"""simple docstring"""


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class        a				(		nn.Module   ):


    def __init__(					self	:  List[Any]   ,				__lowerCAmelCase	:  int = 16   ,				__lowerCAmelCase	:  int = 88   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  int = 1   ,				__lowerCAmelCase	:  float = 0.0   ,				__lowerCAmelCase	:  int = 32   ,				__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						 =      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						 =      0.5

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


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Tuple=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				__lowerCAmelCase	:  bool = True   ,				):
      _UpperCAmelCase						 =      hidden_states

      _UpperCAmelCase						 =      []
      _UpperCAmelCase						 =      0
      # attention_mask is not used yet
      for i in range(2					):
        # for each of the two transformers, pass the corresponding condition tokens
        _UpperCAmelCase						 =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
        _UpperCAmelCase						 =      self.transformer_index_for_condition[i]
        _UpperCAmelCase						 =      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						 =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _UpperCAmelCase						 =      output_states + input_states

      if not return_dict:
        return (output_states,)

      return TransformeraDModelOutput(sample=__lowerCAmelCase					)





"""simple docstring"""


# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase__  		=						abspath(join(dirname(dirname(__file__)), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  from diffusers.utils.testing_utils import pytest_addoption_shared

  pytest_addoption_shared(lowercase		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  from diffusers.utils.testing_utils import pytest_terminal_summary_main

  _UpperCAmelCase						 =      terminalreporter.config.getoption("""--make-reports"""		)
  if make_reports:
    pytest_terminal_summary_main(lowercase   ,id=lowercase		)
