





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 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					PriorTransformer
					__lowerCAmelCase					=					'''hidden_states'''







					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					4
									__a	:  List[str]         =					8
									__a	:  List[str]         =					7

									__a	:  List[str]         =					floats_tensor((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )

									__a	:  str         =					floats_tensor((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )
									__a	:  Union[str, Any]         =					floats_tensor((batch_size, num_embeddings, embedding_dim)       ).to(_UpperCAmelCase       )

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







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=0       ):
									torch.manual_seed(_UpperCAmelCase       )
									__a	:  Any         =					4
									__a	:  Dict         =					8
									__a	:  Tuple         =					7

									__a	:  List[str]         =					torch.randn((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )

									__a	:  Any         =					torch.randn((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )
									__a	:  List[Any]         =					torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(_UpperCAmelCase       )

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







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







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







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







					def 	_lowerCamelCase						(  self       ):
									__a	:  str         =					PriorTransformer.from_pretrained(
									    '''hf-internal-testing/prior-dummy'''		,     output_loading_info=_UpperCAmelCase       )
									self.assertIsNotNone(_UpperCAmelCase       )
									self.assertEqual(len(loading_info['''missing_keys''']       )		,     0       )

									model.to(_UpperCAmelCase       )
									__a	:  Dict         =					model(**self.dummy_input       )[0]

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					self.prepare_init_args_and_inputs_for_common()

									__a	:  int         =					self.model_class(**_UpperCAmelCase       )
									__a	:  Dict         =					inspect.signature(model.forward       )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__a	:  List[Any]         =					[*signature.parameters.keys()]

									__a	:  str         =					['''hidden_states''', '''timestep''']
									self.assertListEqual(arg_names[:2]		,     _UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  str         =					PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''       )
									__a	:  Union[str, Any]         =					model.to(_UpperCAmelCase       )

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

									__a	:  List[str]         =					self.get_dummy_seed_input()

									with torch.no_grad():
													__a	:  Optional[Any]         =					model(**_UpperCAmelCase       )[0]

									__a	:  str         =					output[0, :5].flatten().cpu()
									print(_UpperCAmelCase       )

									# 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	:  int         =					torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]       )
									self.assertTrue(torch_all_close(_UpperCAmelCase		,     _UpperCAmelCase		,     rtol=1e-2       )       )






@slow
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=1		,     _UpperCAmelCase=768		,     _UpperCAmelCase=77		,     _UpperCAmelCase=0       ):
									torch.manual_seed(_UpperCAmelCase       )
									__a	:  Tuple         =					batch_size
									__a	:  Optional[int]         =					embedding_dim
									__a	:  int         =					num_embeddings

									__a	:  str         =					torch.randn((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )

									__a	:  Tuple         =					torch.randn((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )
									__a	:  Optional[Any]         =					torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(_UpperCAmelCase       )

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







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







					@parameterized.expand(
					    [
					        # fmt: off
					        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
					        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
					        # fmt: on
					    ]       )
					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  str         =					PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''		,     subfolder='''prior'''       )
									model.to(_UpperCAmelCase       )
									__a	:  str         =					self.get_dummy_seed_input(seed=_UpperCAmelCase       )

									with torch.no_grad():
													__a	:  Any         =					model(**_UpperCAmelCase       )[0]

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

									__a	:  Optional[int]         =					sample[0, :8].flatten().cpu()
									print(_UpperCAmelCase       )
									__a	:  Any         =					torch.tensor(_UpperCAmelCase       )

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






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A	     =					{
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InstructBlipQFormerModel''',
		    '''InstructBlipPreTrainedModel''',
		    '''InstructBlipForConditionalGeneration''',
		    '''InstructBlipVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    InstructBlipConfig,
		    InstructBlipQFormerConfig,
		    InstructBlipVisionConfig,
		)
		from .processing_instructblip import InstructBlipProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_instructblip import (
				    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InstructBlipForConditionalGeneration,
				    InstructBlipPreTrainedModel,
				    InstructBlipQFormerModel,
				    InstructBlipVisionModel,
				)

else:
		import sys

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