



'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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     UpperCAmelCase_       (       _a		,		_a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		IFInpaintingSuperResolutionPipeline
    lowercase					=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
    lowercase					=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}	)
    lowercase					=		PipelineTesterMixin.required_optional_params - {"latents"}




    def lowerCamelCase       (    self					:     Optional[Any]						):
          return self._get_superresolution_dummy_components()




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     Optional[int] ,     snake_case_					:     Optional[int]=0						):
          if str(snake_case_						).startswith("""mps"""						):
                snake_case__  :    int    						=      torch.manual_seed(snake_case_						)
          else:
                snake_case__  :    Union[str, Any]    						=      torch.Generator(device=snake_case_						).manual_seed(snake_case_						)

          snake_case__  :    Tuple    						=      floats_tensor((1, 3, 16, 16) ,     rng=random.Random(snake_case_						)						).to(snake_case_						)
          snake_case__  :    Optional[Any]    						=      floats_tensor((1, 3, 32, 32) ,     rng=random.Random(snake_case_						)						).to(snake_case_						)
          snake_case__  :    List[Any]    						=      floats_tensor((1, 3, 32, 32) ,     rng=random.Random(snake_case_						)						).to(snake_case_						)

          snake_case__  :    Optional[Any]    						=      {
              """prompt""": """A painting of a squirrel eating a burger""",
              """image""": image,
              """original_image""": original_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					:     Tuple						):
          self._test_save_load_optional_components()




    @unittest.skipIf(torch_device != """cuda""" ,     reason="""float16 requires CUDA"""						)
    def lowerCamelCase       (    self					:     str						):
          # 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					:     Any						):
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)




    def lowerCamelCase       (    self					:     Any						):
          self._test_save_load_local()




    def lowerCamelCase       (    self					:     str						):
          self._test_inference_batch_single_identical(
              expected_max_diff=1E-2 ,     )







from collections import OrderedDict
from typing import 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/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		"mobilenet_v2"




   def __init__(    self,		__magic_name__=3,		__magic_name__=224,		__magic_name__=1.0,		__magic_name__=8,		__magic_name__=8,		__magic_name__=6,		__magic_name__=32,		__magic_name__=True,		__magic_name__=True,		__magic_name__="relu6",		__magic_name__=True,		__magic_name__=0.8,		__magic_name__=0.02,		__magic_name__=0.001,		__magic_name__=255,		**__magic_name__,		) ->      List[Any]:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       if depth_multiplier <= 0:
           raise ValueError('''depth_multiplier must be greater than zero.'''							)

       UpperCamelCase__							:				Union[str, Any]          =      num_channels
       UpperCamelCase__							:				int          =      image_size
       UpperCamelCase__							:				int          =      depth_multiplier
       UpperCamelCase__							:				Tuple          =      depth_divisible_by
       UpperCamelCase__							:				List[str]          =      min_depth
       UpperCamelCase__							:				Optional[int]          =      expand_ratio
       UpperCamelCase__							:				Optional[int]          =      output_stride
       UpperCamelCase__							:				Tuple          =      first_layer_is_expansion
       UpperCamelCase__							:				Union[str, Any]          =      finegrained_output
       UpperCamelCase__							:				str          =      hidden_act
       UpperCamelCase__							:				Optional[Any]          =      tf_padding
       UpperCamelCase__							:				Optional[int]          =      classifier_dropout_prob
       UpperCamelCase__							:				int          =      initializer_range
       UpperCamelCase__							:				Union[str, Any]          =      layer_norm_eps
       UpperCamelCase__							:				Tuple          =      semantic_loss_ignore_index






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Union[str, Any]   		=		version.parse("1.11"				)




   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       return OrderedDict([('''pixel_values''', {0: '''batch'''})]							)




   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]							)
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]							)




   @property
   def       UpperCamelCase__  (    self							) ->      float:






       """simple docstring"""
       return 1E-4

