




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 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Optional[int]			=						tmp_path / """file.csv"""
      _A							:     int			=						textwrap.dedent(
          """\
        header1,header2
        1,2
        10,20
        """     )
      with open(snake_case_,"""w"""     ) as f:
            f.write(snake_case_     )
      return str(snake_case_     )







@pytest.fixture
def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Optional[int]			=						tmp_path / """malformed_file.csv"""
      _A							:     str			=						textwrap.dedent(
          """\
        header1,header2
        1,2
        10,20,
        """     )
      with open(snake_case_,"""w"""     ) as f:
            f.write(snake_case_     )
      return str(snake_case_     )







@pytest.fixture
def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Optional[int]			=						tmp_path / """csv_with_image.csv"""
      _A							:     str			=						textwrap.dedent(
          f'''\
        image
        {image_file}
        '''     )
      with open(snake_case_,"""w"""     ) as f:
            f.write(snake_case_     )
      return str(snake_case_     )







@pytest.fixture
def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[str]			=						tmp_path / """csv_with_label.csv"""
      _A							:     Optional[int]			=						textwrap.dedent(
          """\
        label
        good
        bad
        good
        """     )
      with open(snake_case_,"""w"""     ) as f:
            f.write(snake_case_     )
      return str(snake_case_     )







@pytest.fixture
def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[Any]			=						tmp_path / """csv_with_int_list.csv"""
      _A							:     Tuple			=						textwrap.dedent(
          """\
        int_list
        1 2 3
        4 5 6
        7 8 9
        """     )
      with open(snake_case_,"""w"""     ) as f:
            f.write(snake_case_     )
      return str(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Dict			=						Csv()
      _A							:     List[Any]			=						csv._generate_tables([[csv_file, malformed_csv_file]]     )
      with pytest.raises(snake_case_,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(snake_case_     ) in record.message
          for record in caplog.records     )







@require_pil
def 				lowerCAmelCase_	(     snake_case_     ):
      with open(snake_case_,encoding="""utf-8"""     ) as f:
            _A							:     str			=						f.read().splitlines()[1]
      _A							:     Union[str, Any]			=						Csv(encoding="""utf-8""",features=Features({"""image""": Image()}     )     )
      _A							:     List[Any]			=						csv._generate_tables([[csv_file_with_image]]     )
      _A							:     Optional[int]			=						pa.concat_tables([table for _, table in generator]     )
      assert pa_table.schema.field("""image"""     ).type == Image()()
      _A							:     Dict			=						pa_table.to_pydict()["""image"""]
      assert generated_content == [{"path": image_file, "bytes": None}]







def 				lowerCAmelCase_	(     snake_case_     ):
      with open(snake_case_,encoding="""utf-8"""     ) as f:
            _A							:     List[str]			=						f.read().splitlines()[1:]
      _A							:     Optional[Any]			=						Csv(encoding="""utf-8""",features=Features({"""label""": ClassLabel(names=["""good""", """bad"""]     )}     )     )
      _A							:     Tuple			=						csv._generate_tables([[csv_file_with_label]]     )
      _A							:     Union[str, Any]			=						pa.concat_tables([table for _, table in generator]     )
      assert pa_table.schema.field("""label"""     ).type == ClassLabel(names=["""good""", """bad"""]     )()
      _A							:     Tuple			=						pa_table.to_pydict()["""label"""]
      assert generated_content == [ClassLabel(names=["""good""", """bad"""]     ).straint(snake_case_     ) for label in labels]







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[str]			=						Csv(encoding="""utf-8""",sep=""",""",converters={"""int_list""": lambda snake_case_     : [int(snake_case_     ) for i in x.split()]}     )
      _A							:     List[Any]			=						csv._generate_tables([[csv_file_with_int_list]]     )
      _A							:     Any			=						pa.concat_tables([table for _, table in generator]     )
      assert pa.types.is_list(pa_table.schema.field("""int_list"""     ).type     )
      _A							:     str			=						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 typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

           _A							:     str			=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           _A							:     List[str]			=						beta_t.flatten()
           while len(beta_t.shape				) < len(x.shape				):
                 _A							:     Union[str, Any]			=						beta_t.unsqueeze(-1				)
           _A							:     Tuple			=						-0.5 * beta_t * x

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



     def __len__(  self				)						->							Optional[Any]:
           return self.config.num_train_timesteps

