

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 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							PriorTransformer
    lowerCamelCase		     =							'hidden_states'


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





        '''simple docstring'''







        A__   				=       4
        A__   				=       8
        A__   				=       7

        A__   				=       floats_tensor((batch_size, embedding_dim)		).to(lowercase_		)

        A__   				=       floats_tensor((batch_size, embedding_dim)		).to(lowercase_		)
        A__   				=       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			:	Tuple,lowercase_			:	List[str]=0		)->   Union[str, Any]:





        '''simple docstring'''







        torch.manual_seed(lowercase_		)
        A__   				=       4
        A__   				=       8
        A__   				=       7

        A__   				=       torch.randn((batch_size, embedding_dim)		).to(lowercase_		)

        A__   				=       torch.randn((batch_size, embedding_dim)		).to(lowercase_		)
        A__   				=       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]		)->   Dict:





        '''simple docstring'''







        return (4, 8)


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





        '''simple docstring'''







        return (4, 8)


    def 						snake_case__					(				self			:	Any		)->   int:





        '''simple docstring'''







        A__   				=       {
            'num_attention_heads': 2,
            'attention_head_dim': 4,
            'num_layers': 2,
            'embedding_dim': 8,
            'num_embeddings': 7,
            'additional_embeddings': 4,
        }
        A__   				=       self.dummy_input
        return init_dict, inputs_dict


    def 						snake_case__					(				self			:	Any		)->   int:





        '''simple docstring'''







        A__				,						A__   				=       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_		)
        A__   				=       model(**self.dummy_input		)[0]

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


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





        '''simple docstring'''







        A__				,						A__   				=       self.prepare_init_args_and_inputs_for_common()

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

        A__   				=       ['hidden_states', 'timestep']
        self.assertListEqual(arg_names[:2],lowercase_		)


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





        '''simple docstring'''







        A__   				=       PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'		)
        A__   				=       model.to(lowercase_		)

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

        A__   				=       self.get_dummy_seed_input()

        with torch.no_grad():
            A__   				=       model(**lowercase_		)[0]

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




@slow
class 			A						(							unittest.TestCase			):







    """simple docstring"""





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





        '''simple docstring'''







        torch.manual_seed(lowercase_		)
        A__   				=       batch_size
        A__   				=       embedding_dim
        A__   				=       num_embeddings

        A__   				=       torch.randn((batch_size, embedding_dim)		).to(lowercase_		)

        A__   				=       torch.randn((batch_size, embedding_dim)		).to(lowercase_		)
        A__   				=       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			:	Optional[Any]		)->   int:





        '''simple docstring'''







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


    @parameterized.expand(
        [
            # fmt: off
            [1_3, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
            [3_7, [-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 						snake_case__					(				self			:	Optional[int],lowercase_			:	Optional[Any],lowercase_			:	Optional[Any]		)->   Dict:





        '''simple docstring'''







        A__   				=       PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior',subfolder='prior'		)
        model.to(lowercase_		)
        A__   				=       self.get_dummy_seed_input(seed=lowercase_		)

        with torch.no_grad():
            A__   				=       model(**lowercase_		)[0]

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

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

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




import random
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				tuple:





    '''simple docstring'''






    A__				,						A__				,						A__   				=       [], [], []
    for element in data:
        if element < pivot:
            less.append(SCREAMING_SNAKE_CASE__				)
        elif element > pivot:
            greater.append(SCREAMING_SNAKE_CASE__				)
        else:
            equal.append(SCREAMING_SNAKE_CASE__				)
    return less, equal, greater

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				str:





    '''simple docstring'''






    if index >= len(SCREAMING_SNAKE_CASE__				) or index < 0:
        return None

    A__   				=       items[random.randint(0   ,      len(SCREAMING_SNAKE_CASE__				) - 1				)]
    A__   				=       0
    A__				,						A__				,						A__   				=       _partition(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    A__   				=       len(SCREAMING_SNAKE_CASE__				)
    A__   				=       len(SCREAMING_SNAKE_CASE__				)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    # must be in larger
    else:
        return quick_select(SCREAMING_SNAKE_CASE__   ,      index - (m + count)				)

