
'''simple docstring'''

import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import MaskFormerSwinBackbone
     from transformers.models.maskformer import MaskFormerSwinModel




class    lowerCAmelCase       :


 def __init__(    self  : Dict			,							a__  : Union[str, Any]			,							a__  : Dict=13			,							a__  : int=32			,							a__  : str=2			,							a__  : Union[str, Any]=3			,							a__  : Union[str, Any]=16			,							a__  : Union[str, Any]=[1, 2, 1]			,							a__  : Optional[int]=[2, 2, 4]			,							a__  : str=2			,							a__  : int=2.0			,							a__  : Union[str, Any]=True			,							a__  : Any=0.0			,							a__  : Optional[Any]=0.0			,							a__  : str=0.1			,							a__  : List[str]="gelu"			,							a__  : Any=False			,							a__  : List[Any]=True			,							a__  : List[str]=0.02			,							a__  : Union[str, Any]=1e-5			,							a__  : List[Any]=True			,							a__  : Optional[Any]=None			,							a__  : Union[str, Any]=True			,							a__  : Any=10			,							a__  : str=8			,							a__  : str=["stage1", "stage2", "stage3"]			,							a__  : List[Any]=[1, 2, 3]			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	parent
  lowerCAmelCase__     :   Tuple     		=	batch_size
  lowerCAmelCase__     :   Dict     		=	image_size
  lowerCAmelCase__     :   List[Any]     		=	patch_size
  lowerCAmelCase__     :   List[str]     		=	num_channels
  lowerCAmelCase__     :   str     		=	embed_dim
  lowerCAmelCase__     :   Union[str, Any]     		=	depths
  lowerCAmelCase__     :   Optional[Any]     		=	num_heads
  lowerCAmelCase__     :   int     		=	window_size
  lowerCAmelCase__     :   Tuple     		=	mlp_ratio
  lowerCAmelCase__     :   Optional[Any]     		=	qkv_bias
  lowerCAmelCase__     :   List[Any]     		=	hidden_dropout_prob
  lowerCAmelCase__     :   List[Any]     		=	attention_probs_dropout_prob
  lowerCAmelCase__     :   Union[str, Any]     		=	drop_path_rate
  lowerCAmelCase__     :   Union[str, Any]     		=	hidden_act
  lowerCAmelCase__     :   List[Any]     		=	use_absolute_embeddings
  lowerCAmelCase__     :   Optional[int]     		=	patch_norm
  lowerCAmelCase__     :   Union[str, Any]     		=	layer_norm_eps
  lowerCAmelCase__     :   Optional[int]     		=	initializer_range
  lowerCAmelCase__     :   List[str]     		=	is_training
  lowerCAmelCase__     :   str     		=	scope
  lowerCAmelCase__     :   Union[str, Any]     		=	use_labels
  lowerCAmelCase__     :   Union[str, Any]     		=	type_sequence_label_size
  lowerCAmelCase__     :   Optional[Any]     		=	encoder_stride
  lowerCAmelCase__     :   List[Any]     		=	out_features
  lowerCAmelCase__     :   Union[str, Any]     		=	out_indices


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

  lowerCAmelCase__     :   Tuple     		=	None
  if self.use_labels:
   lowerCAmelCase__     :   int     		=	ids_tensor([self.batch_size]			,							self.type_sequence_label_size    )

  lowerCAmelCase__     :   Any     		=	self.get_config()

  return config, pixel_values, labels


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  return MaskFormerSwinConfig(
      image_size=self.image_size			,							patch_size=self.patch_size			,							num_channels=self.num_channels			,							embed_dim=self.embed_dim			,							depths=self.depths			,							num_heads=self.num_heads			,							window_size=self.window_size			,							mlp_ratio=self.mlp_ratio			,							qkv_bias=self.qkv_bias			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							drop_path_rate=self.drop_path_rate			,							hidden_act=self.hidden_act			,							use_absolute_embeddings=self.use_absolute_embeddings			,							path_norm=self.patch_norm			,							layer_norm_eps=self.layer_norm_eps			,							initializer_range=self.initializer_range			,							encoder_stride=self.encoder_stride			,							out_features=self.out_features			,							out_indices=self.out_indices			,							)


