'''simple docstring'''


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class       _a    (		nn.Module      ):


  """simple docstring"""

  def __init__(		self      :       Optional[Any]   ,				lowercase_      :       int = 16   ,				lowercase_      :       int = 88   ,				lowercase_      :       Optional[int] = None   ,				lowercase_      :       int = 1   ,				lowercase_      :       float = 0.0   ,				lowercase_      :       int = 32   ,				lowercase_      :       Optional[int] = None   ,				lowercase_      :       bool = False   ,				lowercase_      :       Optional[int] = None   ,				lowercase_      :       Optional[int] = None   ,				lowercase_      :       str = "geglu"   ,				lowercase_      :       Optional[int] = None   ,				):







    '''simple docstring'''


    super().__init__()
    lowercase_     =       nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=lowercase_   ,				attention_head_dim=lowercase_   ,				in_channels=lowercase_   ,				num_layers=lowercase_   ,				dropout=lowercase_   ,				norm_num_groups=lowercase_   ,				cross_attention_dim=lowercase_   ,				attention_bias=lowercase_   ,				sample_size=lowercase_   ,				num_vector_embeds=lowercase_   ,				activation_fn=lowercase_   ,				num_embeds_ada_norm=lowercase_   ,				)
            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
    lowercase_     =       0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    lowercase_     =       [77, 257]

    # 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])`
    lowercase_     =       [1, 0]





  def 		lowerCamelCase__  (		self      :       str   ,				lowercase_      :       int   ,				lowercase_      :       Dict   ,				lowercase_      :       Tuple=None   ,				lowercase_      :       Optional[Any]=None   ,				lowercase_      :       str=None   ,				lowercase_      :       bool = True   ,				):







    '''simple docstring'''


    lowercase_     =       hidden_states

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

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

    if not return_dict:
      return (output_states,)

    return TransformeraDModelOutput(sample=lowercase_      )


'''simple docstring'''


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


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''mra'''




  def __init__(		self      :       Any   ,				lowercase_      :       List[str]=50_265   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       Optional[Any]=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Optional[int]=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       str=512   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Tuple=0.0_2   ,				lowercase_      :       List[Any]=1e-5   ,				lowercase_      :       Optional[Any]="absolute"   ,				lowercase_      :       int=4   ,				lowercase_      :       int="full"   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       str=0   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       Optional[Any]=2   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


    super().__init__(pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )

    lowercase_     =       vocab_size
    lowercase_     =       max_position_embeddings
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       initializer_range
    lowercase_     =       type_vocab_size
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       block_per_row
    lowercase_     =       approx_mode
    lowercase_     =       initial_prior_first_n_blocks
    lowercase_     =       initial_prior_diagonal_n_blocks

