


"""simple docstring"""
import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:    Dict		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    Tuple		=    {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}





class      __A     (lowercase__):





							'''simple docstring'''

							__lowercase:		Tuple							      =    """mvp"""
							__lowercase:		Union[str, Any]							      =    ["""past_key_values"""]
							__lowercase:		Tuple							      =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



							def __init__(				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Any=50_267		,       UpperCAmelCase_							:      str=1_024		,       UpperCAmelCase_							:      Dict=12		,       UpperCAmelCase_							:      Any=4_096		,       UpperCAmelCase_							:      Union[str, Any]=16		,       UpperCAmelCase_							:      Union[str, Any]=12		,       UpperCAmelCase_							:      List[Any]=4_096		,       UpperCAmelCase_							:      List[str]=16		,       UpperCAmelCase_							:      Optional[int]=0.0		,       UpperCAmelCase_							:      List[Any]=0.0		,       UpperCAmelCase_							:      Optional[Any]="gelu"		,       UpperCAmelCase_							:      Optional[int]=1_024		,       UpperCAmelCase_							:      List[Any]=0.1		,       UpperCAmelCase_							:      Any=0.0		,       UpperCAmelCase_							:      List[str]=0.0		,       UpperCAmelCase_							:      Union[str, Any]=0.02		,       UpperCAmelCase_							:      Optional[int]=0.0		,       UpperCAmelCase_							:      str=False		,       UpperCAmelCase_							:      List[str]=True		,       UpperCAmelCase_							:      Dict=1		,       UpperCAmelCase_							:      Union[str, Any]=0		,       UpperCAmelCase_							:      Tuple=2		,       UpperCAmelCase_							:      Any=True		,       UpperCAmelCase_							:      Dict=2		,       UpperCAmelCase_							:      int=2		,       UpperCAmelCase_							:      Optional[int]=False		,       UpperCAmelCase_							:      int=100		,       UpperCAmelCase_							:      Optional[Any]=800		,       **UpperCAmelCase_							:      List[str]		,       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		vocab_size
														snake_case_           =		max_position_embeddings
														snake_case_           =		d_model
														snake_case_           =		encoder_ffn_dim
														snake_case_           =		encoder_layers
														snake_case_           =		encoder_attention_heads
														snake_case_           =		decoder_ffn_dim
														snake_case_           =		decoder_layers
														snake_case_           =		decoder_attention_heads
														snake_case_           =		dropout
														snake_case_           =		attention_dropout
														snake_case_           =		activation_dropout
														snake_case_           =		activation_function
														snake_case_           =		init_std
														snake_case_           =		encoder_layerdrop
														snake_case_           =		decoder_layerdrop
														snake_case_           =		classifier_dropout
														snake_case_           =		use_cache
														snake_case_           =		encoder_layers
														snake_case_           =		scale_embedding  # scale factor will be sqrt(d_model) if True
														snake_case_           =		use_prompt
														snake_case_           =		prompt_length
														snake_case_           =		prompt_mid_dim

														super().__init__(
														    pad_token_id=__lowerCamelCase		,       bos_token_id=__lowerCamelCase		,       eos_token_id=__lowerCamelCase		,       is_encoder_decoder=__lowerCamelCase		,       decoder_start_token_id=__lowerCamelCase		,       forced_eos_token_id=__lowerCamelCase		,       **__lowerCamelCase		,       )

														if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""		,       __lowerCamelCase       ):
																					snake_case_           =		self.bos_token_id
																					warnings.warn(
																					    F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
																					    """The config can simply be saved and uploaded again to be fixed."""       )







"""simple docstring"""
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		str							      =    VQModel
							__lowercase:		Union[str, Any]							      =    """sample"""



							@property
							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      List[str]=(32, 32)       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		4
														snake_case_           =		3

														snake_case_           =		floats_tensor((batch_size, num_channels) + sizes       ).to(UpperCAmelCase_       )

														return {"sample": image}



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														return (3, 32, 32)



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



														"""simple docstring"""



														return (3, 32, 32)



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



														"""simple docstring"""



														snake_case_           =		{
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														snake_case_           =		self.dummy_input
														return init_dict, inputs_dict



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Dict:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														pass



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



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""		,       output_loading_info=UpperCAmelCase_       )
														self.assertIsNotNone(UpperCAmelCase_       )
														self.assertEqual(len(loading_info["""missing_keys"""]       )		,       0       )

														model.to(UpperCAmelCase_       )
														snake_case_           =		model(**self.dummy_input       )

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



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""       )
														model.to(UpperCAmelCase_       ).eval()

														torch.manual_seed(0       )
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0       )

														snake_case_           =		torch.randn(1		,       model.config.in_channels		,       model.config.sample_size		,       model.config.sample_size       )
														snake_case_           =		image.to(UpperCAmelCase_       )
														with torch.no_grad():
																					snake_case_           =		model(UpperCAmelCase_       ).sample

														snake_case_           =		output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														snake_case_           =		torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]       )
														# fmt: on
														self.assertTrue(torch.allclose(UpperCAmelCase_		,       UpperCAmelCase_		,       atol=1E-3       )       )