 def    _A	(    self  : Any			,							a__  : Tuple			,							a__  : Union[str, Any]			,							a__  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	MaskFormerSwinModel(config=a__    )
  model.to(a__    )
  model.eval()
  lowerCAmelCase__     :   Union[str, Any]     		=	model(a__    )

  lowerCAmelCase__     :   Any     		=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
  lowerCAmelCase__     :   str     		=	int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

  self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, expected_seq_len, expected_dim)    )


 def    _A	(    self  : Tuple			,							a__  : Tuple			,							a__  : List[str]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	MaskFormerSwinBackbone(config=a__    )
  model.to(a__    )
  model.eval()
  lowerCAmelCase__     :   List[str]     		=	model(a__    )

  # verify feature maps
  self.parent.assertEqual(len(result.feature_maps    )			,							len(config.out_features    )    )
  self.parent.assertListEqual(list(result.feature_maps[0].shape    )			,							[13, 16, 16, 16]    )

  # verify channels
  self.parent.assertEqual(len(model.channels    )			,							len(config.out_features    )    )
  self.parent.assertListEqual(model.channels			,							[16, 32, 64]    )

  # verify ValueError
  with self.parent.assertRaises(a__    ):
   lowerCAmelCase__     :   Optional[Any]     		=	["stem"]
   lowerCAmelCase__     :   Dict     		=	MaskFormerSwinBackbone(config=a__    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	self.prepare_config_and_inputs()
  lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__     :   Dict     		=	config_and_inputs
  lowerCAmelCase__     :   List[str]     		=	{"pixel_values": pixel_values}
  return config, inputs_dict




@require_torch
class    lowerCAmelCase       (     UpperCamelCase_    ,  UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				List[Any]	=			(
     (
         MaskFormerSwinModel,
         MaskFormerSwinBackbone,
     )
     if is_torch_available()
     else ()
 )
 A_				:				List[Any]	=			{"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
 A_				:				List[Any]	=			False
 A_				:				str	=			False
 A_				:				int	=			False
 A_				:				Optional[Any]	=			False
 A_				:				Any	=			False


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	MaskFormerSwinModelTester(self    )
  lowerCAmelCase__     :   List[Any]     		=	ConfigTester(self			,							config_class=a__			,							embed_dim=37    )


 @require_torch_multi_gpu
 @unittest.skip(
     reason=(
         "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
         " `nn.DataParallel`"
     )    )
 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  pass


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.create_and_test_config_common_properties()
  self.config_tester.create_and_test_config_to_json_string()
  self.config_tester.create_and_test_config_to_json_file()
  self.config_tester.create_and_test_config_from_and_save_pretrained()
  self.config_tester.create_and_test_config_with_num_labels()
  self.config_tester.check_config_can_be_init_without_params()
  self.config_tester.check_config_arguments_init()


 def    _A	(    self  : int    ):
  '''simple docstring'''

  return


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*a__    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_backbone(*a__    )


 @unittest.skip("Swin does not use inputs_embeds"    )
 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  pass


 @unittest.skip("Swin does not support feedforward chunking"    )
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  pass


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   lowerCAmelCase__     :   List[Any]     		=	model_class(a__    )
   self.assertIsInstance(model.get_input_embeddings()			,							(nn.Module)    )
   lowerCAmelCase__     :   str     		=	model.get_output_embeddings()
   self.assertTrue(x is None or isinstance(a__			,							nn.Linear    )    )


