code: from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  lowerCamelCase__           =				logging.get_logger(__name__)
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  					__UpperCamelCase						=   """mt5"""
  					__UpperCamelCase						=   ["""past_key_values"""]
  
  
  
  
  					def __init__( self :					Optional[Any]      ,_lowerCAmelCase :					str=250_112      ,_lowerCAmelCase :					Union[str, Any]=512      ,_lowerCAmelCase :					str=64      ,_lowerCAmelCase :					Optional[Any]=1_024      ,_lowerCAmelCase :					Optional[Any]=8      ,_lowerCAmelCase :					Optional[Any]=None      ,_lowerCAmelCase :					List[str]=6      ,_lowerCAmelCase :					Optional[int]=32      ,_lowerCAmelCase :					str=128      ,_lowerCAmelCase :					Optional[int]=0.1      ,_lowerCAmelCase :					Dict=1E-6      ,_lowerCAmelCase :					Any=1.0      ,_lowerCAmelCase :					Optional[int]="gated-gelu"      ,_lowerCAmelCase :					int=True      ,_lowerCAmelCase :					Union[str, Any]=True      ,_lowerCAmelCase :					Dict="T5Tokenizer"      ,_lowerCAmelCase :					Union[str, Any]=False      ,_lowerCAmelCase :					List[Any]=0      ,_lowerCAmelCase :					int=1      ,_lowerCAmelCase :					Tuple=0      ,**_lowerCAmelCase :					Any      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().__init__(
  							    is_encoder_decoder=_lowerCAmelCase      ,tokenizer_class=_lowerCAmelCase      ,tie_word_embeddings=_lowerCAmelCase      ,pad_token_id=_lowerCAmelCase      ,eos_token_id=_lowerCAmelCase      ,decoder_start_token_id=_lowerCAmelCase      ,**_lowerCAmelCase      ,)
  							__snake_case		       =     vocab_size
  							__snake_case		       =     d_model
  							__snake_case		       =     d_kv
  							__snake_case		       =     d_ff
  							__snake_case		       =     num_layers
  							__snake_case		       =     (
  							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
  							)  # default = symmetry
  							__snake_case		       =     num_heads
  							__snake_case		       =     relative_attention_num_buckets
  							__snake_case		       =     relative_attention_max_distance
  							__snake_case		       =     dropout_rate
  							__snake_case		       =     layer_norm_epsilon
  							__snake_case		       =     initializer_factor
  							__snake_case		       =     feed_forward_proj
  							__snake_case		       =     use_cache
  
  							__snake_case		       =     self.feed_forward_proj.split("-"						)
  							__snake_case		       =     act_info[-1]
  							__snake_case		       =     act_info[0] == "gated"
  
  							if len(_lowerCAmelCase						) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase						) > 2:
  									raise ValueError(
  									    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
  									    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
  									    "'gated-gelu' or 'relu'"						)
  
  							# for backwards compatibility
  							if feed_forward_proj == "gated-gelu":
  									__snake_case		       =     "gelu_new"
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self.d_model
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self.num_heads
  
  
  
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self.num_layers
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  
  
  
  
  					@property
  					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
  					def 							UpperCamelCase_						( self :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     {
  							    "input_ids": {0: "batch", 1: "encoder_sequence"},
  							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
  							}
  							if self.use_past:
  									__snake_case		       =     "past_encoder_sequence + sequence"
  									__snake_case		       =     {0: "batch"}
  									__snake_case		       =     {0: "batch", 1: "past_decoder_sequence + sequence"}
  							else:
  									__snake_case		       =     {0: "batch", 1: "decoder_sequence"}
  									__snake_case		       =     {0: "batch", 1: "decoder_sequence"}
  
  							if self.use_past:
  									self.fill_with_past_key_values_(_lowerCAmelCase      ,direction="inputs"						)
  
  							return common_inputs
  
  
  
  
  					@property
  					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return 13
  
  
  
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return 5E-4
  
  
  
