




'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_	 =		logging.get_logger(__name__)

lowerCamelCase_	 =		{
    'google/mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'
}







class 	lowercase_					(				A     ):


   """simple docstring"""

   lowerCamelCase_       						=		MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   lowerCamelCase_       						=		'''mobilebert'''



   def __init__(					self      :			List[Any]						,   __lowerCamelCase      :			int=3_0_5_2_2						,   __lowerCamelCase      :			int=5_1_2						,   __lowerCamelCase      :			Tuple=2_4						,   __lowerCamelCase      :			Optional[Any]=4						,   __lowerCamelCase      :			List[Any]=5_1_2						,   __lowerCamelCase      :			List[Any]="relu"						,   __lowerCamelCase      :			Dict=0.0						,   __lowerCamelCase      :			Dict=0.1						,   __lowerCamelCase      :			Optional[Any]=5_1_2						,   __lowerCamelCase      :			Optional[int]=2						,   __lowerCamelCase      :			Tuple=0.0_2						,   __lowerCamelCase      :			Any=1e-12						,   __lowerCamelCase      :			Tuple=0						,   __lowerCamelCase      :			List[str]=1_2_8						,   __lowerCamelCase      :			Any=True						,   __lowerCamelCase      :			List[Any]=True						,   __lowerCamelCase      :			Tuple=1_2_8						,   __lowerCamelCase      :			Optional[int]=False						,   __lowerCamelCase      :			Optional[int]=True						,   __lowerCamelCase      :			int=4						,   __lowerCamelCase      :			Optional[int]="no_norm"						,   __lowerCamelCase      :			List[str]=True						,   __lowerCamelCase      :			List[Any]=None						,   **__lowerCamelCase      :			Any						,   ):

      """simple docstring"""
      super().__init__(pad_token_id=__lowerCamelCase						,   **__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   vocab_size
      _SCREAMING_SNAKE_CASE =   hidden_size
      _SCREAMING_SNAKE_CASE =   num_hidden_layers
      _SCREAMING_SNAKE_CASE =   num_attention_heads
      _SCREAMING_SNAKE_CASE =   hidden_act
      _SCREAMING_SNAKE_CASE =   intermediate_size
      _SCREAMING_SNAKE_CASE =   hidden_dropout_prob
      _SCREAMING_SNAKE_CASE =   attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE =   max_position_embeddings
      _SCREAMING_SNAKE_CASE =   type_vocab_size
      _SCREAMING_SNAKE_CASE =   initializer_range
      _SCREAMING_SNAKE_CASE =   layer_norm_eps
      _SCREAMING_SNAKE_CASE =   embedding_size
      _SCREAMING_SNAKE_CASE =   trigram_input
      _SCREAMING_SNAKE_CASE =   use_bottleneck
      _SCREAMING_SNAKE_CASE =   intra_bottleneck_size
      _SCREAMING_SNAKE_CASE =   use_bottleneck_attention
      _SCREAMING_SNAKE_CASE =   key_query_shared_bottleneck
      _SCREAMING_SNAKE_CASE =   num_feedforward_networks
      _SCREAMING_SNAKE_CASE =   normalization_type
      _SCREAMING_SNAKE_CASE =   classifier_activation

      if self.use_bottleneck:
         _SCREAMING_SNAKE_CASE =   intra_bottleneck_size
      else:
         _SCREAMING_SNAKE_CASE =   hidden_size

      _SCREAMING_SNAKE_CASE =   classifier_dropout







class 	lowercase_					(				A     ):


   """simple docstring"""

   @property
   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE =   {0: "batch", 1: "choice", 2: "sequence"}
      else:
         _SCREAMING_SNAKE_CASE =   {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ]					)







'''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 	lowercase_					(				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 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)						,   layers_per_block=2						,   sample_size=3_2						,   in_channels=9						,   out_channels=4						,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,   cross_attention_dim=3_2						,   )
      _SCREAMING_SNAKE_CASE =   PNDMScheduler(skip_prk_steps=__lowerCamelCase					)
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   AutoencoderKL(
          block_out_channels=[3_2, 6_4]						,   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=3_2						,   projection_dim=3_2						,   intermediate_size=3_7						,   layer_norm_eps=1e-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   image_size=3_2						,   patch_size=4						,   )
      _SCREAMING_SNAKE_CASE =   PaintByExampleImageEncoder(__lowerCamelCase						,   proj_size=3_2					)
      _SCREAMING_SNAKE_CASE =   CLIPImageProcessor(crop_size=3_2						,   size=3_2					)

      _SCREAMING_SNAKE_CASE =   {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "image_encoder": image_encoder,
          "safety_checker": None,
          "feature_extractor": feature_extractor,
      }
      return components



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			str					):

      """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					) / 1_2_7.5 - 1.0
      return image



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			Optional[int]="cpu"						,   __lowerCamelCase      :			Union[str, Any]=0					):

      """simple docstring"""
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      _SCREAMING_SNAKE_CASE =   floats_tensor((1, 3, 3_2, 3_2)						,   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((6_4, 6_4)					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((6_4, 6_4)					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					).resize((3_2, 3_2)					)

      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 							lowerCAmelCase_    (					self      :			List[str]					):

      """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, 6_4, 6_4, 3)
      _SCREAMING_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 							lowerCAmelCase_    (					self      :			Tuple					):

      """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, 6_4, 6_4, 3)
      assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5e-2



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      super().test_inference_batch_single_identical(expected_max_diff=3e-3					)







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


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      # make sure here that pndm scheduler skips prk
      _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(3_2_1					)
      _SCREAMING_SNAKE_CASE =   pipe(
          image=__lowerCamelCase						,   mask_image=__lowerCamelCase						,   example_image=__lowerCamelCase						,   generator=__lowerCamelCase						,   guidance_scale=5.0						,   num_inference_steps=5_0						,   output_type="np"						,   )

      _SCREAMING_SNAKE_CASE =   output.images

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

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      _SCREAMING_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