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   Any     		=	self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   lowerCAmelCase__     :   Optional[int]     		=	model_class(a__    )
   lowerCAmelCase__     :   str     		=	inspect.signature(model.forward    )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   lowerCAmelCase__     :   str     		=	[*signature.parameters.keys()]

   lowerCAmelCase__     :   Optional[int]     		=	["pixel_values"]
   self.assertListEqual(arg_names[:1]			,							a__    )


 @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions"    )
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  pass


 @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone"    )
 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  pass


 def    _A	(    self  : List[str]			,							a__  : Tuple			,							a__  : int			,							a__  : Optional[Any]			,							a__  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	model_class(a__    )
  model.to(a__    )
  model.eval()

  with torch.no_grad():
   lowerCAmelCase__     :   Union[str, Any]     		=	model(**self._prepare_for_class(a__			,							a__    )    )

  lowerCAmelCase__     :   Dict     		=	outputs.hidden_states

  lowerCAmelCase__     :   Tuple     		=	getattr(
      self.model_tester			,							"expected_num_hidden_layers"			,							len(self.model_tester.depths    ) + 1    )
  self.assertEqual(len(a__    )			,							a__    )

  # Swin has a different seq_length
  lowerCAmelCase__     :   Any     		=	(
      config.patch_size
      if isinstance(config.patch_size			,							collections.abc.Iterable    )
      else (config.patch_size, config.patch_size)
  )

  lowerCAmelCase__     :   int     		=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

  self.assertListEqual(
      list(hidden_states[0].shape[-2:]    )			,							[num_patches, self.model_tester.embed_dim]			,							)


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

  lowerCAmelCase__     :   Union[str, Any]     		=	(
      self.model_tester.image_size
      if isinstance(self.model_tester.image_size			,							collections.abc.Iterable    )
      else (self.model_tester.image_size, self.model_tester.image_size)
  )

  for model_class in self.all_model_classes:
   lowerCAmelCase__     :   int     		=	True
   self.check_hidden_states_output(a__			,							a__			,							a__			,							a__    )

   # check that output_hidden_states also work using config
   del inputs_dict["output_hidden_states"]
   lowerCAmelCase__     :   List[Any]     		=	True

   self.check_hidden_states_output(a__			,							a__			,							a__			,							a__    )


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()
  lowerCAmelCase__     :   Dict     		=	3

  lowerCAmelCase__     :   str     		=	(
      self.model_tester.image_size
      if isinstance(self.model_tester.image_size			,							collections.abc.Iterable    )
      else (self.model_tester.image_size, self.model_tester.image_size)
  )
  lowerCAmelCase__     :   str     		=	(
      config.patch_size
      if isinstance(config.patch_size			,							collections.abc.Iterable    )
      else (config.patch_size, config.patch_size)
  )

  lowerCAmelCase__     :   Union[str, Any]     		=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
  lowerCAmelCase__     :   str     		=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

  for model_class in self.all_model_classes:
   lowerCAmelCase__     :   List[Any]     		=	True
   self.check_hidden_states_output(a__			,							a__			,							a__			,							(padded_height, padded_width)    )

   # check that output_hidden_states also work using config
   del inputs_dict["output_hidden_states"]
   lowerCAmelCase__     :   Optional[Any]     		=	True
   self.check_hidden_states_output(a__			,							a__			,							a__			,							(padded_height, padded_width)    )


 @unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints"    )
 def    _A	(    self  : str    ):
  '''simple docstring'''

  pass


 @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"    )
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  pass


 @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"    )
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  pass


