


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(  _a     ,							unittest.TestCase   ):
					snake_case_		:				int										=	PriorTransformer
					snake_case_		:				List[Any]										=	"""hidden_states"""


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		4
											__snake_case				=		8
											__snake_case				=		7

											__snake_case				=		floats_tensor((batch_size, embedding_dim)						).to(SCREAMING_SNAKE_CASE						)

											__snake_case				=		floats_tensor((batch_size, embedding_dim)						).to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		floats_tensor((batch_size, num_embeddings, embedding_dim)						).to(SCREAMING_SNAKE_CASE						)

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0						)			->	List[str]:




											'''simple docstring'''

											torch.manual_seed(SCREAMING_SNAKE_CASE						)
											__snake_case				=		4
											__snake_case				=		8
											__snake_case				=		7

											__snake_case				=		torch.randn((batch_size, embedding_dim)						).to(SCREAMING_SNAKE_CASE						)

											__snake_case				=		torch.randn((batch_size, embedding_dim)						).to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		torch.randn((batch_size, num_embeddings, embedding_dim)						).to(SCREAMING_SNAKE_CASE						)

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


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




											'''simple docstring'''

											return (4, 8)


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Tuple:




											'''simple docstring'''

											return (4, 8)


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




											'''simple docstring'''

											__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 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	Tuple:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		PriorTransformer.from_pretrained(
											    "hf-internal-testing/prior-dummy"     ,    output_loading_info=SCREAMING_SNAKE_CASE						)
											self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
											self.assertEqual(len(loading_info["missing_keys"]						)     ,    0						)

											model.to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		model(**self.dummy_input						)[0]

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	str:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.prepare_init_args_and_inputs_for_common()

											__snake_case				=		self.model_class(**SCREAMING_SNAKE_CASE						)
											__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]     ,    SCREAMING_SNAKE_CASE						)






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




											'''simple docstring'''

											__snake_case				=		PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"						)
											__snake_case				=		model.to(SCREAMING_SNAKE_CASE						)

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

											__snake_case				=		self.get_dummy_seed_input()

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE						)[0]

											__snake_case				=		output[0, :5].flatten().cpu()
											print(SCREAMING_SNAKE_CASE						)

											# 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([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]						)
											self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    rtol=1e-2						)						)






@slow
class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1     ,    SCREAMING_SNAKE_CASE	:		Dict=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=7_7     ,    SCREAMING_SNAKE_CASE	:		Any=0						)			->	int:




											'''simple docstring'''

											torch.manual_seed(SCREAMING_SNAKE_CASE						)
											__snake_case				=		batch_size
											__snake_case				=		embedding_dim
											__snake_case				=		num_embeddings

											__snake_case				=		torch.randn((batch_size, embedding_dim)						).to(SCREAMING_SNAKE_CASE						)

											__snake_case				=		torch.randn((batch_size, embedding_dim)						).to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		torch.randn((batch_size, num_embeddings, embedding_dim)						).to(SCREAMING_SNAKE_CASE						)

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


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




											'''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 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	Any:




											'''simple docstring'''

											__snake_case				=		PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"     ,    subfolder="prior"						)
											model.to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.get_dummy_seed_input(seed=SCREAMING_SNAKE_CASE						)

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE						)[0]

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

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

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









class     UpperCamelCase:


					def __init__(						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		0
											__snake_case				=		0
											__snake_case				=		{}


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	Optional[Any]:




											'''simple docstring'''

											if vertex not in self.adjacency:
																	__snake_case				=		{}
																	self.num_vertices += 1


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




											'''simple docstring'''

											self.add_vertex(SCREAMING_SNAKE_CASE						)
											self.add_vertex(SCREAMING_SNAKE_CASE						)

											if head == tail:
																	return

											__snake_case				=		weight
											__snake_case				=		weight


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




											'''simple docstring'''

											__snake_case				=		self.get_edges()
											for edge in edges:
																	__snake_case		,					__snake_case		,					__snake_case				=		edge
																	edges.remove((tail, head, weight)						)
											for i in range(len(SCREAMING_SNAKE_CASE						)						):
																	__snake_case				=		list(edges[i]						)

											edges.sort(key=lambda SCREAMING_SNAKE_CASE						: e[2]						)
											for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
																	if edges[i][2] >= edges[i + 1][2]:
																							__snake_case				=		edges[i][2] + 1
											for edge in edges:
																	__snake_case		,					__snake_case		,					__snake_case				=		edge
																	__snake_case				=		weight
																	__snake_case				=		weight


