

'''simple docstring'''







from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class 					A__      (      nn.Module			):



    def __init__(					self		,				UpperCamelCase__ = 16		,				UpperCamelCase__ = 88		,				UpperCamelCase__ = None		,				UpperCamelCase__ = 1		,				UpperCamelCase__ = 0.0		,				UpperCamelCase__ = 32		,				UpperCamelCase__ = None		,				UpperCamelCase__ = False		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = "geglu"		,				UpperCamelCase__ = None		,				)						->      str:





           '''simple docstring'''
           super().__init__()
           A_						=      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
           A_						=      0.5

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







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__ = True		,				)						->      str:





           '''simple docstring'''
           A_						=      hidden_states

           A_						=      []
           A_						=      0
           # attention_mask is not used yet
           for i in range(2       ):
                  # for each of the two transformers, pass the corresponding condition tokens
                  A_						=      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
                  A_						=      self.transformer_index_for_condition[i]
                  A_						=      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]

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

           if not return_dict:
                  return (output_states,)

           return TransformeraDModelOutput(sample=UpperCamelCase__       )



'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      FileLock(str(tmpdir / """foo.lock"""				)				)
       A_						=      FileLock(str(tmpdir / """foo.lock"""				)				)
       A_						=      0.01
       with locka.acquire():
              with pytest.raises(UpperCAmelCase__				):
                     A_						=      time.time()
                     locka.acquire(UpperCAmelCase__				)
                     assert time.time() - _start > timeout







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Dict:
       A_						=      """a""" * 10_00 + """.lock"""
       A_						=      FileLock(str(tmpdir / filename				)				)
       assert locka._lock_file.endswith(""".lock"""				)
       assert not locka._lock_file.endswith(UpperCAmelCase__				)
       assert len(os.path.basename(locka._lock_file				)				) <= 2_55
       A_						=      FileLock(tmpdir / filename				)
       with locka.acquire():
              with pytest.raises(UpperCAmelCase__				):
                     locka.acquire(0				)