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

  def set_nan_tensor_to_zero(a__  : str    ):
   lowerCAmelCase__     :   Optional[Any]     		=	0
   return t

  def check_equivalence(a__  : Union[str, Any]			,							a__  : Dict			,							a__  : Optional[Any]			,							a__  : int={}    ):
   with torch.no_grad():
    lowerCAmelCase__     :   List[str]     		=	model(**a__			,							return_dict=a__			,							**a__    )
    lowerCAmelCase__     :   List[Any]     		=	model(**a__			,							return_dict=a__			,							**a__    ).to_tuple()

    def recursive_check(a__  : int			,							a__  : List[Any]    ):
     if isinstance(a__			,							(List, Tuple)    ):
      for tuple_iterable_value, dict_iterable_value in zip(a__			,							a__    ):
       recursive_check(a__			,							a__    )
     elif isinstance(a__			,							a__    ):
      for tuple_iterable_value, dict_iterable_value in zip(
          tuple_object.values()			,							dict_object.values()    ):
       recursive_check(a__			,							a__    )
     elif tuple_object is None:
      return
     else:
      self.assertTrue(
          torch.allclose(
              set_nan_tensor_to_zero(a__    )			,							set_nan_tensor_to_zero(a__    )			,							atol=1e-5    )			,							msg=(
              "Tuple and dict output are not equal. Difference:"
              F''' {torch.max(torch.abs(tuple_object - dict_object    )    )}. Tuple has `nan`:'''
              F''' {torch.isnan(a__    ).any()} and `inf`: {torch.isinf(a__    )}. Dict has'''
              F''' `nan`: {torch.isnan(a__    ).any()} and `inf`: {torch.isinf(a__    )}.'''
          )			,							)

    recursive_check(a__			,							a__    )

  for model_class in self.all_model_classes:
   lowerCAmelCase__     :   Optional[int]     		=	model_class(a__    )
   model.to(a__    )
   model.eval()

   lowerCAmelCase__     :   List[str]     		=	self._prepare_for_class(a__			,							a__    )
   lowerCAmelCase__     :   List[str]     		=	self._prepare_for_class(a__			,							a__    )
   check_equivalence(a__			,							a__			,							a__    )

   lowerCAmelCase__     :   Tuple     		=	self._prepare_for_class(a__			,							a__			,							return_labels=a__    )
   lowerCAmelCase__     :   List[str]     		=	self._prepare_for_class(a__			,							a__			,							return_labels=a__    )
   check_equivalence(a__			,							a__			,							a__    )

   lowerCAmelCase__     :   Optional[int]     		=	self._prepare_for_class(a__			,							a__    )
   lowerCAmelCase__     :   List[str]     		=	self._prepare_for_class(a__			,							a__    )
   check_equivalence(a__			,							a__			,							a__			,							{"output_hidden_states": True}    )

   lowerCAmelCase__     :   Union[str, Any]     		=	self._prepare_for_class(a__			,							a__			,							return_labels=a__    )
   lowerCAmelCase__     :   int     		=	self._prepare_for_class(a__			,							a__			,							return_labels=a__    )
   check_equivalence(a__			,							a__			,							a__			,							{"output_hidden_states": True}    )




@require_torch
class    lowerCAmelCase       (     unittest.TestCase    ,  UpperCamelCase_  ):
 A_				:				Tuple	=			(MaskFormerSwinBackbone,) if is_torch_available() else ()
 A_				:				List[Any]	=			MaskFormerSwinConfig


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	MaskFormerSwinModelTester(self    )


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()
  lowerCAmelCase__     :   List[str]     		=	inputs_dict["pixel_values"].shape[0]

  for backbone_class in self.all_model_classes:
   lowerCAmelCase__     :   int     		=	backbone_class(a__    )
   backbone.to(a__    )
   backbone.eval()

   lowerCAmelCase__     :   Dict     		=	backbone(**a__    )

   # Test default outputs and verify feature maps
   self.assertIsInstance(outputs.feature_maps			,							a__    )
   self.assertTrue(len(outputs.feature_maps    ) == len(backbone.channels    )    )
   for feature_map, n_channels in zip(outputs.feature_maps			,							backbone.channels    ):
    self.assertTrue(feature_map.shape[:2]			,							(batch_size, n_channels)    )
   self.assertIsNone(outputs.hidden_states    )
   self.assertIsNone(outputs.attentions    )

