



"""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    __snake_case(					__A			,			unittest.TestCase       ):
    _A       = PaintByExamplePipeline
    _A       = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
    _A       = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _A       = frozenset([]       )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




    def   A   (   self			):





      '''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=A_			)
      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(A_    ,							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   A   (   self    ,							A_			):





      '''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(A_			).to(dtype=torch.floataa			) / 127.5 - 1.0
      return image




    def   A   (   self    ,							A_="cpu"    ,							A_=0			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			floats_tensor((1, 3, 32, 32)    ,							rng=random.Random(A_			)			).to(A_			)
      _SCREAMING_SNAKE_CASE        =			image.cpu().permute(0    ,							2    ,							3    ,							1			)[0]
      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(A_			)			).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(A_			)			).convert('''RGB'''			).resize((32, 32)			)

      if str(A_			).startswith('''mps'''			):
        _SCREAMING_SNAKE_CASE        =			torch.manual_seed(A_			)
      else:
        _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(A_			)
      _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   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()

      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE        =			PaintByExamplePipeline(**A_			)
      _SCREAMING_SNAKE_CASE        =			pipe.to('''cpu'''			)
      pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs()
      _SCREAMING_SNAKE_CASE        =			pipe(**A_			)
      _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([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]			)

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




    def   A   (   self			):





      '''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(A_			)
      pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			pipe(image=A_    ,							mask_image=mask_image[:, 0]    ,							**A_			)
      _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(A_			)			).convert('''RGB'''			)
      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(A_			)			).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   A   (   self			):





      '''simple docstring'''



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




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




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''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(A_			)
      pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(321			)
      _SCREAMING_SNAKE_CASE        =			pipe(
          image=A_    ,							mask_image=A_    ,							example_image=A_    ,							generator=A_    ,							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([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]			)

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





"""simple docstring"""






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			CanineConfig()
  _SCREAMING_SNAKE_CASE        =			CanineModel(UpperCamelCase__    )
  model.eval()

  print(F'''Building PyTorch model from configuration: {config}'''    )

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    )

  # Save pytorch-model (weights and configuration)
  print(F'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(UpperCamelCase__    )

  # Save tokenizer files
  _SCREAMING_SNAKE_CASE        =			CanineTokenizer()
  print(F'''Save tokenizer files to {pytorch_dump_path}'''    )
  tokenizer.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
    lowerCamelCase				:     Dict								=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
    )
    parser.add_argument(
        """--pytorch_dump_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to a folder where the PyTorch model will be placed.""",
    )
    lowerCamelCase				:     str								=  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
