

'''simple docstring'''



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 		lowerCAmelCase__     (   a				,      unittest.TestCase      ):



 """simple docstring"""



 lowerCAmelCase__						      =   PaintByExamplePipeline
 lowerCAmelCase__						      =   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCAmelCase__						      =   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__						      =   frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
 def    UpperCAmelCase__	(	self      : int    )						->				Dict:


      """simple docstring"""




      torch.manual_seed(0    )
      __SCREAMING_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      ,							)
      __SCREAMING_SNAKE_CASE						     =   PNDMScheduler(skip_prk_steps=__lowerCamelCase    )
      torch.manual_seed(0    )
      __SCREAMING_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    )
      __SCREAMING_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      ,							)
      __SCREAMING_SNAKE_CASE						     =   PaintByExampleImageEncoder(__lowerCamelCase      ,							proj_size=32    )
      __SCREAMING_SNAKE_CASE						     =   CLIPImageProcessor(crop_size=32      ,							size=32    )

      __SCREAMING_SNAKE_CASE						     =   {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "image_encoder": image_encoder,
          "safety_checker": None,
          "feature_extractor": feature_extractor,
      }
      return components
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : str    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   np.array(image.convert("""RGB"""    )    )
      __SCREAMING_SNAKE_CASE						     =   image[None].transpose(0      ,							3      ,							1      ,							2    )
      __SCREAMING_SNAKE_CASE						     =   torch.from_numpy(__lowerCamelCase    ).to(dtype=torch.floataa    ) / 127.5 - 1.0
      return image
 def    UpperCAmelCase__	(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[Any]="cpu"      ,							__SCREAMING_SNAKE_CASE      : str=0    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(__lowerCamelCase    )    ).to(__lowerCamelCase    )
      __SCREAMING_SNAKE_CASE						     =   image.cpu().permute(0      ,							2      ,							3      ,							1    )[0]
      __SCREAMING_SNAKE_CASE						     =   Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert("""RGB"""    ).resize((64, 64)    )
      __SCREAMING_SNAKE_CASE						     =   Image.fromarray(np.uinta(image + 4    )    ).convert("""RGB"""    ).resize((64, 64)    )
      __SCREAMING_SNAKE_CASE						     =   Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert("""RGB"""    ).resize((32, 32)    )

      if str(__lowerCamelCase    ).startswith("""mps"""    ):
           __SCREAMING_SNAKE_CASE						     =   torch.manual_seed(__lowerCamelCase    )
      else:
           __SCREAMING_SNAKE_CASE						     =   torch.Generator(device=__lowerCamelCase    ).manual_seed(__lowerCamelCase    )
      __SCREAMING_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      : Optional[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.get_dummy_components()

      # make sure here that pndm scheduler skips prk
      __SCREAMING_SNAKE_CASE						     =   PaintByExamplePipeline(**__lowerCamelCase    )
      __SCREAMING_SNAKE_CASE						     =   pipe.to("""cpu"""    )
      pipe.set_progress_bar_config(disable=__lowerCamelCase    )

      __SCREAMING_SNAKE_CASE						     =   self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE						     =   pipe(**__lowerCamelCase    )
      __SCREAMING_SNAKE_CASE						     =   output.images

      __SCREAMING_SNAKE_CASE						     =   image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __SCREAMING_SNAKE_CASE						     =   np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
 def    UpperCAmelCase__	(	self      : List[str]    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   "cpu"
      __SCREAMING_SNAKE_CASE						     =   self.get_dummy_inputs()
      inputs.pop("""mask_image"""    )
      __SCREAMING_SNAKE_CASE						     =   self.convert_to_pt(inputs.pop("""image"""    )    )
      __SCREAMING_SNAKE_CASE						     =   image.clamp(0      ,							1    ) / 2

      # make sure here that pndm scheduler skips prk
      __SCREAMING_SNAKE_CASE						     =   PaintByExamplePipeline(**self.get_dummy_components()    )
      __SCREAMING_SNAKE_CASE						     =   pipe.to(__lowerCamelCase    )
      pipe.set_progress_bar_config(disable=__lowerCamelCase    )

      __SCREAMING_SNAKE_CASE						     =   pipe(image=__lowerCamelCase      ,							mask_image=mask_image[:, 0]      ,							**__lowerCamelCase    )
      __SCREAMING_SNAKE_CASE						     =   output.images

      __SCREAMING_SNAKE_CASE						     =   image.cpu().permute(0      ,							2      ,							3      ,							1    )[0]
      __SCREAMING_SNAKE_CASE						     =   mask_image.cpu().permute(0      ,							2      ,							3      ,							1    )[0]

      __SCREAMING_SNAKE_CASE						     =   Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert("""RGB"""    )
      __SCREAMING_SNAKE_CASE						     =   Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert("""RGB"""    )

