





import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__lowerCamelCase       :     Tuple   =     (720, 1280)  # Height, Width
__lowerCamelCase       :     int   =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowerCamelCase       :     int   =     1 / 100
__lowerCamelCase       :     Any   =     """"""
__lowerCamelCase       :     List[str]   =     """"""
__lowerCamelCase       :     List[Any]   =     """"""
__lowerCamelCase       :     Tuple   =     250



def 		A_				(     )						-> None:
 UpperCamelCase	,    UpperCamelCase	:					Tuple       	=   get_dataset(_lowerCAmelCase							,   _lowerCAmelCase     )
 for index in range(_lowerCAmelCase     ):
  UpperCamelCase	:					Union[str, Any]       	=   random.sample(range(len(_lowerCAmelCase     )     )							,   4     )
  UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					str       	=   update_image_and_anno(
      _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   filter_scale=_lowerCAmelCase							,   )

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  UpperCamelCase	:					Union[str, Any]       	=   random_chars(32     )
  UpperCamelCase	:					Optional[Any]       	=   path.split(os.sep     )[-1].rsplit("."							,   1     )[0]
  UpperCamelCase	:					Optional[Any]       	=   F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
  cva.imwrite(F"""{file_root}.jpg"""							,   _lowerCAmelCase							,   [cva.IMWRITE_JPEG_QUALITY, 85]     )
  print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""     )
  UpperCamelCase	:					int       	=   []
  for anno in new_annos:
   UpperCamelCase	:					Dict       	=   anno[3] - anno[1]
   UpperCamelCase	:					Union[str, Any]       	=   anno[4] - anno[2]
   UpperCamelCase	:					Optional[int]       	=   anno[1] + width / 2
   UpperCamelCase	:					Tuple       	=   anno[2] + height / 2
   UpperCamelCase	:					Dict       	=   F"""{anno[0]} {x_center} {y_center} {width} {height}"""
   annos_list.append(_lowerCAmelCase     )
  with open(F"""{file_root}.txt"""							,   "w"     ) as outfile:
   outfile.write("\n".join(line for line in annos_list     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> tuple[list, list]:
 UpperCamelCase	:					int       	=   []
 UpperCamelCase	:					Tuple       	=   []
 for label_file in glob.glob(os.path.join(_lowerCAmelCase							,   "*.txt"     )     ):
  UpperCamelCase	:					Union[str, Any]       	=   label_file.split(os.sep     )[-1].rsplit("."							,   1     )[0]
  with open(_lowerCAmelCase     ) as in_file:
   UpperCamelCase	:					int       	=   in_file.readlines()
  UpperCamelCase	:					Optional[Any]       	=   os.path.join(_lowerCAmelCase							,   F"""{label_name}.jpg"""     )

  UpperCamelCase	:					str       	=   []
  for obj_list in obj_lists:
   UpperCamelCase	:					Optional[int]       	=   obj_list.rstrip("\n"     ).split(" "     )
   UpperCamelCase	:					Dict       	=   float(obj[1]     ) - float(obj[3]     ) / 2
   UpperCamelCase	:					List[str]       	=   float(obj[2]     ) - float(obj[4]     ) / 2
   UpperCamelCase	:					Dict       	=   float(obj[1]     ) + float(obj[3]     ) / 2
   UpperCamelCase	:					int       	=   float(obj[2]     ) + float(obj[4]     ) / 2

   boxes.append([int(obj[0]     ), xmin, ymin, xmax, ymax]     )
  if not boxes:
   continue
  img_paths.append(_lowerCAmelCase     )
  labels.append(_lowerCAmelCase     )
 return img_paths, labels



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase = 0.0							,   )						-> tuple[list, list, str]:
 UpperCamelCase	:					str       	=   np.zeros([output_size[0], output_size[1], 3]							,   dtype=np.uinta     )
 UpperCamelCase	:					List[Any]       	=   scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 UpperCamelCase	:					Dict       	=   scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 UpperCamelCase	:					Dict       	=   int(scale_x * output_size[1]     )
 UpperCamelCase	:					Tuple       	=   int(scale_y * output_size[0]     )

 UpperCamelCase	:					Union[str, Any]       	=   []
 UpperCamelCase	:					int       	=   []
 for i, index in enumerate(_lowerCAmelCase     ):
  UpperCamelCase	:					str       	=   all_img_list[index]
  path_list.append(_lowerCAmelCase     )
  UpperCamelCase	:					Tuple       	=   all_annos[index]
  UpperCamelCase	:					Union[str, Any]       	=   cva.imread(_lowerCAmelCase     )
  if i == 0:  # top-left
   UpperCamelCase	:					int       	=   cva.resize(_lowerCAmelCase							,   (divid_point_x, divid_point_y)     )
   UpperCamelCase	:					int       	=   img
   for bbox in img_annos:
    UpperCamelCase	:					Any       	=   bbox[1] * scale_x
    UpperCamelCase	:					Optional[Any]       	=   bbox[2] * scale_y
    UpperCamelCase	:					str       	=   bbox[3] * scale_x
    UpperCamelCase	:					Any       	=   bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]     )
  elif i == 1:  # top-right
   UpperCamelCase	:					Any       	=   cva.resize(_lowerCAmelCase							,   (output_size[1] - divid_point_x, divid_point_y)     )
   UpperCamelCase	:					str       	=   img
   for bbox in img_annos:
    UpperCamelCase	:					str       	=   scale_x + bbox[1] * (1 - scale_x)
    UpperCamelCase	:					Tuple       	=   bbox[2] * scale_y
    UpperCamelCase	:					int       	=   scale_x + bbox[3] * (1 - scale_x)
    UpperCamelCase	:					str       	=   bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]     )
  elif i == 2:  # bottom-left
   UpperCamelCase	:					Dict       	=   cva.resize(_lowerCAmelCase							,   (divid_point_x, output_size[0] - divid_point_y)     )
   UpperCamelCase	:					Dict       	=   img
   for bbox in img_annos:
    UpperCamelCase	:					str       	=   bbox[1] * scale_x
    UpperCamelCase	:					List[str]       	=   scale_y + bbox[2] * (1 - scale_y)
    UpperCamelCase	:					Dict       	=   bbox[3] * scale_x
    UpperCamelCase	:					Optional[int]       	=   scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]     )
  else:  # bottom-right
   UpperCamelCase	:					str       	=   cva.resize(
       _lowerCAmelCase							,   (output_size[1] - divid_point_x, output_size[0] - divid_point_y)     )
   UpperCamelCase	:					Optional[int]       	=   img
   for bbox in img_annos:
    UpperCamelCase	:					Dict       	=   scale_x + bbox[1] * (1 - scale_x)
    UpperCamelCase	:					Any       	=   scale_y + bbox[2] * (1 - scale_y)
    UpperCamelCase	:					int       	=   scale_x + bbox[3] * (1 - scale_x)
    UpperCamelCase	:					Union[str, Any]       	=   scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]     )

