




"""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       lowerCAmelCase__       (			lowerCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     str       =      PriorTransformer
				__UpperCAmelCase :     Tuple       =      "hidden_states"



				@property
				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		List[Any]												=				4
						__lowercase				:		List[str]												=				8
						__lowercase				:		Optional[int]												=				7

						__lowercase				:		Optional[int]												=				floats_tensor((batch_size, embedding_dim)						).to(lowercase__						)

						__lowercase				:		Tuple												=				floats_tensor((batch_size, embedding_dim)						).to(lowercase__						)
						__lowercase				:		List[str]												=				floats_tensor((batch_size, num_embeddings, embedding_dim)						).to(lowercase__						)

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



				def       snake_case    (     self					:	List[Any]    ,   lowercase__					:	int=0						):
						torch.manual_seed(lowercase__						)
						__lowercase				:		Optional[int]												=				4
						__lowercase				:		Optional[int]												=				8
						__lowercase				:		List[str]												=				7

						__lowercase				:		int												=				torch.randn((batch_size, embedding_dim)						).to(lowercase__						)

						__lowercase				:		Any												=				torch.randn((batch_size, embedding_dim)						).to(lowercase__						)
						__lowercase				:		int												=				torch.randn((batch_size, num_embeddings, embedding_dim)						).to(lowercase__						)

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



				@property
				def       snake_case    (     self					:	Union[str, Any]						):
						return (4, 8)



				@property
				def       snake_case    (     self					:	Dict						):
						return (4, 8)



				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase				:		Optional[Any]												=				{
						    "num_attention_heads": 2,
						    "attention_head_dim": 4,
						    "num_layers": 2,
						    "embedding_dim": 8,
						    "num_embeddings": 7,
						    "additional_embeddings": 4,
						}
						__lowercase				:		Any												=				self.dummy_input
						return init_dict, inputs_dict



				def       snake_case    (     self					:	str						):
						__lowercase						,__lowercase				:		Union[str, Any]												=				PriorTransformer.from_pretrained(
						    "hf-internal-testing/prior-dummy"    ,   output_loading_info=lowercase__						)
						self.assertIsNotNone(lowercase__						)
						self.assertEqual(len(loading_info["missing_keys"]						)    ,   0						)

						model.to(lowercase__						)
						__lowercase				:		str												=				model(**self.dummy_input						)[0]

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



				def       snake_case    (     self					:	int						):
						__lowercase						,__lowercase				:		List[str]												=				self.prepare_init_args_and_inputs_for_common()

						__lowercase				:		Dict												=				self.model_class(**lowercase__						)
						__lowercase				:		Union[str, Any]												=				inspect.signature(model.forward						)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						__lowercase				:		List[str]												=				[*signature.parameters.keys()]

						__lowercase				:		Optional[int]												=				["hidden_states", "timestep"]
						self.assertListEqual(arg_names[:2]    ,   lowercase__						)



				def       snake_case    (     self					:	int						):
						__lowercase				:		List[Any]												=				PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"						)
						__lowercase				:		List[str]												=				model.to(lowercase__						)

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

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

						with torch.no_grad():
								__lowercase				:		Tuple												=				model(**lowercase__						)[0]

						__lowercase				:		Optional[Any]												=				output[0, :5].flatten().cpu()
						print(lowercase__						)

						# 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.
						__lowercase				:		Union[str, Any]												=				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(lowercase__    ,   lowercase__    ,   rtol=1e-2						)						)





@slow
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	int    ,   lowercase__					:	Optional[int]=1    ,   lowercase__					:	Dict=7_6_8    ,   lowercase__					:	List[Any]=7_7    ,   lowercase__					:	Tuple=0						):
						torch.manual_seed(lowercase__						)
						__lowercase				:		List[str]												=				batch_size
						__lowercase				:		str												=				embedding_dim
						__lowercase				:		List[Any]												=				num_embeddings

						__lowercase				:		Any												=				torch.randn((batch_size, embedding_dim)						).to(lowercase__						)

						__lowercase				:		List[str]												=				torch.randn((batch_size, embedding_dim)						).to(lowercase__						)
						__lowercase				:		Dict												=				torch.randn((batch_size, num_embeddings, embedding_dim)						).to(lowercase__						)

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



				def       snake_case    (     self					:	List[Any]						):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



				@parameterized.expand(
				    [
				        # fmt: off
				        [1_3, [-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]],
				        [3_7, [-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       snake_case    (     self					:	int    ,   lowercase__					:	str    ,   lowercase__					:	List[str]						):
						__lowercase				:		Union[str, Any]												=				PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"    ,   subfolder="prior"						)
						model.to(lowercase__						)
						__lowercase				:		Optional[Any]												=				self.get_dummy_seed_input(seed=lowercase__						)

						with torch.no_grad():
								__lowercase				:		Union[str, Any]												=				model(**lowercase__						)[0]

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

						__lowercase				:		Any												=				sample[0, :8].flatten().cpu()
						print(lowercase__						)
						__lowercase				:		int												=				torch.tensor(lowercase__						)

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






"""simple docstring"""







from __future__ import annotations

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->list[list[str]]:






		"""simple docstring"""


		__lowercase				:		List[Any]												=				word_bank or []
		# create a table
		__lowercase				:		int												=				len(_lowerCamelCase				) + 1

		__lowercase				:		list[list[list[str]]]												=				[]
		for _ in range(_lowerCamelCase				):
				table.append([]				)
		# seed value
		__lowercase				:		Any												=				[[]]  # because empty string has empty combination

		# iterate through the indices
		for i in range(_lowerCamelCase				):
				# condition
				if table[i] != []:
						for word in word_bank:
								# slice condition
								if target[i : i + len(_lowerCamelCase				)] == word:
										__lowercase				:		list[list[str]]												=				[
										    [word, *way] for way in table[i]
										]
										# adds the word to every combination the current position holds
										# now,push that combination to the table[i+len(word)]
										table[i + len(_lowerCamelCase				)] += new_combinations

    # combinations are in reverse order so reverse for better output
		for combination in table[len(_lowerCamelCase				)]:
				combination.reverse()

		return table[len(_lowerCamelCase				)]


if __name__ == "__main__":
						print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
						print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
						print(
						    all_construct(
						        'hexagonosaurus',
						        ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
						    )
						)