      __SCREAMING_SNAKE_CASE						     =   pipe(**self.get_dummy_inputs()    )
      __SCREAMING_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      : Tuple    )						->				Union[str, Any]:


      """simple docstring"""




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







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



 """simple docstring"""



 def    UpperCAmelCase__	(	self      : int    )						->				List[str]:


      """simple docstring"""




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


 def    UpperCAmelCase__	(	self      : Tuple    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/paint_by_example/dog_in_bucket.png"""    )
      __SCREAMING_SNAKE_CASE						     =   load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/paint_by_example/mask.png"""    )
      __SCREAMING_SNAKE_CASE						     =   load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/paint_by_example/panda.jpg"""    )

      __SCREAMING_SNAKE_CASE						     =   PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""    )
      __SCREAMING_SNAKE_CASE						     =   pipe.to(__lowerCamelCase    )
      pipe.set_progress_bar_config(disable=__lowerCamelCase    )

      __SCREAMING_SNAKE_CASE						     =   torch.manual_seed(321    )
      __SCREAMING_SNAKE_CASE						     =   pipe(
          image=__lowerCamelCase      ,							mask_image=__lowerCamelCase      ,							example_image=__lowerCamelCase      ,							generator=__lowerCamelCase      ,							guidance_scale=5.0      ,							num_inference_steps=50      ,							output_type="""np"""      ,							)

      __SCREAMING_SNAKE_CASE						     =   output.images

      __SCREAMING_SNAKE_CASE						     =   image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE						     =   np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2









'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


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







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "mt5"
 lowerCAmelCase__						      =   ["past_key_values"]
 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=250_112      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=512      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=64      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=1_024      ,							__SCREAMING_SNAKE_CASE      : List[str]=8      ,							__SCREAMING_SNAKE_CASE      : Any=None      ,							__SCREAMING_SNAKE_CASE      : List[str]=6      ,							__SCREAMING_SNAKE_CASE      : Any=32      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=128      ,							__SCREAMING_SNAKE_CASE      : List[str]=0.1      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=1E-6      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=1.0      ,							__SCREAMING_SNAKE_CASE      : Tuple="gated-gelu"      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple="T5Tokenizer"      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=False      ,							__SCREAMING_SNAKE_CASE      : Tuple=0      ,							__SCREAMING_SNAKE_CASE      : str=1      ,							__SCREAMING_SNAKE_CASE      : Dict=0      ,							**__SCREAMING_SNAKE_CASE      : str      ,							)						->				List[Any]:


      """simple docstring"""




      super().__init__(
          is_encoder_decoder=__SCREAMING_SNAKE_CASE      ,							tokenizer_class=__SCREAMING_SNAKE_CASE      ,							tie_word_embeddings=__SCREAMING_SNAKE_CASE      ,							pad_token_id=__SCREAMING_SNAKE_CASE      ,							eos_token_id=__SCREAMING_SNAKE_CASE      ,							decoder_start_token_id=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE      ,							)
      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   d_model
      __SCREAMING_SNAKE_CASE						     =   d_kv
      __SCREAMING_SNAKE_CASE						     =   d_ff
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   relative_attention_num_buckets
      __SCREAMING_SNAKE_CASE						     =   relative_attention_max_distance
      __SCREAMING_SNAKE_CASE						     =   dropout_rate
      __SCREAMING_SNAKE_CASE						     =   layer_norm_epsilon
      __SCREAMING_SNAKE_CASE						     =   initializer_factor
      __SCREAMING_SNAKE_CASE						     =   feed_forward_proj
      __SCREAMING_SNAKE_CASE						     =   use_cache

      __SCREAMING_SNAKE_CASE						     =   self.feed_forward_proj.split("""-"""    )
      __SCREAMING_SNAKE_CASE						     =   act_info[-1]
      __SCREAMING_SNAKE_CASE						     =   act_info[0] == """gated"""

      if len(__SCREAMING_SNAKE_CASE    ) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE    ) > 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":
           __SCREAMING_SNAKE_CASE						     =   """gelu_new"""
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      return self.d_model
 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				int:


      """simple docstring"""




      return self.num_heads


 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Union[str, Any]:


      """simple docstring"""




      return self.num_layers







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
          """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
      }
      if self.use_past:
           __SCREAMING_SNAKE_CASE						     =   """past_encoder_sequence + sequence"""
           __SCREAMING_SNAKE_CASE						     =   {0: """batch"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}

      if self.use_past:
           self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE      ,							direction="""inputs"""    )

      return common_inputs
 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				int:


      """simple docstring"""




      return 13


 @property
 def    UpperCAmelCase__	(	self      : Tuple    )						->				float:


      """simple docstring"""




      return 5E-4






