

"""simple docstring"""


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

import cva
__A						       =      ""
__A						       =      ""
__A						       =      ""
__A						       =      1  # (0 is vertical, 1 is horizontal)






def    a__   (   )     ->     None:
		__lowerCAmelCase				,			__lowerCAmelCase:	Dict   				=      get_dataset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		print("Processing..."			)
		__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      update_image_and_anno(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

		for index, image in enumerate(__SCREAMING_SNAKE_CASE			):
				# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
				__lowerCAmelCase:	Optional[int]   				=      random_chars(3_2			)
				__lowerCAmelCase:	Optional[int]   				=      paths[index].split(os.sep			)[-1].rsplit("." ,			1			)[0]
				__lowerCAmelCase:	Tuple   				=      F"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
				cva.imwrite(F"/{file_root}.jpg" ,			__SCREAMING_SNAKE_CASE ,			[cva.IMWRITE_JPEG_QUALITY, 8_5]			)
				print(F"Success {index+1}/{len(__SCREAMING_SNAKE_CASE			)} with {file_name}"			)
				__lowerCAmelCase:	List[Any]   				=      []
				for anno in new_annos[index]:
						__lowerCAmelCase:	int   				=      F"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
						annos_list.append(__SCREAMING_SNAKE_CASE			)
				with open(F"/{file_root}.txt" ,			"w"			) as outfile:
						outfile.write("\n".join(line for line in annos_list			)			)






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

				__lowerCAmelCase:	int   				=      []
				for obj_list in obj_lists:
						__lowerCAmelCase:	int   				=      obj_list.rstrip("\n"			).split(" "			)
						boxes.append(
						    [
						        int(obj[0]			),
						        float(obj[1]			),
						        float(obj[2]			),
						        float(obj[3]			),
						        float(obj[4]			),
						    ]			)
				if not boxes:
						continue
				img_paths.append(__SCREAMING_SNAKE_CASE			)
				labels.append(__SCREAMING_SNAKE_CASE			)
		return img_paths, labels






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 1			)     ->     tuple[list, list, list]:
		__lowerCAmelCase:	Dict   				=      []
		__lowerCAmelCase:	str   				=      []
		__lowerCAmelCase:	Any   				=      []
		for idx in range(len(__SCREAMING_SNAKE_CASE			)			):
				__lowerCAmelCase:	Tuple   				=      []
				__lowerCAmelCase:	List[Any]   				=      img_list[idx]
				path_list.append(__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Union[str, Any]   				=      anno_list[idx]
				__lowerCAmelCase:	Any   				=      cva.imread(__SCREAMING_SNAKE_CASE			)
				if flip_type == 1:
						__lowerCAmelCase:	List[str]   				=      cva.flip(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
						for bbox in img_annos:
								__lowerCAmelCase:	List[str]   				=      1 - bbox[1]
								new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
				elif flip_type == 0:
						__lowerCAmelCase:	Any   				=      cva.flip(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
						for bbox in img_annos:
								__lowerCAmelCase:	Optional[Any]   				=      1 - bbox[2]
								new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
				new_annos_lists.append(__SCREAMING_SNAKE_CASE			)
				new_imgs_list.append(__SCREAMING_SNAKE_CASE			)
		return new_imgs_list, new_annos_lists, path_list






def    a__   (   __SCREAMING_SNAKE_CASE = 3_2			)     ->     str:
		assert number_char > 1, "The number of character should greater than 1"
		__lowerCAmelCase:	Any   				=      ascii_lowercase + digits
		return "".join(random.choice(__SCREAMING_SNAKE_CASE			) for _ in range(__SCREAMING_SNAKE_CASE			)			)


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





"""simple docstring"""


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    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  PriorTransformer
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  """hidden_states"""

				@property
				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      4
						__lowerCAmelCase:	int   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	str   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      floats_tensor((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    str=0)->       str:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      4
						__lowerCAmelCase:	Dict   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	List[str]   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Tuple   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

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



						'''simple docstring'''


						return (4, 8)

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



						'''simple docstring'''


						return (4, 8)

				def 							lowercase_					(     self							:    Optional[int])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      {
						    "num_attention_heads": 2,
						    "attention_head_dim": 4,
						    "num_layers": 2,
						    "embedding_dim": 8,
						    "num_embeddings": 7,
						    "additional_embeddings": 4,
						}
						__lowerCAmelCase:	Any   				=      self.dummy_input
						return init_dict, inputs_dict

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



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      PriorTransformer.from_pretrained(
						    "hf-internal-testing/prior-dummy"					,  output_loading_info=UpperCamelCase__)
						self.assertIsNotNone(UpperCamelCase__)
						self.assertEqual(len(loading_info["missing_keys"])					,  0)

						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      model(**self.dummy_input)[0]

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

				def 							lowercase_					(     self							:    List[str])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      self.prepare_init_args_and_inputs_for_common()

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

						__lowerCAmelCase:	Any   				=      ["hidden_states", "timestep"]
						self.assertListEqual(arg_names[:2]					,  UpperCamelCase__)




				def 							lowercase_					(     self							:    Optional[int])->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy")
						__lowerCAmelCase:	Union[str, Any]   				=      model.to(UpperCamelCase__)

						if hasattr(UpperCamelCase__					,  "set_default_attn_processor"):
								model.set_default_attn_processor()

						__lowerCAmelCase:	str   				=      self.get_dummy_seed_input()

						with torch.no_grad():
								__lowerCAmelCase:	Dict   				=      model(**UpperCamelCase__)[0]

						__lowerCAmelCase:	Dict   				=      output[0, :5].flatten().cpu()
						print(UpperCamelCase__)

						# 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.
						__lowerCAmelCase:	List[str]   				=      torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
						self.assertTrue(torch_all_close(UpperCamelCase__					,  UpperCamelCase__					,  rtol=1e-2))





@slow
class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict=1					,  UpperCamelCase__							:    str=7_6_8					,  UpperCamelCase__							:    int=7_7					,  UpperCamelCase__							:    Any=0)->       Union[str, Any]:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      batch_size
						__lowerCAmelCase:	Any   				=      embedding_dim
						__lowerCAmelCase:	Dict   				=      num_embeddings

						__lowerCAmelCase:	Dict   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	str   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

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



						'''simple docstring'''


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




				@parameterized.expand(
				    [
				        # fmt: off
				        [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
				        [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
				        # fmt: on
				    ])
				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    int)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"					,  subfolder="prior")
						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      self.get_dummy_seed_input(seed=UpperCamelCase__)

						with torch.no_grad():
								__lowerCAmelCase:	Optional[Any]   				=      model(**UpperCamelCase__)[0]

						assert list(sample.shape) == [1, 7_6_8]

						__lowerCAmelCase:	Dict   				=      sample[0, :8].flatten().cpu()
						print(UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor(UpperCamelCase__)

						assert torch_all_close(UpperCamelCase__					,  UpperCamelCase__					,  atol=1e-3)


