



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 						_UpperCamelCase  (							nn.Module   ):


			'''simple docstring'''

			def __init__(							self					:			str							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Optional[Any]=1.0						):





					"""simple docstring"""






					super().__init__()

					__SCREAMING_SNAKE_CASE	:		int   	=		cut_size
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		cut_power



			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Optional[int]						):





					"""simple docstring"""






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


def     lowerCAmelCase_       (     _lowerCamelCase:       Tuple	,				_lowerCamelCase:       Tuple ):
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		F.normalize(_UpperCamelCase	,				dim=-1 )
		__SCREAMING_SNAKE_CASE	:		Any   	=		F.normalize(_UpperCamelCase	,				dim=-1 )
		return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )


def     lowerCAmelCase_       (     _lowerCamelCase:       Optional[Any]	,				_lowerCamelCase:       Dict ):




		for param in model.parameters():
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		value






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			def __init__(							self					:			int							,					lowerCAmelCase__					:			AutoencoderKL							,					lowerCAmelCase__					:			CLIPTextModel							,					lowerCAmelCase__					:			CLIPModel							,					lowerCAmelCase__					:			CLIPTokenizer							,					lowerCAmelCase__					:			UNetaDConditionModel							,					lowerCAmelCase__					:			Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]							,					lowerCAmelCase__					:			CLIPImageProcessor							,					):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		transforms.Normalize(mean=feature_extractor.image_mean							,					std=feature_extractor.image_std						)
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		(
					    feature_extractor.size
					    if isinstance(feature_extractor.size							,					lowerCAmelCase__						)
					    else feature_extractor.size["shortest_edge"]
					)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		MakeCutouts(self.cut_out_size						)

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

			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Optional[Union[str, int]] = "auto"						):





					"""simple docstring"""






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

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					self.enable_attention_slicing(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					set_requires_grad(self.vae							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					set_requires_grad(self.vae							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					set_requires_grad(self.unet							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					set_requires_grad(self.unet							,					lowerCAmelCase__						)

			@torch.enable_grad()
			def UpperCamelCase__ (							self					:			int							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			str=True							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		latents.detach().requires_grad_()

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.scheduler.scale_model_input(lowerCAmelCase__							,					lowerCAmelCase__						)

					# predict the noise residual
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.unet(lowerCAmelCase__							,					lowerCAmelCase__							,					encoder_hidden_states=lowerCAmelCase__						).sample

					if isinstance(self.scheduler							,					(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
							__SCREAMING_SNAKE_CASE	:		str   	=		self.scheduler.alphas_cumprod[timestep]
							__SCREAMING_SNAKE_CASE	:		str   	=		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
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

							__SCREAMING_SNAKE_CASE	:		Tuple   	=		torch.sqrt(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		pred_original_sample * (fac) + latents * (1 - fac)
					elif isinstance(self.scheduler							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Any   	=		self.scheduler.sigmas[index]
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		latents - sigma * noise_pred
					else:
							raise ValueError(F"scheduler type {type(self.scheduler						)} not supported"						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		1 / self.vae.config.scaling_factor * sample
					__SCREAMING_SNAKE_CASE	:		int   	=		self.vae.decode(lowerCAmelCase__						).sample
					__SCREAMING_SNAKE_CASE	:		int   	=		(image / 2 + 0.5).clamp(0							,					1						)

					if use_cutouts:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.make_cutouts(lowerCAmelCase__							,					lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		transforms.Resize(self.cut_out_size						)(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.normalize(lowerCAmelCase__						).to(latents.dtype						)

					__SCREAMING_SNAKE_CASE	:		int   	=		self.clip_model.get_image_features(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		image_embeddings_clip / image_embeddings_clip.norm(p=2							,					dim=-1							,					keepdim=lowerCAmelCase__						)

					if use_cutouts:
							__SCREAMING_SNAKE_CASE	:		Dict   	=		spherical_dist_loss(lowerCAmelCase__							,					lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE	:		int   	=		dists.view([num_cutouts, sample.shape[0], -1]						)
							__SCREAMING_SNAKE_CASE	:		str   	=		dists.sum(2						).mean(0						).sum() * clip_guidance_scale
					else:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		spherical_dist_loss(lowerCAmelCase__							,					lowerCAmelCase__						).mean() * clip_guidance_scale

