

'''simple docstring'''


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


lowerCAmelCase__						:       List[Any]										=						logging.get_logger(__name__)

lowerCAmelCase__						:       Union[str, Any]										=						{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """biogpt"""







						def __init__(							self					:      List[Any]			,			snake_case_					:      List[str]=4_2_3_8_4			,			snake_case_					:      Union[str, Any]=1_0_2_4			,			snake_case_					:      List[str]=2_4			,			snake_case_					:      Any=1_6			,			snake_case_					:      Any=4_0_9_6			,			snake_case_					:      Dict="gelu"			,			snake_case_					:      int=0.1			,			snake_case_					:      Any=0.1			,			snake_case_					:      Dict=1_0_2_4			,			snake_case_					:      str=0.0_2			,			snake_case_					:      Tuple=1e-12			,			snake_case_					:      int=True			,			snake_case_					:      List[Any]=True			,			snake_case_					:      List[Any]=0.0			,			snake_case_					:      Tuple=0.0			,			snake_case_					:      List[Any]=1			,			snake_case_					:      Any=0			,			snake_case_					:      Tuple=2			,			**snake_case_					:      Optional[Any]			,			):



								'''simple docstring'''



								snake_case__						:       Any       						=  vocab_size
								snake_case__						:       List[str]       						=  max_position_embeddings
								snake_case__						:       Tuple       						=  hidden_size
								snake_case__						:       Tuple       						=  num_hidden_layers
								snake_case__						:       List[str]       						=  num_attention_heads
								snake_case__						:       Dict       						=  intermediate_size
								snake_case__						:       Union[str, Any]       						=  hidden_act
								snake_case__						:       List[str]       						=  hidden_dropout_prob
								snake_case__						:       List[str]       						=  attention_probs_dropout_prob
								snake_case__						:       List[str]       						=  initializer_range
								snake_case__						:       Optional[int]       						=  layer_norm_eps
								snake_case__						:       List[Any]       						=  scale_embedding
								snake_case__						:       Optional[Any]       						=  use_cache
								snake_case__						:       Optional[Any]       						=  layerdrop
								snake_case__						:       List[str]       						=  activation_dropout
								super().__init__(pad_token_id=snake_case_			,			bos_token_id=snake_case_			,			eos_token_id=snake_case_			,			**snake_case_  )






'''simple docstring'''


import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
					import jax




@require_flax
class     a			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    FlaxAutoencoderKL







						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  4
								snake_case__						:       Optional[Any]       						=  3
								snake_case__						:       Optional[int]       						=  (3_2, 3_2)

								snake_case__						:       Optional[int]       						=  jax.random.PRNGKey(0  )
								snake_case__						:       Union[str, Any]       						=  jax.random.uniform(snake_case_			,			((batch_size, num_channels) + sizes)  )

								return {"sample": image, "prng_key": prng_key}







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  {
								    '''block_out_channels''': [3_2, 6_4],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								snake_case__						:       Dict       						=  self.dummy_input
								return init_dict, inputs_dict