  
  
  
style_context: import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig
  
  from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
  from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
  from diffusers.utils import floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  class 					UpperCamelCase	(     snake_case__			,      unittest.TestCase      ):
  					__UpperCamelCase						=   PaintByExamplePipeline
  					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  					__UpperCamelCase						=   frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							torch.manual_seed(0						)
  							__snake_case		       =     UNetaDConditionModel(
  							    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,cross_attention_dim=32      ,)
  							__snake_case		       =     PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
  							torch.manual_seed(0						)
  							__snake_case		       =     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      ,)
  							torch.manual_seed(0						)
  							__snake_case		       =     CLIPVisionConfig(
  							    hidden_size=32      ,projection_dim=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,image_size=32      ,patch_size=4      ,)
  							__snake_case		       =     PaintByExampleImageEncoder(_lowerCAmelCase      ,proj_size=32						)
  							__snake_case		       =     CLIPImageProcessor(crop_size=32      ,size=32						)
  
  							__snake_case		       =     {
  							    "unet": unet,
  							    "scheduler": scheduler,
  							    "vae": vae,
  							    "image_encoder": image_encoder,
  							    "safety_checker": None,
  							    "feature_extractor": feature_extractor,
  							}
  							return components
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     np.array(image.convert("RGB"						)						)
  							__snake_case		       =     image[None].transpose(0      ,3      ,1      ,2						)
  							__snake_case		       =     torch.from_numpy(_lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
  							return image
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any="cpu"      ,_lowerCAmelCase :					List[str]=0						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)
  							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((64, 64)						)
  							__snake_case		       =     Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((64, 64)						)
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((32, 32)						)
  
  							if str(_lowerCAmelCase						).startswith("mps"						):
  									__snake_case		       =     torch.manual_seed(_lowerCAmelCase						)
  							else:
  									__snake_case		       =     torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
  							__snake_case		       =     {
  							    "example_image": example_image,
  							    "image": init_image,
  							    "mask_image": mask_image,
  							    "generator": generator,
  							    "num_inference_steps": 2,
  							    "guidance_scale": 6.0,
  							    "output_type": "numpy",
  							}
  							return inputs
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_dummy_components()
  
  							# make sure here that pndm scheduler skips prk
  							__snake_case		       =     PaintByExamplePipeline(**_lowerCAmelCase						)
  							__snake_case		       =     pipe.to("cpu"						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     self.get_dummy_inputs()
  							__snake_case		       =     pipe(**_lowerCAmelCase						)
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 64, 64, 3)
  							__snake_case		       =     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)
  
  							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     "cpu"
  							__snake_case		       =     self.get_dummy_inputs()
  							inputs.pop("mask_image"						)
  							__snake_case		       =     self.convert_to_pt(inputs.pop("image"						)						)
  							__snake_case		       =     image.clamp(0      ,1						) / 2
  
  							# make sure here that pndm scheduler skips prk
  							__snake_case		       =     PaintByExamplePipeline(**self.get_dummy_components()						)
  							__snake_case		       =     pipe.to(_lowerCAmelCase						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     pipe(image=_lowerCAmelCase      ,mask_image=mask_image[:, 0]      ,**_lowerCAmelCase						)
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
  							__snake_case		       =     mask_image.cpu().permute(0      ,2      ,3      ,1						)[0]
  
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
  
  							__snake_case		       =     pipe(**self.get_dummy_inputs()						)
  							__snake_case		       =     output.images
  
  							assert out_a.shape == (1, 64, 64, 3)
  							assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5E-2
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().test_inference_batch_single_identical(expected_max_diff=3E-3						)
  
  
  
  
  @slow
  @require_torch_gpu
  class 					UpperCamelCase	(     unittest.TestCase      ):
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().tearDown()
  							gc.collect()
  							torch.cuda.empty_cache()
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/dog_in_bucket.png"						)
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/mask.png"						)
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/panda.jpg"						)
  
  							__snake_case		       =     PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"						)
  							__snake_case		       =     pipe.to(_lowerCAmelCase						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     torch.manual_seed(321						)
  							__snake_case		       =     pipe(
  							    image=_lowerCAmelCase      ,mask_image=_lowerCAmelCase      ,example_image=_lowerCAmelCase      ,generator=_lowerCAmelCase      ,guidance_scale=5.0      ,num_inference_steps=50      ,output_type="np"      ,)
  
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 512, 512, 3)
  							__snake_case		       =     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)
  
  							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  
  