


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 						_snake_case  (				_snake_case						,   _snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	IFInpaintingSuperResolutionPipeline
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}							)
 SCREAMING_SNAKE_CASE__				=	PipelineTesterMixin.required_optional_params - {'latents'}




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return self._get_superresolution_dummy_components()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=0   ):
       if str(_lowerCamelCase   ).startswith('''mps'''   ):
             a						:Any										=	torch.manual_seed(_lowerCamelCase   )
       else:
             a						:Tuple										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )

       a						:List[Any]										=	floats_tensor((1, 3, 16, 16) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )
       a						:Union[str, Any]										=	floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )
       a						:int										=	floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )

       a						:List[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 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_save_load_optional_components()




 @unittest.skipIf(torch_device != '''cuda''' ,  reason='''float16 requires CUDA'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # 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 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_save_load_local()




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









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


snake_case    :   Union[str, Any]	    =					logging.get_logger(__name__)

snake_case    :   List[str]	    =					{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'efficientnet'




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

       a						:Optional[int]										=	num_channels
       a						:List[str]										=	image_size
       a						:int										=	width_coefficient
       a						:Optional[Any]										=	depth_coefficient
       a						:Any										=	depth_divisor
       a						:Any										=	kernel_sizes
       a						:Tuple										=	in_channels
       a						:Union[str, Any]										=	out_channels
       a						:Any										=	depthwise_padding
       a						:Any										=	strides
       a						:Optional[Any]										=	num_block_repeats
       a						:Tuple										=	expand_ratios
       a						:Dict										=	squeeze_expansion_ratio
       a						:int										=	hidden_act
       a						:Dict										=	hidden_dim
       a						:Tuple										=	pooling_type
       a						:Any										=	initializer_range
       a						:Tuple										=	batch_norm_eps
       a						:Optional[int]										=	batch_norm_momentum
       a						:List[Any]										=	dropout_rate
       a						:Optional[int]										=	drop_connect_rate
       a						:Tuple										=	sum(_lowerCamelCase   ) * 4







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	version.parse('1.11'							)




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




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





