




import warnings
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_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """segformer"""

						def __init__(		self :List[str]     ,			__snake_case :str=3     ,			__snake_case :Optional[Any]=4     ,			__snake_case :List[Any]=[2, 2, 2, 2]     ,			__snake_case :Dict=[8, 4, 2, 1]     ,			__snake_case :Optional[int]=[32, 64, 1_60, 2_56]     ,			__snake_case :Union[str, Any]=[7, 3, 3, 3]     ,			__snake_case :Optional[Any]=[4, 2, 2, 2]     ,			__snake_case :Tuple=[1, 2, 5, 8]     ,			__snake_case :List[Any]=[4, 4, 4, 4]     ,			__snake_case :Optional[Any]="gelu"     ,			__snake_case :Tuple=0.0     ,			__snake_case :Dict=0.0     ,			__snake_case :Optional[int]=0.1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :Tuple=0.1     ,			__snake_case :Union[str, Any]=1E-6     ,			__snake_case :int=2_56     ,			__snake_case :Optional[int]=2_55     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)

										if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
														warnings.warn(
														    """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
														    """ removed, as the behaviour will default to that of reshape_last_stage = True."""     ,			__snake_case     ,			)

										__magic_name__						:						Dict										=num_channels
										__magic_name__						:						str										=num_encoder_blocks
										__magic_name__						:						List[Any]										=depths
										__magic_name__						:						Optional[Any]										=sr_ratios
										__magic_name__						:						List[str]										=hidden_sizes
										__magic_name__						:						List[str]										=patch_sizes
										__magic_name__						:						Any										=strides
										__magic_name__						:						Optional[Any]										=mlp_ratios
										__magic_name__						:						str										=num_attention_heads
										__magic_name__						:						int										=hidden_act
										__magic_name__						:						List[Any]										=hidden_dropout_prob
										__magic_name__						:						Optional[Any]										=attention_probs_dropout_prob
										__magic_name__						:						Optional[Any]										=classifier_dropout_prob
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						List[str]										=drop_path_rate
										__magic_name__						:						List[Any]										=layer_norm_eps
										__magic_name__						:						List[str]										=decoder_hidden_size
										__magic_name__						:						Union[str, Any]										=kwargs.get("""reshape_last_stage"""     ,			__snake_case			)
										__magic_name__						:						Dict										=semantic_loss_ignore_index




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       version.parse("""1.11"""      )

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return OrderedDict(
										    [
										        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
										    ]			)

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										return 1E-4


						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										return 12










import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       KandinskyInpaintPipeline
						UpperCamelCase      =       ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						UpperCamelCase      =       [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						UpperCamelCase      =       [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						UpperCamelCase      =       False

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return self.time_input_dim

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return self.time_input_dim * 4

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return 1_00

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
										return tokenizer

						@property
						def      A__    (		self :str			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=MCLIPConfig(
										    numDims=self.cross_attention_dim     ,			transformerDimensions=self.text_embedder_hidden_size     ,			hidden_size=self.text_embedder_hidden_size     ,			intermediate_size=37     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			vocab_size=10_05     ,			)

										__magic_name__						:						Tuple										=MultilingualCLIP(__snake_case			)
										__magic_name__						:						Optional[int]										=text_encoder.eval()

										return text_encoder

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[Any]										={
										    """in_channels""": 9,
										    # Out channels is double in channels because predicts mean and variance
										    """out_channels""": 8,
										    """addition_embed_type""": """text_image""",
										    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
										    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
										    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
										    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    """layers_per_block""": 1,
										    """encoder_hid_dim""": self.text_embedder_hidden_size,
										    """encoder_hid_dim_type""": """text_image_proj""",
										    """cross_attention_dim""": self.cross_attention_dim,
										    """attention_head_dim""": 4,
										    """resnet_time_scale_shift""": """scale_shift""",
										    """class_embed_type""": None,
										}

										__magic_name__						:						Union[str, Any]										=UNetaDConditionModel(**__snake_case			)
										return model

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return {
										    "block_out_channels": [32, 64],
										    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}

						@property
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Dict										=VQModel(**self.dummy_movq_kwargs			)
										return model

