






'''simple docstring'''







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	str:
							lowerCamelCase__ :     Union[str, Any]	  =	[]
							for line in lines:
														lowerCamelCase__ :     str	  =	re.sub(r"""#.*"""    ,      """"""    ,      UpperCamelCase				)  # remove comments
														if line:
																					filtered_lines.append(UpperCamelCase				)
							lowerCamelCase__ :     Tuple	  =	"""\n""".join(UpperCamelCase				)

							# Make a hash from all this code
							lowerCamelCase__ :     int	  =	full_str.encode("""utf-8"""				)
							return shaaaa(UpperCamelCase				).hexdigest()


# get importable module names and hash for caching
_A     :      Tuple   					={
    '''csv''': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    '''json''': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    '''pandas''': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    '''parquet''': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    '''arrow''': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    '''text''': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    '''imagefolder''': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    '''audiofolder''': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_A     :      Union[str, Any]   					={
    '''.csv''': ('''csv''', {}),
    '''.tsv''': ('''csv''', {'''sep''': '''\t'''}),
    '''.json''': ('''json''', {}),
    '''.jsonl''': ('''json''', {}),
    '''.parquet''': ('''parquet''', {}),
    '''.arrow''': ('''arrow''', {}),
    '''.txt''': ('''text''', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_A     :      List[str]   					={'''imagefolder''', '''audiofolder'''}

# Used to filter data files based on extensions given a module name
_A     :      Dict[str, List[str]]   					={}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('''.zip''')



_MODULE_TO_EXTENSIONS["audiofolder"].append('''.zip''')









'''simple docstring'''







from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_A     :      Union[str, Any]   					=8





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase=BITS				)				->	Tuple:
							lowerCamelCase__ :     List[str]	  =	x.device

							lowerCamelCase__ :     Any	  =	(x * 255).int().clamp(0    ,      255				)

							lowerCamelCase__ :     Optional[int]	  =	2 ** torch.arange(bits - 1    ,      -1    ,      -1    ,      device=UpperCamelCase				)
							lowerCamelCase__ :     int	  =	rearrange(UpperCamelCase    ,      """d -> d 1 1"""				)
							lowerCamelCase__ :     List[str]	  =	rearrange(UpperCamelCase    ,      """b c h w -> b c 1 h w"""				)

							lowerCamelCase__ :     Tuple	  =	((x & mask) != 0).float()
							lowerCamelCase__ :     List[Any]	  =	rearrange(UpperCamelCase    ,      """b c d h w -> b (c d) h w"""				)
							lowerCamelCase__ :     Optional[int]	  =	bits * 2 - 1
							return bits





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase=BITS				)				->	List[Any]:
							lowerCamelCase__ :     List[Any]	  =	x.device

							lowerCamelCase__ :     Dict	  =	(x > 0).int()
							lowerCamelCase__ :     Optional[Any]	  =	2 ** torch.arange(bits - 1    ,      -1    ,      -1    ,      device=UpperCamelCase    ,      dtype=torch.intaa				)

							lowerCamelCase__ :     List[Any]	  =	rearrange(UpperCamelCase    ,      """d -> d 1 1"""				)
							lowerCamelCase__ :     List[str]	  =	rearrange(UpperCamelCase    ,      """b (c d) h w -> b c d h w"""    ,      d=8				)
							lowerCamelCase__ :     List[Any]	  =	reduce(x * mask    ,      """b c d h w -> b c h w"""    ,      """sum"""				)
							return (dec / 255).clamp(0.0    ,      1.0				)





def 			SCREAMING_SNAKE_CASE_	(self    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase = 0.0    ,      UpperCamelCase = True    ,      UpperCamelCase=None    ,      UpperCamelCase = True    ,      )				->	Union[DDIMSchedulerOutput, Tuple]:
							if self.num_inference_steps is None:
														raise ValueError(
														    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""				)

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							lowerCamelCase__ :     Optional[int]	  =	timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							lowerCamelCase__ :     str	  =	self.alphas_cumprod[timestep]
							lowerCamelCase__ :     List[str]	  =	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							lowerCamelCase__ :     Optional[int]	  =	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
							lowerCamelCase__ :     Optional[int]	  =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							lowerCamelCase__ :     Dict	  =	self.bit_scale
							if self.config.clip_sample:
														lowerCamelCase__ :     Optional[Any]	  =	torch.clamp(UpperCamelCase    ,      -scale    ,      UpperCamelCase				)

							# 5. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							lowerCamelCase__ :     Tuple	  =	self._get_variance(UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     Optional[int]	  =	eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														lowerCamelCase__ :     int	  =	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

							# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							lowerCamelCase__ :     Optional[Any]	  =	(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

