





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      UpperCamelCase_						(					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		PriorTransformer
					lowerCAmelCase_							=		'''hidden_states'''




					@property
					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			4
										_snake_case													=			8
										_snake_case													=			7

										_snake_case													=			floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase_					)

										_snake_case													=			floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase_					)
										_snake_case													=			floats_tensor((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase_					)

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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_=0					)						->							Union[str, Any]:
										torch.manual_seed(lowerCAmelCase_					)
										_snake_case													=			4
										_snake_case													=			8
										_snake_case													=			7

										_snake_case													=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase_					)

										_snake_case													=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase_					)
										_snake_case													=			torch.randn((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase_					)

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




					@property
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										return (4, 8)




					@property
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										return (4, 8)




					def 		lowerCAmelCase     (						self					)						->							str:
										_snake_case													=			{
										    'num_attention_heads': 2,
										    'attention_head_dim': 4,
										    'num_layers': 2,
										    'embedding_dim': 8,
										    'num_embeddings': 7,
										    'additional_embeddings': 4,
										}
										_snake_case													=			self.dummy_input
										return init_dict, inputs_dict




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case							,       _snake_case													=			PriorTransformer.from_pretrained(
										    'hf-internal-testing/prior-dummy'			,  output_loading_info=lowerCAmelCase_					)
										self.assertIsNotNone(lowerCAmelCase_					)
										self.assertEqual(len(loading_info['missing_keys']					)			,  0					)

										model.to(lowerCAmelCase_					)
										_snake_case													=			model(**self.dummy_input					)[0]

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




					def 		lowerCAmelCase     (						self					)						->							str:
										_snake_case							,       _snake_case													=			self.prepare_init_args_and_inputs_for_common()

										_snake_case													=			self.model_class(**lowerCAmelCase_					)
										_snake_case													=			inspect.signature(model.forward					)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										_snake_case													=			[*signature.parameters.keys()]

										_snake_case													=			['hidden_states', 'timestep']
										self.assertListEqual(arg_names[:2]			,  lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self					)						->							Dict:
										_snake_case													=			PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'					)
										_snake_case													=			model.to(lowerCAmelCase_					)

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

										_snake_case													=			self.get_dummy_seed_input()

										with torch.no_grad():
															_snake_case													=			model(**lowerCAmelCase_					)[0]

										_snake_case													=			output[0, :5].flatten().cpu()
										print(lowerCAmelCase_					)

										# 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													=			torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]					)
										self.assertTrue(torch_all_close(lowerCAmelCase_			,  lowerCAmelCase_			,  rtol=1E-2					)					)





@slow
class      UpperCamelCase_						(					unittest.TestCase   ):




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_=1			,  lowerCAmelCase_=768			,  lowerCAmelCase_=77			,  lowerCAmelCase_=0					)						->							str:
										torch.manual_seed(lowerCAmelCase_					)
										_snake_case													=			batch_size
										_snake_case													=			embedding_dim
										_snake_case													=			num_embeddings

										_snake_case													=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase_					)

										_snake_case													=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase_					)
										_snake_case													=			torch.randn((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase_					)

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




					def 		lowerCAmelCase     (						self					)						->							Dict:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






					@parameterized.expand(
					    [
					        # fmt: off
					        [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
					        [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
					        # fmt: on
					    ]					)
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Any:
										_snake_case													=			PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'			,  subfolder='prior'					)
										model.to(lowerCAmelCase_					)
										_snake_case													=			self.get_dummy_seed_input(seed=lowerCAmelCase_					)

										with torch.no_grad():
															_snake_case													=			model(**lowerCAmelCase_					)[0]

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

										_snake_case													=			sample[0, :8].flatten().cpu()
										print(lowerCAmelCase_					)
										_snake_case													=			torch.tensor(lowerCAmelCase_					)

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










import random





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    float    ,      UpperCamelCase__     :    bool = False  )     ->	dict:



					'''simple docstring'''




					_snake_case													=			{i: [] for i in range(UpperCamelCase__  )}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(UpperCamelCase__  )
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(UpperCamelCase__  ):
										for j in range(i + 1    ,      UpperCamelCase__  ):
															if random.random() < probability:
																				graph[i].append(UpperCamelCase__  )
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(UpperCamelCase__  )
					return graph





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	dict:



					'''simple docstring'''




					return {
					    i: [j for j in range(UpperCamelCase__  ) if i != j] for i in range(UpperCamelCase__  )
					}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