   # Test output_hidden_states=True
   lowerCAmelCase__     :   Tuple     		=	backbone(**a__			,							output_hidden_states=a__    )
   self.assertIsNotNone(outputs.hidden_states    )
   self.assertTrue(len(outputs.hidden_states    )			,							len(backbone.stage_names    )    )
   # We skip the stem layer
   for hidden_states, n_channels in zip(outputs.hidden_states[1:]			,							backbone.channels    ):
    for hidden_state in hidden_states:
     # Hidden states are in the format (batch_size, (height * width), n_channels)
     lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__     :   Any     		=	hidden_state.shape
     self.assertTrue((h_batch_size, h_n_channels)			,							(batch_size, n_channels)    )

            # Test output_attentions=True
   if self.has_attentions:
    lowerCAmelCase__     :   Any     		=	backbone(**a__			,							output_attentions=a__    )
    self.assertIsNotNone(outputs.attentions    )







'''simple docstring'''

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class    lowerCAmelCase       (     UpperCamelCase_    ,  UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				int	=			StableDiffusionDiffEditPipeline
 A_				:				List[Any]	=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
 A_				:				str	=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
 A_				:				Dict	=			frozenset(
     []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 A_				:				Union[str, Any]	=			frozenset([]  )


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   str     		=	UNetaDConditionModel(
      block_out_channels=(32, 64)			,							layers_per_block=2			,							sample_size=32			,							in_channels=4			,							out_channels=4			,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,							cross_attention_dim=32			,							attention_head_dim=(2, 4)			,							use_linear_projection=a__			,							)
  lowerCAmelCase__     :   str     		=	DDIMScheduler(
      beta_start=0.00085			,							beta_end=0.012			,							beta_schedule="scaled_linear"			,							clip_sample=a__			,							set_alpha_to_one=a__			,							)
  lowerCAmelCase__     :   str     		=	DDIMInverseScheduler(
      beta_start=0.00085			,							beta_end=0.012			,							beta_schedule="scaled_linear"			,							clip_sample=a__			,							set_alpha_to_zero=a__			,							)
  torch.manual_seed(0    )
  lowerCAmelCase__     :   int     		=	AutoencoderKL(
      block_out_channels=[32, 64]			,							in_channels=3			,							out_channels=3			,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,							latent_channels=4			,							sample_size=128			,							)
  torch.manual_seed(0    )
  lowerCAmelCase__     :   Dict     		=	CLIPTextConfig(
      bos_token_id=0			,							eos_token_id=2			,							hidden_size=32			,							intermediate_size=37			,							layer_norm_eps=1e-05			,							num_attention_heads=4			,							num_hidden_layers=5			,							pad_token_id=1			,							vocab_size=1000			,							hidden_act="gelu"			,							projection_dim=512			,							)
  lowerCAmelCase__     :   Tuple     		=	CLIPTextModel(a__    )
  lowerCAmelCase__     :   int     		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

  lowerCAmelCase__     :   List[Any]     		=	{
      "unet": unet,
      "scheduler": scheduler,
      "inverse_scheduler": inverse_scheduler,
      "vae": vae,
      "text_encoder": text_encoder,
      "tokenizer": tokenizer,
      "safety_checker": None,
      "feature_extractor": None,
  }

  return components


 def    _A	(    self  : Dict			,							a__  : List[Any]			,							a__  : str=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	floats_tensor((1, 16, 16)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	floats_tensor((1, 2, 4, 16, 16)			,							rng=random.Random(a__    )    ).to(a__    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   Any     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   int     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	{
      "prompt": "a dog and a newt",
      "mask_image": mask,
      "image_latents": latents,
      "generator": generator,
      "num_inference_steps": 2,
      "inpaint_strength": 1.0,
      "guidance_scale": 6.0,
      "output_type": "numpy",
  }