    # Remove bounding box small than scale of filter
 if filter_scale > 0:
  UpperCamelCase	:					List[str]       	=   [
      anno
      for anno in new_anno
      if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
  ]

 return output_img, new_anno, path_list[0]



def 		A_				(     _lowerCAmelCase     )						-> str:
 assert number_char > 1, "The number of character should greater than 1"
 UpperCamelCase	:					Optional[Any]       	=   ascii_lowercase + digits
 return "".join(random.choice(_lowerCAmelCase     ) for _ in range(_lowerCAmelCase     )     )


if __name__ == "__main__":
    main()
    print("""DONE ✅""")







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 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:List[str]						    =   KandinskyInpaintPipeline
      _UpperCAmelCase	:List[str]						    =   ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
      _UpperCAmelCase	:Dict						    =   [
          'prompt',
          'negative_prompt',
          'image_embeds',
          'negative_image_embeds',
          'image',
          'mask_image',
      ]
      _UpperCAmelCase	:Optional[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	:int						    =   False





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return 32





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return 32





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.time_input_dim





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.time_input_dim * 4





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return 100





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"   )
       return tokenizer





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(0   )
       UpperCamelCase	:					Optional[int]       	=   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       ,							)

       UpperCamelCase	:					Optional[int]       	=   MultilingualCLIP(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   text_encoder.eval()

       return text_encoder





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(0   )

       UpperCamelCase	:					Optional[int]       	=   {
           "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,
       }

       UpperCamelCase	:					List[Any]       	=   UNetaDConditionModel(**A_   )
       return model





      @property
      def        __UpperCamelCase( self   ):
       '''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        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(0   )
       UpperCamelCase	:					List[str]       	=   VQModel(**self.dummy_movq_kwargs   )
       return model





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.dummy_text_encoder
       UpperCamelCase	:					str       	=   self.dummy_tokenizer
       UpperCamelCase	:					List[Any]       	=   self.dummy_unet
       UpperCamelCase	:					Optional[Any]       	=   self.dummy_movq

       UpperCamelCase	:					Union[str, Any]       	=   DDIMScheduler(
           num_train_timesteps=1000       ,							beta_schedule="linear"       ,							beta_start=0.0_00_85       ,							beta_end=0.0_12       ,							clip_sample=A_       ,							set_alpha_to_one=A_       ,							steps_offset=1       ,							prediction_type="epsilon"       ,							thresholding=A_       ,							)

       UpperCamelCase	:					Optional[Any]       	=   {
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components





      def        __UpperCamelCase( self       ,							A_       ,							A_=0   ):
       '''simple docstring'''




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

       if str(A_   ).startswith("mps"   ):
        UpperCamelCase	:					int       	=   torch.manual_seed(A_   )
       else:
        UpperCamelCase	:					Tuple       	=   torch.Generator(device=A_   ).manual_seed(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   {
           "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        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   "cpu"

       UpperCamelCase	:					Tuple       	=   self.get_dummy_components()

       UpperCamelCase	:					str       	=   self.pipeline_class(**A_   )
       UpperCamelCase	:					Tuple       	=   pipe.to(A_   )

       pipe.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					Any       	=   pipe(**self.get_dummy_inputs(A_   )   )
       UpperCamelCase	:					List[Any]       	=   output.images

       UpperCamelCase	:					List[Any]       	=   pipe(
           **self.get_dummy_inputs(A_   )       ,							return_dict=A_       ,							)[0]

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

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

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

       UpperCamelCase	:					Union[str, Any]       	=   np.array(
           [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]   )

       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        __UpperCamelCase( self   ):
       '''simple docstring'''




       super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"   )

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

       UpperCamelCase	:					List[Any]       	=   "a hat"

       UpperCamelCase	:					Tuple       	=   KandinskyPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-prior"       ,							torch_dtype=torch.floataa   )
       pipe_prior.to(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   KandinskyInpaintPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-inpaint"       ,							torch_dtype=torch.floataa   )
       UpperCamelCase	:					Optional[Any]       	=   pipeline.to(A_   )
       pipeline.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					List[Any]       	=   torch.Generator(device="cpu"   ).manual_seed(0   )
       UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   pipe_prior(
           A_       ,							generator=A_       ,							num_inference_steps=5       ,							negative_prompt=""       ,							).to_tuple()

       UpperCamelCase	:					Dict       	=   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"       ,							)

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

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

       assert_mean_pixel_difference(A_       ,							A_   )
