"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		KandinskyVaaControlnetPipeline
      a__        =		["""image_embeds""", """negative_image_embeds""", """hint"""]
      a__        =		["""image_embeds""", """negative_image_embeds""", """hint"""]
      a__        =		[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      a__        =		False



      @property
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return self.time_input_dim



      @property
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        return self.time_input_dim * 4



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        return 1_00



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     Optional[int]										=	{
            'in_channels': 8,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 8,
            'addition_embed_type': 'image_hint',
            '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__:     Any										=	UNetaDConditionModel(**lowerCamelCase__)
        return model



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





        '''simple docstring'''





        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Dict										=	VQModel(**self.dummy_movq_kwargs)
        return model



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     List[Any]										=	self.dummy_unet
        a__:     Tuple										=	self.dummy_movq

        a__:     Tuple										=	DDIMScheduler(
            num_train_timesteps=10_00    ,			beta_schedule='linear'    ,			beta_start=0.00085    ,			beta_end=0.012    ,			clip_sample=lowerCamelCase__    ,			set_alpha_to_one=lowerCamelCase__    ,			steps_offset=1    ,			prediction_type='epsilon'    ,			thresholding=lowerCamelCase__    ,			)

        a__:     Any										=	{
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }
        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Dict										=	floats_tensor((1, self.text_embedder_hidden_size)    ,			rng=random.Random(lowerCamelCase__)).to(lowerCamelCase__)
        a__:     Dict										=	floats_tensor((1, self.text_embedder_hidden_size)    ,			rng=random.Random(seed + 1)).to(
            lowerCamelCase__)

        # create hint
        a__:     List[str]										=	floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(lowerCamelCase__)).to(lowerCamelCase__)

        if str(lowerCamelCase__).startswith('mps'):
          a__:     List[str]										=	torch.manual_seed(lowerCamelCase__)
        else:
          a__:     str										=	torch.Generator(device=lowerCamelCase__).manual_seed(lowerCamelCase__)
        a__:     int										=	{
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'hint': hint,
            'generator': generator,
            'height': 64,
            'width': 64,
            'guidance_scale': 4.0,
            'num_inference_steps': 2,
            'output_type': 'np',
        }
        return inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     int										=	'cpu'

        a__:     str										=	self.get_dummy_components()

        a__:     int										=	self.pipeline_class(**lowerCamelCase__)
        a__:     Optional[Any]										=	pipe.to(lowerCamelCase__)

        pipe.set_progress_bar_config(disable=lowerCamelCase__)

        a__:     List[str]										=	pipe(**self.get_dummy_inputs(lowerCamelCase__))
        a__:     Dict										=	output.images

        a__:     Optional[Any]										=	pipe(
            **self.get_dummy_inputs(lowerCamelCase__)    ,			return_dict=lowerCamelCase__    ,			)[0]

        a__:     List[Any]										=	image[0, -3:, -3:, -1]
        a__:     Tuple										=	image_from_tuple[0, -3:, -3:, -1]

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

        a__:     List[Any]										=	np.array(
            [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595])

        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    __snake_case       (     unittest.TestCase				):



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





        '''simple docstring'''





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy')

        a__:     Any										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/hint_image_cat.png')
        a__:     Any										=	torch.from_numpy(np.array(lowerCamelCase__)).float() / 2_55.0
        a__:     Dict										=	hint.permute(2    ,			0    ,			1).unsqueeze(0)

        a__:     Optional[int]										=	KandinskyVaaPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-prior'    ,			torch_dtype=torch.floataa)
        pipe_prior.to(lowerCamelCase__)

        a__:     Any										=	KandinskyVaaControlnetPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-controlnet-depth'    ,			torch_dtype=torch.floataa)
        a__:     Optional[Any]										=	pipeline.to(lowerCamelCase__)
        pipeline.set_progress_bar_config(disable=lowerCamelCase__)

        a__:     Tuple										=	'A robot, 4k photo'

        a__:     Optional[int]										=	torch.Generator(device='cuda').manual_seed(0)
        a__							,    a__:     Optional[int]										=	pipe_prior(
            lowerCamelCase__    ,			generator=lowerCamelCase__    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

        a__:     Union[str, Any]										=	torch.Generator(device='cuda').manual_seed(0)
        a__:     Optional[int]										=	pipeline(
            image_embeds=lowerCamelCase__    ,			negative_image_embeds=lowerCamelCase__    ,			hint=lowerCamelCase__    ,			generator=lowerCamelCase__    ,			num_inference_steps=1_00    ,			output_type='np'    ,			)

        a__:     List[str]										=	output.images[0]

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

        assert_mean_pixel_difference(lowerCamelCase__    ,			lowerCamelCase__)








import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any=1024      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    [], []

				__lowerCamelCase		       =    list(zip(UpperCamelCase__ ,     UpperCamelCase__      )      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    sorted_examples[0]

				def is_too_big(UpperCamelCase__				:      List[str]      ):
								return tok(UpperCamelCase__ ,     return_tensors='pt'      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								__lowerCamelCase		       =    new_src + ' ' + src
								__lowerCamelCase		       =    new_tgt + ' ' + tgt
								if is_too_big(UpperCamelCase__      ) or is_too_big(UpperCamelCase__      ):  # cant fit, finalize example
												finished_src.append(UpperCamelCase__      )
												finished_tgt.append(UpperCamelCase__      )
												__lowerCamelCase       ,						__lowerCamelCase		       =    src, tgt
								else:  # can fit, keep adding
												__lowerCamelCase       ,						__lowerCamelCase		       =    cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(UpperCamelCase__      )
								finished_tgt.append(UpperCamelCase__      )
				return finished_src, finished_tgt







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path(UpperCamelCase__      )
				save_path.mkdir(exist_ok=UpperCamelCase__      )
				for split in ["train"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase       ,						__lowerCamelCase		       =    pack_examples(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""packed {split} split from {len(UpperCamelCase__      )} examples -> {len(UpperCamelCase__      )}."""      )
								Path(save_path / F"""{split}.source"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
								Path(save_path / F"""{split}.target"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
				for split in ["val", "test"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.source"""      )
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.target"""      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument('--tok_name' ,     type=UpperCamelCase__ ,     help='like facebook/bart-large-cnn,t5-base, etc.'      )
				parser.add_argument('--max_seq_len' ,     type=UpperCamelCase__ ,     default=128      )
				parser.add_argument('--data_dir' ,     type=UpperCamelCase__      )
				parser.add_argument('--save_path' ,     type=UpperCamelCase__      )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(UpperCamelCase__ ,     Path(args.data_dir      ) ,     args.max_seq_len ,     args.save_path      )


if __name__ == "__main__":
					packer_cli()


