
"""simple docstring"""


import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil






@pytest.fixture
def  _snake_case			(						snake_case__				:       List[str]       ):
 A  				=				tmp_path / 'text.txt'
 A  				=				textwrap.dedent(
     '\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        '       )
 with open(__A		, 'w'		, encoding='utf-8'       ) as f:
  f.write(__A       )
 return str(__A       )






@pytest.fixture
def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       int       ):
 A  				=				tmp_path / 'text_with_image.txt'
 with open(__A		, 'w'		, encoding='utf-8'       ) as f:
  f.write(__A       )
 return str(__A       )






@pytest.mark.parametrize('keep_linebreaks'		, [True, False]       )
def  _snake_case			(						snake_case__				:       Optional[Any]		, snake_case__				:       List[str]       ):



 with open(__A		, encoding='utf-8'       ) as f:
  A  				=				f.read().splitlines(keepends=__A       )
 A  				=				Text(keep_linebreaks=__A		, encoding='utf-8'       )
 A  				=				text._generate_tables([[text_file]]       )
 A  				=				pa.concat_tables([table for _, table in generator]       ).to_pydict()['text']
 assert generated_content == expected_content






@require_pil
def  _snake_case			(						snake_case__				:       Optional[Any]       ):



 with open(__A		, encoding='utf-8'       ) as f:
  A  				=				f.read().splitlines()[0]
 A  				=				Text(encoding='utf-8'		, features=Features({'image': Image()}       )       )
 A  				=				text._generate_tables([[text_file_with_image]]       )
 A  				=				pa.concat_tables([table for _, table in generator]       )
 assert pa_table.schema.field('image'       ).type == Image()()
 A  				=				pa_table.to_pydict()['image']
 assert generated_content == [{"path": image_file, "bytes": None}]






@pytest.mark.parametrize('sample_by'		, ['line', 'paragraph', 'document']       )
def  _snake_case			(						snake_case__				:       Optional[Any]		, snake_case__				:       str       ):



 with open(__A		, encoding='utf-8'       ) as f:
  A  				=				f.read()
 if sample_by == "line":
  A  				=				expected_content.splitlines()
 elif sample_by == "paragraph":
  A  				=				expected_content.split('\n\n'       )
 elif sample_by == "document":
  A  				=				[expected_content]
 A  				=				Text(sample_by=__A		, encoding='utf-8'		, chunksize=100       )
 A  				=				text._generate_tables([[text_file]]       )
 A  				=				pa.concat_tables([table for _, table in generator]       ).to_pydict()['text']
 assert generated_content == expected_content

"""simple docstring"""


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

import numpy as np
import torch

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


_lowercase											=			logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      torch.FloatTensor







class 			lowerCAmelCase_			( _lowercase						,   _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      Dict =      1


						@register_to_config
						def __init__(	self       : List[Any]					,A_       : int = 40					,A_       : float = 0.0_02					,A_       : float = 80.0					,A_       : float = 0.5					,A_       : float = 1.0					,A_       : float = 7.0					,A_       : bool = True					,)       ->				List[str]:
							# standard deviation of the initial noise distribution
							A  				=				sigma_max

							A  				=				np.linspace(0					,1					,A_ )
							A  				=				self._convert_to_karras(A_ )
							A  				=				self.sigma_to_t(A_ )

							# setable values
							A  				=				None
							A  				=				torch.from_numpy(A_ )
							A  				=				torch.from_numpy(A_ )
							A  				=				False
							A  				=				False


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : List[Any]					,A_       : List[Any]=None )       ->				List[Any]:
							if schedule_timesteps is None:
								A  				=				self.timesteps

							A  				=				(schedule_timesteps == timestep).nonzero()
							return indices.item()


						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor] )       ->				torch.FloatTensor:
							# Get sigma corresponding to timestep
							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )
							A  				=				self.index_for_timestep(A_ )
							A  				=				self.sigmas[step_idx]

							A  				=				sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

							A  				=				True
							return sample


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : Union[float, np.ndarray] )       ->				Tuple:
							if not isinstance(A_					,np.ndarray ):
								A  				=				np.array(A_					,dtype=np.floataa )

							A  				=				1000 * 0.25 * np.log(sigmas + 1e-44 )

							return timesteps


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : Optional[int] = None					,A_       : Union[str, torch.device] = None					,A_       : Optional[List[int]] = None					,)       ->				Optional[Any]:
							if num_inference_steps is None and timesteps is None:
								raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.' )

							if num_inference_steps is not None and timesteps is not None:
								raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.' )

