


'''simple docstring'''





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


__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"cvt"


 def __init__(      self							:	Optional[Any]			,       __lowerCamelCase							:	Dict=3			,       __lowerCamelCase							:	Optional[Any]=[7, 3, 3]			,       __lowerCamelCase							:	Union[str, Any]=[4, 2, 2]			,       __lowerCamelCase							:	str=[2, 1, 1]			,       __lowerCamelCase							:	Optional[int]=[6_4, 1_9_2, 3_8_4]			,       __lowerCamelCase							:	Tuple=[1, 3, 6]			,       __lowerCamelCase							:	Dict=[1, 2, 1_0]			,       __lowerCamelCase							:	Dict=[4.0, 4.0, 4.0]			,       __lowerCamelCase							:	Union[str, Any]=[0.0, 0.0, 0.0]			,       __lowerCamelCase							:	str=[0.0, 0.0, 0.0]			,       __lowerCamelCase							:	List[Any]=[0.0, 0.0, 0.1]			,       __lowerCamelCase							:	Any=[True, True, True]			,       __lowerCamelCase							:	int=[False, False, True]			,       __lowerCamelCase							:	Optional[Any]=["dw_bn", "dw_bn", "dw_bn"]			,       __lowerCamelCase							:	Dict=[3, 3, 3]			,       __lowerCamelCase							:	Optional[int]=[1, 1, 1]			,       __lowerCamelCase							:	Optional[Any]=[2, 2, 2]			,       __lowerCamelCase							:	Optional[Any]=[1, 1, 1]			,       __lowerCamelCase							:	Optional[Any]=[1, 1, 1]			,       __lowerCamelCase							:	int=0.02			,       __lowerCamelCase							:	Tuple=1e-12			,       **__lowerCamelCase							:	Optional[Any]			,       ):
    super().__init__(**__lowerCamelCase )
    UpperCAmelCase__	:List[str]      =      num_channels
    UpperCAmelCase__	:Dict      =      patch_sizes
    UpperCAmelCase__	:str      =      patch_stride
    UpperCAmelCase__	:Any      =      patch_padding
    UpperCAmelCase__	:List[str]      =      embed_dim
    UpperCAmelCase__	:Optional[Any]      =      num_heads
    UpperCAmelCase__	:List[str]      =      depth
    UpperCAmelCase__	:Any      =      mlp_ratio
    UpperCAmelCase__	:Any      =      attention_drop_rate
    UpperCAmelCase__	:Optional[int]      =      drop_rate
    UpperCAmelCase__	:Any      =      drop_path_rate
    UpperCAmelCase__	:Dict      =      qkv_bias
    UpperCAmelCase__	:List[str]      =      cls_token
    UpperCAmelCase__	:str      =      qkv_projection_method
    UpperCAmelCase__	:List[Any]      =      kernel_qkv
    UpperCAmelCase__	:List[str]      =      padding_kv
    UpperCAmelCase__	:str      =      stride_kv
    UpperCAmelCase__	:int      =      padding_q
    UpperCAmelCase__	:Optional[int]      =      stride_q
    UpperCAmelCase__	:Optional[Any]      =      initializer_range
    UpperCAmelCase__	:str      =      layer_norm_eps







'''simple docstring'''





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	int = 1_6			,       __lowerCamelCase							:	int = 8_8			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "geglu"			,       __lowerCamelCase							:	Optional[int] = None			,       ):
    super().__init__()
    UpperCAmelCase__	:Optional[int]      =      nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=__lowerCamelCase			,       attention_head_dim=__lowerCamelCase			,       in_channels=__lowerCamelCase			,       num_layers=__lowerCamelCase			,       dropout=__lowerCamelCase			,       norm_num_groups=__lowerCamelCase			,       cross_attention_dim=__lowerCamelCase			,       attention_bias=__lowerCamelCase			,       sample_size=__lowerCamelCase			,       num_vector_embeds=__lowerCamelCase			,       activation_fn=__lowerCamelCase			,       num_embeds_ada_norm=__lowerCamelCase			,       )
            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
    UpperCAmelCase__	:Tuple      =      0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase__	:int      =      [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])`
    UpperCAmelCase__	:Any      =      [1, 0]





 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	bool = True			,       ):
    UpperCAmelCase__	:Optional[int]      =      hidden_states

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

    UpperCAmelCase__	:Optional[int]      =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase__	:Optional[Any]      =      output_states + input_states

    if not return_dict:
       return (output_states,)

    return TransformeraDModelOutput(sample=__lowerCamelCase )