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

							if eta > 0:
														# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
														lowerCamelCase__ :     Dict	  =	model_output.device if torch.is_tensor(UpperCamelCase				) else """cpu"""
														lowerCamelCase__ :     str	  =	torch.randn(model_output.shape    ,      dtype=model_output.dtype    ,      generator=UpperCamelCase				).to(UpperCamelCase				)
														lowerCamelCase__ :     Optional[Any]	  =	self._get_variance(UpperCamelCase    ,      UpperCamelCase				) ** 0.5 * eta * noise

														lowerCamelCase__ :     int	  =	prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=UpperCamelCase    ,      pred_original_sample=UpperCamelCase				)





def 			SCREAMING_SNAKE_CASE_	(self    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase="epsilon"    ,      UpperCamelCase=None    ,      UpperCamelCase = True    ,      )				->	Union[DDPMSchedulerOutput, Tuple]:
							lowerCamelCase__ :     List[Any]	  =	timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
														lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	torch.split(UpperCamelCase    ,      sample.shape[1]    ,      dim=1				)
							else:
														lowerCamelCase__ :     List[str]	  =	None

							# 1. compute alphas, betas
							lowerCamelCase__ :     str	  =	self.alphas_cumprod[t]
							lowerCamelCase__ :     List[str]	  =	self.alphas_cumprod[t - 1] if t > 0 else self.one
							lowerCamelCase__ :     str	  =	1 - alpha_prod_t
							lowerCamelCase__ :     List[Any]	  =	1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if prediction_type == "epsilon":
														lowerCamelCase__ :     Union[str, Any]	  =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														lowerCamelCase__ :     Optional[Any]	  =	model_output
							else:
														raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''				)

							# 3. Clip "predicted x_0"
							lowerCamelCase__ :     str	  =	self.bit_scale
							if self.config.clip_sample:
														lowerCamelCase__ :     List[Any]	  =	torch.clamp(UpperCamelCase    ,      -scale    ,      UpperCamelCase				)

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowerCamelCase__ :     Tuple	  =	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							lowerCamelCase__ :     Tuple	  =	self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowerCamelCase__ :     int	  =	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							lowerCamelCase__ :     Optional[Any]	  =	0
							if t > 0:
														lowerCamelCase__ :     Optional[Any]	  =	torch.randn(
														    model_output.size()    ,      dtype=model_output.dtype    ,      layout=model_output.layout    ,      generator=UpperCamelCase				).to(model_output.device				)
														lowerCamelCase__ :     str	  =	(self._get_variance(UpperCamelCase    ,      predicted_variance=UpperCamelCase				) ** 0.5) * noise

							lowerCamelCase__ :     Optional[int]	  =	pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=UpperCamelCase    ,      pred_original_sample=UpperCamelCase				)



class _lowercase     (    _lowercase		):




							def __init__(							self:    List[str]      ,							UpperCamelCase__:    UNetaDConditionModel      ,							UpperCamelCase__:    Union[DDIMScheduler, DDPMScheduler]      ,							UpperCamelCase__:    Optional[float] = 1.0      ,							):
														super().__init__()
														lowerCamelCase__ :     Optional[int]	  =	bit_scale
														lowerCamelCase__ :     List[Any]	  =	(
														    ddim_bit_scheduler_step if isinstance(UpperCamelCase__      ,							UpperCamelCase__					) else ddpm_bit_scheduler_step
														)

														self.register_modules(unet=UpperCamelCase__      ,							scheduler=UpperCamelCase__					)




							@torch.no_grad()
							def __call__(							self:    Union[str, Any]      ,							UpperCamelCase__:    Optional[int] = 256      ,							UpperCamelCase__:    Optional[int] = 256      ,							UpperCamelCase__:    Optional[int] = 50      ,							UpperCamelCase__:    Optional[torch.Generator] = None      ,							UpperCamelCase__:    Optional[int] = 1      ,							UpperCamelCase__:    Optional[str] = "pil"      ,							UpperCamelCase__:    bool = True      ,							**UpperCamelCase__:    int      ,							):
														lowerCamelCase__ :     List[Any]	  =	torch.randn(
														    (batch_size, self.unet.config.in_channels, height, width)      ,							generator=UpperCamelCase__      ,							)
														lowerCamelCase__ :     Union[str, Any]	  =	decimal_to_bits(UpperCamelCase__					) * self.bit_scale
														lowerCamelCase__ :     Union[str, Any]	  =	latents.to(self.device					)

														self.scheduler.set_timesteps(UpperCamelCase__					)

														for t in self.progress_bar(self.scheduler.timesteps					):
																					# predict the noise residual
																					lowerCamelCase__ :     Tuple	  =	self.unet(UpperCamelCase__      ,							UpperCamelCase__					).sample

																					# compute the previous noisy sample x_t -> x_t-1
																					lowerCamelCase__ :     Any	  =	self.scheduler.step(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					).prev_sample

														lowerCamelCase__ :     Dict	  =	bits_to_decimal(UpperCamelCase__					)

														if output_type == "pil":
																					lowerCamelCase__ :     int	  =	self.numpy_to_pil(UpperCamelCase__					)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=UpperCamelCase__					)

