






import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


lowerCamelCase_						     =  """none"""
lowerCamelCase_						     =  """none"""
def      lowerCamelCase					(						a_ ,						a_	)	->			List[Any]:
							lowerCAmelCase_										=     value.split(a_	)
							return len(a_	) > 1 and any((len(a_	) == 3 for x in split)	)
def      lowerCamelCase					(						a_	)	->			str:
							if isinstance(a_ ,						a_	):
														return value
							if isinstance(a_ ,						a_	):
														return float(a_	)
							if not isinstance(a_ ,						a_	):
														raise ValueError('Argument value is not a string. Can\'t parse it as float'	)
							lowerCAmelCase_										=     value

							try:
														# Example: 1,000.7
														if "." in sanitized and "," in sanitized:
																					return float(sanitized.replace(',' ,						''	)	)
														# 1,000
														if "," in sanitized and _split_thousands(',' ,						a_	):
																					return float(sanitized.replace(',' ,						''	)	)
														# 5,5556
														if "," in sanitized and sanitized.count(','	) == 1 and not _split_thousands(',' ,						a_	):
																					return float(sanitized.replace(',' ,						'.'	)	)
														# 0.0.0.1
														if sanitized.count('.'	) > 1:
																					return float(sanitized.replace('.' ,						''	)	)
														# 0,0,0,1
														if sanitized.count(','	) > 1:
																					return float(sanitized.replace(',' ,						''	)	)
														return float(a_	)
							except ValueError:
														# Avoid adding the sanitized value in the error message.
														raise ValueError('Unable to convert value to float'	)
def      lowerCamelCase					(						a_	)	->			Any:
							if answer is None:
														return None
							try:
														lowerCAmelCase_										=     convert_to_float(a_	)
														if isinstance(a_ ,						a_	) and math.isnan(a_	):
																					return None
														return value
							except ValueError:
														return answer.lower()


lowerCamelCase_						     =  {
    """text""": lambda x: x,
    """real""": convert_to_float,
}




class 							a_ (						enum.Enum		):


							'''simple docstring'''






							__a:	Any     =    0
							__a:	Union[str, Any]     =    1
							__a:	int     =    2
							__a:	List[str]     =    3
							__a:	int     =    4
							__a:	Tuple     =    5




class 							a_ (						enum.Enum		):


							'''simple docstring'''






							__a:	Dict     =    0
							__a:	Optional[Any]     =    1
							__a:	Any     =    2




@dataclasses.dataclass
class 							a_ :


							'''simple docstring'''






							__a:	Text
							__a:	_Operator
							__a:	Any


lowerCamelCase_						     =  re.compile(r"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)
def      lowerCamelCase					(						a_	)	->			str:
							return list(_TOKENIZER.findall(x.lower()	)	)
def      lowerCamelCase					(						a_ ,						a_ ,						a_	)	->			Tuple:
							if operator == _Operator.EQUALS:
														return src == tgt
							elif operator == _Operator.GREATER:
														return src > tgt
							elif operator == _Operator.LESSER:
														return src < tgt
							raise ValueError(F'''Unknown operator: {operator}'''	)
def      lowerCamelCase					(						a_ ,						a_ ,						a_	)	->			str:
							lowerCAmelCase_										=     table['types']
							return _TYPE_CONVERTER[types[column]](a_	)
def      lowerCamelCase					(						a_	)	->			Dict:
							return isinstance(a_ ,						a_	)
def      lowerCamelCase					(						a_ ,						a_ ,						a_	)	->			Tuple:
							for cond in conditions:
														lowerCAmelCase_										=     row[cond.column]

														lowerCAmelCase_										=     _parse_value(a_ ,						cond.column ,						cond.cmp_value	)

														if _is_string(a_	) and _is_string(a_	):
																					lowerCAmelCase_										=     _normalize_for_match(a_	)
																					lowerCAmelCase_										=     _normalize_for_match(a_	)

														if not isinstance(a_ ,						type(a_	)	):
																					raise ValueError('Type difference {} != {}'.format(type(a_	) ,						type(a_	)	)	)

														if not _compare(cond.operator ,						a_ ,						a_	):
																					return False
							return True
def      lowerCamelCase					(						a_ ,						a_ ,						a_	)	->			Optional[Any]:
							if not answer_coordinates:
														if aggregation_op == _Aggregation.COUNT:
																					return 0.0
														else:
																					return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
							if aggregation_op == _Aggregation.COUNT:
														return float(len(a_	)	)

