



"""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        __A       (	A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Union[str, Any]   	=				PriorTransformer
		lowerCAmelCase			:					int   	=				"hidden_states"







		@property
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    Dict				=			4
				lowercase__   :    List[Any]				=			8
				lowercase__   :    Any				=			7

				lowercase__   :    int				=			floats_tensor((batch_size, embedding_dim)						).to(_snake_case						)

				lowercase__   :    Any				=			floats_tensor((batch_size, embedding_dim)						).to(_snake_case						)
				lowercase__   :    int				=			floats_tensor((batch_size, num_embeddings, embedding_dim)						).to(_snake_case						)

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







		def 					UpperCAmelCase    (	self						:  str			,_snake_case						:  str=0						)					->		Optional[Any]:






				"""simple docstring"""



				torch.manual_seed(_snake_case						)
				lowercase__   :    Optional[Any]				=			4
				lowercase__   :    Optional[int]				=			8
				lowercase__   :    Any				=			7

				lowercase__   :    str				=			torch.randn((batch_size, embedding_dim)						).to(_snake_case						)

				lowercase__   :    List[Any]				=			torch.randn((batch_size, embedding_dim)						).to(_snake_case						)
				lowercase__   :    Dict				=			torch.randn((batch_size, num_embeddings, embedding_dim)						).to(_snake_case						)

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







		@property
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		List[Any]:






				"""simple docstring"""



				return (4, 8)







		@property
		def 					UpperCAmelCase    (	self						:  Any						)					->		List[str]:






				"""simple docstring"""



				return (4, 8)







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






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  int						)					->		int:






				"""simple docstring"""



				lowercase__		,			lowercase__   :    Tuple				=			PriorTransformer.from_pretrained(
				    '''hf-internal-testing/prior-dummy'''			,output_loading_info=_snake_case						)
				self.assertIsNotNone(_snake_case						)
				self.assertEqual(len(loading_info['''missing_keys''']						)			,0						)

				model.to(_snake_case						)
				lowercase__   :    Optional[Any]				=			model(**self.dummy_input						)[0]

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







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Dict:






				"""simple docstring"""



				lowercase__		,			lowercase__   :    Any				=			self.prepare_init_args_and_inputs_for_common()

				lowercase__   :    List[str]				=			self.model_class(**_snake_case						)
				lowercase__   :    Optional[Any]				=			inspect.signature(model.forward						)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				lowercase__   :    Optional[Any]				=			[*signature.parameters.keys()]

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







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






				"""simple docstring"""



				lowercase__   :    Any				=			PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''						)
				lowercase__   :    Dict				=			model.to(_snake_case						)

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

				lowercase__   :    Dict				=			self.get_dummy_seed_input()

				with torch.no_grad():
						lowercase__   :    Optional[int]				=			model(**_snake_case						)[0]

				lowercase__   :    str				=			output[0, :5].flatten().cpu()
				print(_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.
				lowercase__   :    Optional[Any]				=			torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]						)
				self.assertTrue(torch_all_close(_snake_case			,_snake_case			,rtol=1e-2						)						)





@slow
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  List[str]=1			,_snake_case						:  Tuple=768			,_snake_case						:  List[Any]=77			,_snake_case						:  Optional[Any]=0						)					->		List[Any]:






				"""simple docstring"""



				torch.manual_seed(_snake_case						)
				lowercase__   :    Tuple				=			batch_size
				lowercase__   :    List[str]				=			embedding_dim
				lowercase__   :    Tuple				=			num_embeddings

				lowercase__   :    int				=			torch.randn((batch_size, embedding_dim)						).to(_snake_case						)

				lowercase__   :    str				=			torch.randn((batch_size, embedding_dim)						).to(_snake_case						)
				lowercase__   :    List[str]				=			torch.randn((batch_size, num_embeddings, embedding_dim)						).to(_snake_case						)

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







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






				"""simple docstring"""



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







		@parameterized.expand(
		    [
		        # fmt: off
		        [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
		        [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
		        # fmt: on
		    ]						)
		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  Optional[Any]			,_snake_case						:  Optional[Any]						)					->		int:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''			,subfolder='''prior'''						)
				model.to(_snake_case						)
				lowercase__   :    List[str]				=			self.get_dummy_seed_input(seed=_snake_case						)

				with torch.no_grad():
						lowercase__   :    List[str]				=			model(**_snake_case						)[0]

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

				lowercase__   :    List[str]				=			sample[0, :8].flatten().cpu()
				print(_snake_case						)
				lowercase__   :    Optional[Any]				=			torch.tensor(_snake_case						)

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





from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_:List[Any]             =						TypeVar("""KEY""")
SCREAMING_SNAKE_CASE_:Dict             =						TypeVar("""VAL""")






@dataclass(frozen=SCREAMING_SNAKE_CASE__ ,						slots=SCREAMING_SNAKE_CASE__    )
class SCREAMING_SNAKE_CASE__     (		Generic[KEY, VAL]    ):




    '''simple docstring'''






    __lowerCamelCase			: KEY
    __lowerCamelCase			: VAL






class SCREAMING_SNAKE_CASE__     (		_Item    ):




    '''simple docstring'''






    def __init__(       self		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__		)






    def __bool__(       self		):
        return False


SCREAMING_SNAKE_CASE_:Optional[Any]             =						_DeletedItem()






class SCREAMING_SNAKE_CASE__     (		MutableMapping[KEY, VAL]    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 8,		lowerCamelCase__ = 0.75		):
        A :       List[str]          =   initial_block_size
        A :       list[_Item | None]          =   [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        A :       Dict          =   capacity_factor
        A :       Optional[Any]          =   0






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return hash(lowerCamelCase__		) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return (ind + 1) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       List[Any]          =   self._buckets[ind]
        if not stored:
            A :       Optional[int]          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            self._len += 1
            return True
        elif stored.key == key:
            A :       int          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            return True
        else:
            return False






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   len(self._buckets		) * self._capacity_factor
        return len(self		) >= int(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        if len(self._buckets		) <= self._initial_block_size:
            return False
        A :       Optional[Any]          =   len(self._buckets		) * self._capacity_factor / 2
        return len(self		) < limit






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Dict          =   self._buckets
        A :       Optional[Any]          =   [None] * new_size
        A :       List[str]          =   0
        for item in old_buckets:
            if item:
                self._add_item(item.key,		item.val		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) * 2		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) // 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Union[str, Any]          =   self._get_bucket_index(lowerCamelCase__		)
        for _ in range(len(self._buckets		)		):
            yield ind
            A :       Dict          =   self._get_next_ind(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            if self._try_set(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
                break






    def __setitem__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if self._is_full():
            self._size_up()

        self._add_item(lowerCamelCase__,		lowerCamelCase__		)






    def __delitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       Tuple          =   self._buckets[ind]
            if item is None:
                raise KeyError(lowerCamelCase__		)
            if item is _deleted:
                continue
            if item.key == key:
                A :       int          =   _deleted
                self._len -= 1
                break
        if self._is_sparse():
            self._size_down()






    def __getitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       int          =   self._buckets[ind]
            if item is None:
                break
            if item is _deleted:
                continue
            if item.key == key:
                return item.val
        raise KeyError(lowerCamelCase__		)






    def __len__(       self		):
        return self._len






    def __iter__(       self		):
        yield from (item.key for item in self._buckets if item)






    def __repr__(       self		):
        A :       Union[str, Any]          =   """ ,""".join(
            f'''{item.key}: {item.val}''' for item in self._buckets if item		)
        return f'''HashMap({val_string})'''


