

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	__magic_name__				(       snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :Any								=			KandinskyPipeline
     UpperCamelCase_  :str								=			[
         """prompt""",
         """image_embeds""",
         """negative_image_embeds""",
     ]
     UpperCamelCase_  :Union[str, Any]								=			["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
     UpperCamelCase_  :int								=			[
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     UpperCamelCase_  :Optional[Any]								=			False






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    str:
         return self.time_input_dim






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return self.time_input_dim * 4






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         return 100






     @property
     def   UpperCAmelCase_							(							self  )->    List[Any]:
         UpperCamelCase_      =	XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"  )
         return tokenizer






     @property
     def   UpperCAmelCase_							(							self  )->    int:
         torch.manual_seed(0  )
         UpperCamelCase_      =	MCLIPConfig(
             numDims=self.cross_attention_dim		,	transformerDimensions=self.text_embedder_hidden_size		,	hidden_size=self.text_embedder_hidden_size		,	intermediate_size=37		,	num_attention_heads=4		,	num_hidden_layers=5		,	vocab_size=1_005		,	)

         UpperCamelCase_      =	MultilingualCLIP(_lowercase  )
         UpperCamelCase_      =	text_encoder.eval()

         return text_encoder






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         torch.manual_seed(0  )

         UpperCamelCase_      =	{
             "in_channels": 4,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "text_image",
             "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
             "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
             "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "layers_per_block": 1,
             "encoder_hid_dim": self.text_embedder_hidden_size,
             "encoder_hid_dim_type": "text_image_proj",
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": None,
         }

         UpperCamelCase_      =	UNetaDConditionModel(**_lowercase  )
         return model






     @property
     def   UpperCAmelCase_							(							self  )->    Dict:
         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }






     @property
     def   UpperCAmelCase_							(							self  )->    Tuple:
         torch.manual_seed(0  )
         UpperCamelCase_      =	VQModel(**self.dummy_movq_kwargs  )
         return model






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	self.dummy_text_encoder
         UpperCamelCase_      =	self.dummy_tokenizer
         UpperCamelCase_      =	self.dummy_unet
         UpperCamelCase_      =	self.dummy_movq

         UpperCamelCase_      =	DDIMScheduler(
             num_train_timesteps=1_000		,	beta_schedule="linear"		,	beta_start=0.00_085		,	beta_end=0.012		,	clip_sample=_lowercase		,	set_alpha_to_one=_lowercase		,	steps_offset=1		,	prediction_type="epsilon"		,	thresholding=_lowercase		,	)

         UpperCamelCase_      =	{
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase=0  )->    Union[str, Any]:
         UpperCamelCase_      =	floats_tensor((1, self.cross_attention_dim)		,	rng=random.Random(_lowercase  )  ).to(_lowercase  )
         UpperCamelCase_      =	floats_tensor((1, self.cross_attention_dim)		,	rng=random.Random(seed + 1  )  ).to(_lowercase  )
         if str(_lowercase  ).startswith("mps"  ):
             UpperCamelCase_      =	torch.manual_seed(_lowercase  )
         else:
             UpperCamelCase_      =	torch.Generator(device=_lowercase  ).manual_seed(_lowercase  )
         UpperCamelCase_      =	{
             "prompt": "horse",
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs






     def   UpperCAmelCase_							(							self  )->    Tuple:
         UpperCamelCase_      =	"cpu"

         UpperCamelCase_      =	self.get_dummy_components()

         UpperCamelCase_      =	self.pipeline_class(**_lowercase  )
         UpperCamelCase_      =	pipe.to(_lowercase  )

         pipe.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	pipe(**self.get_dummy_inputs(_lowercase  )  )
         UpperCamelCase_      =	output.images

         UpperCamelCase_      =	pipe(
             **self.get_dummy_inputs(_lowercase  )		,	return_dict=_lowercase		,	)[0]

         UpperCamelCase_      =	image[0, -3:, -3:, -1]
         UpperCamelCase_      =	image_from_tuple[0, -3:, -3:, -1]

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

         UpperCamelCase_      =	np.array(
             [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

         assert (
             np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
         ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
         ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






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






     def   UpperCAmelCase_							(							self  )->    Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy"  )

         UpperCamelCase_      =	KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior"		,	torch_dtype=torch.floataa  )
         pipe_prior.to(_lowercase  )

         UpperCamelCase_      =	KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"		,	torch_dtype=torch.floataa  )
         UpperCamelCase_      =	pipeline.to(_lowercase  )
         pipeline.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	"red cat, 4k photo"

         UpperCamelCase_      =	torch.Generator(device="cuda"  ).manual_seed(0  )
         UpperCamelCase_     ,					UpperCamelCase_      =	pipe_prior(
             _lowercase		,	generator=_lowercase		,	num_inference_steps=5		,	negative_prompt=""		,	).to_tuple()

         UpperCamelCase_      =	torch.Generator(device="cuda"  ).manual_seed(0  )
         UpperCamelCase_      =	pipeline(
             _lowercase		,	image_embeds=_lowercase		,	negative_image_embeds=_lowercase		,	generator=_lowercase		,	num_inference_steps=100		,	output_type="np"		,	)

