



"""simple docstring"""
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class  SCREAMING_SNAKE_CASE__			(				nn.Module ):

    def __init__(					self      :					Optional[Any]			,	lowerCAmelCase_      :					Dict = 1_6			,	lowerCAmelCase_      :					Any = 8_8			,	lowerCAmelCase_      :					Optional[int] = None			,	lowerCAmelCase_      :					Union[str, Any] = 1			,	lowerCAmelCase_      :					Optional[int] = 0.0			,	lowerCAmelCase_      :					Optional[Any] = 3_2			,	lowerCAmelCase_      :					Optional[int] = None			,	lowerCAmelCase_      :					List[str] = False			,	lowerCAmelCase_      :					Union[str, Any] = None			,	lowerCAmelCase_      :					List[str] = None			,	lowerCAmelCase_      :					int = "geglu"			,	lowerCAmelCase_      :					Optional[int] = None			,	):







      """simple docstring"""







      super().__init__()
      lowercase_             =			nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=__a			,	attention_head_dim=__a			,	in_channels=__a			,	num_layers=__a			,	dropout=__a			,	norm_num_groups=__a			,	cross_attention_dim=__a			,	attention_bias=__a			,	sample_size=__a			,	num_vector_embeds=__a			,	activation_fn=__a			,	num_embeds_ada_norm=__a			,	)
              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_             =			[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])`
      lowercase_             =			[1, 0]




    def        _UpperCAmelCase						(					self      :					Any			,	lowerCAmelCase_      :					Optional[Any]			,	lowerCAmelCase_      :					str			,	lowerCAmelCase_      :					List[str]=None			,	lowerCAmelCase_      :					str=None			,	lowerCAmelCase_      :					List[str]=None			,	lowerCAmelCase_      :					Optional[Any] = 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](
            __a			,	encoder_hidden_states=__a			,	timestep=__a			,	cross_attention_kwargs=__a			,	return_dict=__a			,	)[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=__a)





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A	:  Union[str, Any]            =     {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:  Optional[int]            =     [
	    "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TimesformerModel",
	    "TimesformerForVideoClassification",
	    "TimesformerPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	A	:  List[str]            =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




