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				):
						a__		:List[str]      =			PriorTransformer
						a__		:Any      =			'''hidden_states'''





						@property
						def        A_							(self						)				->				Optional[Any]:
												UpperCamelCase_		:							int  =		4
												UpperCamelCase_		:							List[Any]  =		8
												UpperCamelCase_		:							int  =		7

												UpperCamelCase_		:							List[str]  =		floats_tensor((batch_size, embedding_dim)						).to(__UpperCamelCase						)

												UpperCamelCase_		:							Union[str, Any]  =		floats_tensor((batch_size, embedding_dim)						).to(__UpperCamelCase						)
												UpperCamelCase_		:							Optional[int]  =		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        A_							(self			,     __UpperCamelCase=0						)				->				Union[str, Any]:
												torch.manual_seed(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		4
												UpperCamelCase_		:							Optional[int]  =		8
												UpperCamelCase_		:							List[Any]  =		7

												UpperCamelCase_		:							Optional[int]  =		torch.randn((batch_size, embedding_dim)						).to(__UpperCamelCase						)

												UpperCamelCase_		:							str  =		torch.randn((batch_size, embedding_dim)						).to(__UpperCamelCase						)
												UpperCamelCase_		:							Optional[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,
												}





						@property
						def        A_							(self						)				->				int:
												return (4, 8)





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





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





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_,UpperCamelCase_		:							Dict  =		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						)
												UpperCamelCase_		:							str  =		model(**self.dummy_input						)[0]

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





						def        A_							(self						)				->				int:
												UpperCamelCase_,UpperCamelCase_		:							Tuple  =		self.prepare_init_args_and_inputs_for_common()

												UpperCamelCase_		:							Optional[Any]  =		self.model_class(**__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		inspect.signature(model.forward						)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCamelCase_		:							Optional[Any]  =		[*signature.parameters.keys()]

												UpperCamelCase_		:							int  =		["""hidden_states""", """timestep"""]
												self.assertListEqual(arg_names[:2]			,     __UpperCamelCase						)





						def        A_							(self						)				->				Optional[Any]:
												UpperCamelCase_		:							Tuple  =		PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""						)
												UpperCamelCase_		:							Any  =		model.to(__UpperCamelCase						)

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

												UpperCamelCase_		:							Dict  =		self.get_dummy_seed_input()

												with torch.no_grad():
																		UpperCamelCase_		:							Optional[int]  =		model(**__UpperCamelCase						)[0]

												UpperCamelCase_		:							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.
												UpperCamelCase_		:							int  =		torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]						)
												self.assertTrue(torch_all_close(__UpperCamelCase			,     __UpperCamelCase			,     rtol=1E-2						)						)


@slow
class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=1			,     __UpperCamelCase=768			,     __UpperCamelCase=77			,     __UpperCamelCase=0						)				->				List[str]:
												torch.manual_seed(__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		batch_size
												UpperCamelCase_		:							List[Any]  =		embedding_dim
												UpperCamelCase_		:							Optional[int]  =		num_embeddings

												UpperCamelCase_		:							Optional[Any]  =		torch.randn((batch_size, embedding_dim)						).to(__UpperCamelCase						)

												UpperCamelCase_		:							int  =		torch.randn((batch_size, embedding_dim)						).to(__UpperCamelCase						)
												UpperCamelCase_		:							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        A_							(self						)				->				int:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





						@parameterized.expand(
						    [
						        # fmt: off
						        [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
						        [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
						        # fmt: on
						    ]						)
						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Any:
												UpperCamelCase_		:							Tuple  =		PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""			,     subfolder="""prior"""						)
												model.to(__UpperCamelCase						)
												UpperCamelCase_		:							int  =		self.get_dummy_seed_input(seed=__UpperCamelCase						)

												with torch.no_grad():
																		UpperCamelCase_		:							Any  =		model(**__UpperCamelCase						)[0]

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

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

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







from __future__ import annotations






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, ):
						if (electron_conc, hole_conc, intrinsic_conc).count(0			) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""			)
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative in a semiconductor"""			)
						elif hole_conc < 0:
												raise ValueError("""Hole concentration cannot be negative in a semiconductor"""			)
						elif intrinsic_conc < 0:
												raise ValueError(
												    """Intrinsic concentration cannot be negative in a semiconductor"""			)
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    intrinsic_conc**2 / hole_conc,
												)
						elif hole_conc == 0:
												return (
												    "hole_conc",
												    intrinsic_conc**2 / electron_conc,
												)
						elif intrinsic_conc == 0:
												return (
												    "intrinsic_conc",
												    (electron_conc * hole_conc) ** 0.5,
												)
						else:
												return (-1, -1)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