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		-torch.autograd.grad(lowerCAmelCase__							,					lowerCAmelCase__						)[0]

					if isinstance(self.scheduler							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		latents.detach() + grads * (sigma**2)
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		noise_pred_original
					else:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		noise_pred_original - torch.sqrt(lowerCAmelCase__						) * grads
					return noise_pred, latents



			@torch.no_grad()
			def __call__(							self					:			Tuple							,					lowerCAmelCase__					:			Union[str, List[str]]							,					lowerCAmelCase__					:			Optional[int] = 5_1_2							,					lowerCAmelCase__					:			Optional[int] = 5_1_2							,					lowerCAmelCase__					:			Optional[int] = 5_0							,					lowerCAmelCase__					:			Optional[float] = 7.5							,					lowerCAmelCase__					:			Optional[int] = 1							,					lowerCAmelCase__					:			float = 0.0							,					lowerCAmelCase__					:			Optional[float] = 1_0_0							,					lowerCAmelCase__					:			Optional[Union[str, List[str]]] = None							,					lowerCAmelCase__					:			Optional[int] = 4							,					lowerCAmelCase__					:			Optional[bool] = True							,					lowerCAmelCase__					:			Optional[torch.Generator] = None							,					lowerCAmelCase__					:			Optional[torch.FloatTensor] = None							,					lowerCAmelCase__					:			Optional[str] = "pil"							,					lowerCAmelCase__					:			bool = True							,					):





					"""simple docstring"""






					if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		str   	=		1
					elif isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Any   	=		len(lowerCAmelCase__						)
					else:
							raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__						)}"						)

					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
					__SCREAMING_SNAKE_CASE	:		str   	=		self.tokenizer(
					    lowerCAmelCase__							,					padding="""max_length"""							,					max_length=self.tokenizer.model_max_length							,					truncation=lowerCAmelCase__							,					return_tensors="""pt"""							,					)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.text_encoder(text_input.input_ids.to(self.device						)						)[0]
					# duplicate text embeddings for each generation per prompt
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		text_embeddings.repeat_interleave(lowerCAmelCase__							,					dim=0						)

					if clip_guidance_scale > 0:
							if clip_prompt is not None:
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.tokenizer(
									    lowerCAmelCase__							,					padding="""max_length"""							,					max_length=self.tokenizer.model_max_length							,					truncation=lowerCAmelCase__							,					return_tensors="""pt"""							,					).input_ids.to(self.device						)
							else:
									__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		text_input.input_ids.to(self.device						)
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.clip_model.get_text_features(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE	:		Dict   	=		text_embeddings_clip / text_embeddings_clip.norm(p=2							,					dim=-1							,					keepdim=lowerCAmelCase__						)
							# duplicate text embeddings clip for each generation per prompt
							__SCREAMING_SNAKE_CASE	:		int   	=		text_embeddings_clip.repeat_interleave(lowerCAmelCase__							,					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.
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		text_input.input_ids.shape[-1]
							__SCREAMING_SNAKE_CASE	:		str   	=		self.tokenizer([""""""]							,					padding="""max_length"""							,					max_length=lowerCAmelCase__							,					return_tensors="""pt"""						)
							__SCREAMING_SNAKE_CASE	:		str   	=		self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
							# duplicate unconditional embeddings for each generation per prompt
							__SCREAMING_SNAKE_CASE	:		Dict   	=		uncond_embeddings.repeat_interleave(lowerCAmelCase__							,					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
							__SCREAMING_SNAKE_CASE	:		Dict   	=		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`.
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					__SCREAMING_SNAKE_CASE	:		str   	=		text_embeddings.dtype
					if latents is None:
							if self.device.type == "mps":
									# randn does not work reproducibly on mps
									__SCREAMING_SNAKE_CASE	:		Any   	=		torch.randn(lowerCAmelCase__							,					generator=lowerCAmelCase__							,					device="""cpu"""							,					dtype=lowerCAmelCase__						).to(
									    self.device						)
							else:
									__SCREAMING_SNAKE_CASE	:		int   	=		torch.randn(lowerCAmelCase__							,					generator=lowerCAmelCase__							,					device=self.device							,					dtype=lowerCAmelCase__						)
					else:
							if latents.shape != latents_shape:
									raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"						)
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		latents.to(self.device						)