  return inputs


 def    _A	(    self  : Union[str, Any]			,							a__  : Union[str, Any]			,							a__  : Tuple=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	floats_tensor((1, 3, 32, 32)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	image.cpu().permute(0			,							2			,							3			,							1    )[0]
  lowerCAmelCase__     :   Tuple     		=	Image.fromarray(np.uinta(a__    )    ).convert("RGB"    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   str     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   List[str]     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Dict     		=	{
      "image": image,
      "source_prompt": "a cat and a frog",
      "target_prompt": "a dog and a newt",
      "generator": generator,
      "num_inference_steps": 2,
      "num_maps_per_mask": 2,
      "mask_encode_strength": 1.0,
      "guidance_scale": 6.0,
      "output_type": "numpy",
  }

  return inputs


 def    _A	(    self  : Tuple			,							a__  : Any			,							a__  : Optional[Any]=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	floats_tensor((1, 3, 32, 32)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   List[str]     		=	image.cpu().permute(0			,							2			,							3			,							1    )[0]
  lowerCAmelCase__     :   Tuple     		=	Image.fromarray(np.uinta(a__    )    ).convert("RGB"    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   Dict     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   Tuple     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Dict     		=	{
      "image": image,
      "prompt": "a cat and a frog",
      "generator": generator,
      "num_inference_steps": 2,
      "inpaint_strength": 1.0,
      "guidance_scale": 6.0,
      "decode_latents": True,
      "output_type": "numpy",
  }
  return inputs


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  if not hasattr(self.pipeline_class			,							"_optional_components"    ):
   return

  lowerCAmelCase__     :   int     		=	self.get_dummy_components()
  lowerCAmelCase__     :   Optional[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  # set all optional components to None and update pipeline config accordingly
  for optional_component in pipe._optional_components:
   setattr(a__			,							a__			,							a__    )
  pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

  lowerCAmelCase__     :   str     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   List[Any]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   Tuple     		=	self.pipeline_class.from_pretrained(a__    )
   pipe_loaded.to(a__    )
   pipe_loaded.set_progress_bar_config(disable=a__    )

  for optional_component in pipe._optional_components:
   self.assertTrue(
       getattr(a__			,							a__    ) is None			,							F'''`{optional_component}` did not stay set to None after loading.'''			,							)

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   int     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   Dict     		=	np.abs(output - output_loaded    ).max()
  self.assertLess(a__			,							1e-4    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	"cpu"

  lowerCAmelCase__     :   Any     		=	self.get_dummy_components()
  lowerCAmelCase__     :   List[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_mask_inputs(a__    )
  lowerCAmelCase__     :   List[str]     		=	pipe.generate_mask(**a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	mask[0, -3:, -3:]

  self.assertEqual(mask.shape			,							(1, 16, 16)    )
  lowerCAmelCase__     :   str     		=	np.array([0] * 9    )
  lowerCAmelCase__     :   Dict     		=	np.abs(mask_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )
  self.assertEqual(mask[0, -3, -4]			,							0    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	"cpu"

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_components()
  lowerCAmelCase__     :   List[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   List[str]     		=	self.get_dummy_inversion_inputs(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	pipe.invert(**a__    ).images
  lowerCAmelCase__     :   Dict     		=	image[0, -1, -3:, -3:]

  self.assertEqual(image.shape			,							(2, 32, 32, 3)    )
  lowerCAmelCase__     :   Tuple     		=	np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,							)
  lowerCAmelCase__     :   Union[str, Any]     		=	np.abs(image_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  super().test_inference_batch_single_identical(expected_max_diff=5e-3    )