         UpperCamelCase_      =	output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(_lowercase		,	_lowercase  )



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


SCREAMING_SNAKE_CASE		:List[Any]				  =    ["""CoLA""", """SST""", """MRPC""", """QQP""", """STS""", """MNLI""", """SNLI""", """QNLI""", """RTE""", """WNLI""", """diagnostic"""]
SCREAMING_SNAKE_CASE		: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""",
}

SCREAMING_SNAKE_CASE		:str				  =    """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"""
SCREAMING_SNAKE_CASE		:int				  =    """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"""




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Tuple:



    """simple docstring"""






    print(f"Downloading and extracting {task}..." )
    UpperCamelCase_      =	f"{task}.zip"
    urllib.request.urlretrieve(TASKaPATH[task] ,	SCREAMING_SNAKE_CASE_ )
    with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ ) as zip_ref:
        zip_ref.extractall(SCREAMING_SNAKE_CASE_ )
    os.remove(SCREAMING_SNAKE_CASE_ )
    print("\tCompleted!" )




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Optional[Any]:



    """simple docstring"""






    print("Processing MRPC..." )
    UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"MRPC" )
    if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
        os.mkdir(SCREAMING_SNAKE_CASE_ )
    if path_to_data:
        UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"msr_paraphrase_train.txt" )
        UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"msr_paraphrase_test.txt" )
    else:
        print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN )
        UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"msr_paraphrase_train.txt" )
        UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"msr_paraphrase_test.txt" )
        urllib.request.urlretrieve(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )
        urllib.request.urlretrieve(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )
    if not os.path.isfile(SCREAMING_SNAKE_CASE_ ):
        raise ValueError(f"Train data not found at {mrpc_train_file}" )
    if not os.path.isfile(SCREAMING_SNAKE_CASE_ ):
        raise ValueError(f"Test data not found at {mrpc_test_file}" )
    urllib.request.urlretrieve(TASKaPATH["MRPC"] ,	os.path.join(SCREAMING_SNAKE_CASE_ ,	"dev_ids.tsv" ) )

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

    with open(SCREAMING_SNAKE_CASE_ ,	encoding="utf8" ) as data_fh, open(
        os.path.join(SCREAMING_SNAKE_CASE_ ,	"train.tsv" ) ,	"w" ,	encoding="utf8" ) as train_fh, open(os.path.join(SCREAMING_SNAKE_CASE_ ,	"dev.tsv" ) ,	"w" ,	encoding="utf8" ) as dev_fh:
        UpperCamelCase_      =	data_fh.readline()
        train_fh.write(SCREAMING_SNAKE_CASE_ )
        dev_fh.write(SCREAMING_SNAKE_CASE_ )
        for row in data_fh:
            UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	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(SCREAMING_SNAKE_CASE_ ,	encoding="utf8" ) as data_fh, open(
        os.path.join(SCREAMING_SNAKE_CASE_ ,	"test.tsv" ) ,	"w" ,	encoding="utf8" ) as test_fh:
        UpperCamelCase_      =	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(SCREAMING_SNAKE_CASE_ ):
            UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	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(			SCREAMING_SNAKE_CASE_ )->	Tuple:



    """simple docstring"""






    print("Downloading and extracting diagnostic..." )
    if not os.path.isdir(os.path.join(SCREAMING_SNAKE_CASE_ ,	"diagnostic" ) ):
        os.mkdir(os.path.join(SCREAMING_SNAKE_CASE_ ,	"diagnostic" ) )
    UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"diagnostic" ,	"diagnostic.tsv" )
    urllib.request.urlretrieve(TASKaPATH["diagnostic"] ,	SCREAMING_SNAKE_CASE_ )
    print("\tCompleted!" )
    return




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	Any:



    """simple docstring"""






    UpperCamelCase_      =	task_names.split("," )
    if "all" in task_names:
        UpperCamelCase_      =	TASKS
    else:
        UpperCamelCase_      =	[]
        for task_name in task_names:
            if task_name not in TASKS:
                raise ValueError(f"Task {task_name} not found!" )
            tasks.append(SCREAMING_SNAKE_CASE_ )
    return tasks




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    UpperCamelCase_      =	argparse.ArgumentParser()
    parser.add_argument("--data_dir" ,	help="directory to save data to" ,	type=SCREAMING_SNAKE_CASE_ ,	default="glue_data" )
    parser.add_argument(
        "--tasks" ,	help="tasks to download data for as a comma separated string" ,	type=SCREAMING_SNAKE_CASE_ ,	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=SCREAMING_SNAKE_CASE_ ,	default="" ,	)
    UpperCamelCase_      =	parser.parse_args(SCREAMING_SNAKE_CASE_ )

    if not os.path.isdir(args.data_dir ):
        os.mkdir(args.data_dir )
    UpperCamelCase_      =	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(SCREAMING_SNAKE_CASE_ ,	args.data_dir )


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