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




											'''simple docstring'''

											__snake_case				=		""
											for tail in self.adjacency:
																	for head in self.adjacency[tail]:
																							__snake_case				=		self.adjacency[head][tail]
																							string += f'''{head} -> {tail} == {weight}\n'''
											return string.rstrip("\n"						)


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




											'''simple docstring'''

											__snake_case				=		[]
											for tail in self.adjacency:
																	for head in self.adjacency[tail]:
																							output.append((tail, head, self.adjacency[head][tail])						)
											return output


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[Any]:




											'''simple docstring'''

											return self.adjacency.keys()


					@staticmethod
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		Any=None     ,    SCREAMING_SNAKE_CASE	:		List[Any]=None						)			->	int:




											'''simple docstring'''

											__snake_case				=		Graph()
											if vertices is None:
																	__snake_case				=		[]
											if edges is None:
																	__snake_case				=		[]
											for vertex in vertices:
																	g.add_vertex(SCREAMING_SNAKE_CASE						)
											for edge in edges:
																	g.add_edge(*SCREAMING_SNAKE_CASE						)
											return g






					class     UpperCamelCase:


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




																'''simple docstring'''

																__snake_case				=		{}
																__snake_case				=		{}


										def __len__(						self	:		List[str]						)			->	Dict:




																'''simple docstring'''

																return len(self.parent						)


										def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	List[str]:




																'''simple docstring'''

																if item in self.parent:
																						return self.find(SCREAMING_SNAKE_CASE						)

																__snake_case				=		item
																__snake_case				=		0
																return item


										def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	Any:




																'''simple docstring'''

																if item not in self.parent:
																						return self.make_set(SCREAMING_SNAKE_CASE						)
																if item != self.parent[item]:
																						__snake_case				=		self.find(self.parent[item]						)
																return self.parent[item]






										def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Dict:




																'''simple docstring'''

																__snake_case				=		self.find(SCREAMING_SNAKE_CASE						)
																__snake_case				=		self.find(SCREAMING_SNAKE_CASE						)

																if roota == roota:
																						return roota

																if self.rank[roota] > self.rank[roota]:
																						__snake_case				=		roota
																						return roota

																if self.rank[roota] < self.rank[roota]:
																						__snake_case				=		roota
																						return roota

																if self.rank[roota] == self.rank[roota]:
																						self.rank[roota] += 1
																						__snake_case				=		roota
																						return roota
																return None




					@staticmethod
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		str						)			->	Any:




											'''simple docstring'''

											__snake_case				=		graph.num_vertices

											__snake_case				=		Graph.UnionFind()
											__snake_case				=		[]
											while num_components > 1:
																	__snake_case				=		{}
																	for vertex in graph.get_vertices():
																							__snake_case				=		-1

																	__snake_case				=		graph.get_edges()
																	for edge in edges:
																							__snake_case		,					__snake_case		,					__snake_case				=		edge
																							edges.remove((tail, head, weight)						)
																	for edge in edges:
																							__snake_case		,					__snake_case		,					__snake_case				=		edge
																							__snake_case				=		union_find.find(SCREAMING_SNAKE_CASE						)
																							__snake_case				=		union_find.find(SCREAMING_SNAKE_CASE						)
																							if seta != seta:
																													if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																			__snake_case				=		[head, tail, weight]

																													if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																			__snake_case				=		[head, tail, weight]
																	for vertex in cheap_edge:
																							if cheap_edge[vertex] != -1:
																													__snake_case		,					__snake_case		,					__snake_case				=		cheap_edge[vertex]
																													if union_find.find(SCREAMING_SNAKE_CASE						) != union_find.find(SCREAMING_SNAKE_CASE						):
																																			union_find.union(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																																			mst_edges.append(cheap_edge[vertex]						)
																																			__snake_case				=		num_components - 1
											__snake_case				=		Graph.build(edges=SCREAMING_SNAKE_CASE						)
											return mst





