



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class      __lowerCAmelCase     (		nn.Module      ):
					"""simple docstring"""






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






								"""simple docstring"""


								super().__init__()
								A__       =    nn.ModuleList(
								    [
								        TransformeraDModel(
								            num_attention_heads=_snake_case				,					attention_head_dim=_snake_case				,					in_channels=_snake_case				,					num_layers=_snake_case				,					dropout=_snake_case				,					norm_num_groups=_snake_case				,					cross_attention_dim=_snake_case				,					attention_bias=_snake_case				,					sample_size=_snake_case				,					num_vector_embeds=_snake_case				,					activation_fn=_snake_case				,					num_embeds_ada_norm=_snake_case				,					)
								        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, 2_57]

								# 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     _a							(     self					:       Optional[Any]				,					_snake_case					:       Any				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       str=None				,					_snake_case					:       Optional[int]=None				,					_snake_case					:       Union[str, Any]=None				,					_snake_case					:       bool = True				,					):






								"""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](
											    _snake_case				,					encoder_hidden_states=_snake_case				,					timestep=_snake_case				,					cross_attention_kwargs=_snake_case				,					return_dict=_snake_case				,					)[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=_snake_case					)









import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Any			       =    IFInpaintingPipeline
					A__				:		Dict			       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					A__				:		List[Any]			       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__				:		Dict			       =    PipelineTesterMixin.required_optional_params - {"latents"}



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self._get_dummy_components()



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Any				,					_snake_case					:       str=0					):






								"""simple docstring"""


								if str(_snake_case					).startswith('mps'					):
											A__       =    torch.manual_seed(_snake_case					)
								else:
											A__       =    torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)
								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)

								A__       =    {
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': image,
								    'mask_image': mask_image,
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}

								return inputs



					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()				,					reason='XFormers attention is only available with CUDA and `xformers` installed'				,					)
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self._test_save_load_optional_components()



					@unittest.skipIf(torch_device != 'cuda'				,					reason='float16 requires CUDA'					)
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								super().test_save_load_floataa(expected_max_diff=1E-1					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_save_load_local()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2				,					)