					# set timesteps
					__SCREAMING_SNAKE_CASE	:		str   	=		"offset" in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		{}
					if accepts_offset:
							__SCREAMING_SNAKE_CASE	:		Any   	=		1

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

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.scheduler.timesteps.to(self.device						)

					# scale the initial noise by the standard deviation required by the scheduler
					__SCREAMING_SNAKE_CASE	:		int   	=		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]
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		"eta" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		{}
					if accepts_eta:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		eta

					# check if the scheduler accepts generator
					__SCREAMING_SNAKE_CASE	:		str   	=		"generator" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
					if accepts_generator:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		generator

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

							# predict the noise residual
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.unet(lowerCAmelCase__							,					lowerCAmelCase__							,					encoder_hidden_states=lowerCAmelCase__						).sample

							# perform classifier free guidance
							if do_classifier_free_guidance:
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		noise_pred.chunk(2						)
									__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

							# perform clip guidance
							if clip_guidance_scale > 0:
									__SCREAMING_SNAKE_CASE	:		List[str]   	=		(
									    text_embeddings.chunk(2						)[1] if do_classifier_free_guidance else text_embeddings
									)
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.cond_fn(
									    lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					)

							# compute the previous noisy sample x_t -> x_t-1
							__SCREAMING_SNAKE_CASE	:		int   	=		self.scheduler.step(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					**lowerCAmelCase__						).prev_sample

					# scale and decode the image latents with vae
					__SCREAMING_SNAKE_CASE	:		int   	=		1 / self.vae.config.scaling_factor * latents
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.vae.decode(lowerCAmelCase__						).sample

					__SCREAMING_SNAKE_CASE	:		int   	=		(image / 2 + 0.5).clamp(0							,					1						)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		image.cpu().permute(0							,					2							,					3							,					1						).numpy()

					if output_type == "pil":
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.numpy_to_pil(lowerCAmelCase__						)

					if not return_dict:
							return (image, None)

					return StableDiffusionPipelineOutput(images=lowerCAmelCase__							,					nsfw_content_detected=lowerCAmelCase__						)




'''simple docstring'''




import argparse
import os
import sys
import urllib.request
import zipfile


UpperCamelCase__	:     str    =			['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
UpperCamelCase__	:     List[Any]    =			{
    '''CoLA''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4''',
    '''SST''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8''',
    '''MRPC''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc''',
    '''QQP''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5''',
    '''STS''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5''',
    '''MNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce''',
    '''SNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df''',
    '''QNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601''',
    '''RTE''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb''',
    '''WNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf''',
    '''diagnostic''': '''https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D''',
}

UpperCamelCase__	:     Optional[int]    =			'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
UpperCamelCase__	:     Any    =			'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''


def     lowerCAmelCase_       (     _lowerCamelCase:       Union[str, Any]	,				_lowerCamelCase:       Dict ):
		print(F"Downloading and extracting {task}..." )
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		F"{task}.zip"
		urllib.request.urlretrieve(TASKaPATH[task]	,				_lowerCamelCase )
		with zipfile.ZipFile(_lowerCamelCase ) as zip_ref:
				zip_ref.extractall(_lowerCamelCase )
		os.remove(_lowerCamelCase )
		print("""\tCompleted!""" )


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       Optional[int] ):
		print("""Processing MRPC...""" )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		os.path.join(_lowerCamelCase	,				"""MRPC""" )
		if not os.path.isdir(_lowerCamelCase ):
				os.mkdir(_lowerCamelCase )
		if path_to_data:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_train.txt""" )
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_test.txt""" )
		else:
				print("""Local MRPC data not specified, downloading data from %s""" % MRPC_TRAIN )
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_train.txt""" )
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_test.txt""" )
				urllib.request.urlretrieve(_lowerCamelCase	,				_lowerCamelCase )
				urllib.request.urlretrieve(_lowerCamelCase	,				_lowerCamelCase )
		if not os.path.isfile(_lowerCamelCase ):
				raise ValueError(F"Train data not found at {mrpc_train_file}" )
		if not os.path.isfile(_lowerCamelCase ):
				raise ValueError(F"Test data not found at {mrpc_test_file}" )
		urllib.request.urlretrieve(TASKaPATH["""MRPC"""]	,				os.path.join(_lowerCamelCase	,				"""dev_ids.tsv""" ) )

