




"""simple docstring"""







from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 			UpperCamelCase_     (			nn.Module):

	"""simple docstring"""





	def __init__(			self      :    Optional[int]      ,      UpperCAmelCase__      :    int = 1_6      ,      UpperCAmelCase__      :    int = 8_8      ,      UpperCAmelCase__      :    Optional[int] = None      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 3_2      ,      UpperCAmelCase__      :    Optional[int] = None      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    Optional[int] = None      ,      UpperCAmelCase__      :    Optional[int] = None      ,      UpperCAmelCase__      :    str = "geglu"      ,      UpperCAmelCase__      :    Optional[int] = None      ,      )	->     str:
						super().__init__()
						__SCREAMING_SNAKE_CASE						       =    nn.ModuleList(
						    [
						        TransformeraDModel(
						            num_attention_heads=UpperCAmelCase__      ,      attention_head_dim=UpperCAmelCase__      ,      in_channels=UpperCAmelCase__      ,      num_layers=UpperCAmelCase__      ,      dropout=UpperCAmelCase__      ,      norm_num_groups=UpperCAmelCase__      ,      cross_attention_dim=UpperCAmelCase__      ,      attention_bias=UpperCAmelCase__      ,      sample_size=UpperCAmelCase__      ,      num_vector_embeds=UpperCAmelCase__      ,      activation_fn=UpperCAmelCase__      ,      num_embeds_ada_norm=UpperCAmelCase__      ,      )
						        for _ in range(2       )
						    ]       )

						# Variables that can be set by a pipeline:

						# The ratio of transformer1 to transformer2's output states to be combined during inference
						__SCREAMING_SNAKE_CASE						       =    0.5

						# The shape of `encoder_hidden_states` is expected to be
						# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
						__SCREAMING_SNAKE_CASE						       =    [7_7, 2_5_7]

						# Which transformer to use to encode which condition.
						# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
						__SCREAMING_SNAKE_CASE						       =    [1, 0]




	def   UpperCAmelCase_				(			self      :    List[str]      ,      UpperCAmelCase__      :    Optional[int]      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    List[str]=None      ,      UpperCAmelCase__      :    List[Any]=None      ,      UpperCAmelCase__      :    int=None      ,      UpperCAmelCase__      :    bool = True      ,      )	->     str:
						__SCREAMING_SNAKE_CASE						       =    hidden_states

						__SCREAMING_SNAKE_CASE						       =    []
						__SCREAMING_SNAKE_CASE						       =    0
						# attention_mask is not used yet
						for i in range(2       ):
											# for each of the two transformers, pass the corresponding condition tokens
											__SCREAMING_SNAKE_CASE						       =    encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
											__SCREAMING_SNAKE_CASE						       =    self.transformer_index_for_condition[i]
											__SCREAMING_SNAKE_CASE						       =    self.transformers[transformer_index](
											    UpperCAmelCase__      ,      encoder_hidden_states=UpperCAmelCase__      ,      timestep=UpperCAmelCase__      ,      cross_attention_kwargs=UpperCAmelCase__      ,      return_dict=UpperCAmelCase__      ,      )[0]
											encoded_states.append(encoded_state - input_states       )
											tokens_start += self.condition_lengths[i]

						__SCREAMING_SNAKE_CASE						       =    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
						__SCREAMING_SNAKE_CASE						       =    output_states + input_states

						if not return_dict:
											return (output_states,)

						return TransformeraDModelOutput(sample=UpperCAmelCase__       )




"""simple docstring"""







from typing import TYPE_CHECKING

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


a__    :							List[str]            =					{
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					[
		    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BioGptForCausalLM''',
		    '''BioGptForTokenClassification''',
		    '''BioGptForSequenceClassification''',
		    '''BioGptModel''',
		    '''BioGptPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a__    :							Optional[int]            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
