

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     snake_case_			(				_a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =PriorTransformer
							__UpperCAmelCase       ="""hidden_states"""




							@property
							def        A__  ( self							):
												__lowerCAmelCase					=							4
												__lowerCAmelCase					=							8
												__lowerCAmelCase					=							7

												__lowerCAmelCase					=							floats_tensor((batch_size, embedding_dim)							).to(_A							)

												__lowerCAmelCase					=							floats_tensor((batch_size, embedding_dim)							).to(_A							)
												__lowerCAmelCase					=							floats_tensor((batch_size, num_embeddings, embedding_dim)							).to(_A							)

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




							def        A__  ( self					,						_A=0							):
												torch.manual_seed(_A							)
												__lowerCAmelCase					=							4
												__lowerCAmelCase					=							8
												__lowerCAmelCase					=							7

												__lowerCAmelCase					=							torch.randn((batch_size, embedding_dim)							).to(_A							)

												__lowerCAmelCase					=							torch.randn((batch_size, embedding_dim)							).to(_A							)
												__lowerCAmelCase					=							torch.randn((batch_size, num_embeddings, embedding_dim)							).to(_A							)

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




							@property
							def        A__  ( self							):
												return (4, 8)




							@property
							def        A__  ( self							):
												return (4, 8)




							def        A__  ( self							):
												__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							):
												__lowerCAmelCase,	__lowerCAmelCase					=							PriorTransformer.from_pretrained(
												    'hf-internal-testing/prior-dummy'					,						output_loading_info=_A							)
												self.assertIsNotNone(_A							)
												self.assertEqual(len(loading_info['missing_keys']							)					,						0							)

												model.to(_A							)
												__lowerCAmelCase					=							model(**self.dummy_input							)[0]

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




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.prepare_init_args_and_inputs_for_common()

												__lowerCAmelCase					=							self.model_class(**_A							)
												__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]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'							)
												__lowerCAmelCase					=							model.to(_A							)

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

												__lowerCAmelCase					=							self.get_dummy_seed_input()

												with torch.no_grad():
																	__lowerCAmelCase					=							model(**_A							)[0]

												__lowerCAmelCase					=							output[0, :5].flatten().cpu()
												print(_A							)

												# 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(_A					,						_A					,						rtol=1e-2							)							)





@slow
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def        A__  ( self					,						_A=1					,						_A=7_6_8					,						_A=7_7					,						_A=0							):
												torch.manual_seed(_A							)
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							embedding_dim
												__lowerCAmelCase					=							num_embeddings

												__lowerCAmelCase					=							torch.randn((batch_size, embedding_dim)							).to(_A							)

												__lowerCAmelCase					=							torch.randn((batch_size, embedding_dim)							).to(_A							)
												__lowerCAmelCase					=							torch.randn((batch_size, num_embeddings, embedding_dim)							).to(_A							)

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




							def        A__  ( self							):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							@parameterized.expand(
							    [
							        # fmt: off
							        [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
							        [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
							        # fmt: on
							    ]							)
							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'					,						subfolder='prior'							)
												model.to(_A							)
												__lowerCAmelCase					=							self.get_dummy_seed_input(seed=_A							)

												with torch.no_grad():
																	__lowerCAmelCase					=							model(**_A							)[0]

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

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

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









from typing import TYPE_CHECKING

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


lowerCamelCase      				=     {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecAudioForAudioFrameClassification''',
		    '''Data2VecAudioForCTC''',
		    '''Data2VecAudioForSequenceClassification''',
		    '''Data2VecAudioForXVector''',
		    '''Data2VecAudioModel''',
		    '''Data2VecAudioPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecTextForCausalLM''',
		    '''Data2VecTextForMaskedLM''',
		    '''Data2VecTextForMultipleChoice''',
		    '''Data2VecTextForQuestionAnswering''',
		    '''Data2VecTextForSequenceClassification''',
		    '''Data2VecTextForTokenClassification''',
		    '''Data2VecTextModel''',
		    '''Data2VecTextPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecVisionForImageClassification''',
		    '''Data2VecVisionForMaskedImageModeling''',
		    '''Data2VecVisionForSemanticSegmentation''',
		    '''Data2VecVisionModel''',
		    '''Data2VecVisionPreTrainedModel''',
		]

if is_tf_available():
		lowerCamelCase      				=     [
		    '''TFData2VecVisionForImageClassification''',
		    '''TFData2VecVisionForSemanticSegmentation''',
		    '''TFData2VecVisionModel''',
		    '''TFData2VecVisionPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
		from .configuration_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecTextConfig,
		    DataaVecTextOnnxConfig,
		)
		from .configuration_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecVisionConfig,
		    DataaVecVisionOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dataavec_audio import (
				    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecAudioForAudioFrameClassification,
				    DataaVecAudioForCTC,
				    DataaVecAudioForSequenceClassification,
				    DataaVecAudioForXVector,
				    DataaVecAudioModel,
				    DataaVecAudioPreTrainedModel,
				)
				from .modeling_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecTextForCausalLM,
				    DataaVecTextForMaskedLM,
				    DataaVecTextForMultipleChoice,
				    DataaVecTextForQuestionAnswering,
				    DataaVecTextForSequenceClassification,
				    DataaVecTextForTokenClassification,
				    DataaVecTextModel,
				    DataaVecTextPreTrainedModel,
				)
				from .modeling_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForMaskedImageModeling,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				    DataaVecVisionPreTrainedModel,
				)
		if is_tf_available():
				from .modeling_tf_dataavec_vision import (
				    TFDataaVecVisionForImageClassification,
				    TFDataaVecVisionForSemanticSegmentation,
				    TFDataaVecVisionModel,
				    TFDataaVecVisionPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