							# If we have just one answer, if float returns it or try a conversion.
							lowerCAmelCase_										=     [table['rows'][i][j] for (i, j) in answer_coordinates]
							if len(a_	) == 1:
														try:
																					return convert_to_float(values[0]	)
														except ValueError as e:
																					if aggregation_op != _Aggregation.NONE:
																												raise e

							if aggregation_op == _Aggregation.NONE:
														return None

							# Other aggregation only support numeric values. Bail out if we have strings.
							if not all((isinstance(a_ ,						(int, float)	) for v in values)	):
														return None

							if aggregation_op == _Aggregation.SUM:
														return float(sum(a_	)	)
							elif aggregation_op == _Aggregation.AVERAGE:
														return sum(a_	) / len(a_	)
							else:
														raise ValueError(F'''Unknown aggregation: {aggregation_op}'''	)
def      lowerCamelCase					(						a_ ,						a_	)	->			str:
							lowerCAmelCase_										=     sql_query['agg']
							if aggregation_op_index >= 3:
														lowerCAmelCase_										=     _Aggregation(a_	)
							else:
														lowerCAmelCase_										=     _Aggregation.NONE

							lowerCAmelCase_										=     sql_query['sel']
							lowerCAmelCase_										=     [
							    _Condition(a_ ,						_Operator(a_	) ,						a_	)
							    for column, operator, cmp_value in zip(
							        sql_query['conds']['column_index'] ,						sql_query['conds']['operator_index'] ,						sql_query['conds']['condition']	)
							]

							lowerCAmelCase_										=     []
							for row in range(len(table['rows']	)	):
														if _respect_conditions(a_ ,						table['rows'][row] ,						a_	):
																					indices.append((row, target_column)	)

							if not indices:
														return [], aggregation_op

							if len(a_	) == 1:
														return indices, aggregation_op

							# Parsing of MIN/MAX.
							if aggregation_op_index in (1, 2):
														lowerCAmelCase_										=     {2: min, 1: max}
														lowerCAmelCase_										=     [(table['rows'][i][j], index) for index, (i, j) in enumerate(a_	)]
														lowerCAmelCase_										=     functools.reduce(operators[sql_query['agg']] ,						a_	)

														lowerCAmelCase_										=     [indices[reduced[1]]]
														return ret, _Aggregation.NONE

							return indices, aggregation_op
def      lowerCamelCase					(						a_ ,						a_ ,						a_	)	->			List[Any]:
							if float_answer is not None:
														return [str(a_	)]
							return [str(table['real_rows'][r][c]	) for r, c in answer_coordinates]



def      lowerCamelCase					(						a_ ,						a_	)	->			List:
							lowerCAmelCase_						,   lowerCAmelCase_										=     _get_answer_coordinates(a_ ,						a_	)
							lowerCAmelCase_										=     _get_float_answer(a_ ,						a_ ,						a_	)
							lowerCAmelCase_										=     _get_answer_text(a_ ,						a_ ,						a_	)
							# keep the original data the same with TaPas
							if len(a_	) == 0:
														lowerCAmelCase_										=     [EMPTY_ANSWER]
							return answer_text











import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_=1.0   )					->					Any:
														'''simple docstring'''

														super().__init__()

														lowerCAmelCase_										=     cut_size
														lowerCAmelCase_										=     cut_power






							def      _lowercase       (		self			,			lowercase_			,			lowercase_   )					->					int:
														'''simple docstring'''

														lowerCAmelCase_						,   lowerCAmelCase_										=     pixel_values.shape[2:4]
														lowerCAmelCase_										=     min(lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     min(lowercase_			,			lowercase_			,			self.cut_size   )
														lowerCAmelCase_										=     []
														for _ in range(lowercase_   ):
																					lowerCAmelCase_										=     int(torch.rand([]   ) ** self.cut_power * (max_size - min_size) + min_size   )
																					lowerCAmelCase_										=     torch.randint(0			,			sideX - size + 1			,			()   )
																					lowerCAmelCase_										=     torch.randint(0			,			sideY - size + 1			,			()   )
																					lowerCAmelCase_										=     pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
																					cutouts.append(F.adaptive_avg_poolad(lowercase_			,			self.cut_size   )   )
														return torch.cat(lowercase_   )


