
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__		:int   =    logging.get_logger(__name__)

lowercase__		:Optional[int]   =    {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Dict       					='''swin'''

							lowercase_       :       Optional[int]       					={
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}




							def __init__(				self   ,A__=2_2_4   ,A__=4   ,A__=3   ,A__=9_6   ,A__=[2, 2, 6, 2]   ,A__=[3, 6, 1_2, 2_4]   ,A__=7   ,A__=4.0   ,A__=True   ,A__=0.0   ,A__=0.0   ,A__=0.1   ,A__="gelu"   ,A__=False   ,A__=0.02   ,A__=1E-5   ,A__=3_2   ,A__=None   ,A__=None   ,**A__   ,):
												super().__init__(**A__)

												lowercase							=						image_size
												lowercase							=						patch_size
												lowercase							=						num_channels
												lowercase							=						embed_dim
												lowercase							=						depths
												lowercase							=						len(A__)
												lowercase							=						num_heads
												lowercase							=						window_size
												lowercase							=						mlp_ratio
												lowercase							=						qkv_bias
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						drop_path_rate
												lowercase							=						hidden_act
												lowercase							=						use_absolute_embeddings
												lowercase							=						layer_norm_eps
												lowercase							=						initializer_range
												lowercase							=						encoder_stride
												# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
												# this indicates the channel dimension after the last stage of the model
												lowercase							=						int(embed_dim * 2 ** (len(A__) - 1))
												lowercase							=						['''stem'''] + [f'stage{idx}' for idx in range(1   ,len(A__) + 1)]
												lowercase	,						lowercase							=						get_aligned_output_features_output_indices(
												    out_features=A__   ,out_indices=A__   ,stage_names=self.stage_names)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       str       					=version.parse('''1.11'''		)




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





							@property
							def  A__      (				self):
												return 1E-4






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 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''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):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




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




							def  A__      (				self):
												self._test_save_load_local()





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