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	"cpu"

  lowerCAmelCase__     :   Optional[int]     		=	self.get_dummy_components()

  lowerCAmelCase__     :   List[Any]     		=	{"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
  lowerCAmelCase__     :   List[Any]     		=	DPMSolverMultistepScheduler(**a__    )
  lowerCAmelCase__     :   List[Any]     		=	DPMSolverMultistepInverseScheduler(**a__    )

  lowerCAmelCase__     :   Any     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Dict     		=	self.get_dummy_inversion_inputs(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	pipe.invert(**a__    ).images
  lowerCAmelCase__     :   Any     		=	image[0, -1, -3:, -3:]

  self.assertEqual(image.shape			,							(2, 32, 32, 3)    )
  lowerCAmelCase__     :   Dict     		=	np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,							)
  lowerCAmelCase__     :   int     		=	np.abs(image_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )




@require_torch_gpu
@slow
class    lowerCAmelCase       (     unittest.TestCase  ):


 def    _A	(    self  : int    ):
  '''simple docstring'''

  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()


 @classmethod
 def    _A	(    cls  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	load_image(
      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"    )

  lowerCAmelCase__     :   Dict     		=	raw_image.convert("RGB"    ).resize((768, 768)    )

  lowerCAmelCase__     :   str     		=	raw_image


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	torch.manual_seed(0    )

  lowerCAmelCase__     :   str     		=	StableDiffusionDiffEditPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2-1"			,							safety_checker=a__			,							torch_dtype=torch.floataa    )
  lowerCAmelCase__     :   Optional[Any]     		=	DDIMScheduler.from_config(pipe.scheduler.config    )
  lowerCAmelCase__     :   int     		=	DDIMInverseScheduler.from_config(pipe.scheduler.config    )
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Optional[Any]     		=	"a bowl of fruit"
  lowerCAmelCase__     :   Union[str, Any]     		=	"a bowl of pears"

  lowerCAmelCase__     :   Optional[Any]     		=	pipe.generate_mask(
      image=self.raw_image			,							source_prompt=a__			,							target_prompt=a__			,							generator=a__			,							)

  lowerCAmelCase__     :   str     		=	pipe.invert(
      prompt=a__			,							image=self.raw_image			,							inpaint_strength=0.7			,							generator=a__    ).latents

  lowerCAmelCase__     :   Tuple     		=	pipe(
      prompt=a__			,							mask_image=a__			,							image_latents=a__			,							generator=a__			,							negative_prompt=a__			,							inpaint_strength=0.7			,							output_type="numpy"			,							).images[0]

  lowerCAmelCase__     :   List[str]     		=	(
      np.array(
          load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/diffedit/pears.png"    ).resize((768, 768)    )    )
      / 255
  )
  assert np.abs((expected_image - image).max()    ) < 5e-1


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	torch.manual_seed(0    )

  lowerCAmelCase__     :   str     		=	StableDiffusionDiffEditPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2-1"			,							safety_checker=a__			,							torch_dtype=torch.floataa    )
  lowerCAmelCase__     :   int     		=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
  lowerCAmelCase__     :   Any     		=	DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Any     		=	"a bowl of fruit"
  lowerCAmelCase__     :   Optional[int]     		=	"a bowl of pears"

  lowerCAmelCase__     :   Optional[Any]     		=	pipe.generate_mask(
      image=self.raw_image			,							source_prompt=a__			,							target_prompt=a__			,							generator=a__			,							)

  lowerCAmelCase__     :   Optional[int]     		=	pipe.invert(
      prompt=a__			,							image=self.raw_image			,							inpaint_strength=0.7			,							generator=a__			,							num_inference_steps=25			,							).latents

  lowerCAmelCase__     :   Any     		=	pipe(
      prompt=a__			,							mask_image=a__			,							image_latents=a__			,							generator=a__			,							negative_prompt=a__			,							inpaint_strength=0.7			,							num_inference_steps=25			,							output_type="numpy"			,							).images[0]

  lowerCAmelCase__     :   Optional[int]     		=	(
      np.array(
          load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/diffedit/pears.png"    ).resize((768, 768)    )    )
      / 255
  )
  assert np.abs((expected_image - image).max()    ) < 5e-1