def      lowerCamelCase					(						a_ ,						a_	)	->			Optional[int]:
							lowerCAmelCase_										=     F.normalize(a_ ,						dim=-1	)
							lowerCAmelCase_										=     F.normalize(a_ ,						dim=-1	)
							return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)
def      lowerCamelCase					(						a_ ,						a_	)	->			int:
							for param in model.parameters():
														lowerCAmelCase_										=     value




class 							a_ (						a_		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)					->					str:
														'''simple docstring'''

														super().__init__()
														self.register_modules(
														    vae=lowercase_			,			text_encoder=lowercase_			,			clip_model=lowercase_			,			tokenizer=lowercase_			,			unet=lowercase_			,			scheduler=lowercase_			,			feature_extractor=lowercase_			,			)

														lowerCAmelCase_										=     transforms.Normalize(mean=feature_extractor.image_mean			,			std=feature_extractor.image_std   )
														lowerCAmelCase_										=     (
														    feature_extractor.size
														    if isinstance(feature_extractor.size			,			lowercase_   )
														    else feature_extractor.size['shortest_edge']
														)
														lowerCAmelCase_										=     MakeCutouts(self.cut_out_size   )

														set_requires_grad(self.text_encoder			,			lowercase_   )
														set_requires_grad(self.clip_model			,			lowercase_   )

							def      _lowercase       (		self			,			lowercase_ = "auto"   )					->					Union[str, Any]:
														'''simple docstring'''

														if slice_size == "auto":
																					# half the attention head size is usually a good trade-off between
																					# speed and memory
																					lowerCAmelCase_										=     self.unet.config.attention_head_dim // 2
														self.unet.set_attention_slice(lowercase_   )

							def      _lowercase       (		self   )					->					List[Any]:
														'''simple docstring'''

														self.enable_attention_slicing(lowercase_   )

							def      _lowercase       (		self   )					->					str:
														'''simple docstring'''

														set_requires_grad(self.vae			,			lowercase_   )

							def      _lowercase       (		self   )					->					List[str]:
														'''simple docstring'''

														set_requires_grad(self.vae			,			lowercase_   )

							def      _lowercase       (		self   )					->					List[str]:
														'''simple docstring'''

														set_requires_grad(self.unet			,			lowercase_   )

							def      _lowercase       (		self   )					->					Tuple:
														'''simple docstring'''

														set_requires_grad(self.unet			,			lowercase_   )

							@torch.enable_grad()
							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_=True			,			)					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     latents.detach().requires_grad_()

														lowerCAmelCase_										=     self.scheduler.scale_model_input(lowercase_			,			lowercase_   )

														# predict the noise residual
														lowerCAmelCase_										=     self.unet(lowercase_			,			lowercase_			,			encoder_hidden_states=lowercase_   ).sample

														if isinstance(self.scheduler			,			(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
																					lowerCAmelCase_										=     self.scheduler.alphas_cumprod[timestep]
																					lowerCAmelCase_										=     1 - alpha_prod_t
																					# compute predicted original sample from predicted noise also called
																					# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
																					lowerCAmelCase_										=     (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																					lowerCAmelCase_										=     torch.sqrt(lowercase_   )
																					lowerCAmelCase_										=     pred_original_sample * (fac) + latents * (1 - fac)
														elif isinstance(self.scheduler			,			lowercase_   ):
																					lowerCAmelCase_										=     self.scheduler.sigmas[index]
																					lowerCAmelCase_										=     latents - sigma * noise_pred
														else:
																					raise ValueError(f'''scheduler type {type(self.scheduler   )} not supported'''   )

														lowerCAmelCase_										=     1 / self.vae.config.scaling_factor * sample
														lowerCAmelCase_										=     self.vae.decode(lowercase_   ).sample
														lowerCAmelCase_										=     (image / 2 + 0.5).clamp(0			,			1   )