							# Follow DDPMScheduler custom timesteps logic
							if timesteps is not None:
								for i in range(1					,len(A_ ) ):
									if timesteps[i] >= timesteps[i - 1]:
										raise ValueError('`timesteps` must be in descending order.' )

								if timesteps[0] >= self.config.num_train_timesteps:
									raise ValueError(
									    F'`timesteps` must start before `self.config.train_timesteps`:'
									    F' {self.config.num_train_timesteps}.' )

								A  				=				np.array(A_					,dtype=np.intaa )
								A  				=				True
							else:
								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  				=				num_inference_steps

								A  				=				self.config.num_train_timesteps // self.num_inference_steps
								A  				=				(np.arange(0					,A_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
								A  				=				False

							# Map timesteps to Karras sigmas directly for multistep sampling
							# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
							A  				=				self.config.num_train_timesteps
							A  				=				timesteps[::-1].copy()
							A  				=				ramp / (num_train_timesteps - 1)
							A  				=				self._convert_to_karras(A_ )
							A  				=				self.sigma_to_t(A_ )

							A  				=				np.concatenate([sigmas, [self.sigma_min]] ).astype(np.floataa )
							A  				=				torch.from_numpy(A_ ).to(device=A_ )

							if str(A_ ).startswith('mps' ):
								# mps does not support float64
								A  				=				torch.from_numpy(A_ ).to(A_					,dtype=torch.floataa )
							else:
								A  				=				torch.from_numpy(A_ ).to(device=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Any:
							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_max

							A  				=				self.config.rho
							A  				=				sigma_min ** (1 / rho)
							A  				=				sigma_max ** (1 / rho)
							A  				=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : Dict )       ->				Any:
							A  				=				self.config.sigma_data

							A  				=				sigma_data**2 / (sigma**2 + sigma_data**2)
							A  				=				sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Dict:
							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_data

							A  				=				sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
							A  				=				(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor]					,A_       : torch.FloatTensor					,A_       : Optional[torch.Generator] = None					,A_       : bool = True					,)       ->				Union[CMStochasticIterativeSchedulerOutput, Tuple]:

							if (
							    isinstance(A_					,A_ )
							    or isinstance(A_					,torch.IntTensor )
							    or isinstance(A_					,torch.LongTensor )
							):
								raise ValueError(
								    (
								        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
								        F' `{self.__class__}.step()` is not supported. Make sure to pass'
								        ' one of the `scheduler.timesteps` as a timestep.'
								    )					,)

							if not self.is_scale_input_called:
								logger.warning(
								    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
								    'See `StableDiffusionPipeline` for a usage example.' )

							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )

							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_max

							A  				=				self.index_for_timestep(A_ )

							# sigma_next corresponds to next_t in original implementation
							A  				=				self.sigmas[step_index]
							if step_index + 1 < self.config.num_train_timesteps:
								A  				=				self.sigmas[step_index + 1]
							else:
								# Set sigma_next to sigma_min
								A  				=				self.sigmas[-1]

							# Get scalings for boundary conditions
							A				, A  				=				self.get_scalings_for_boundary_condition(A_ )

							# 1. Denoise model output using boundary conditions
							A  				=				c_out * model_output + c_skip * sample
							if self.config.clip_denoised:
								A  				=				denoised.clamp(-1					,1 )

							# 2. Sample z ~ N(0, s_noise^2 * I)
							# Noise is not used for onestep sampling.
							if len(self.timesteps ) > 1:
								A  				=				randn_tensor(
								    model_output.shape					,dtype=model_output.dtype					,device=model_output.device					,generator=A_ )
							else:
								A  				=				torch.zeros_like(A_ )
							A  				=				noise * self.config.s_noise

							A  				=				sigma_next.clamp(min=A_					,max=A_ )

							# 3. Return noisy sample
							# tau = sigma_hat, eps = sigma_min
							A  				=				denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

							if not return_dict:
								return (prev_sample,)

							return CMStochasticIterativeSchedulerOutput(prev_sample=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor					,)       ->				torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							A  				=				self.sigmas.to(device=original_samples.device					,dtype=original_samples.dtype )
							if original_samples.device.type == "mps" and torch.is_floating_point(A_ ):
								# mps does not support float64
								A  				=				self.timesteps.to(original_samples.device					,dtype=torch.floataa )
								A  				=				timesteps.to(original_samples.device					,dtype=torch.floataa )
							else:
								A  				=				self.timesteps.to(original_samples.device )
								A  				=				timesteps.to(original_samples.device )

							A  				=				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

							A  				=				sigmas[step_indices].flatten()
							while len(sigma.shape ) < len(original_samples.shape ):
								A  				=				sigma.unsqueeze(-1 )

							A  				=				original_samples + noise * sigma
							return noisy_samples




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