
'''simple docstring'''





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


UpperCAmelCase            = ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
UpperCAmelCase            = {
    "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            = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
UpperCAmelCase            = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"

def   A			(							A_				:       Tuple     ,				A_				:       Tuple							):
						print(F"""Downloading and extracting {task}..."""							)
						snake_case							:  List[str]			=     F"""{task}.zip"""
						urllib.request.urlretrieve(TASKaPATH[task]     ,				A_							)
						with zipfile.ZipFile(A_							) as zip_ref:
												zip_ref.extractall(A_							)
						os.remove(A_							)
						print('''\tCompleted!'''							)

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

						snake_case							:  int			=     []
						with open(os.path.join(A_     ,				'''dev_ids.tsv'''							)     ,				encoding='''utf8'''							) as ids_fh:
												for row in ids_fh:
																		dev_ids.append(row.strip().split('''\t'''							)							)

						with open(A_     ,				encoding='''utf8'''							) as data_fh, open(
						    os.path.join(A_     ,				'''train.tsv'''							)     ,				'''w'''     ,				encoding='''utf8'''							) as train_fh, open(os.path.join(A_     ,				'''dev.tsv'''							)     ,				'''w'''     ,				encoding='''utf8'''							) as dev_fh:
												snake_case							:  Dict			=     data_fh.readline()
												train_fh.write(A_							)
												dev_fh.write(A_							)
												for row in data_fh:
																		snake_case,       snake_case,       snake_case,       snake_case,       snake_case							:  Any			=     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(A_     ,				encoding='''utf8'''							) as data_fh, open(
						    os.path.join(A_     ,				'''test.tsv'''							)     ,				'''w'''     ,				encoding='''utf8'''							) as test_fh:
												snake_case							:  Optional[int]			=     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(A_							):
																		snake_case,       snake_case,       snake_case,       snake_case,       snake_case							:  List[str]			=     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   A			(							A_				:       Dict							):
						print('''Downloading and extracting diagnostic...'''							)
						if not os.path.isdir(os.path.join(A_     ,				'''diagnostic'''							)							):
												os.mkdir(os.path.join(A_     ,				'''diagnostic'''							)							)
						snake_case							:  Any			=     os.path.join(A_     ,				'''diagnostic'''     ,				'''diagnostic.tsv'''							)
						urllib.request.urlretrieve(TASKaPATH['''diagnostic''']     ,				A_							)
						print('''\tCompleted!'''							)
						return

def   A			(							A_				:       int							):
						snake_case							:  int			=     task_names.split(''','''							)
						if "all" in task_names:
												snake_case							:  Tuple			=     TASKS
						else:
												snake_case							:  str			=     []
												for task_name in task_names:
																		if task_name not in TASKS:
																								raise ValueError(F"""Task {task_name} not found!"""							)
																		tasks.append(A_							)
						return tasks

def   A			(							A_				:       List[Any]							):
						snake_case							:  int			=     argparse.ArgumentParser()
						parser.add_argument('''--data_dir'''     ,				help='''directory to save data to'''     ,				type=A_     ,				default='''glue_data'''							)
						parser.add_argument(
						    '''--tasks'''     ,				help='''tasks to download data for as a comma separated string'''     ,				type=A_     ,				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=A_     ,				default=''''''     ,				)
						snake_case							:  Dict			=     parser.parse_args(A_							)

						if not os.path.isdir(args.data_dir							):
												os.mkdir(args.data_dir							)
						snake_case							:  List[Any]			=     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(A_     ,				args.data_dir							)


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



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE		,						SCREAMING_SNAKE_CASE		,						unittest.TestCase				):
						__A:						Optional[Any]				   =      StableDiffusionParadigmsPipeline
						__A:						Optional[int]				   =      TEXT_TO_IMAGE_PARAMS
						__A:						Dict				   =      TEXT_TO_IMAGE_BATCH_PARAMS
						__A:						Any				   =      TEXT_TO_IMAGE_IMAGE_PARAMS
						__A:						List[Any]				   =      TEXT_TO_IMAGE_IMAGE_PARAMS