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

										__magic_name__						:						List[str]										=self.dummy_text_encoder
										__magic_name__						:						Optional[Any]										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_unet
										__magic_name__						:						Tuple										=self.dummy_movq

										__magic_name__						:						List[str]										=DDIMScheduler(
										    num_train_timesteps=10_00     ,			beta_schedule="""linear"""     ,			beta_start=0.00085     ,			beta_end=0.012     ,			clip_sample=__snake_case     ,			set_alpha_to_one=__snake_case     ,			steps_offset=1     ,			prediction_type="""epsilon"""     ,			thresholding=__snake_case     ,			)

										__magic_name__						:						str										={
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components

						def      A__    (		self :str     ,			__snake_case :Optional[Any]     ,			__snake_case :int=0			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						Dict										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(seed + 1			)			).to(__snake_case			)
										# create init_image
										__magic_name__						:						str										=floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						int										=image.cpu().permute(0     ,			2     ,			3     ,			1			)[0]
										__magic_name__						:						str										=Image.fromarray(np.uinta(__snake_case			)			).convert("""RGB"""			).resize((2_56, 2_56)			)
										# create mask
										__magic_name__						:						Dict										=np.ones((64, 64)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										if str(__snake_case			).startswith("""mps"""			):
														__magic_name__						:						Dict										=torch.manual_seed(__snake_case			)
										else:
														__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
										__magic_name__						:						List[Any]										={
										    """prompt""": """horse""",
										    """image""": init_image,
										    """mask_image""": mask,
										    """image_embeds""": image_embeds,
										    """negative_image_embeds""": negative_image_embeds,
										    """generator""": generator,
										    """height""": 64,
										    """width""": 64,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 4.0,
										    """output_type""": """np""",
										}
										return inputs

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

										__magic_name__						:						Tuple										="""cpu"""

										__magic_name__						:						List[Any]										=self.get_dummy_components()

										__magic_name__						:						Union[str, Any]										=self.pipeline_class(**__snake_case			)
										__magic_name__						:						Tuple										=pipe.to(__snake_case			)

										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Tuple										=pipe(**self.get_dummy_inputs(__snake_case			)			)
										__magic_name__						:						List[Any]										=output.images

										__magic_name__						:						Any										=pipe(
										    **self.get_dummy_inputs(__snake_case			)     ,			return_dict=__snake_case     ,			)[0]

										__magic_name__						:						int										=image[0, -3:, -3:, -1]
										__magic_name__						:						str										=image_from_tuple[0, -3:, -3:, -1]

										print(f"image.shape {image.shape}"			)

										assert image.shape == (1, 64, 64, 3)

										__magic_name__						:						Optional[Any]										=np.array(
										    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]			)

										assert (
										    np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
										), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2
										), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"


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

										super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




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

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

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


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

										__magic_name__						:						List[str]										=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""			)

										__magic_name__						:						int										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""			)
										__magic_name__						:						List[Any]										=np.ones((7_68, 7_68)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										__magic_name__						:						int										="""a hat"""

										__magic_name__						:						int										=KandinskyPriorPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-prior"""     ,			torch_dtype=torch.floataa			)
										pipe_prior.to(__snake_case			)

										__magic_name__						:						Dict										=KandinskyInpaintPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-inpaint"""     ,			torch_dtype=torch.floataa			)
										__magic_name__						:						int										=pipeline.to(__snake_case			)
										pipeline.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Union[str, Any]										=torch.Generator(device="""cpu"""			).manual_seed(0			)
										__magic_name__					,			__magic_name__						:						Dict										=pipe_prior(
										    __snake_case     ,			generator=__snake_case     ,			num_inference_steps=5     ,			negative_prompt=""""""     ,			).to_tuple()

										__magic_name__						:						Optional[Any]										=pipeline(
										    __snake_case     ,			image=__snake_case     ,			mask_image=__snake_case     ,			image_embeds=__snake_case     ,			negative_image_embeds=__snake_case     ,			generator=__snake_case     ,			num_inference_steps=1_00     ,			height=7_68     ,			width=7_68     ,			output_type="""np"""     ,			)

										__magic_name__						:						Optional[int]										=output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(__snake_case     ,			__snake_case			)