														if use_cutouts:
																					lowerCAmelCase_										=     self.make_cutouts(lowercase_			,			lowercase_   )
														else:
																					lowerCAmelCase_										=     transforms.Resize(self.cut_out_size   )(lowercase_   )
														lowerCAmelCase_										=     self.normalize(lowercase_   ).to(latents.dtype   )

														lowerCAmelCase_										=     self.clip_model.get_image_features(lowercase_   )
														lowerCAmelCase_										=     image_embeddings_clip / image_embeddings_clip.norm(p=2			,			dim=-1			,			keepdim=lowercase_   )

														if use_cutouts:
																					lowerCAmelCase_										=     spherical_dist_loss(lowercase_			,			lowercase_   )
																					lowerCAmelCase_										=     dists.view([num_cutouts, sample.shape[0], -1]   )
																					lowerCAmelCase_										=     dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
														else:
																					lowerCAmelCase_										=     spherical_dist_loss(lowercase_			,			lowercase_   ).mean() * clip_guidance_scale

														lowerCAmelCase_										=     -torch.autograd.grad(lowercase_			,			lowercase_   )[0]

														if isinstance(self.scheduler			,			lowercase_   ):
																					lowerCAmelCase_										=     latents.detach() + grads * (sigma**2)
																					lowerCAmelCase_										=     noise_pred_original
														else:
																					lowerCAmelCase_										=     noise_pred_original - torch.sqrt(lowercase_   ) * grads
														return noise_pred, latents






							@torch.no_grad()
							def __call__(		self			,			lowercase_			,			lowercase_ = 5_1_2			,			lowercase_ = 5_1_2			,			lowercase_ = 5_0			,			lowercase_ = 7.5			,			lowercase_ = 1			,			lowercase_ = 0.0			,			lowercase_ = 1_0_0			,			lowercase_ = None			,			lowercase_ = 4			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = "pil"			,			lowercase_ = True			,			)					->					str:
														'''simple docstring'''

														if isinstance(lowercase_			,			lowercase_   ):
																					lowerCAmelCase_										=     1
														elif isinstance(lowercase_			,			lowercase_   ):
																					lowerCAmelCase_										=     len(lowercase_   )
														else:
																					raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowercase_   )}'''   )

														if height % 8 != 0 or width % 8 != 0:
																					raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

														# get prompt text embeddings
														lowerCAmelCase_										=     self.tokenizer(
														    lowercase_			,			padding='max_length'			,			max_length=self.tokenizer.model_max_length			,			truncation=lowercase_			,			return_tensors='pt'			,			)
														lowerCAmelCase_										=     self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
														# duplicate text embeddings for each generation per prompt
														lowerCAmelCase_										=     text_embeddings.repeat_interleave(lowercase_			,			dim=0   )

														if clip_guidance_scale > 0:
																					if clip_prompt is not None:
																												lowerCAmelCase_										=     self.tokenizer(
																												    lowercase_			,			padding='max_length'			,			max_length=self.tokenizer.model_max_length			,			truncation=lowercase_			,			return_tensors='pt'			,			).input_ids.to(self.device   )
																					else:
																												lowerCAmelCase_										=     text_input.input_ids.to(self.device   )
																					lowerCAmelCase_										=     self.clip_model.get_text_features(lowercase_   )
																					lowerCAmelCase_										=     text_embeddings_clip / text_embeddings_clip.norm(p=2			,			dim=-1			,			keepdim=lowercase_   )
																					# duplicate text embeddings clip for each generation per prompt
																					lowerCAmelCase_										=     text_embeddings_clip.repeat_interleave(lowercase_			,			dim=0   )

														# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
														# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
														# corresponds to doing no classifier free guidance.
														lowerCAmelCase_										=     guidance_scale > 1.0
														# get unconditional embeddings for classifier free guidance
														if do_classifier_free_guidance:
																					lowerCAmelCase_										=     text_input.input_ids.shape[-1]
																					lowerCAmelCase_										=     self.tokenizer(['']			,			padding='max_length'			,			max_length=lowercase_			,			return_tensors='pt'   )
																					lowerCAmelCase_										=     self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
																					# duplicate unconditional embeddings for each generation per prompt
																					lowerCAmelCase_										=     uncond_embeddings.repeat_interleave(lowercase_			,			dim=0   )

