

'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, 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    A__ ( _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						KandinskyVaaInpaintPipeline
    lowercase		   =						["image_embeds", "negative_image_embeds", "image", "mask_image"]
    lowercase		   =						[
        "image_embeds",
        "negative_image_embeds",
        "image",
        "mask_image",
    ]
    lowercase		   =						[
        "generator",
        "height",
        "width",
        "latents",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    lowercase		   =						False



    @property
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           return 32



    @property
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           return 32



    @property
    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           return self.time_input_dim



    @property
    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           return self.time_input_dim * 4



    @property
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           return 100



    @property
    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           torch.manual_seed(0    )

           A_		   = {
               """in_channels""": 9,
               # Out channels is double in channels because predicts mean and variance
               """out_channels""": 8,
               """addition_embed_type""": """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""": """image_proj""",
               """cross_attention_dim""": self.cross_attention_dim,
               """attention_head_dim""": 4,
               """resnet_time_scale_shift""": """scale_shift""",
               """class_embed_type""": None,
           }

           A_		   = UNetaDConditionModel(**UpperCamelCase__    )
           return model



    @property
    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           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    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = VQModel(**self.dummy_movq_kwargs    )
           return model



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = self.dummy_unet
           A_		   = self.dummy_movq

           A_		   = DDIMScheduler(
               num_train_timesteps=1000						,			beta_schedule="""linear"""						,			beta_start=0.00085						,			beta_end=0.012						,			clip_sample=UpperCamelCase__						,			set_alpha_to_one=UpperCamelCase__						,			steps_offset=1						,			prediction_type="""epsilon"""						,			thresholding=UpperCamelCase__						,			)

           A_		   = {
               """unet""": unet,
               """scheduler""": scheduler,
               """movq""": movq,
           }

           return components



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__=0    )  -> List[Any]:





           '''simple docstring'''
           A_		   = floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(UpperCamelCase__    )    ).to(UpperCamelCase__    )
           A_		   = floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(seed + 1    )    ).to(
               UpperCamelCase__    )
           # create init_image
           A_		   = floats_tensor((1, 3, 64, 64)						,			rng=random.Random(UpperCamelCase__    )    ).to(UpperCamelCase__    )
           A_		   = image.cpu().permute(0						,			2						,			3						,			1    )[0]
           A_		   = Image.fromarray(np.uinta(UpperCamelCase__    )    ).convert("""RGB"""    ).resize((256, 256)    )
           # create mask
           A_		   = np.ones((64, 64)						,			dtype=np.floataa    )
           A_		   = 0

           if str(UpperCamelCase__    ).startswith("""mps"""    ):
                  A_		   = torch.manual_seed(UpperCamelCase__    )
           else:
                  A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(UpperCamelCase__    )
           A_		   = {
               """image""": init_image,
               """mask_image""": mask,
               """image_embeds""": image_embeds,
               """negative_image_embeds""": negative_image_embeds,
               """generator""": generator,
               """height""": 64,
               """width""": 64,
               """num_inference_steps""": 2,
               """guidance_scale""": 4.0,
               """output_type""": """np""",
           }
           return inputs



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = """cpu"""

           A_		   = self.get_dummy_components()

           A_		   = self.pipeline_class(**UpperCamelCase__    )
           A_		   = pipe.to(UpperCamelCase__    )

           pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = pipe(**self.get_dummy_inputs(UpperCamelCase__    )    )
           A_		   = output.images

           A_		   = pipe(
               **self.get_dummy_inputs(UpperCamelCase__    )						,			return_dict=UpperCamelCase__						,			)[0]

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

           print(f'''image.shape {image.shape}'''    )

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

           A_		   = np.array(
               [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]    )

           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()}'''







    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           super().test_inference_batch_single_identical(expected_max_diff=3e-3    )


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



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""    )

           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""    )
           A_		   = np.ones((768, 768)						,			dtype=np.floataa    )
           A_		   = 0

           A_		   = """a hat"""

           A_		   = KandinskyVaaPriorPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-prior"""						,			torch_dtype=torch.floataa    )
           pipe_prior.to(UpperCamelCase__    )

           A_		   = KandinskyVaaInpaintPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-decoder-inpaint"""						,			torch_dtype=torch.floataa    )
           A_		   = pipeline.to(UpperCamelCase__    )
           pipeline.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = torch.Generator(device="""cpu"""    ).manual_seed(0    )
           A_   ,     A_		   = pipe_prior(
               UpperCamelCase__						,			generator=UpperCamelCase__						,			num_inference_steps=5						,			negative_prompt=""""""						,			).to_tuple()

           A_		   = pipeline(
               image=UpperCamelCase__						,			mask_image=UpperCamelCase__						,			image_embeds=UpperCamelCase__						,			negative_image_embeds=UpperCamelCase__						,			generator=UpperCamelCase__						,			num_inference_steps=100						,			height=768						,			width=768						,			output_type="""np"""						,			)

           A_		   = output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(UpperCamelCase__						,			UpperCamelCase__    )



'''simple docstring'''







import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCamelCase						    =      logging.get_logger(__name__)


@dataclass
class    A__ :
    lowercase		   =						field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()     )}     )
    lowercase		   =						field(
        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}     )
    lowercase		   =						field(
        default=128  ,  metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        }  ,  )
    lowercase		   =						field(
        default=_snake_case  ,  metadata={"help": "Overwrite the cached training and evaluation sets"}     )



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = self.task_name.lower()


class    A__ ( _snake_case     ):
    lowercase		   =						"train"
    lowercase		   =						"dev"
    lowercase		   =						"test"


class    A__ ( _snake_case     ):
    lowercase		   =						42
    lowercase		   =						42
    lowercase		   =						42



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = Split.train						,			UpperCamelCase__ = None						,			)  -> List[Any]:





           '''simple docstring'''
           warnings.warn(
               """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
               """library. You can have a look at this example script for pointers: """
               """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""						,			UpperCamelCase__						,			)
           A_		   = args
           A_		   = glue_processors[args.task_name]()
           A_		   = glue_output_modes[args.task_name]
           if isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                  try:
                         A_		   = Split[mode]
                  except KeyError:
                         raise KeyError("""mode is not a valid split name"""    )
        # Load data features from cache or dataset file
           A_		   = os.path.join(
               cache_dir if cache_dir is not None else args.data_dir						,			f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''						,			)
           A_		   = self.processor.get_labels()
           if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
               "RobertaTokenizer",
               "RobertaTokenizerFast",
               "XLMRobertaTokenizer",
               "BartTokenizer",
               "BartTokenizerFast",
           ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  A_   ,     A_		   = label_list[2], label_list[1]
           A_		   = label_list

           # Make sure only the first process in distributed training processes the dataset,
           # and the others will use the cache.
           A_		   = cached_features_file + """.lock"""
           with FileLock(UpperCamelCase__    ):
                  if os.path.exists(UpperCamelCase__    ) and not args.overwrite_cache:
                         A_		   = time.time()
                         A_		   = torch.load(UpperCamelCase__    )
                         logger.info(
                             f'''Loading features from cached file {cached_features_file} [took %.3f s]'''						,			time.time() - start    )
                  else:
                         logger.info(f'''Creating features from dataset file at {args.data_dir}'''    )

                         if mode == Split.dev:
                                A_		   = self.processor.get_dev_examples(args.data_dir    )
                         elif mode == Split.test:
                                A_		   = self.processor.get_test_examples(args.data_dir    )
                         else:
                                A_		   = self.processor.get_train_examples(args.data_dir    )
                         if limit_length is not None:
                                A_		   = examples[:limit_length]
                         A_		   = glue_convert_examples_to_features(
                             UpperCamelCase__						,			UpperCamelCase__						,			max_length=args.max_seq_length						,			label_list=UpperCamelCase__						,			output_mode=self.output_mode						,			)
                         A_		   = time.time()
                         torch.save(self.features						,			UpperCamelCase__    )
                         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                         logger.info(
                             f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''    )



    def __len__(   self    )  -> str:





           '''simple docstring'''
           return len(self.features    )



    def __getitem__(   self						,			UpperCamelCase__    )  -> InputFeatures:





           '''simple docstring'''
           return self.features[i]







    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           return self.label_list
