


'''simple docstring'''







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__	(    lowerCamelCase					:      Union[str, Any]       ):
       _A					:     List[Any]     = tmp_path / 'file.csv'
       _A					:     Dict     = textwrap.dedent(
           '\\n        header1,header2\n        1,2\n        10,20\n        '       )
       with open(lowerCamelCase  ,'w'       ) as f:
              f.write(lowerCamelCase       )
       return str(lowerCamelCase       )






@pytest.fixture
def 				lowerCAmelCase__	(    lowerCamelCase					:      Dict       ):
       _A					:     List[Any]     = tmp_path / 'malformed_file.csv'
       _A					:     Union[str, Any]     = textwrap.dedent(
           '\\n        header1,header2\n        1,2\n        10,20,\n        '       )
       with open(lowerCamelCase  ,'w'       ) as f:
              f.write(lowerCamelCase       )
       return str(lowerCamelCase       )






@pytest.fixture
def 				lowerCAmelCase__	(    lowerCamelCase					:      Any  ,lowerCamelCase					:      str       ):
       _A					:     Dict     = tmp_path / 'csv_with_image.csv'
       _A					:     int     = textwrap.dedent(
           F'\\n        image\n        {image_file}\n        '       )
       with open(lowerCamelCase  ,'w'       ) as f:
              f.write(lowerCamelCase       )
       return str(lowerCamelCase       )






@pytest.fixture
def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]       ):
       _A					:     str     = tmp_path / 'csv_with_label.csv'
       _A					:     Any     = textwrap.dedent(
           '\\n        label\n        good\n        bad\n        good\n        '       )
       with open(lowerCamelCase  ,'w'       ) as f:
              f.write(lowerCamelCase       )
       return str(lowerCamelCase       )






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






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






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






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]       ):
       _A					:     Tuple     = Csv(encoding='utf-8'  ,sep=','  ,converters={'int_list': lambda lowerCamelCase       : [int(lowerCamelCase       ) for i in x.split()]}       )
       _A					:     Tuple     = csv._generate_tables([[csv_file_with_int_list]]       )
       _A					:     Dict     = pa.concat_tables([table for _, table in generator]       )
       assert pa.types.is_list(pa_table.schema.field('int_list'       ).type       )
       _A					:     Optional[int]     = pa_table.to_pydict()['int_list']
       assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]










'''simple docstring'''







# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   42
    a													=   None






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      Tuple=0.999  ,lowerCamelCase					:      int="cosine"  ,):
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowerCamelCase					:      str       ):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(lowerCamelCase					:      Dict       ):
                     return math.exp(t * -12.0       )

       else:
              raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'       )

       _A					:     Tuple     = []
       for i in range(lowerCamelCase       ):
              _A					:     Optional[Any]     = i / num_diffusion_timesteps
              _A					:     Tuple     = (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(lowerCamelCase       ) / alpha_bar_fn(lowerCamelCase       )  ,lowerCamelCase       )       )
       return torch.tensor(lowerCamelCase  ,dtype=torch.floataa       )


class 			__lowerCamelCase			(	a_    , a_	):
    """simple docstring"""
    a													=   1