						def 						a__ (					self		:			Optional[Any]			):
													torch.manual_seed(0			)
													_UpperCAmelCase  :			str	       =		UNetaDConditionModel(
													    block_out_channels=(32, 64)	,						layers_per_block=2	,						sample_size=32	,						in_channels=4	,						out_channels=4	,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,						cross_attention_dim=32	,						attention_head_dim=(2, 4)	,						use_linear_projection=_lowerCamelCase	,						)
													_UpperCAmelCase  :			Dict	       =		DDIMParallelScheduler(
													    beta_start=0.0_00_85	,						beta_end=0.0_12	,						beta_schedule="scaled_linear"	,						clip_sample=_lowerCamelCase	,						set_alpha_to_one=_lowerCamelCase	,						)
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Tuple	       =		AutoencoderKL(
													    block_out_channels=[32, 64]	,						in_channels=3	,						out_channels=3	,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,						latent_channels=4	,						sample_size=1_28	,						)
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Optional[Any]	       =		CLIPTextConfig(
													    bos_token_id=0	,						eos_token_id=2	,						hidden_size=32	,						intermediate_size=37	,						layer_norm_eps=1E-05	,						num_attention_heads=4	,						num_hidden_layers=5	,						pad_token_id=1	,						vocab_size=10_00	,						hidden_act="gelu"	,						projection_dim=5_12	,						)
													_UpperCAmelCase  :			Tuple	       =		CLIPTextModel(_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													_UpperCAmelCase  :			Optional[int]	       =		{
													    "unet": unet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components

						def 						a__ (					self		:			Dict	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			int=0			):
													if str(_lowerCamelCase			).startswith("mps"			):
																				_UpperCAmelCase  :			Tuple	       =		torch.manual_seed(_lowerCamelCase			)
													else:
																				_UpperCAmelCase  :			Optional[int]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		{
													    "prompt": "a photograph of an astronaut riding a horse",
													    "generator": generator,
													    "num_inference_steps": 10,
													    "guidance_scale": 6.0,
													    "output_type": "numpy",
													    "parallel": 3,
													    "debug": True,
													}
													return inputs

						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Union[str, Any]	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			List[str]	       =		self.get_dummy_components()
													_UpperCAmelCase  :			List[Any]	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		sd_pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			Tuple	       =		image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			str	       =		np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Dict	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			Dict	       =		self.get_dummy_components()
													torch.manual_seed(0			)
													_UpperCAmelCase  :			str	       =		DDPMParallelScheduler()
													torch.manual_seed(0			)
													_UpperCAmelCase  :			int	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			Any	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			int	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		sd_pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			str	       =		image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Optional[Any]	       =		np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

						def 						a__ (					self		:			List[str]			):
													super().test_inference_batch_consistent(batch_sizes=[1, 2]			)

						def 						a__ (					self		:			List[str]			):
													super().test_inference_batch_single_identical(batch_size=2	,						expected_max_diff=3E-3			)






						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			int	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			Tuple	       =		self.get_dummy_components()
													_UpperCAmelCase  :			Dict	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			Optional[int]	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[Any]	       =		"french fries"
													_UpperCAmelCase  :			Tuple	       =		sd_pipe(**_lowerCamelCase	,						negative_prompt=_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[Any]	       =		output.images
													_UpperCAmelCase  :			Union[str, Any]	       =		image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Optional[Any]	       =		np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







@slow
@require_torch_gpu
class    _UpperCamelCase(   unittest.TestCase				):

						def 						a__ (					self		:			int			):
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

						def 						a__ (					self		:			Tuple	,						_lowerCamelCase		:			Optional[int]=0			):
													_UpperCAmelCase  :			Union[str, Any]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		{
													    "prompt": "a photograph of an astronaut riding a horse",
													    "generator": generator,
													    "num_inference_steps": 10,
													    "guidance_scale": 7.5,
													    "output_type": "numpy",
													    "parallel": 3,
													    "debug": True,
													}
													return inputs






						def 						a__ (					self		:			Any			):
													_UpperCAmelCase  :			str	       =		"stabilityai/stable-diffusion-2-base"
													_UpperCAmelCase  :			List[str]	       =		DDIMParallelScheduler.from_pretrained(_lowerCamelCase	,						subfolder="scheduler"			)
													_UpperCAmelCase  :			Union[str, Any]	       =		StableDiffusionParadigmsPipeline.from_pretrained(_lowerCamelCase	,						scheduler=_lowerCamelCase	,						safety_checker=_lowerCamelCase			)
													pipe.to(_lowerCamelCase			)
													pipe.set_progress_bar_config(disable=_lowerCamelCase			)
													pipe.enable_attention_slicing()

													_UpperCAmelCase  :			Optional[Any]	       =		self.get_inputs()
													_UpperCAmelCase  :			List[str]	       =		pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			Tuple	       =		image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 5_12, 5_12, 3)

													_UpperCAmelCase  :			Union[str, Any]	       =		np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]			)

													assert np.abs(expected_slice - image_slice			).max() < 1E-2