																					# For classifier free guidance, we need to do two forward passes.
																					# Here we concatenate the unconditional and text embeddings into a single batch
																					# to avoid doing two forward passes
																					lowerCAmelCase_										=     torch.cat([uncond_embeddings, text_embeddings]   )

														# get the initial random noise unless the user supplied it

														# Unlike in other pipelines, latents need to be generated in the target device
														# for 1-to-1 results reproducibility with the CompVis implementation.
														# However this currently doesn't work in `mps`.
														lowerCAmelCase_										=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
														lowerCAmelCase_										=     text_embeddings.dtype
														if latents is None:
																					if self.device.type == "mps":
																												# randn does not work reproducibly on mps
																												lowerCAmelCase_										=     torch.randn(lowercase_			,			generator=lowercase_			,			device='cpu'			,			dtype=lowercase_   ).to(
																												    self.device   )
																					else:
																												lowerCAmelCase_										=     torch.randn(lowercase_			,			generator=lowercase_			,			device=self.device			,			dtype=lowercase_   )
														else:
																					if latents.shape != latents_shape:
																												raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
																					lowerCAmelCase_										=     latents.to(self.device   )

														# set timesteps
														lowerCAmelCase_										=     'offset' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
														lowerCAmelCase_										=     {}
														if accepts_offset:
																					lowerCAmelCase_										=     1

														self.scheduler.set_timesteps(lowercase_			,			**lowercase_   )

														# Some schedulers like PNDM have timesteps as arrays
														# It's more optimized to move all timesteps to correct device beforehand
														lowerCAmelCase_										=     self.scheduler.timesteps.to(self.device   )

														# scale the initial noise by the standard deviation required by the scheduler
														lowerCAmelCase_										=     latents * self.scheduler.init_noise_sigma

														# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
														# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
														# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
														# and should be between [0, 1]
														lowerCAmelCase_										=     'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
														lowerCAmelCase_										=     {}
														if accepts_eta:
																					lowerCAmelCase_										=     eta

														# check if the scheduler accepts generator
														lowerCAmelCase_										=     'generator' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
														if accepts_generator:
																					lowerCAmelCase_										=     generator

														for i, t in enumerate(self.progress_bar(lowercase_   )   ):
																					# expand the latents if we are doing classifier free guidance
																					lowerCAmelCase_										=     torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
																					lowerCAmelCase_										=     self.scheduler.scale_model_input(lowercase_			,			lowercase_   )

																					# predict the noise residual
																					lowerCAmelCase_										=     self.unet(lowercase_			,			lowercase_			,			encoder_hidden_states=lowercase_   ).sample

																					# perform classifier free guidance
																					if do_classifier_free_guidance:
																												lowerCAmelCase_						,   lowerCAmelCase_										=     noise_pred.chunk(2   )
																												lowerCAmelCase_										=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																					# perform clip guidance
																					if clip_guidance_scale > 0:
																												lowerCAmelCase_										=     (
																												    text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
																												)
																												lowerCAmelCase_						,   lowerCAmelCase_										=     self.cond_fn(
																												    lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)

																					# compute the previous noisy sample x_t -> x_t-1
																					lowerCAmelCase_										=     self.scheduler.step(lowercase_			,			lowercase_			,			lowercase_			,			**lowercase_   ).prev_sample

														# scale and decode the image latents with vae
														lowerCAmelCase_										=     1 / self.vae.config.scaling_factor * latents
														lowerCAmelCase_										=     self.vae.decode(lowercase_   ).sample

														lowerCAmelCase_										=     (image / 2 + 0.5).clamp(0			,			1   )
														lowerCAmelCase_										=     image.cpu().permute(0			,			2			,			3			,			1   ).numpy()

														if output_type == "pil":
																					lowerCAmelCase_										=     self.numpy_to_pil(lowercase_   )

														if not return_dict:
																					return (image, None)

														return StableDiffusionPipelineOutput(images=lowercase_			,			nsfw_content_detected=lowercase_   )



