





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

import cva
import numpy as np

# Parrameters
_SCREAMING_SNAKE_CASE      	=		(7_20, 12_80)  # Height, Width
_SCREAMING_SNAKE_CASE      	=		(0.4, 0.6)  # if height or width lower than this scale, drop it.
_SCREAMING_SNAKE_CASE      	=		1 / 1_00
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		2_50


def 							SCREAMING_SNAKE_CASE__						(    ):
 snake_case_		:			Any											=	get_dataset(__a   , __a   )
 for index in range(__a   ):
  snake_case_		:			Dict											=	random.sample(range(len(__a   )   )   , 4   )
  snake_case_		:			Optional[int]											=	update_image_and_anno(
      __a   , __a   , __a   , __a   , __a   , filter_scale=__a   , )

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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
 snake_case_		:			List[Any]											=	[]
 snake_case_		:			Optional[int]											=	[]
 for label_file in glob.glob(os.path.join(__a   , '*.txt'   )   ):
  snake_case_		:			List[str]											=	label_file.split(os.sep   )[-1].rsplit('.'   , 1   )[0]
  with open(__a   ) as in_file:
   snake_case_		:			Dict											=	in_file.readlines()
  snake_case_		:			int											=	os.path.join(__a   , f"""{label_name}.jpg"""   )

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

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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   , __a = 0.0   , ):
 snake_case_		:			str											=	np.zeros([output_size[0], output_size[1], 3]   , dtype=np.uinta   )
 snake_case_		:			Tuple											=	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 snake_case_		:			int											=	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 snake_case_		:			Optional[Any]											=	int(scale_x * output_size[1]   )
 snake_case_		:			int											=	int(scale_y * output_size[0]   )

 snake_case_		:			str											=	[]
 snake_case_		:			Dict											=	[]
 for i, index in enumerate(__a   ):
  snake_case_		:			Any											=	all_img_list[index]
  path_list.append(__a   )
  snake_case_		:			List[str]											=	all_annos[index]
  snake_case_		:			Any											=	cva.imread(__a   )
  if i == 0:  # top-left
   snake_case_		:			Optional[Any]											=	cva.resize(__a   , (divid_point_x, divid_point_y)   )
   snake_case_		:			Dict											=	img
   for bbox in img_annos:
    snake_case_		:			Dict											=	bbox[1] * scale_x
    snake_case_		:			Tuple											=	bbox[2] * scale_y
    snake_case_		:			Optional[Any]											=	bbox[3] * scale_x
    snake_case_		:			str											=	bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 1:  # top-right
   snake_case_		:			Tuple											=	cva.resize(__a   , (output_size[1] - divid_point_x, divid_point_y)   )
   snake_case_		:			Optional[Any]											=	img
   for bbox in img_annos:
    snake_case_		:			int											=	scale_x + bbox[1] * (1 - scale_x)
    snake_case_		:			Dict											=	bbox[2] * scale_y
    snake_case_		:			Union[str, Any]											=	scale_x + bbox[3] * (1 - scale_x)
    snake_case_		:			Union[str, Any]											=	bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 2:  # bottom-left
   snake_case_		:			Optional[Any]											=	cva.resize(__a   , (divid_point_x, output_size[0] - divid_point_y)   )
   snake_case_		:			int											=	img
   for bbox in img_annos:
    snake_case_		:			int											=	bbox[1] * scale_x
    snake_case_		:			List[str]											=	scale_y + bbox[2] * (1 - scale_y)
    snake_case_		:			List[str]											=	bbox[3] * scale_x
    snake_case_		:			Union[str, Any]											=	scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  else:  # bottom-right
   snake_case_		:			str											=	cva.resize(
       __a   , (output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
   snake_case_		:			Dict											=	img
   for bbox in img_annos:
    snake_case_		:			Dict											=	scale_x + bbox[1] * (1 - scale_x)
    snake_case_		:			Tuple											=	scale_y + bbox[2] * (1 - scale_y)
    snake_case_		:			Union[str, Any]											=	scale_x + bbox[3] * (1 - scale_x)
    snake_case_		:			List[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:
  snake_case_		:			Dict											=	[
      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 							SCREAMING_SNAKE_CASE__						(    __a   ):
 assert number_char > 1, "The number of character should greater than 1"
 snake_case_		:			Tuple											=	ascii_lowercase + digits
 return "".join(random.choice(__a   ) for _ in range(__a   )   )


