

'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					KandinskyInpaintPipeline
 __SCREAMING_SNAKE_CASE					:Dict      	=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					[
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
     """mask_image""",
 ]
 __SCREAMING_SNAKE_CASE					:Any      	=					[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __SCREAMING_SNAKE_CASE					:Dict      	=					False



 @property
 def 			snake_case__  (   self		:   List[Any]      ):
      return 32



 @property
 def 			snake_case__  (   self		:   Union[str, Any]      ):
      return 32



 @property
 def 			snake_case__  (   self		:   int      ):
      return self.time_input_dim



 @property
 def 			snake_case__  (   self		:   Tuple      ):
      return self.time_input_dim * 4



 @property
 def 			snake_case__  (   self		:   List[Any]      ):
      return 100



 @property
 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
      return tokenizer



 @property
 def 			snake_case__  (   self		:   Tuple      ):
      torch.manual_seed(0      )
      __magic_name__						       =    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=1005			,				)

      __magic_name__						       =    MultilingualCLIP(a__      )
      __magic_name__						       =    text_encoder.eval()

      return text_encoder



 @property
 def 			snake_case__  (   self		:   Dict      ):
      torch.manual_seed(0      )

      __magic_name__						       =    {
          '''in_channels''': 9,
          # 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,
      }

      __magic_name__						       =    UNetaDConditionModel(**a__      )
      return model



 @property
 def 			snake_case__  (   self		:   str      ):
      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		:   List[Any]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    VQModel(**self.dummy_movq_kwargs      )
      return model



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    self.dummy_text_encoder
      __magic_name__						       =    self.dummy_tokenizer
      __magic_name__						       =    self.dummy_unet
      __magic_name__						       =    self.dummy_movq

      __magic_name__						       =    DDIMScheduler(
          num_train_timesteps=1000			,				beta_schedule='''linear'''			,				beta_start=0.00_085			,				beta_end=0.012			,				clip_sample=a__			,				set_alpha_to_one=a__			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=a__			,				)

      __magic_name__						       =    {
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''movq''': movq,
      }

      return components



 def 			snake_case__  (   self		:   List[str]			,				a__		:   Union[str, Any]			,				a__		:   Dict=0      ):
      __magic_name__						       =    floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(a__      )      ).to(a__      )
      __magic_name__						       =    floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1      )      ).to(a__      )
      # create init_image
      __magic_name__						       =    floats_tensor((1, 3, 64, 64)			,				rng=random.Random(a__      )      ).to(a__      )
      __magic_name__						       =    image.cpu().permute(0			,				2			,				3			,				1      )[0]
      __magic_name__						       =    Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((256, 256)      )
      # create mask
      __magic_name__						       =    np.ones((64, 64)			,				dtype=np.floataa      )
      __magic_name__						       =    0

      if str(a__      ).startswith('''mps'''      ):
           __magic_name__						       =    torch.manual_seed(a__      )
      else:
           __magic_name__						       =    torch.Generator(device=a__      ).manual_seed(a__      )
      __magic_name__						       =    {
          '''prompt''': '''horse''',
          '''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		:   int      ):
      __magic_name__						       =    '''cpu'''

      __magic_name__						       =    self.get_dummy_components()

      __magic_name__						       =    self.pipeline_class(**a__      )
      __magic_name__						       =    pipe.to(a__      )

      pipe.set_progress_bar_config(disable=a__      )

      __magic_name__						       =    pipe(**self.get_dummy_inputs(a__      )      )
      __magic_name__						       =    output.images

      __magic_name__						       =    pipe(
          **self.get_dummy_inputs(a__      )			,				return_dict=a__			,				)[0]

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

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

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

      __magic_name__						       =    np.array(
          [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]      )

      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		:   List[Any]      ):
      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






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



 def 			snake_case__  (   self		:   int      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''      )

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

      __magic_name__						       =    '''a hat'''

      __magic_name__						       =    KandinskyPriorPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-1-prior'''			,				torch_dtype=torch.floataa      )
      pipe_prior.to(a__      )

      __magic_name__						       =    KandinskyInpaintPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-1-inpaint'''			,				torch_dtype=torch.floataa      )
      __magic_name__						       =    pipeline.to(a__      )
      pipeline.set_progress_bar_config(disable=a__      )

      __magic_name__						       =    torch.Generator(device='''cpu'''      ).manual_seed(0      )
      __magic_name__       ,  __magic_name__						       =    pipe_prior(
          a__			,				generator=a__			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

      __magic_name__						       =    pipeline(
          a__			,				image=a__			,				mask_image=a__			,				image_embeds=a__			,				negative_image_embeds=a__			,				generator=a__			,				num_inference_steps=100			,				height=768			,				width=768			,				output_type='''np'''			,				)

      __magic_name__						       =    output.images[0]

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

      assert_mean_pixel_difference(a__			,				a__      )









'''simple docstring'''




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 						UpperCamelCase	(			a		,						a		,						a		,						a=1024			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    [], []

     __magic_name__						       =    list(zip(a		,						a			)			)
     __magic_name__       ,  __magic_name__						       =    sorted_examples[0]

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

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

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




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     Any:






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




def 						UpperCamelCase	(			)				->     List[str]:






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


if __name__ == "__main__":
      packer_cli()






