"""simple docstring"""


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 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     str   							=							IFInpaintingPipeline
    _snake_case   :     Any   							=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
    _snake_case   :     int   							=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _snake_case   :     List[Any]   							=							PipelineTesterMixin.required_optional_params - {'latents'}


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      return self._get_dummy_components()


    def    lowerCAmelCase_     (    self		:     int      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     List[Any]=0    ):
      if str(__lowerCAmelCase    ).startswith("""mps"""    ):
        _UpperCAmelCase         =					torch.manual_seed(__lowerCAmelCase    )
      else:
        _UpperCAmelCase         =					torch.Generator(device=__lowerCAmelCase    ).manual_seed(__lowerCAmelCase    )

      _UpperCAmelCase         =					floats_tensor((1, 3, 32, 32)      , rng=random.Random(__lowerCAmelCase    )    ).to(__lowerCAmelCase    )
      _UpperCAmelCase         =					floats_tensor((1, 3, 32, 32)      , rng=random.Random(__lowerCAmelCase    )    ).to(__lowerCAmelCase    )

      _UpperCAmelCase         =					{
          """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    lowerCAmelCase_     (    self		:     List[Any]    ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )


    def    lowerCAmelCase_     (    self		:     int    ):
      self._test_save_load_optional_components()


    @unittest.skipIf(torch_device != """cuda"""      , reason="""float16 requires CUDA"""    )
    def    lowerCAmelCase_     (    self		:     int    ):
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1e-1    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      self._test_save_load_local()


    def    lowerCAmelCase_     (    self		:     Dict    ):
      self._test_inference_batch_single_identical(
          expected_max_diff=1e-2      , )

"""simple docstring"""


from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Any   							=							'efficientnet'


    def __init__(    self		:     Any      , __lowerCAmelCase		:     int = 3      , __lowerCAmelCase		:     int = 600      , __lowerCAmelCase		:     float = 2.0      , __lowerCAmelCase		:     float = 3.1      , __lowerCAmelCase		:     int = 8      , __lowerCAmelCase		:     List[int] = [3, 3, 5, 3, 5, 5, 3]      , __lowerCAmelCase		:     List[int] = [32, 16, 24, 40, 80, 112, 192]      , __lowerCAmelCase		:     List[int] = [16, 24, 40, 80, 112, 192, 320]      , __lowerCAmelCase		:     List[int] = []      , __lowerCAmelCase		:     List[int] = [1, 2, 2, 2, 1, 2, 1]      , __lowerCAmelCase		:     List[int] = [1, 2, 2, 3, 3, 4, 1]      , __lowerCAmelCase		:     List[int] = [1, 6, 6, 6, 6, 6, 6]      , __lowerCAmelCase		:     float = 0.25      , __lowerCAmelCase		:     str = "swish"      , __lowerCAmelCase		:     int = 2560      , __lowerCAmelCase		:     str = "mean"      , __lowerCAmelCase		:     float = 0.02      , __lowerCAmelCase		:     float = 0.001      , __lowerCAmelCase		:     float = 0.99      , __lowerCAmelCase		:     float = 0.5      , __lowerCAmelCase		:     float = 0.2      , **__lowerCAmelCase		:     List[Any]      , ):
      super().__init__(**__lowerCAmelCase    )

      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					width_coefficient
      _UpperCAmelCase         =					depth_coefficient
      _UpperCAmelCase         =					depth_divisor
      _UpperCAmelCase         =					kernel_sizes
      _UpperCAmelCase         =					in_channels
      _UpperCAmelCase         =					out_channels
      _UpperCAmelCase         =					depthwise_padding
      _UpperCAmelCase         =					strides
      _UpperCAmelCase         =					num_block_repeats
      _UpperCAmelCase         =					expand_ratios
      _UpperCAmelCase         =					squeeze_expansion_ratio
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					hidden_dim
      _UpperCAmelCase         =					pooling_type
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					batch_norm_eps
      _UpperCAmelCase         =					batch_norm_momentum
      _UpperCAmelCase         =					dropout_rate
      _UpperCAmelCase         =					drop_connect_rate
      _UpperCAmelCase         =					sum(__lowerCAmelCase    ) * 4



class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Dict   							=							version.parse('1.11'      )


    @property
    def    lowerCAmelCase_     (    self		:     Any    ):
      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]    )


    @property
    def    lowerCAmelCase_     (    self		:     int    ):
      return 1e-5
