




				def __len__(  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.config.num_train_timesteps




