






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_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Optional[int]		      =    PriorTransformer
							UpperCamelCase_		:int		      =    'hidden_states'




							@property
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						4
											lowerCAmelCase__      			=						8
											lowerCAmelCase__      			=						7

											lowerCAmelCase__      			=						floats_tensor((batch_size, embedding_dim)	).to(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						floats_tensor((batch_size, embedding_dim)	).to(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Dict=0	):
											torch.manual_seed(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						4
											lowerCAmelCase__      			=						8
											lowerCAmelCase__      			=						7

											lowerCAmelCase__      			=						torch.randn((batch_size, embedding_dim)	).to(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						torch.randn((batch_size, embedding_dim)	).to(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Union[str, Any]	):
											return (4, 8)




							@property
							def 						__snake_case					(   self     :   List[Any]	):
											return (4, 8)




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						{
											    '''num_attention_heads''': 2,
											    '''attention_head_dim''': 4,
											    '''num_layers''': 2,
											    '''embedding_dim''': 8,
											    '''num_embeddings''': 7,
											    '''additional_embeddings''': 4,
											}
											lowerCAmelCase__      			=						self.dummy_input
											return init_dict, inputs_dict




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__			,		lowerCAmelCase__      			=						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_	)
											lowerCAmelCase__      			=						model(**self.dummy_input	)[0]

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




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.prepare_init_args_and_inputs_for_common()

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

											lowerCAmelCase__      			=						['''hidden_states''', '''timestep''']
											self.assertListEqual(arg_names[:2] ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Any	):
											lowerCAmelCase__      			=						PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''	)
											lowerCAmelCase__      			=						model.to(SCREAMING_SNAKE_CASE_	)

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

											lowerCAmelCase__      			=						self.get_dummy_seed_input()

											with torch.no_grad():
															lowerCAmelCase__      			=						model(**SCREAMING_SNAKE_CASE_	)[0]

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



@slow
class  lowerCAmelCase_       (				unittest.TestCase					):




							def 						__snake_case					(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Any=1 ,							SCREAMING_SNAKE_CASE_     :   List[str]=768 ,							SCREAMING_SNAKE_CASE_     :   Tuple=77 ,							SCREAMING_SNAKE_CASE_     :   Tuple=0	):
											torch.manual_seed(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						embedding_dim
											lowerCAmelCase__      			=						num_embeddings

											lowerCAmelCase__      			=						torch.randn((batch_size, embedding_dim)	).to(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						torch.randn((batch_size, embedding_dim)	).to(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   str	):
											# 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 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Any	):
											lowerCAmelCase__      			=						PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior''' ,							subfolder='''prior'''	)
											model.to(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						self.get_dummy_seed_input(seed=SCREAMING_SNAKE_CASE_	)

											with torch.no_grad():
															lowerCAmelCase__      			=						model(**SCREAMING_SNAKE_CASE_	)[0]

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

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

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


_UpperCAmelCase			:		Optional[Any]   				=    {
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ErnieForCausalLM",
			    "ErnieForMaskedLM",
			    "ErnieForMultipleChoice",
			    "ErnieForNextSentencePrediction",
			    "ErnieForPreTraining",
			    "ErnieForQuestionAnswering",
			    "ErnieForSequenceClassification",
			    "ErnieForTokenClassification",
			    "ErnieModel",
			    "ErniePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ernie import (
						    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ErnieForCausalLM,
						    ErnieForMaskedLM,
						    ErnieForMultipleChoice,
						    ErnieForNextSentencePrediction,
						    ErnieForPreTraining,
						    ErnieForQuestionAnswering,
						    ErnieForSequenceClassification,
						    ErnieForTokenClassification,
						    ErnieModel,
						    ErniePreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





