


'''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__			(			UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Optional[Any]          =     PriorTransformer
			lowerCAmelCase :	Dict          =     "hidden_states"




			@property
			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      4
					_UpperCAmelCase		:       List[str]								=      8
					_UpperCAmelCase		:       int								=      7

					_UpperCAmelCase		:       Dict								=      floats_tensor((batch_size, embedding_dim) ).to(lowerCamelCase__ )

					_UpperCAmelCase		:       Union[str, Any]								=      floats_tensor((batch_size, embedding_dim) ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       str								=      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  :     str	,  lowerCamelCase__  :     Optional[int]=0 )   ->Optional[int]:



					'''simple docstring'''







					torch.manual_seed(lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      4
					_UpperCAmelCase		:       Any								=      8
					_UpperCAmelCase		:       int								=      7

					_UpperCAmelCase		:       List[Any]								=      torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase__ )

					_UpperCAmelCase		:       Union[str, Any]								=      torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       str								=      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  :     Dict )   ->Union[str, Any]:



					'''simple docstring'''







					return (4, 8)




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



					'''simple docstring'''







					return (4, 8)




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->List[str]:



					'''simple docstring'''







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




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



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Tuple								=      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__ )
					_UpperCAmelCase		:       Dict								=      model(**self.dummy_input )[0]

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




			def 							lowerCAmelCase__    (		self  :     Dict )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       List[Any]								=      self.prepare_init_args_and_inputs_for_common()

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

					_UpperCAmelCase		:       List[str]								=      ["hidden_states", "timestep"]
					self.assertListEqual(arg_names[:2]	,  lowerCamelCase__ )






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



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy" )
					_UpperCAmelCase		:       Any								=      model.to(lowerCamelCase__ )

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

					_UpperCAmelCase		:       Any								=      self.get_dummy_seed_input()

					with torch.no_grad():
							_UpperCAmelCase		:       str								=      model(**lowerCamelCase__ )[0]

					_UpperCAmelCase		:       int								=      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.
					_UpperCAmelCase		:       Dict								=      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(lowerCamelCase__	,  lowerCamelCase__	,  rtol=1E-2 ) )






@slow
class 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     Tuple=1	,  lowerCamelCase__  :     List[Any]=7_68	,  lowerCamelCase__  :     Tuple=77	,  lowerCamelCase__  :     Dict=0 )   ->Union[str, Any]:



					'''simple docstring'''







					torch.manual_seed(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      batch_size
					_UpperCAmelCase		:       Any								=      embedding_dim
					_UpperCAmelCase		:       int								=      num_embeddings

					_UpperCAmelCase		:       Tuple								=      torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase__ )

					_UpperCAmelCase		:       Any								=      torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       Dict								=      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  :     Optional[int] )   ->Tuple:



					'''simple docstring'''







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






			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-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]],
			        [37, [-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 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     Union[str, Any] )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"	,  subfolder="prior" )
					model.to(lowerCamelCase__ )
					_UpperCAmelCase		:       List[Any]								=      self.get_dummy_seed_input(seed=lowerCamelCase__ )

					with torch.no_grad():
							_UpperCAmelCase		:       List[Any]								=      model(**lowerCamelCase__ )[0]

					assert list(sample.shape ) == [1, 7_68]

					_UpperCAmelCase		:       Optional[int]								=      sample[0, :8].flatten().cpu()
					print(lowerCamelCase__ )
					_UpperCAmelCase		:       Tuple								=      torch.tensor(lowerCamelCase__ )

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







'''simple docstring'''


from __future__ import annotations

import numpy as np






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return np.maximum(0      , __lowerCAmelCase   )


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