		__SCREAMING_SNAKE_CASE	:		Tuple   	=		[]
		with open(os.path.join(_lowerCamelCase	,				"""dev_ids.tsv""" )	,				encoding="""utf8""" ) as ids_fh:
				for row in ids_fh:
						dev_ids.append(row.strip().split("""\t""" ) )

		with open(_lowerCamelCase	,				encoding="""utf8""" ) as data_fh, open(
		    os.path.join(_lowerCamelCase	,				"""train.tsv""" )	,				"""w"""	,				encoding="""utf8""" ) as train_fh, open(os.path.join(_lowerCamelCase	,				"""dev.tsv""" )	,				"""w"""	,				encoding="""utf8""" ) as dev_fh:
				__SCREAMING_SNAKE_CASE	:		Any   	=		data_fh.readline()
				train_fh.write(_lowerCamelCase )
				dev_fh.write(_lowerCamelCase )
				for row in data_fh:
						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		row.strip().split("""\t""" )
						if [ida, ida] in dev_ids:
								dev_fh.write("""%s\t%s\t%s\t%s\t%s\n""" % (label, ida, ida, sa, sa) )
						else:
								train_fh.write("""%s\t%s\t%s\t%s\t%s\n""" % (label, ida, ida, sa, sa) )

		with open(_lowerCamelCase	,				encoding="""utf8""" ) as data_fh, open(
		    os.path.join(_lowerCamelCase	,				"""test.tsv""" )	,				"""w"""	,				encoding="""utf8""" ) as test_fh:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		data_fh.readline()
				test_fh.write("""index\t#1 ID\t#2 ID\t#1 String\t#2 String\n""" )
				for idx, row in enumerate(_lowerCamelCase ):
						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Dict   	=		row.strip().split("""\t""" )
						test_fh.write("""%d\t%s\t%s\t%s\t%s\n""" % (idx, ida, ida, sa, sa) )
		print("""\tCompleted!""" )


def     lowerCAmelCase_       (     _lowerCamelCase:       Dict ):
		print("""Downloading and extracting diagnostic...""" )
		if not os.path.isdir(os.path.join(_lowerCamelCase	,				"""diagnostic""" ) ):
				os.mkdir(os.path.join(_lowerCamelCase	,				"""diagnostic""" ) )
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		os.path.join(_lowerCamelCase	,				"""diagnostic"""	,				"""diagnostic.tsv""" )
		urllib.request.urlretrieve(TASKaPATH["""diagnostic"""]	,				_lowerCamelCase )
		print("""\tCompleted!""" )
		return


def     lowerCAmelCase_       (     _lowerCamelCase:       int ):
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		task_names.split(""",""" )
		if "all" in task_names:
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		TASKS
		else:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
				for task_name in task_names:
						if task_name not in TASKS:
								raise ValueError(F"Task {task_name} not found!" )
						tasks.append(_lowerCamelCase )
		return tasks


def     lowerCAmelCase_       (     _lowerCamelCase:       Tuple ):
		__SCREAMING_SNAKE_CASE	:		int   	=		argparse.ArgumentParser()
		parser.add_argument("""--data_dir"""	,				help="""directory to save data to"""	,				type=_lowerCamelCase	,				default="""glue_data""" )
		parser.add_argument(
		    """--tasks"""	,				help="""tasks to download data for as a comma separated string"""	,				type=_lowerCamelCase	,				default="""all""" )
		parser.add_argument(
		    """--path_to_mrpc"""	,				help="""path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt"""	,				type=_lowerCamelCase	,				default=""""""	,				)
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		parser.parse_args(_lowerCamelCase )

		if not os.path.isdir(args.data_dir ):
				os.mkdir(args.data_dir )
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		get_tasks(args.tasks )

		for task in tasks:
				if task == "MRPC":
						format_mrpc(args.data_dir	,				args.path_to_mrpc )
				elif task == "diagnostic":
						download_diagnostic(args.data_dir )
				else:
						download_and_extract(_lowerCamelCase	,				args.data_dir )


if __name__ == "__main__":
						sys.exit(main(sys.argv[1:]))