
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__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Optional[int]        =		PriorTransformer
		a__       :						Optional[int]        =		"""hidden_states"""






		@property
		def  __A       (		self		:							int  )     ->							Optional[int]:
						__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  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							str=0  )     ->							List[Any]:
						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  __A       (		self		:							str  )     ->							Optional[Any]:
						return (4, 8)






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






		def  __A       (		self		:							Tuple  )     ->							Optional[Any]:
						__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  __A       (		self		:							Tuple  )     ->							List[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  __A       (		self		:							Dict  )     ->							List[str]:
						__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  __A       (		self		:							Optional[Any]  )     ->							int:
						__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([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]  )
						self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			rtol=1e-2  )  )





@slow
class    lowerCAmelCase__     (	unittest.TestCase			):






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							List[str]=1							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=7_68							,			SCREAMING_SNAKE_CASE__		:							str=77							,			SCREAMING_SNAKE_CASE__		:							int=0  )     ->							int:
						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  __A       (		self		:							Dict  )     ->							Any:
						# clean up the VRAM after each test
						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  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Tuple  )     ->							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, 7_68]

						__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_torch_available


SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "WavLMForAudioFrameClassification",
					    "WavLMForCTC",
					    "WavLMForSequenceClassification",
					    "WavLMForXVector",
					    "WavLMModel",
					    "WavLMPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_wavlm import (
										    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WavLMForAudioFrameClassification,
										    WavLMForCTC,
										    WavLMForSequenceClassification,
										    WavLMForXVector,
										    WavLMModel,
										    WavLMPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			int												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