    @register_to_config
    def __init__(  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int = 1000				,							SCREAMING_SNAKE_CASE      :							float = 0.0001				,							SCREAMING_SNAKE_CASE      :							float = 0.02				,							SCREAMING_SNAKE_CASE      :							str = "linear"				,							SCREAMING_SNAKE_CASE      :							Optional[Union[np.ndarray, List[float]]] = None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							int = 0				,							SCREAMING_SNAKE_CASE      :							str = "epsilon"				,							SCREAMING_SNAKE_CASE      :							float = 1.0				,							**SCREAMING_SNAKE_CASE      :							List[str]				,							):
           if kwargs.get('set_alpha_to_one'				,							SCREAMING_SNAKE_CASE) is not None:
                  _A					:     Tuple     = (
                      'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
                  )
                  deprecate('set_alpha_to_one'				,							'1.0.0'				,							SCREAMING_SNAKE_CASE				,							standard_warn=SCREAMING_SNAKE_CASE)
                  _A					:     Tuple     = kwargs['set_alpha_to_one']
           if trained_betas is not None:
                  _A					:     Any     = torch.tensor(SCREAMING_SNAKE_CASE				,							dtype=torch.floataa)
           elif beta_schedule == "linear":
                  _A					:     List[Any]     = torch.linspace(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							dtype=torch.floataa)
           elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  _A					:     List[str]     = (
                      torch.linspace(beta_start**0.5				,							beta_end**0.5				,							SCREAMING_SNAKE_CASE				,							dtype=torch.floataa) ** 2
                  )
           elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  _A					:     List[Any]     = betas_for_alpha_bar(SCREAMING_SNAKE_CASE)
           else:
                  raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

           _A					:     Optional[int]     = 1.0 - self.betas
           _A					:     Union[str, Any]     = torch.cumprod(self.alphas				,							dim=0)

           # At every step in inverted ddim, we are looking into the next alphas_cumprod
           # For the final step, there is no next alphas_cumprod, and the index is out of bounds
           # `set_alpha_to_zero` decides whether we set this parameter simply to zero
           # in this case, self.step() just output the predicted noise
           # or whether we use the final alpha of the "non-previous" one.
           _A					:     Optional[int]     = torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

           # standard deviation of the initial noise distribution
           _A					:     Union[str, Any]     = 1.0

           # setable values
           _A					:     List[str]     = None
           _A					:     Dict     = torch.from_numpy(np.arange(0				,							SCREAMING_SNAKE_CASE).copy().astype(np.intaa))


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							Optional[int] = None):
           return sample


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Union[str, torch.device] = None):

           if num_inference_steps > self.config.num_train_timesteps:
                  raise ValueError(
                      F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                      F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                      F' maximal {self.config.num_train_timesteps} timesteps.')

           _A					:     Optional[Any]     = num_inference_steps
           _A					:     List[Any]     = self.config.num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           _A					:     List[str]     = (np.arange(0				,							SCREAMING_SNAKE_CASE) * step_ratio).round().copy().astype(np.intaa)
           _A					:     int     = torch.from_numpy(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
           self.timesteps += self.config.steps_offset


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							float = 0.0				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							Optional[torch.FloatTensor] = None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							):
           # 1. get previous step value (=t+1)
           _A					:     Union[str, Any]     = timestep + self.config.num_train_timesteps // self.num_inference_steps

           # 2. compute alphas, betas
           # change original implementation to exactly match noise levels for analogous forward process
           _A					:     List[str]     = self.alphas_cumprod[timestep]
           _A					:     List[str]     = (
               self.alphas_cumprod[prev_timestep]
               if prev_timestep < self.config.num_train_timesteps
               else self.final_alpha_cumprod
           )

           _A					:     List[str]     = 1 - alpha_prod_t

           # 3. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           if self.config.prediction_type == "epsilon":
                  _A					:     Any     = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                  _A					:     List[Any]     = model_output
           elif self.config.prediction_type == "sample":
                  _A					:     List[Any]     = model_output
                  _A					:     Dict     = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
           elif self.config.prediction_type == "v_prediction":
                  _A					:     List[str]     = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                  _A					:     Optional[int]     = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
           else:
                  raise ValueError(
                      F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                      ' `v_prediction`')

           # 4. Clip or threshold "predicted x_0"
           if self.config.clip_sample:
                  _A					:     str     = pred_original_sample.clamp(
                      -self.config.clip_sample_range				,							self.config.clip_sample_range)

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           _A					:     Any     = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

           # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           _A					:     Tuple     = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

           if not return_dict:
                  return (prev_sample, pred_original_sample)
           return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE				,							pred_original_sample=SCREAMING_SNAKE_CASE)


    def __len__(  self      :							List[Any]):
           return self.config.num_train_timesteps