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








import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Dict       =       PriorTransformer
						__magic_name__:			str       =       "hidden_states"






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







							"""simple docstring"""


							snake_case_		:			Any											=	4
							snake_case_		:			int											=	8
							snake_case_		:			Dict											=	7

							snake_case_		:			Union[str, Any]											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			int											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			str											=	floats_tensor((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[Any]=0	)							->    str:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			List[Any]											=	4
							snake_case_		:			str											=	8
							snake_case_		:			Any											=	7

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			Optional[int]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			List[Any]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






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







							"""simple docstring"""


							return (4, 8)






						@property
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							return (4, 8)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	{
							    'num_attention_heads': 2,
							    'attention_head_dim': 4,
							    'num_layers': 2,
							    'embedding_dim': 8,
							    'num_embeddings': 7,
							    'additional_embeddings': 4,
							}
							snake_case_		:			Tuple											=	self.dummy_input
							return init_dict, inputs_dict






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_						,snake_case_		:			str											=	PriorTransformer.from_pretrained(
							    'hf-internal-testing/prior-dummy'		,   output_loading_info=_A	)
							self.assertIsNotNone(_A	)
							self.assertEqual(len(loading_info['missing_keys']	)		,   0	)

							model.to(_A	)
							snake_case_		:			Optional[Any]											=	model(**self.dummy_input	)[0]

							assert hidden_states is not None, "Make sure output is not None"






						def UpperCAmelCase_				(      self      :      Tuple	)							->    List[Any]:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Optional[int]											=	self.prepare_init_args_and_inputs_for_common()

							snake_case_		:			Tuple											=	self.model_class(**_A	)
							snake_case_		:			Any											=	inspect.signature(model.forward	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case_		:			List[str]											=	[*signature.parameters.keys()]

							snake_case_		:			int											=	['hidden_states', 'timestep']
							self.assertListEqual(arg_names[:2]		,   _A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'	)
							snake_case_		:			str											=	model.to(_A	)

							if hasattr(_A		,   'set_default_attn_processor'	):
								model.set_default_attn_processor()

							snake_case_		:			Optional[int]											=	self.get_dummy_seed_input()

							with torch.no_grad():
								snake_case_		:			Any											=	model(**_A	)[0]

							snake_case_		:			Any											=	output[0, :5].flatten().cpu()
							print(_A	)

							# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
							# the expected output slices are not the same for CPU and GPU.
							snake_case_		:			str											=	torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]	)
							self.assertTrue(torch_all_close(_A		,   _A		,   rtol=1E-2	)	)
@slow
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      int=1		,   _A      :      int=768		,   _A      :      str=77		,   _A      :      List[str]=0	)							->    Tuple:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			Dict											=	batch_size
							snake_case_		:			Any											=	embedding_dim
							snake_case_		:			int											=	num_embeddings

							snake_case_		:			Dict											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			Optional[int]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Optional[int]:







							"""simple docstring"""


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






						@parameterized.expand(
						    [
						        # fmt: off
						        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
						        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
						        # fmt: on
						    ]	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      List[Any]		,   _A      :      List[str]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'		,   subfolder='prior'	)
							model.to(_A	)
							snake_case_		:			Optional[Any]											=	self.get_dummy_seed_input(seed=_A	)

							with torch.no_grad():
								snake_case_		:			str											=	model(**_A	)[0]

							assert list(sample.shape	) == [1, 768]

							snake_case_		:			Optional[Any]											=	sample[0, :8].flatten().cpu()
							print(_A	)
							snake_case_		:			int											=	torch.tensor(_A	)

							assert torch_all_close(_A		,   _A		,   atol=1E-3	)

